Podcasts about latent space

What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company's most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @satyanadella | @Microsoft | @latentspacepod | @swyx Chapters: 00:00 – Satya Nadella Introduction 01:48 – Reflections from Microsoft Build 03:12 – Microsoft's AI Training Strategy 05:48 – Complexity of Real-World Deployment of AI 07:33 – Augmenting Human Capital 09:37 – Harnesses for Enterprise 11:49 – Developer Value 15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence? 15:51 – Modern Definition of IP 17:38 – Future of Vendor vs. Enterprise Agents 21:48 – Near-Term Predictions on Model Pricing 24:02 – Durability of SaaS 25:58 – What Satya's Building 28:18 – Future of Engineering Roles 30:54 – How Microsoft Can Be More Ambitious 34:36 – Data Centers and Community Impact 38:01 – AI's Impact on Society 39:52 - AI and Education 42:28 – Conclusion

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

The future of war has been evolving before our eyes in Ukraine, yet the west still plans to fight the last war. In this special episode, guest host Noah Smith (@noahpinion) and Brandon Anderson sit down with Yaroslav Azhnyuk (@YaroslavAzhnyuk), a serial tech founder who went from building PetCube to founding The Fourth Law, one of the world's most advanced AI-guided drone companies. Over two hours we cover the technology, tactics, and geopolitics of drone warfare, and why the modern battlefield has already left the West behind:* Yaroslav's personal history and the Ukraine war [00:01:04 – 00:14:01]* The modern drone tech stack: why FPV drones are the new god of war, the future of the rifleman, fiber optic vs. AI, five levels of autonomy, and the eight dimensions of the autonomous battlefield [00:14:01 – 01:05:13]* The geopolitics and economics of drones: China's manufacturing advantage, the drone race, Western defense readiness, countermeasures, and why the gap is widening [01:05:13 – 01:58:57]For those looking for Noah Smith's commentary, it really gets going around the 00:51:31 mark.Yaroslav Azhnyuk / The Fourth Law:* X: https://x.com/YaroslavAzhnyuk* LinkedIn: https://www.linkedin.com/in/yaroslavazhnyuk/* The Fourth Law: https://thefourthlaw.aiNoah Smith:* Substack: Noah Smith * X: https://x.com/noahpinionTimestamps00:00:00 Cold Open: China's 4 Billion Drones and the Cameras-to-Explosives Pipeline00:01:04 Introduction: Brandon, Noah Smith, and Yaroslav Azhnyuk00:05:41 From Tech Entrepreneur to Defense: PetCube, Brave One, and the D3 Fund00:10:42 The Ethics of Building Weapons: Dual-Use Technology and the Wolf at the Door00:14:01 The Tech Stack: Cameras, Autonomy Modules, Interceptors, and a Semiconductor Fab00:18:47 Fiber Optic vs. AI: The Radio Horizon Problem and $32/km Cable00:25:32 FPV Drones: The New God of War — 70–80% of Frontline Casualties00:28:28 The Five Levels of Drone Autonomy: From Terminal Guidance to Full Autonomy00:41:37 The Eight Dimensions of the Autonomous Battlefield00:45:32 AI Safety and the Morality of Autonomous Weapons00:51:31 The End of the Rifleman? Noah's 2013 Prediction vs. Battlefield Reality01:05:13 China's Manufacturing Advantage and Western Vulnerabilities01:24:21 Policy Advice for Western Defense: Defense Valley and the Widening Gap01:32:54 The Drone Race: Who's Ahead, Category by Category01:41:57 Countermeasures: Shotguns, Jammers, Lasers, and Fishnets01:58:19 The Wedding and Final Takeaway: Be Prepared for WarTranscriptCold Open: China, FPV Drones, and the New Warning SignYaroslav [00:00:00]: Think about this. Last year, Ukraine produced 4 million FPV drones. Ukraine is not the most industrious nation in the world. China can produce 4 billion of these FPV drones.Noah [00:00:10]: Would you say that right now China is now the supreme conventional military power on Earth, given its ability to manufacture and deploy drones in the quantity and quality that you just described?Yaroslav [00:00:20]: I don't think we have all the information to claim that but we cannot count it out, and that alone should be a big warning sign. As I say, at some point in my life I went from making cameras that fling treats to pets to cameras that fling explosives to the occupiers. So that's the short story. And when you think about what your nation, what your patriots are going through, you realize that's the only morally right thing to do is to fight back, and it is immoral not to fight back, and then the choice becomes very clear.Introduction: Yaroslav Azhnyuk, Petcube, and the Last Flight into KyivBrandon [00:01:04]: Welcome to Latent Space. I'm Brandon. I normally do science podcasts, but today we're going to do something a little bit different. I'm joined by Noah Smith of Noahpinion on Substack and Twitter. And he has lots of interesting things to say about drones. And as a guest, we have Yaroslav Azhnyuk, founder of The Fourth Law and several other, drone-related startups. To get started, it is February 23rd, 2022. You are running a pet startup. You're connecting pets with their owners. Let's go in just a little bit of background. How did you get started in tech, and what were you working on before the Ukrainian war started?Yaroslav [00:01:50]: Good to be here. Thank you. On February 23rd, late in the evening, 11:00 PM Kyiv time, my wife and I landed in Kyiv. Actually, then she was a fiance. We came from Lviv, where we were looking at a church, where our wedding should have taken place. And we got into this cab ride from the airport to our home, and the driver was like, “You crazy. Like, everyone's leaving Kyiv. Why do you come?” We're like, “What? Nothing's going to happen. Dude, chill.” And then obviously, eight minutes later, or eight hours later, the bombs fell in the city. It was quite surreal. We probably landed on the last flight that landed in Kyiv, or one of those last flights. My background, I'm a tech guy. Studied applied mathematics in Kyiv Polytechnics, born and raised in Kyiv. My parents are old PhDs from academia, and grandparents too. Like, everything, from linguistics to nuclear physics. And I'm an entrepreneur, so I've built a bunch of companies. Petcube is the one you were referencing. So I lived in San Francisco 2014 to 2020, building Petcube, which is one of the leading, pet device companies in the world, selling lots of pet cameras. And then, yeah, as I say, at some point in my life I went from making cameras that fling treats to pets to cameras that fling explosives to the occupiers. So that's the short story.February 24th: Leaving Kyiv as the Invasion BeginsNoah [00:03:28]: February 24th, I guess a few hours after you, go to check out your wedding chapel, what do you do?Yaroslav [00:03:37]: We had a plan for this situation. So my parents and family live in Kyiv, and we're like, “Okay, this has actually started. The worst has, come true.” And so we basically packed our belongings and got in the car and spent 17 hours driving west. And that was pretty sure most people in our audience watched at least one apocalyptic movie in their life, so that was exactly like that. Like, felt exactly like that. Missiles are falling. Like, there was smoke in Kyiv. Like, my dad and I went, like, to central part of the cities. It's probably, likeYaroslav [00:04:20]: 800 meters from presidential office, to pick some stuff up at his workplace. Because he's, like, the head of an academic institution, so he had to get some of the things with him. And super surreal. Like, the streets are empty. Like, the gas stations are out of gas. Like, we found some gas station. We didn't have, like, spare canisters with us, so we're like, We figured out, like, the car was diesel, so like, we figured out, if it's diesel, you can actually store it in plastic, canisters, and we bought some window wash for the cars. We poured it out of the canisters, and we poured the diesel into that. Yeah, so it was like that. And then, like, helping friends get out, like my friend and his dog. Like, we found Like, my brother was also, like, riding in a separate car. We found a place for my friend who didn't have a car. It was like, yeah, it was like, totally surreal. And we didn't know of course, and you didn't know this will last for so long. You didn't know whether Ukraine will be able to defend Kyiv. And it was like, yeah, very little information and very little insight into future.From Pet Cameras to Defense Tech: Building for Ukraine and the Free WorldNoah [00:05:42]: What are your thoughts with regards to how do you, defend, Ukraine? So you eventually start building drones Like, what is the process to get from there from where you were building, devices that connect owners with pets to building drones, and what other things did you do to help the war effort in the process?Yaroslav [00:06:07]: It's definitely non-trivial, right? Like, I didn't go, to I didn't get any, like, military education when I was a student. Like, normally, in Ukraine, you would, you would go to like, this military school even if you're getting higher education in any other, sphere. I decided to skip that which is like, an unusual way to go. And I never thought that I will be somehow engaged in a war effort. Like, what is war? Of course, wars are over. It's the end of history. So one thing you got to understand about, like, many Ukrainians and like, I guess, it's also true about most of the people I met here in the US, that your who you are in terms of your nationality is a big part of your identity. So when that gets under attack, it's something deeper than just the country you live in gets under attack, right? And I Day one, I figured I'm going to I'm going to fight back with everything I can, right? But I didn't think on day one that I'm actually going to do, weapons. And a bunch of things. We were reaching out to a number of American, congresspeople and senators, and basically advocating for support of Ukraine, for voting for lend lease, which has happened in May 2022, but didn't actually work as expected. We helped start, Brave One, which is now a very important defense innovation cluster, sort of like a DIU here in the US. We helped start, a fund called D3. It's like, it was started or co-started by Eric Schmidt, former CEO of Google. So a bunch of these odd things, but then eventually I was like, “Okay,”by 2023 it was obvious this thing, A is going to last a lot more time, and B, that the whole world is shifting and that there's going to be a new arms race, that the warfare is redefined by drones as platforms. And for the first time in history, you have a platform that is software defined, that can increase your battlefield capabilities, in a in a step change just overnight. So it's like if you were able to push a software update and get all of your Roman legionnaires a new helmet? That has never been possible before. It's the first time in the history of war this is possible. So all of that and many other things like, supply chain fragilization, and the impact that AI is going to have on all of this all these things have become evident to me in 2023, and it's like, “Okay, I should do what I do best, or what I know how to do best, start a tech company, and sort of leverage the global techno capitalist machine, to provide, defensibility to Ukraine and the free world.” So that's literally the mission of the company, increase defensibility of Ukraine and the free world. And then there was some sort of soul-searching and like, asking yourself. It's like, “Okay, am I Actually, I know nothing about weapons. Am I actually, like, ready to make, things that other people use to kill other bad people?”Yaroslav [00:09:36]: When you think about what your nation, what your Compatriots are going through And think about all the terror of places like Bucha, the occupied cities in the east and south, the abducted children, the raped women, all the economic damage that's being done, and the intention to destroy a whole nation, to genocide the people of Ukraine, you realize that's the only morally right thing to do is to fight back, and it is immoral not to fight back. And then the choice becomes very clear. And look, we're just passing the ammunition. We're not doing the actual job. The actual fighters and defenders and heroes are people in the armed forces. We're just support.The Moral Question: Weapons, Responsibility, and Fighting BackNoah [00:10:33]: I have so many questions. Actually, I know you seem to have a question. Do you want to ask anything?Yaroslav [00:10:38]: No, I'm just listening. Go ahead.Noah [00:10:40]: I do want to talk about, some of let's say, the moral issues, like you just said. You endYaroslav [00:10:50]: I think there are no issues there.Yaroslav [00:10:52]: What would an example of a moral question be in this case?Noah [00:10:55]: No, I mean Okay. As you just said, you are creating the tools, but others are using them.Noah [00:11:05]: I was maybe thinking of having this conversation later, but one of the questions is like, is it actually you are going to be building them for your homeland, which you are building it for your homeland, which is I think, very a strong morally defensible position, but this technology is not going to stay with you, right?Noah [00:11:26]: This you will probably be selling these to other people Yeah. So the future is really where the moral issues may come into playYaroslav [00:11:38]: The this question becomes, easier and more complete if we ask this not about a particular technology or particular weapon, if we think that this question actually applies to any kind of technology Right? So -Knife or fire. You can use knife to do surgery and save people's lives, or you can use it as a weapon to take people's lives.Noah [00:12:06]: Cut tomatoes, too.Yaroslav [00:12:08]: Cut tomatoes too.Noah [00:12:09]: Yes, knife.Yaroslav [00:12:09]: That's helpful.Noah [00:12:10]: In Japan, sword and knife, they, call the same word.Yaroslav [00:12:14]: It's like, it's with any technology. Large language models, right? Look at how powerful they are and yet they're available to anyone in North Korea or in Russia.Yaroslav [00:12:29]: That's one side of the argument. The other side is As a maker, what is your responsibility for how the tools you're creating, will be used? There's definitely some responsibility, right? Then How should the decision process look like? Should you, like, try to calculate all the possible scenarios before starting to work on something? Or do you create something that is needed now to save people's lives, and then think about, addressing the unwanted edge cases later? In ideal world where there's like, or okay, it's not ideal world. In a mythical world where there is some one governing party and it gets to decide everything, and there is no other country, that can, decide on their own, you could say, “Well, we need to calculate for all the consequences, and only then, maybe build this building, by replacing this park because, maybe we need this park in the city,”right? So that kind of situation. But when you're in a situation where you're in a forest, in front of a wolf, you first going to deal with the wolf that wants to eat you, and then you're going to go consult Greenpeace. So that's kind of situation that Ukraine is in.The Fourth Law, Odd Systems, and Ukraine's Drone StackNoah [00:13:59]: Enough. Because this is a tech podcast, I did want to spend some time talking about, sort of the tech in that you've developed and what you've been working on. So can you explain, I guess, first of all, like, the problem that you were trying to solve from a technical standpoint? And I think, and then maybe, like, go into some of the solutions and some of the design process that led you from designing, little laser-guided, guiding lasers with a with an iPhone versus Having drones.Yaroslav [00:14:34]: Like, it so happened, that my partners and I, we sort of So I started one company called The Fourth Law, and its goal was and is to Make, massively scalable on-drone autonomy. And then In parallel with that together with my, Petcube co-founders, partners, and friends, we started another company called Odd Systems Which, was focused on making thermal cameras. Cameras, thermal cameras are seeing thermal radiation and are used to see at night. And we're now sort of those companies are getting closer and closer together and we're probably going to merge them. And this group of companies is currently the leading, team in on-drone AI and thermal imaging on the Ukrainian battlefield, and Likely one of the leading, if not the leading in the world. So We have these, like, three sort of business units, which are cameras, drone autonomy, and drones. So the cameras and drone autonomy sell daytime and nighttime cameras and different types of drone autonomous modules to other drone manufacturers, over 200 drone manufacturers in Ukraine. And then the UAV, business unit sells the drones themselves to the armed forces of Ukraine, Ukrainian government. And there are different types of drones. Those are sort of front strike, as we call them, so those are sort of FPV strike drones and the bombers, and then interceptors. And there are different kinds of interceptors. We do Shahed interceptors and we do ISR interceptors. We don't do the deep strike-FPV Drones, Interceptors, and Battery-Powered WarfareNoah [00:16:32]: What's an ISR interceptor?Yaroslav [00:16:33]: ISR is stands for intelligence, surveillance, reconnaissance, and those are basically drones which are which, Russians are using to watch over positions and then communicate where, the targets are coming.Noah [00:16:48]: It's a reconnaissance.Yaroslav [00:16:48]: That's, the ISR is sort of a classical term for a for a reconnaissance drone.Noah [00:16:53]: Are all of these battery-powered drones that you just described? ‘Cause I know that the sort of deep strike drones still have, like Some sort ofYaroslav [00:17:01]: Internal combustion engine?Noah [00:17:02]: Internal combustion engine. Are all the things you're talking about battery-powered?Yaroslav [00:17:06]: What we're working on is all battery-powered, right? We don't do the deep strikes, right? And then in terms of autonomy-Noah [00:17:12]: You can catch a Shahed with a battery-powered thing. It's not Fast to catch.Yaroslav [00:17:17]: No, absolutely. Look, Shahed interceptor, like ours, it's called Zero, it goes up to 326 kilometers per hour.Noah [00:17:26]: For reference, how fast is a Shahed?Yaroslav [00:17:28]: Eight, like, in internal phase it could be 280, but in cruise phase it's, like, 220-ish.Yaroslav [00:17:36]: Yeah. And sorry, I'm not like you can convert that into miles if you're interested.Noah [00:17:41]: No, that's fine.Noah [00:17:41]: Multiply by two thirds or point six or something.Yaroslav [00:17:44]: That's easy. Yeah, I was saying that for autonomy modules, right, we, -We make systems, autonomous systems for frontline, for interceptors and some for deep strikes as well, and then different levels of autonomy. So from terminal guidance, which is like lasts 500 meters, give or take, to autonomous bombing, to autonomous target detection, to autonomous navigation and all of that across day and night, different terrains, different time of the year, different platforms like quadcopters and fixed wing, and maybe some other platforms. So it's quite a wide variety of products. We also have like our own simulation. We have our own training school for the war fighters. And we're about to start construction of two, semiconductor plants to make, sensors for thermal cameras. So that's super exciting for me as a computer science guy is Doing semiconductors. Super cool.Noah [00:18:49]: Like in terms of kind of core drone technologies, you basically are one is an FPV replacement without fiber optics, and the other isYaroslav [00:18:59]: YouNoah [00:18:59]: Signal tracking with interceptorsYaroslav [00:19:00]: With or without fiber optics. Fiber optics Is just like, sort of a communication module.Yaroslav [00:19:05]: You can, you can use classical analog, video link and radio link. Those would be two separate radios. You can do digital, or you can do fiber optic, and then fiber optic Has its own advantages but also adds weight and decreases, the distance and decreases, how fast you can, sort of turn and With a drone. Yeah.Noah [00:19:33]: Do you need AI for fiber optic drones?Yaroslav [00:19:36]: Like you can use AI for fiber optic drones. AI replaces a human, right? Fiber optic is making your communication link more resilient. So those are slightly different goals. Like if you want, you can have, AI controlling hundreds of fiber optic drones instead of having 100 operators for each.Fiber Optics, Radio Horizons, and Terminal GuidanceNoah [00:20:03]: I guess I thought that the key reason that people moved to fiber optic drones was for like electronic, countermeasures. Or I guess to counter those.Yaroslav [00:20:13]: I think that's a correct assessment from sort of a public awareness standpoint. In practice it's somewhat more difficult Because besides electronic countermeasures, you have these issues of a radio horizon For FPV drones, which means that asYaroslav [00:20:36]: I believe Earth is round Some people disagree. But basically if you fly a drone and you have a land station over here and a drone flying over hereYaroslav [00:20:49]: If your drone is flying high, you have good direct radio visibility. If your drone goes low, and usually, Russian infantry and vehicles, they're on the ground and you want to hit them, you need to go low. Lower you go, maybe you'll get behind a hill or behind a forest, and if you're far enough, you'll just get behind the curvature of the earth. You get into what's called a radio shadow. And then That is a real bummer because for the last, be it 60 or 20 meters, you won't be able to see anything and it will be very difficult to hit the target. So to counter that what-- And then the distances that these FPV drones, act on they're, they can be quite large. So for example, here in the US there was this drone dominance program competition, and in drone dominance the furthest distance was about 10 kilometers.Noah [00:21:44]: What was drone dominance? What was that competition?Yaroslav [00:21:47]: Drone, the drone dominance is a is a program started, by the US government, to accelerate the development of drone technology here in the US.Noah [00:21:57]: Got it. And the longest range thing they were using was 10 kilometers.Yaroslav [00:22:00]: Was 10 kilometers, right. In Ukraine, like if your drone doesn't fly at least 20, 25, it just, no one's interested in it, and the usual hits are happening. It was like, okay, many hits are happening between 30 and 40 kilometers, and that's what expected from a regular 10-inch, FPV drone. So at that distance, even at altitudes of like 60 to 100 meters, you might start losing, the link. So some of the earlier AI technology that was fielded in FPV drone was this terminal guidance technology. That was the first product that we ever, launched that helped you as an operator, once you see the target from two, three, 500 meters, you lock onto the target and then, it just, drives the drone towards the target no matter what, even after you lost the visual connection. So optic fiber solves that. However, if you want to go like 20 kilometers with optic fiber, that will add an extra three kilos, of useful weight to your drone. SoNoah [00:23:12]: ‘Cause the cable that you have to unspool as you go weighs.Noah [00:23:15]: It is heavy.Yaroslav [00:23:15]: At first, like the spool is about 800 grams, so a bit less than a kilo, and then, and then think about 10, 10 kilometer optic fiber is another kilo, something like that. That takes away from your useful mass and then now you have like, you need a 15-inch drone and it can only carry maybe one or two kilos of explosives if you want to go, 20 kilometers. If you want to go to 30 or 40, like 30 is probably max. 40 is like very problem problematic on optic fiber. And then the problem with optic fiber is it's actually getting super expensive. So and why? Because of all the data centers for AI. That's literally the same optic fiber-Noah [00:24:01]: We're running out of centersYaroslav [00:24:02]: That's being used there.Yaroslav [00:24:02]: Like when Ukrainians and Russians come to Chinese factories to buy the optic fiber, they're like, “We're out. We sold it out to the Americans.”? That's the craziest thing. So optic fiber went up in price from like, $4 per, kilometer to like, $32 per kilometer in a few months in the beginning of this year. And I'veBrandon [00:24:26]: Claude Code is stopping the Russian drone effort here.Yaroslav [00:24:30]: Ukrainian as well. Yeah.Brandon [00:24:31]: Ukrainian. But I read somewhere that the Russians had grown more dependent on fiber optic drones relative to the Ukrainians, and that's one reason why the Ukrainians have sort of regained the initiative in drones recently.Brandon [00:24:42]: How accurate's that?Yaroslav [00:24:43]: The Russians were the first ones to scale that. I think by as of now, Ukraine has caught up. I think, like, as of maybe three months ago, Ukraine is mostly caught up on fiber optic. Yeah.Brandon [00:24:57]: What percent of damage would you say is in terms of FPV drone damage would you say is now fiber optic versus, like autonomous?FPVs as the New God of War: Tanks, Artillery, and Cost per KillYaroslav [00:25:07]: For our, for our audience, I actually, I cannot answer that question. Like, it's like I know the answer, but I would not disclose that. But for our audience, I think another interesting fact is out of all the casualties on the front line Between 70 and 80% are done by FPV drones.Brandon [00:25:30]: FPV drones are the new weapon of universal weapon of warfare.Yaroslav [00:25:34]: It'sBrandon [00:25:35]: Land warfare, anywayYaroslav [00:25:35]: They used to say that artillery is a god of war because artillery used to cause, like 80% of casualties, and now On that ranking-Brandon [00:25:46]: FPVYaroslav [00:25:47]: FPV drones rule.Brandon [00:25:48]: FPV drones are the god of war.Yaroslav [00:25:51]: Sort of. Dethroned artillery. But it's not to say that artillery is not useful, is not needed. Like, all of these systems are needed. Maybe except cavalry, although Russians still use it. I know, have you seen the videos of Russians using mules and horses?Brandon [00:26:09]: What is the usefulness-Yaroslav [00:26:10]: It'Brandon [00:26:10]: Of a tank in the in the modern-Yaroslav [00:26:11]: That's where we need Greenpeace to say a word, but they're silent. Yeah.Brandon [00:26:15]: What's the use of a tank on the modern battlefield?Yaroslav [00:26:21]: It's diminishing.Brandon [00:26:22]: Diminishing.Yaroslav [00:26:22]: However, I think there might be technologies which will, revive the tank. Look, tank still provides you armor, and armor is important. Like, you still need to armor and firepower, right? Like, you can be an armor personal carrier that provides you, armor. The challenge that currently exists is armor is not very well protected against incoming drones. However, there are ways to do to protect it. We were previously talking about this before the podcast. The CEO of Rheinmetall, recently sort of ridiculed, Ukrainian drone industry, saying that like, there is nothing interesting there, no real innovation, no to stand Compared to like, Rheinmetall or Boeing, and it's all made by housewives. There was like, obviously a ton of memes about this people ridiculing the CEO of Rheinmetall. And one of the best quotes, I heard on this topic is from my friend, Alexey Babenko, who's, the head of and founder of VIARI Drone, which is one of the largest manufacturers of FPV drones. They're our partner. They're using our autonomy. So he said that the drones we manufacture in one day will be more than enough to destroy all the tanks Rheinmetall manufactures in a year.Yaroslav [00:27:52]: Then, yeah, cost-wise, of course, a drone is like, $500 and a Rheinmetall tank is what, probably 5 million-ish or maybe more.Brandon [00:28:00]: Don't mess with those housewives.Yaroslav [00:28:03]: Drone wives.Brandon [00:28:04]: Drone wives.Yaroslav [00:28:06]: That's it.Noah [00:28:06]: There's a classic saying that everyone always fights the last war.Noah [00:28:12]: Yet do How did So from your standpoint, how did we get to the point where tanks became irrelevant in at least for now In a matter of just a few years?Yaroslav [00:28:24]: Look, I think it's the same way, how do we get to the point that calculators become irrelevant?Yaroslav [00:28:31]: Now we have iPhones. Like, why would you need a calculator? Technology progresses and its influence grows non-linearly. It's all exponential. So I can tell you that full autonomy, when you put it on a drone Look, so if you, if you think about a tank and a like, it's not a direct comparison, but even, like, a drone and a artillery shell or like, sort of cost per kill, an artillery shell for 155 caliber, which is a standard NATO caliber Currently market price is about $4,000 per piece. So compare that to say, $400 per drone. That's 10 times more expensive. Account for the amortization of the artillery gun and for how vulnerable it is and what is the sort of tactical, capabilities it gives you as compared to a drone. You'll figure out that an FPV drone is maybe three orders of magnitude, more versatile, more useful, more capable than artillery and many of than a classic artillery. Many of Because there are different types of artillery. Not just, like, one 155. You have mortars, you have all that. But give or take, roughly three orders of magnitude maybe. Again, it doesn't have that firepower. It's not one-to-one comparison still.Yaroslav [00:29:53]: Now, take that FPV drone. When you put full autonomy on that FPV drone, which can be not very expensive, like systems that we're, producing are like, in hundreds of dollars of pure bombFull Autonomy: From Human Pilots to Smartphone-Directed Drone MissionsNoah [00:30:06]: Just interrupt. You said full autonomy Just a second ago you were saying that the autonomy here is guidance, right? It's not decision-making.Yaroslav [00:30:14]: No, I was I was saying that's the f-First and sort of easiest pieces of autonomy that was fielded by us. But if you, if you add full autonomy to a droneBrandon [00:30:24]: He, I think he's asking what does it can you, for the listeners, can you explain What the term full autonomy means?Yaroslav [00:30:29]: Basically, I think a good way to think about an FPV drone is like an iPhone of warfare. It's, like, very inexpensive, very mass producible, very versatile. You don't need a bunch of other things when you have a iPhone in your pocket. You don't have, need an MP3 player, you don't need a calculator, don't need other things. All right? So FPV drone is an iPhone. Or like, okay, Apple please don't sue me, is a smartphone. And then, when you add autonomy to it sort of becomes like Uber or ride sharing. Okay? So what it means is instead of actually being a trained pilot who has this complex remote controller device which requires a couple months of training to actually pilot the drone, and then having to pilot it for 30 minutes, flying towards the target, et cetera, et cetera, now you basically, you have your smartphone, you have a drone, you pick your smartphone, you say, “We are here. The bad guys are here. Go and get them.” And the drone goes up, flies in a given direction, localizes itself on the map, finds the dedicated area where they, the bad guys are supposed to be sees the bad guys, bombs them, return, like, watches, so does a damage assessment, returns back, sits down, and then you can pick it up and watch the video if you didn't have the radio link, right?Noah [00:31:59]: That's a bomber drone.Yaroslav [00:32:00]: That's full autonomy for a bomber drone, right?Noah [00:32:03]: You're saying that no human decision is made in this entire process?Brandon [00:32:06]: That's not, that's not what he's saying.Yaroslav [00:32:07]: A human decision was made at the beginning of the process-Noah [00:32:09]: I get it. I get itYaroslav [00:32:09]: The same way as you would fire an artillery.Yaroslav [00:32:12]: When you fire an artillery, you don't stop at like, 500 meters away from a target and ask it whether, you want to strike or not. That's exactly, a human decision is always made at some point. So when you do that's full autonomy, and such full autonomy is happening as we speak. And such full autonomy increases the capabilities of an FPV drone, which is already, like, three orders more powerful than an artillery shell. Full autonomy increases its capabilities by four orders of magnitude because now you can have 100 times as many people who can use it, because you don't need to train those people, and this is important. You can have 10 times, mission success rate, and you can have 10 times utility per drone because now instead of being one-way kamikaze, it's, it can be a bomber.Brandon [00:33:05]: Now wait, let's, you said 10 times mission success rate, which means that fully autonomous bomber drones succeed in their missions 10 times more often than human piloted bomber drones do. That's an important thing to know.Noah [00:33:17]: Maybe, to push back onBrandon [00:33:19]: They're super, they're superhuman. They're, they' 10X superhuman.Yaroslav [00:33:22]: They're not vulnerable to electronic warfare. They don't care about the radio horizon. They don't lose track during navigation. They are not susceptible to human error when, an artillery shell or other drone blows up besides you and you're like, “Hell no,”like, “I'm getting out of here.” Right? That doesn't happen to an autonomous drone. Like, all of those things. Like, we have, like, one of the brigades that's using our drones with just first level autonomy They literally said that their success rates-Brandon [00:33:53]: What's first level autonomy?Yaroslav [00:33:54]: First level autonomy is just the terminal guidance.Yaroslav [00:33:57]: By the way, we have video of that. We can watch that.Brandon [00:33:59]: Terminal guidance means a human gets it nearby and then the AI takes over.Yaroslav [00:34:03]: The human flies it all the way, like 30 kilometers towards the target, and obviously the target was probably given to that human by someone who's flying some ISR drone, some reconnaissance drone, right? So all the way to the target, and once you see the target from a distance of 500 meters, you do target lock, and from there drone flies autonomous. So just that feature alone, it has increased the guy's, his call sign is Grom, so it has increased his, mission success rate, like precision of mission, yeah, mission success rate from 20% to 71%, and it also increased his kill zone from three kilometers to 10 kilometers, which means there's certain area around the front line which is designated kill zone. Whenever enemy goes into that area, it's almost guaranteed to be to be destroyed by a drone. And then obviously the drones are not launched from like, the zero line. They're usually launched from like, minus 10 kilometer-Mission Success, Failure Modes, and the Five Levels of AutonomyBrandon [00:35:03]: What is a zero line?Yaroslav [00:35:05]: Zero line is sort of an imaginary line of control, of two conflicting forces.Brandon [00:35:14]: It's important to explain these things to a lot of the listeners who areYaroslav [00:35:17]: Thank you for askingBrandon [00:35:18]: Familiar with warfare.Noah [00:35:20]: Myself.Noah [00:35:20]: I'm one of those listeners.Brandon [00:35:20]: You said that level one autonomy, in other words just terminal guidance, just, like, human gets it to the finish line and then it goes over the finish line, increases mission success from 20 something percent to 71%, or something like that.Yaroslav [00:35:33]: Increases the kill zoneBrandon [00:35:34]: Increases the kill zoneYaroslav [00:35:34]: Three kilometers to 10 kilometers.Brandon [00:35:36]: Got it.Yaroslav [00:35:36]: On both parameters-Brandon [00:35:37]: What is full autonomy, dude? AndNoah [00:35:38]: Actually on real quick, can we define mission success and like, maybe in a way, what are the failure modes of missions?Brandon [00:35:44]: I have a guess what mission success is.Noah [00:35:46]: But I couldBrandon [00:35:47]: Get ‘em.Yaroslav [00:35:49]: No, but that's a very good question, in fact, because, even if you fly into the target, well, first the target can be damaged or destroyed. Those are two different modes. Then there can be different targets. A sole infantryman is one kind of target. A dugout where supposed there are some, enemies there is another kind of target, and a some mechanical equipment is another type of target. Radio emitting equipment, which, like, often, like, the targets that the military want to get more than anything else is the some enemy radio tower or something like that or some small radio dish that really makes life difficult in that area, in that combat area. So those are different targets, right? It can be destroyed, can be damaged.Then sometimes, the drone hits but doesn't explode. Like, that happens. And then, there are other failure modes. You didn't even reach the target because you were A jammed by electronic warfare; B, you lost the control over drone because of the radio horizon; C, you were jammed by a different type of electronic warfare that happens way before You hit the target area. It's, impacting your, video receiver. So like jamming on video or jamming on control are two different types of jamming. Then something malfunctioned on a drone, just a mechanical malfunction, maybe like a motor broke or like, whatever. So all of those are different failure modes. Yeah, or maybe you got lost, you're navigate navigating to your, to your target. That happens, too.Noah [00:37:41]: The Level one autonomy, basically you manage to point in a direction.Noah [00:37:49]: You go there, and then the last mile The drone taking over.Yaroslav [00:37:52]: We define this like, I define that but it sort of got picked up by the industry. We define five levels of autonomy. So level one is terminal guidance. It's what we just discussed. Level two is bombing. Level three is autonomous target detection and engagement decision. Level four is autonomous navigation. And level five is autonomous takeoff and landing.Noah [00:38:15]: Those are good things to knowYaroslav [00:38:16]: Those are five levels of autonomy. Now, if youNoah [00:38:19]: I have a question for you.Yaroslav [00:38:19]: Sorry. Like, let me finish withNoah [00:38:21]: SorryYaroslav [00:38:21]: Theoretical part.Noah [00:38:23]: What is Tesla running at right now?Yaroslav [00:38:25]: Tesla?Noah [00:38:25]: No, sorry.Yaroslav [00:38:26]: That's very good point. Like, it's exactly, it was inspired by the levels of self-driving autonomy.Noah [00:38:32]: Waymo's level five, right?Noah [00:38:35]: You just tell it where you want to go, it picks you up, and then you go there.Yaroslav [00:38:36]: I think, like, if you, if you look at the classic definitions of self-driving cars, Waymo is still, like, level four because it still requires even remote, but still, like, human control. It's like if Waymo gets in trouble, there is an operator who takes over and resolves this. So that would still be a level four. It doesn't map directly, but it's also five levels.Brandon [00:38:58]: Can I, can I interject a question here? In terms of an FPV drone that's like a suicide drone that'll just blow itself up killing something, how do what it hit? Like, does it, just transmit back, or do you sort of like, lose track of it and hope it hit? Like, what happens to that?Yaroslav [00:39:16]: That's a great question. SoBrandon [00:39:18]: You need another droneYaroslav [00:39:19]: Like, the current battlefield in Ukraine is saturated with different types of drones. So obviously you have all the FPV drones and last year alone, Ukraine manufactured about 4 million of these, and then Russia's maybe, like, 20% less than that. And for this year, the publicly voiced target was 7 million on Ukrainian side. So it's, like, serious numbers. We're getting in serious numbers here. And then besides those, there are different, reconnaissance drones, ISR as we call them, and there are sort of tactical level ISR where we, both Ukrainians and Russians usually use, Mavic, drone by DJI. And then there are a bunch of locally produced drones, which are sort of fixed wing drones that can stay in the air for much longer than Mavic, maybe, like, half an hour. And then, there are drones that can stay for many hours or even up to a day. And those drones have, are more expensive, have more expensive cameras, et cetera, et cetera. We hunt those drones that Russians launch. The Russians hunt our drones, and so on. But ideally, when you, are a group of soldiers operating an FPV, you'll have someone in your, company, or someone in your platoon who has an ISR asset that will do target designation for you. They'll say, “Oh, like, there's a Russian vehicle over there. Go and get him.”and you go there, you get it, and they're like, “Okay, confirmed.”Battlefield Surveillance and the Eight Dimensions of AutonomyBrandon [00:40:57]: Those guys are watching. They have their own drones in the sky.Yaroslav [00:40:59]: Target destroyed. They have, like, a carousel of drones because One Mavic cannot stay more than 30 minutes. ItBrandon [00:41:06]: They're constantly surveilling the battlefield.Yaroslav [00:41:07]: Almost every spot on the battlefield.Yaroslav [00:41:11]: It's not always the case. Sometimes you will not have a surveillance asset, so then you would launch another FPV just to confirm that there was a hit. Then if you see there was a hit and you're not sure if it completely destroyed, you maybe hit again for good measure.Brandon [00:41:26]: You double tap.Yaroslav [00:41:28]: That's how it works. But I was about to give you another sort of piece of taxonomy. So you have five levels of autonomy, right? Then you have sort of eight dimensions of autonomous battlefield. So what is eight dimensions? It's crucial to understand how autonomy evolves in a modern, battlefield environment. So dimension number one is level of autonomy. What are the capabilities that your asset has? Dimension number two is the platform you're operating on. So it can be a quadcopter, a fixed wing drone, different types of maybe, like, a long range drone or short range drone, but it can also be a missile. You can have autonomy even on an artillery shell or a ground vehicle or a sea vehicle. So all of those are different platforms. Level three would be domain. So it's ground to ground or ground to air as an intersection, or ground to sea or sea to air. They're all, like, all the nuances with different domains. Then level four, would be higher levels of autonomy, such as swarming, drone carriers, drone nests, et cetera.Brandon [00:42:39]: Now when you're saying level, you're talking about dimensions, not about-Yaroslav [00:42:42]: Sorry. YeahBrandon [00:42:43]: Autonomy levels. So dimension four.Yaroslav [00:42:43]: The dimension. Yeah, I used to say I was supposed to say dimension. I say dimension because each of them works with another, right? So you might have, like third level autonomy, fixed wing drone operating in land to air, and stuff like that right? And then operating in a swarm or operating from a nest. Right? Then you have, sort of dimension number five is environment. So is it day or night? Is it summer or winter? Is it, humid, cold, dry? What kind of target is it? Is your target hiding in a forest, or is it, behind a hill or within buildings? So all of that is environment. Then you have, dimension number six is command and control. How are you dealing with or like, tens of thousands of those assets around the battlefield? How are you coordinating that on the higher levels of command? How are you collecting data? All that.Yaroslav [00:43:44]: Dimension number seven would be infrastructure, so things like simulation, data collection tools, security, deployment mechanisms, et cetera. So all those systems have to be developed separately and integrate with all the others. And finally, dimension number eight is sort of distribution. Have you deployed 100 of these systems or 100,000 of these systems? Because those are two very different ballgames. So that now gives you a more broad overview of how autonomy propagates across the battle space.Targeting, Human Responsibility, and Rules of EngagementNoah [00:44:23]: As someone who has done machine learning and had gone out of distribution and had things, go horribly wrong, you were talking several of these, kind of axes of thinking about drone warfare seem like they could be very susceptible to some sort of distribution shift if you start making things autonomous.Yaroslav [00:44:41]: Like what?Noah [00:44:41]: I mean Well, first ofYaroslav [00:44:43]: If the I'm very interested Sort of sort of kinds of scenarios that you're thinking about.Noah [00:44:48]: Like the most obvious one is you, if I assume these are computer vision guided systems for at least the last mile, how do you ensure that oh, well, like you now have some fog roll in or something, and you, the drones just attack the wrong thing? Or maybe, it probably will not turn around and fly back and attack you, but youYaroslav [00:45:10]: Same, the same, the same question, how do you ensure that your mortar fire hits the right thing? Well, it's like mortar fire, give or take half a kilometer could be plus or minus. So maybe you fire one, and then you fire another. So drones are actually, much better in being precise in those scenarios. And I think, to your point, I think five to 10 years from now it will be immoral to use weapons without AI.Yaroslav [00:45:44]: ‘Cause weapons without AI will be more likely to cause, collateral damage or unwanted damage. Same way, it will be immoral to drive your own car manually on a public road because it's more likely to cause, unwanted damage.Noah [00:46:02]: Wow, I never considered that mightBrandon [00:46:04]: Really? That's definitely coming.Yaroslav [00:46:07]: Anyway.Brandon [00:46:07]: No, but that' I don't know, it's an obvious, an obvious thought. I agree with you.Brandon [00:46:12]: I, No, they, obviously they're not going to let you drive once most of the cars on the road are autonomous.Noah [00:46:17]: No, that one, don't I believe.Yaroslav [00:46:19]: No, I think you were you were talking about drones, right?Brandon [00:46:21]: The drones, right. Cool.Yaroslav [00:46:22]: The weapons, right?Brandon [00:46:23]: Friendly fire and collateral damage and stuff like that is all minimized with AI.Brandon [00:46:27]: Here's my question. Take all let's go to level six autonomy. Let's take all of the target selection. Let's take all the battlefield data, integrate it into one big AI, and have that big AI basically be in command of the battlefield And agentically do target selection.Yaroslav [00:46:44]: Be the general, right?Brandon [00:46:44]: It's a general. It's, you've cut humans out of the loop except maybe as dexterous robots, repairing drones and fastening things to drones or maybe something like that because you don't have those robots yet. How soon are we there? AI general.Yaroslav [00:46:58]: The most important thing to ask ourselves is who will be faster to that us or our adversaries?Brandon [00:47:07]: I assume us, but how fast will we be to that? I hope us.Yaroslav [00:47:11]: I hope so too.Brandon [00:47:12]: How fast can we Like when are we looking at that in terms of like horizons years?Yaroslav [00:47:18]: Like technically, it could be done now. The question is of course, there's, some engineering work to be done. The bigger challenge is deployment. Right? So okay, technically Like operation in Iran, right? They, the publicly, it was claimed that I think Palantir system was used for target designation, et cetera, et cetera. So it is not exactly as you say, the AI makes all the decisions, but basically AI goes through all the data you have, gives you these 1,027 different targets and says, “You-- To confirm, please press Okay.” And you look at the targets and you're like, “Yeah, sounds right. Press Okay.”so that's, I think that's where we are now already, or we were a couple weeks ago as we're recording this on April 10th. Another question is how massively deployable it is. Is it, like, every decision being made like that or is it, like, just some of the decisions made like that? And then different levels of command and control. There you have, like, the platoon, the company level, the battalion, et cetera, et cetera, et cetera. But the tricky thing here when we get into that territory, the tricky thing is If your enemy is getting advantage of being Thousand times faster than yourself by deploying such systems What do you do?Yaroslav [00:49:10]: You got to-Brandon [00:49:12]: The if the enemy is a thousand times faster than you at deploying those systems?Yaroslav [00:49:16]: Like, if enemy starts deploying level six autonomy, as you call And you have not started doingBrandon [00:49:22]: You're in troubleYaroslav [00:49:23]: Yes, exactly. So you have to catch up. So my point is that it is very important to think about the safety of these systems, but that thinking should not slow you down in developing them because they are critical for your existential, survival, right? And like, one person who doesn't think, doesn't get to think about the ethics of the war is a dead person. That person surely doesn't get to think about that.Brandon [00:49:52]: What would be the safety risk of such a system?Yaroslav [00:49:55]: Of course-Brandon [00:49:56]: Friendly fire?Yaroslav [00:49:56]: Just wrong decisions, right?Brandon [00:49:59]: I see.Yaroslav [00:49:59]: Maybe, these decisions-AI Command Decisions, Dead Zones, and Complex BattlefieldsBrandon [00:50:06]: Skynet AI decides it's going to useYaroslav [00:50:08]: No, these-Brandon [00:50:08]: Drone army to kill usYaroslav [00:50:09]: Decisions will not only be made about drones. They are likely to made about what the humans should do on your side as well. Then obviously some environments are more like Ukrainian-Russian war, where you haveBrandon [00:50:26]: It will have to choose to risk lives. It will have to choose to sacrifice human lives-Yaroslav [00:50:28]: Of courseBrandon [00:50:29]: On your side.Yaroslav [00:50:29]: Of course. And then some environments are just, like, dead, like, dead zones and there are no civilians there, or virtually no civilians close to the front line because, like, super dangerous. Everyone has evacuated from there. But there are other environments which are more like, okay, there's a counterterrorist operation. There's, like, a group of terrorists or a group of civilians. Or like, it's like the recent operations in Iran, I imagine that the US and Israeli forces do not want to harm civilians. They only targeted the military targets there, right? So in those situations, it's a different level of responsibility for that decision-making as well. And then there is just such a big variety of those military missions, and I'm not even, like, well-informed or well-educated in military science to tell you about all those scenarios. We would need to put some general besides me, and maybe a Ukraine general and American general would have told you very different stories about these things.Brandon [00:51:34]: Got it. Can I ask a few more questions? All right. So in 2013, I wrote one of my first, paid articles ever was about how the era of drones will change human society. I was just sitting around bored thinking about things.Yaroslav [00:51:54]: You were way ahead of your time.Brandon [00:51:55]: I said, I said, “The following will happen.”Yaroslav [00:51:57]: It's, this article is real. I've read it.Yaroslav [00:51:58]: It's actually-Brandon [00:51:59]: I said small autonomous, suicide drones, will cleanse the battlefield of human infantry. Human infantry will not be able to stand against swarms of AI-powered, suicide drones. That was I didn't even know about, like, AlexNet at the time, I think.Yaroslav [00:52:19]: You're just an avid sci-fi reader.Brandon [00:52:23]: I'm an avid sci-fi reader, but also, like, it's not Like, there will be a way to do that. It's a it's a nonlinear multidimensional search problem, and you get enough compute, you'll find some search algorithm that will get you there. And soBrandon [00:52:38]: I, yeah, I think that one sentence describes the bitter lesson right there.Brandon [00:52:41]: It's just like it's a multidimensional search space. You search it somehow. I don't know. Figure out some get a grad student-Yaroslav [00:52:47]: Sooner or laterBrandon [00:52:47]: To make a search algorithm.Brandon [00:52:48]: It's not that hard. Anyway, so but then, but I guess the point is The point is that human infantry on the battlefield will be will be gone at the end. I wrote that in 2013. Many people on social media laughed at me for that called me hysterical, said things like, “Electronic warfare will knock all the drones out of the sky.”like, “You need humans to hold ground.”that's something you still hear from a lot of people on social media today. I feel that this article that I've written has never been directionally wrong. It has gotten more and more right steadily over time, and that we're very reading the battlefield reports from Ukraine, where, human infantry are basically guy, like a few guys hiding in dugouts for months, and I'm not sure what they're doing.Yaroslav [00:53:35]: That's on Ukraine's side. On the Russian side, that's just like a zerg rush.Brandon [00:53:38]: The zerg rush, and then they just die. Then, but they have some guys in dugouts too, right? Like hiding in dugouts for months.Yaroslav [00:53:45]: They have. Yeah.Brandon [00:53:45]: Like, but that like, what are those guys doing in the dugouts? Are providing, like, frontline, like, reconnaissance? Like, what are they doing?Yaroslav [00:53:54]: If there is a guy in a dugout with some bullets and automatic weapon, the other guy cannot come and take the that dugout. That'Brandon [00:54:07]: I seeYaroslav [00:54:08]: They are they're establishing control over territory.Brandon [00:54:10]: I see. So that is so there still is a use for human infantry on the battlefield as of today.Yaroslav [00:54:15]: LikeBrandon [00:54:15]: How long will that last?Yaroslav [00:54:17]: I think it will last for a while. This is funny. There's this whole Layer of the modern culture, a modern Ukraine culture built around the war-related stuff. So there is this -Punk rock band, that is called SZC, I guess in English that would be. Which stands short for like a deserter or something like that. So anyhow, this band has a song titled “2030.” It's basically about the year 2030, and the war still goes on as like the whatever, third world war or whatever. And they basically, they, sang about the AI and like cyborgs and everything, but the simple infantry is still needed, and we're still, like, getting cold in those dugouts, and we're still doing our job. That's sort of the theme of the song. And it seems like that's actually what's going to happen. There areGround Robots, Simulation, and the Limits of World ModelsBrandon [00:55:30]: Ground robots will not replace humans in the dugouts soon.Yaroslav [00:55:34]: I'm very much interested in following the whole humanoid robot theme andBrandon [00:55:39]: What about like a dog robot?Noah [00:55:41]: Or just mobile controlled platforms or something.Brandon [00:55:44]: Spider robot, yeah.Brandon [00:55:45]: Everything evolves into a crab.Brandon [00:55:46]: You build a crab robot.Yaroslav [00:55:47]: A humanoid-Noah [00:55:48]: The carcinization of warfare.Yaroslav [00:55:51]: There is a lot of utility in humanoid robots because the world is designed around humanoids. So I would not, like, 100% disqualify the possibility that sometimes 10 years in the future, humanoid robots, will be actually fighting. So that's an actual Terminator kind of scenario.Brandon [00:56:14]: Yeah, in the first Terminator movie, you look at what they've got on the battlefield, they've got flying bomber drones and humanoid robots.Yaroslav [00:56:20]: Look, the cost of large language models of running them is getting so low, you can have basically an inexpensive computer running, what was a state-of-the-art model a year and a half ago, running it locally on a device with an open source model, which also means that the Chinese can have it, the Russians can have it, the North Koreans can have it, et cetera. So that is already possible. And with when we're looking at the acceleration of the neural nets, I would've, if not the acceleration of the large language models, I would've said that I don't think that humanoid robots will be able to be useful in the battlefield earlier than in 10 years. But if you account for the exponential, it might be five years or so. The problem with all of the autonomous systems, and it's like starts with self-driving cars and even with all the AI, like modern day AI agents, to make them really, useful, you have to solve such a long tail of edge cases, that it's really difficult to make them useful. Like we were promised, self-driving cars, what, like 2007, Sebastian Thrun and Google, and even before that all the challenges, everything. And Elon of course told us it's going to be one year from 2014, and now we still don't have self-driving Teslas everywhere. We have Waymos in SF and some other places, but they're still, like, not perfect. So I think, I expect something similar from self-flying drones and fully autonomous drones, and we saw that firsthand as with each level of autonomy that we're adding, there is a very wide distance between a prototype and something that is ready to be scaled to millions of units and something that has been scaled to millions of units. But the race with like AI coding tools is just insane. So things might accelerate very fast, faster than we can imagine.Noah [00:58:46]: I think your point is that with due to this long tail behavior Level one autonomy as you've defined it, is actually very natural. Like you basically are just solving an image recognition and tracking system.Yaroslav [00:59:02]: It's actually interesting that you say it that way, and I thought about this the very same way, and we have this joke that there are like 200 companies in Ukraine which are trying to solve last mile, targeting or terminal guidance. It seems like we're like the only company that actually solved that because even that problem-Noah [00:59:22]: I'm not saying it's, I'm not saying it's trivial, but it's at least something that you imagine given our current state.Yaroslav [00:59:26]: Like us and Eric Schmidt, like Eric Schmidt's companies are pretty good.Yaroslav [00:59:29]: Like, I actually have lots of respect to what they're doing, and they're, they have been practically influential and helpful on the battlefield, and they have good engineering.Noah [00:59:38]: I wasn't, I wasn't saying it's trivial. I'm just saying this is a something naturally adaptive based upon things that we know work, well. But some of the other domains that where you do have to make decisions and you have a long tail become much harder, and you worry about edge cases more.Yaroslav [00:59:57]: Like the more, the more complex behavior you're trying to simulate, the more edge cases there are right? The more ways to do it wrong there are. And then there are different approaches. It's like if you think about, if you read academic papers about robotics, right? You sort of the robot is represented as something that has the sort of sensor input, and then you have three, levels of sort of logics or decision-making, which are perception, planning, and control, and then you have actuators as output.So pre-neural nets, you would do perception output and control all with classic logics, right? Then, with AlexNet and computer vision, you could do perception with neural nets and the rest with logic. You cannot currently do each of those separately with neural nets, each of those separately with logics, or you can just have one huge neural net that just takes lots of sensory data. It's not just pixels. Could be sound, could be accelerometer, could be everything, as input, and just outputs the controls. And some of the self-driving car companies are doing that or like, experimenting between different ways of doing that. So you can also, like, think about that and the way you implement those features, also influences how much degrees of freedom the system would have, right? Like control, you can do it classical algorithmic control with common filters and PAD filter, PAD controllers, et cetera, or you can do a neural net, that was trained in a gym with a reinforcement learning, et cetera. And those would be two different behaviors of a system.Noah [01:01:53]: I-- Maybe my point was just much more high level. It'Yaroslav [01:01:56]: Or you can If you go even like, if you go high level, you can, you can like train to like have whatever, like Feifei Li and folks who are doing like physical, sortBrandon [01:02:08]: World modelsYaroslav [01:02:08]: World models, right, physical intelligence, they're trying to make these big models and sort of understand the world and then supposedly you have such model and you can tell a drone, “Okay, like, go over that hill and like, find the bad guys and then get them,”or “Make me a video, make me a photo of the guy smiling and get back to me.” Right? That's one way. Another way you have like these subsystems, like one is navigation, another is finding the person, another is like getting to them to take a photo. And those are again, very different behaviors. And then it's not that one is necessarily better than the other, and we might have more technological ability to do one or another. But all of those systems will exist. And then again, you should always keep in mind that it's only the not only the good guys that are developing these systems, the bad guys are developing these systems as well.China's Drone Supply Chain and the West's Manufacturing GapNoah [01:03:00]: I guess where I'm going with this back to Noah's original thought with the end of the end of the soldier. And so in order to replace-Brandon [01:03:10]: Or at least the end of the rifleman.Noah [01:03:11]: Or the end of the rifleman, yeah.Yaroslav [01:03:13]: I'm not seeing that very close, and it was like I'm, as much as I'm a lover of sci-fi and all of that and a technologist, the more I try to beYaroslav [01:03:27]: Like the I try to have certain humility about these things, and like the military, domain and there was just so much human history and blood and tears, dedicated to sort of understanding this art of war and perfecting it and so on. There is so much knowledge in there that I don't feel like I even started to comprehend, a lot of that. But one thing that I really understood is that even though drones are now making eighty percent of the casualties, you go to the actual officers, you talk to the actual, like, brigade commanders, corps commanders, and they explain to you, how all of it fits together, how when you're thinking about an operation that involves a couple thousand people to get this piece of land, out of the enemy's hands, deoccu deoccupy it, how it is so complex, it involves, dozens of different types of drones and then land operations and reconnaissance operations, psychological operations and then aviations and tanks and logistics and all kinds of these different assets. So modern warfare is really very complex, and the fact that the drones are the latest, coolest thing, and then the AI is latest, coolest thing, doesn't mean that now it's that and only that right? So yeah. Whoever's looking into that I think should realize that it's not just what the press talks about, that the reality is much more difficult, much more complex.Brandon [01:05:17]: Let's talk about China and China's manufacturing capabilities. So suppose that someone, like suppose the United States went to war with China. AndYaroslav [01:05:26]: I hope not.Brandon [01:05:27]: I hope not as well. And then but suppose that drones were very essential to that war of all the types of drones that we're talking about here, and that suppose that China said, “All right, well, you need X and Y and Z, to make those drones to fight us, and we control the production of X and Y and Z, so we're just going to cut you right off, and now you have no drones.”Brandon [01:05:47]: I know that a number of countries, including Ukraine and Taiwan, have been making moves to China-proof their drone productions that China couldn't do that. Examples of things they might be able to cut off might include rare earths, fiber optic cable that you were talking about before, various other things that where even if they don't control one hundred percent of the production, they control enough of the production that would be extremely expensive to produce it without relying on Chinese sources. Or the market's fragmented enough, et cetera. What do you see as China's key bottlenecks, and how easy are those to overcome in terms of China-proofing drone production in case of a war against China?Yaroslav [01:06:30]: Let me start with a saying that -Although China does not sell directly to Ukraine and it does sell directly to Russia, a lot of Ukrainian supply chains, they start in China, right?Yaroslav [01:06:49]: We're not in a conflict with China, and we would not want to be in a conflict with China. And we'd hope that China stays a neutral power between Ukraine and Russia and the US as well. That said, the scenario that you're describing, everything is much worse.Yaroslav [01:07:11]: Think about this. Last year, Ukraine produced four million FPV drones. Ukraine is not the most industrious nation in the world.Yaroslav [01:07:19]: China can produce four billion of these FPV drones.Yaroslav [01:07:23]: China can make them not drones with propellers, but fixed-wing drones, which go not forty kilometers far, but maybe two to three hundred kilometers inland.

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Mark Wachholz war 20 Jahre lang Drehbuchautor — und ist jetzt irgendwas anderes. Mit generativer AI baut er statt Blueprints fertige Kurzfilme, gewinnt Festival-Preise und nennt seinen Workflow nicht KI-Filmmaking, sondern Vibe Filmmaking. Wir reden mit ihm über das Verschwinden des AI-Labels, Chatbots als ausgelagertes Gehirn, das limitierte Format Drehbuch und warum man im Latent Space am besten mit Dynamit fischt. „Mit Wein, Vibe und Gesang" — die Episode, in der ein Drehbuchautor zum Filmemacher wird, ohne oft am Set gestanden zu haben.

From building Applied Intuition from YC-era autonomy tooling into a $15B physical AI company, Qasar Younis and Peter Ludwig have spent the last decade living through the full arc of autonomy: from simulation and data infrastructure for robotaxi companies, to operating systems for safety-critical machines, to deploying AI onto cars, trucks, mining equipment, construction vehicles, agriculture, defense systems, and driverless L4 trucks running in Japan today. They join us to explain why “physical AI” is not just LLMs on wheels, why the real bottleneck is no longer model intelligence but deployment onto constrained hardware, and why the future of autonomy may look less like one-off demos and more like Android for every moving machine.We discuss:* Applied Intuition's mission: building physical AI for a safer, more prosperous world, powering cars, trucks, construction and mining equipment, agriculture, defense, and other moving machines* Why physical AI is different from screen-based AI: learned systems can make mistakes in chat or coding, but safety-critical machines like driverless trucks, autonomous vehicles, and robots need much higher reliability* The evolution from autonomy tooling to a broad physical AI platform: starting with simulation and data infrastructure for robotaxi companies, then expanding into 30+ products across simulation, operating systems, autonomy, and AI models* Why tooling companies came back into fashion: Qasar on why developer tooling looked unfashionable in 2016, why Applied Intuition still bet on it, and how the AI boom made workflows and tools central again* The three core buckets of Applied Intuition's technology: simulation and RL infrastructure, true operating systems for vehicles and machines, and fundamental AI models for autonomy and world understanding* Why vehicles need a real AI operating system: real-time control, sensor streaming, latency, memory management, fail-safes, reliable updates, and why “bricking a car” is much worse than bricking an iPad* Physical machines as “phones before Android and iOS”: Peter explains why today's vehicle and machine software stack is fragmented across many operating systems, and why Applied Intuition wants to consolidate the platform layer* Coding agents inside Applied Intuition: Cursor, Claude Code, internal adoption leaderboards, and how AI tools are changing engineering workflows even in embedded systems and safety-critical software* Verification and validation for physical AI: why evals get harder as models improve, how end-to-end autonomy changes simulation requirements, and why neural simulation has to be fast and cheap enough to make RL practical* From deterministic tests to statistical safety: why autonomy validation is shifting from binary pass/fail requirements toward “how many nines” of reliability and mean time between failures* Cruise, Waymo, and public trust: Qasar and Peter discuss why autonomy failures are not just technical issues, how companies interact with regulators, and why Waymo is setting a high bar for the industry* Simulation vs. reality: why no simulator perfectly represents the real world, how sim-to-real validation works, and why real-world testing will never disappear* World models for physical AI: hydroplaning, construction equipment, visual cues, cause-and-effect learning, and where world models help versus where they are not enough* Onboard vs. offboard AI: why data-center models can be huge and slow, but onboard vehicle models need millisecond-level latency, low power, small size, and distillation-like efficiency* Why physical AI is not constrained by model intelligence alone: the hard part is deploying models onto real hardware, under safety, latency, power, cost, and reliability constraints* Legacy autonomy vs. intelligent autonomy: RTK GPS in mining and agriculture, why hand-coded path-following worked for decades, and why modern systems need perception and dynamic intelligence* Planning for physical systems: how “plan mode” applies to robotaxis, mining, defense, and multi-step physical tasks where actions change the state of the world* Why robotics demos are not production: the brittle last 1%, humanoid reliability, DARPA Grand Challenge-style prize policy, and the advanced engineering gap between research and deployment* Applied Intuition's hard-earned lessons: after nearly a decade, Peter says they can look at a robotics demo and predict the next 20 problems the company will hit* Qasar's advice to founders: constrain the commercial problem, avoid copying mature-company strategies too early, and remember that compounding technology only matters if you survive long enough to see it compound* Why 2014 YC advice may not apply in 2026: capital markets, AI company dynamics, and the difference between building in stealth with a deep network versus building as a new founder today* What Applied is hiring for: operating systems, autonomy, dev tooling, model performance, evals, safety-critical systems, hardware/software boundaries, and engineers with deep curiosity about how things workApplied Intuition:* YouTube: https://www.youtube.com/@AppliedIntuitionInc* X: https://x.com/AppliedInt* LinkedIn: https://www.linkedin.com/company/applied-intuition-incQasar Younis:* X: https://x.com/qasar* LinkedIn: https://www.linkedin.com/in/qasar/Peter Ludwig:* LinkedIn: https://www.linkedin.com/in/peterwludwig/Timestamps00:00:00 Introduction: Applied Intuition, Physical AI, and 10 Years of Building00:01:37 Physical AI vs. Screen AI: Why Safety-Critical Changes Everything00:02:51 The Origin Story: Tooling, YC, and the Scale AI Comparison00:05:41 The Three Buckets: Simulation, Operating Systems, and Autonomy Models00:11:10 Hardware, Sensors, and the LiDAR Question00:14:26 The Operating System Layer: Why Vehicles Are Like Pre-Android Phones00:19:13 Customers, Licensing, and the Better-Together Stack00:21:19 AI Coding Adoption: Cursor, Claude Code, and the Bimodal Engineer00:26:41 Verifiable Rewards, Evals, and Neural Simulation00:31:04 Statistical Validation, Regulators, and the Cruise Lesson00:40:25 World Models, Hydroplaning, and Cause-Effect Learning00:43:34 Onboard vs. Offboard: Latency, Embedded ML, and Distillation00:50:57 Plan Mode for Physical Systems and Next-Token Prediction Universally00:53:04 Productionization: The 20 Problems Every Robotics Demo Will Hit00:58:00 Founder Advice: Constraints, Compounding Tech, and Mature-Company Mimicry01:05:41 Hiring Philosophy: Hardware/Software Boundary and Engineering Mindset01:08:50 General Motors Institute, Education, and the Curiosity MindsetTranscriptIntroduction: Applied Intuition, Physical AI, and 10 Years of BuildingAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: And today we're very honored to have the founders of Applied Intuition, Qasar and Peter. Welcome.Qasar [00:00:17]: You guys really know how to turn it on to podcast mode. That was, you guys are real pros at this.Qasar [00:00:23]: They were just joking around right before this, and then they flipped it pretty quick.Alessio [00:00:29]: Oh, yeah, it's good to have you guys. Maybe you just wanna introduce yourself so people know the voice on the mic and they'll know what they're hearing.Peter [00:00:33]: Oh, sure. Yeah, I'm Peter Ludwig. I'm the co-founder and CTO of Applied Intuition.Qasar [00:00:38]: And my name is Qasar Younis. I am the CEO and co-founder with Peter.Alessio [00:00:42]: Nice. Can you guys give the high-level overview of what Applied Intuition is? And I was reading through some of the Congress files, when you went out there, Peter, and eighteen of the top twenty global non-Chinese automakers, you two guys, you have customers in agriculture, defense, construction. I think most people have heard of Applied Intuition tied to YC when it was first started, and then you were kinda in stealth for a long time, so maybe just give people the high-level overview of what it is today, and then we'll dive into the different pieces.Peter [00:01:10]: Yeah. So at Applied Intuition, our mission is to build physical AI for a safer, more prosperous world. And so we work on physical AI for all different types of moving systems, everything from cars to trucks to construction and mining equipment, to defense technologies. And we're a true technology company, so we build and sell the technology, and we sell it to the companies that make the machines. We sell it to the government, really anyone that wants to buy a technology to make machines smart.Physical AI vs. Screen AI: Why Safety-Critical Changes EverythingQasar [00:01:38]: Yeah. And I think in the broader AI landscape, a lot of the focus, rightfully so in the last, three years has been on large language models, and so everything fits in a screen. Like, whether it's code complete products or things like that. And what's different about us is we're deploying intelligence onto a lot of things that don't have screens. they're physical machines. There are sometimes screens within the cabin or for example of a car or a truck or something like that, but most of the value we provide is putting intelligence that is in safety critical environments. So that those two words are really important because learn systems can make mistakes if you're asking for, like, some, so something like, “Tell me about these podcast hostsQasar [00:02:28]: that I'm about to go meet.” But you can't do that obviously when you run, like, as an example, we run driverless trucks in Japan right now, as we speak. We can't have errors. Those are L4 trucks. Yeah.Alessio [00:02:40]: Yeah. Was that always the mission? I remember initially, I think people put you and Scale AI very similarly for some things about being kinda like on the data infrastructure side of things. What was the evolution of the company?The Origin Story: Tooling, YC, and the Scale AI ComparisonPeter [00:02:51]: Well, from the very beginning, we always wanted to, really be a technology company that helped generally push forward the industrial sector. And so we started off working in autonomy. Our very first customers were robotaxi companies. And we started off doing a lot of work in simulation and data infrastructure. And then over the years, we've expanded our portfolios. Now we have, over thirty products, and it's a pretty broad technology play within the landscape of physical AI.Qasar [00:03:19]: Yeah, I think the Scale reason is because we're all YC Universe companies. But it was a very different company. Scale, was, is more of a services company, data labeling company fundamentally. We started and still are, do a lot of tooling. So like, you think developer tooling is now in vogue again, thanks to the AI boom. But honestly, ten years ago, it was out of vogue. It w Like, doing a tooling company in 2016, 2017 was not, like, the thing to do because, I don't know if you remember, the VCs generally, their views was that toolings are They're just workflows, and workflows ultimately are not really interesting. And we've gone and come, full circle with that. But when we started the company, our kind of it's kinda like in the periphery of what the company wants to be. It was like, from our earliest days, like, we wanna deploy software on physical machines, like on cars and on trucks and things like that. And obviously, we didn't know that the transformer boom was gonna happen. We didn't know that autonomy systems would become end-to-end. Those things we didn't know. And why that's important when autonomy systems become end-to-end, it is just now those models can be generalized to, multiple form factors. And so back nine, ten years ago, tooling was a great way, and still is a great way to, build the technology and sell technology to our end customers, a lot of them who wanna build this stuff themselves. And so we just offer like a spectrum of solutions from you can just use like one part of a development suite of tools all the way to buying the full thing. The way to think about the company, or at least the way we think about the company is, as Peter said, a technology provider. It's kinda like, what NVIDIA does or what an AMD, but we just don't do chips.Qasar [00:05:06]: We don't do silicon. But we're a technology provider fundamentally. And I think even, we used to joke when we started the company, like, we're not the guys to build, like, Instagram. Like that was just towards That's not our That's just not us in a most fundamental way. IAlessio [00:05:20]: You have thoughts.Qasar [00:05:21]: Yes.Qasar [00:05:22]: Well, it's, it's I mean, I think it's just like what And I mean, we worked on Maps and stuff, Google Maps. Consumer products are extremely difficult for a lot of different reasons. It just, I think doesn't scratch the itch. I think we're like Michigan guys who are kind of more of that traditional engineering kind of a realm, or lineage. we used to jokeThe Three Buckets: Simulation, Operating Systems, and Autonomy ModelsPeter [00:05:41]: I gotta say, though, what was clear ten years ago was that there was so much more that was possible with software and AI in vehiclesPeter [00:05:47]: and that was generally the space that we started in ten years ago.Peter [00:05:51]: And the precise path that we've taken over the years, I think we've been strategic, and we've adjusted to make sure that we're actually building stuff that's valuable to the market. And like, the technology has changed so much. Like our own technology stack has completely changed, I would say, roughly every two years. And so now we've probably done, let's say, four complete evolutions of our own technology stack. And I sort of see that cadence roughly keeping up.Peter [00:06:13]: And so the way even we think about engineering is almost on this two-year horizon, we're preparing ourselves that, hey, like, we wanna invest the appropriate amount, but then also be very dynamic as the research gets published and as our research team figures out new advancements and adapting to that.Qasar [00:06:27]: Yeah. One thing that has been consistent is the type of people we've, we've recruited. It's engineers who are fall into the sometimes very traditional, like, GoogleQasar [00:06:38]: -gen suite, but way different from, other companies. We are hiring folks who really know the intersection of hardware and software, who know really low-level systems. Obviously, traditional ML researchers and folks who've, actually, put ML systems into production. That's been pretty consistent. I think that, like, you look at the mix of our engineering, eighty-three percent of the company is engineering, so it's, like, a giant list.Qasar [00:07:05]: A lot of engineers.Alessio [00:07:06]: Which, by the way, a thousand engineersQasar [00:07:07]: Yeah. A thousand engineers.Alessio [00:07:08]: that's on your website, so I imagine it's up to date.Qasar [00:07:11]: It is, it is up to date, yes. Yes.Alessio [00:07:12]: okay. And then forty-plus founders.Qasar [00:07:15]: Yeah. We would tend to also, This was more luck than strategy. But we've recruited a lot of ex-founders. It's been a great place for founders, YC and non, ‘cause obviously I know a lot of the YC folks. It's kind of like we recruit a lot of Google people.Qasar [00:07:33]: For them to exercise both their technical and non-technical skills because, we're, we're, we're on the applied side. We have a research team that we do fundamental research, we publish, and we've, we've had great traction there. But fundamentally, the business wants to take this intelligence and deploy it into production and there's, like, a certain type of person that's more interested in that.Alessio [00:07:54]: Yeah. You mentioned the tech stack, Peter, so I just wanted to give you some rein to just go into it. I'm interested in where Wayve Nutrition, starts and ends in some sense, what won't you do? What, do you do that's common among all the verticals that you cover?Peter [00:08:10]: There's a few buckets of work that we do, and we've been at this for almost ten years now, so the technology's pretty broad. But we got startedQasar [00:08:17]: Yeah, with a thousand engineers, like, you could work on lots of things.Peter [00:08:19]: There's lots of stuff, yeah, espe-especially with AI tools to help.Peter [00:08:22]: So we got our start in simulation and simulation tooling and infrastructure. And so generally, if you're trying to build a very complex software system that involves moving machines, you need to test that, and the best way to test it is it's a combination of virtual developments, a simulation, and then also obviously real world testing.Peter [00:08:39]: And then there's a very careful process of that correlation between the simulation results and the real world results and ensuring that the simulator is in fact accurate to that. Simulation's a very deep topic.Peter [00:08:49]: We have a whole suite of products in that, and we could talk for many hours about that specifically. But that is one part of what we do as a company. Reinforcement learning as a subpart of that is also super critical. I think a lot of the a lot of the best advancements happening in a lot of these AI systems right now in some way relate to reinforcement learning, and with now we have lots of compute, and you can do tons of interesting things for reinforcement learning. The second bucket of work that we do is on operating systems technology. true operating systems. Like, think about, schedulers and memory management and middleware and message passing and highly reliable networking and data links. Like, the reality is, if you want to deploy AI onto vehicles, you need a really good operating system. And when we were getting deeper into that space, there wasn't really anything that we were happy with.Peter [00:09:39]: Like, things existed, absolutely, and we were using what was available in the market, and as an engineering organization, we roughly realized these things aren't great. We think we can do this better, and so let's, let's build something. And that was then the that was the moment of inspiration that started our operating systems business, which is now a very real business for us. And in order to write and run great AI, you need a great operating system, and so that-that's what got us into that. And then the third bucket that we work on, it's, it's true fundamental AI technology. Models, we do a lot of work in, as mentioned, the foundational research, but then the also the world models and the actual autonomy models that are running on these physical machines, and that's across cars, trucks, mining, construction, agriculture, and defense, and so that's both land, air, and sea.Qasar [00:10:31]: And also, a smaller subsector of that third bucket is the interaction of humans with those machines.Qasar [00:10:38]: So that's a multimodal, experience. Historically, if you're moving a dirt mover or any of these machines, there are, like, buttons you press, whether they're actual physical tactile buttons or something like a touch screen. That's just That fundamentally is changing to where you're just talking to the machine and the machine and you're teaming with the machine.Alessio [00:10:58]: Voice?Qasar [00:10:59]: Yeah, voice, absolutely, yeah.Alessio [00:11:00]: Oh.Qasar [00:11:00]: And also the machine just being aware of who is in the cabin, what their state is. you can think from a safety systems perspective, the most simple version of this is, like, the driver is tired, right? They're, they're if you get those alerts when you're driving your car and saysHardware, Sensors, and the LiDAR QuestionQasar [00:11:15]: -maybe take a coffee break, that take that times, a couple of order of magnitudes up. But this concept of teaming man and machine is important. When you think about running agents or just running, different instances of, Claude and doing work for you in the background, you can take that analogy out, almost copy and paste and put it into, like, a farm, where you have a farmer who's running a number of machines. So where they interact with the machine is where there's maybe a critical decision or a disengagement or something like that, but generally speaking, the agent on the physical machine is running and making decisions on the behalf of the farmer until there's something maybe critical. And that's also what we work on. So that's not pure autonomy. It's a little bit of a mix, but it falls under, autonomy. In the automotive sense, that's typically defined in SAE levels as an L2++ systemQasar [00:12:05]: -with a human in the loop. But just take that idea, to other verticals.Alessio [00:12:09]: Yeah. You've not mentioned hardware at all, like sensors or obviously we you mentioned you don't do chips. I think even in AV there's, like, a big, cameras versus lidars. Like, what are, like, in your space maybe some of those design decisions that you made, and are they driven by the OEM's ability to put things on the machinery? And like, how much influence do you guys have on co-designing those?Peter [00:12:32]: Yeah. So we don't make sensors. Like, we're, we're not a manufacturer. Obviously, we use a lot of sensors in our autonomy products. in terms of what actually goes on the vehicles, we have a preferred set of sensors that we, let's say fully support, and then our customers, they can sort of choose from those. And obviously if there's a very strong opinion on supporting something else, we'll add that to the platform as well. And the lidar question is at this point sort of the age-old,Peter [00:12:59]: topic in autonomy, and the state of the industry right now is lidar is hands down a useful sensor, specifically for data collection and the R&D phase of autonomy development. if you see, for example, a Tesla R&D vehicle, it actually has lidar on itPeter [00:13:17]: to this day, right? In the Bay Area we see these. you'll see, like, Model Ys or Cybercab that have lidars on them just driving around. So it's, it's useful because it gives you per pixel depth information. So if you can pair a lidar with a camerand you can say that, well, this camera's looking this direction, this lidar's looking this direction, and now for each pixel of the camera I can see how far away is that pixel. you can actually then use that as a part of your model training, and then the that depth information then becomes a learned, a learned state of the camera data. And then when you're doing the production system, you can now remove the lidarPeter [00:13:52]: and now you can actually get depth with just the camera. And so that difference between, like, a highly sensored R&D vehicle and then the down-costed production vehicle, we use that across our whole portfolio of products. And of course the end goal is you want super low cost and super reliable.Peter [00:14:08]: And then in certain use cases you have some more, bespoke things. Like in defense as an example, you do things at night oftentimes, and so you care about sensors like infrared, more so than And you don't, you don't wanna be putting energy out, so you don't wanna use lidar or radar.Peter [00:14:23]: but you still need to be able to see at nighttime. So yeah, we work the whole gamut.The Operating System Layer: Why Vehicles Are Like Pre-Android PhonesAlessio [00:14:27]: Cool. So that's kinda like on the hardware level. Then on the OS level, how does that look like? What is, like, unique? my drive- I drive a Tesla. Whenever I drive some other car that has a screen, it always sucks.Alessio [00:14:38]: It's on, like, cheap Android tablet. It's like, it's laggy and all of that. What does the OS of, like, the autonomy future look like?Peter [00:14:46]: When most people, it's really what you just described. When you think about operating system in a vehicle, you're thinking about the HMI, right? The human machine interface, and absolutely that's a an important part of it, but that's actually only one thin layer on top. So when we talk about operating systems for, like, AI in vehicles, there's many layers that go deep into the CPU critical realm and embedded systems, and you're talking about the real time control ofPeter [00:15:13]: let's say the electric motors or the engine and the actuators, and you have different redundancies for different, let's say, the steering actuation in the vehicle. And all of these things, need very core support in the in the operating system. And then of course for autonomy you have real time sensor data that's streaming in, and the latencies there are really important, right? If you try to Imagine you try to run Microsoft WindowsPeter [00:15:35]: like streaming your sensor data in or controlling the vehicle. Like, the latencies are gonna be absurd. Like, you can never do that. And so what's special about what we do is we really have this system level thinking, right? So we're looking at, we care about every performance characteristics of the entire system, and then we also, because we're doing a lot of the software or all of that software, we can fine-tune and control all of those things. So we can very carefully tune in the latencies for every aspect of the system. We can carefully tune in the memory management. We can have the right, fail-safes and fallbacks, for different things. ‘Cause you have to account for what if, what if there is a critical failure? What if there's a cosmic ray that flipsPeter [00:16:14]: a bit in the middle of the processor that causes some, malfunction? And you have to have a fail-safe to all of that, and so the core operating system is a part of that. And then the one last thing, which is a lot less exciting but is, actually a very big topic, is reliability of updates.Peter [00:16:30]: so the I have a Tesla and you get updates fairly frequently, right?Peter [00:16:36]: Once a month. Most companies that are making vehiclesPeter [00:16:40]: are basically never doing updates, and they're And even if they are doing updates, they're usually only updating maybe one module. Maybe they're updating the HMI module. But they're not able to update, let's say, the CPU critical parts of the system.Peter [00:16:51]: You have to go into the dealer for that. And so with our operating system now we can actually enable highly reliable updates of any system in the vehicle, and that's way easier said than done. Like, there's lots of technical, technically deep stuff, in the tech stack to do that in a way that you're not going to accidentally brick a vehicle.Peter [00:17:08]: And right? If, imagine yourAlessio [00:17:10]: That would be bad.Alessio [00:17:11]: Bad.Peter [00:17:11]: Bricking a car is a very expensivePeter [00:17:13]: and honestly, like across the industry maybe one of the most just pure impactful things that we've done is we've just, we're, we're now enabling the industry to actually do software updates.Alessio [00:17:22]: Just to clarify as well, who is the customer for this? Like, I assume a lot of hardware manufacturers have their own firmware, and I'm sure some of them would just have you write it for them because you're experts. And others would have their own. Like, who pays for this? Who invites you into the house? Is it, is it the end user, or is it, is it the manufacturer?Peter [00:17:41]: Yeah. So let me make an analogy firstly on the on the fragmentation of software. So physical machines today are more akin to the state of the phone market before Android and iOS existed, right? So I worked on Android at Google by the way many years ago, and part of the reason that Larry at Google decided to get into Android was they wanted to run Google products on a bunch of phones, and they bought all of these phones from the industry, and it turned out they had like 50 different operating systems on these phones. And it was virtually impossiblePeter [00:18:17]: for Google to make their app run on all 50 devices equally well. And so the solution was, well, actually what if, what if they created-A really great operating system and made it attractive to all of these phone makers, and that was sort of the genesis for what Android was and why Android existed. It was a way for Google to get their products onto really wide diversity of devices. The state of the physical, industry right now, it's a little bit like that. Like, there's yes, these companies have firmware, but they have so many different operating systems, it's so fragmented, and to actually get a modern AI application to run on these vehicles, you actually, you first have to consolidate the operating system, and so that's, that's why we've done that. And then, your specific question was who are our customers? It's, it's, generally it's the companies that are making these machines.Peter [00:19:06]: And we're, we're, we're selling our technology to them to really simplify the architecture and then enable these AI applications to run on them.Customers, Licensing, and the Better-Together StackSwyx [00:19:13]: How much is reusable across? Like, do you have, like, one OS that is just configured for everything, or is there some more customization that is needed?Peter [00:19:22]: Yeah, highly reusable. So the fundamental technology is quite universal, right? So things that we do have to think about though are, like, chipset support. And so if you're, if you're coding, let's say, an LLM and you have start with an assumption that, “Hey, oh, I'm gonna, I'm gonna use CUDA, and I'm gonna run this, on an NVIDIA chip,” then you don't really have to think about the hardware in that sense. Like, you're just, “Okay, I'm just I'm in the CUDA/NVIDIA ecosystem, and I'm, I'm going to use that.” But the hardware, especially in safety critical systems, it's a lot more diverse. There's not one or one or two players. There's a bunch of different chipsets that we have to support. And so our operating system doesn't just run on, like, the equivalent of X86. It has to, it has to run on a number of different architectures from chips from a bunch of different companies. But again, we've been working on this for a long time now, so we have, we have support for all of those chipsets. And then when you want to then run the AI applications, we can then do that reliably across now a variety of providers.Qasar [00:20:19]: And I think that is, like, heavily inspired by Android, right? Android has a huge suite of testing and it's a reliable operating system that runs on thousands of devices. And we think we can, we can do the same in all these physical moving machines, with the difference that we're really in a safety critical realm. Android isn't.Alessio [00:20:40]: So on Android, I don't need to use Gmail, I can use Superhuman. Like, what about your machinery? Like, can people bring somebody else's automation to it, or is it kinda like all-in-one?Qasar [00:20:50]: You have to use us. No. Yeah. we're If, Yeah. Yeah, it's totally open. Yeah.Peter [00:20:56]: Yeah. our philosophy is that we are a technology company, and so we license our technology to customers to use how they want. And so if a customer wants to If they wanna license our autonomy tech and our operating system, then great, we'll license those. If they just wanna license the operating system and then use different autonomy tech, that's fine also, and we have great documentation andSwyx [00:21:17]: Or if they wanna use developer tooling.Peter [00:21:18]: Yeah, exactly.AI Coding Adoption: Cursor, Claude Code, and the Bimodal EngineerSwyx [00:21:19]: It's, like, a better together if, obviously, if you, if they work together. Is it all C++ I assume is with different compile targets?Peter [00:21:27]: We use a lot of C++.Peter [00:21:28]: Rust is sort of a hot, the new hot kid on the blockPeter [00:21:32]: for a bunch of things as well. But yeah, the lower level you get, especially when you get to real-time constraints, you hit C++ at some point, and at some point maybe you work your way into assembly when needed.Swyx [00:21:44]: Oh, damn.Alessio [00:21:46]: I'm curious about the coding agent adoption, just, like, since you're mentioning more esoteric languages. Like, what's the adoption internally? What have you learned?Peter [00:21:55]: Yeah. We use everything. So Cursor was, I think the hottest tool in the company for a good while. Now Claude Code, I think has taken the reign on that. We have a internal leader, leaderboard that we use just to sort of encourage adoptionPeter [00:22:09]: with-within the company. And yeah, it's, they're phenomenally useful. it's, Honestly, we take inspiration from some of those tools also in how we're adapting some of that mindset of thinking to the physical realm. Like if it's so easy to build an app for this or that thing that lives just on a screen, we can We're taking now a lot of the same ideas and applying that to, “Okay, well, if you wanted a physical machine to do something, how easy can we make that, using our own tooling and platform as well?”Alessio [00:22:40]: Are you changing any of, like, the OS architecture, kinda like the way you expose services to, like, be more AI friendly or?Peter [00:22:48]: Yeah, absolutely. The in the early days of our tools infrastructure work, it was a lot about, You had engineers that were experts in certain topics, but the things that you're dealing with, they're oftentimes more mathematical or more abstract, where actually GUI tools are very useful for certain things. Like as an example, we have a product we call Sensor Studio, which is, it helps you design the sensor suite for your autonomous vehicle, whether, again, it could be a car, it could be a drone, could be a mining equipment, could be a robot. And you place sensors in different places. You There's different, There's a library. You can understand what are the trade-offs that you're making in the design of that system, and that was, like, a very, a very GUI intensive, thing ‘cause it's a little more like a CAD tool in that senseSwyx [00:23:37]: YepPeter [00:23:37]: if you've seen CAD tools. Nowadays, though, right, we expose all of the underlying APIs for that and now using, AI agents, you can actually configure a sensor suite with just text and likely reach a better result than you could've through the GUI in the past, and we're taking that thinking now through the whole product portfolio.Swyx [00:23:57]: Another thing I was thinking about is just in terms of, like, AI, adoption, does it change your hiring at least a little bit, or how do you, how do you sort of manage engineers, differently?Peter [00:24:08]: Yeah. absolutely, it does. we, I think like every company in the Valley right now, are evolving our hiring practicesPeter [00:24:16]: because the skills required to be effective are changing so fast, right? you used to really select for just rote implementation ability and now it is more the AI engineer skill set, right? Where it's like, yeah, how to implement, but actually-Just banging out code is no longer the core job, right? It's, it's actually knowing what questions to ask, knowing how to tie, how to tie together these different AI tools. And so the interviews that we give now I think are way harder than they've ever been.Peter [00:24:46]: But we also allow, right, selective use of AI tools to solve the problems. And I think in that you start to see more of a bimodal distribution of engineers, right? You start to see like wow, there's, there's this subset of people that they really get it. Like they're, they're all in and they've, they've clearly invested the hours needed to learn these tools and how to be effective.Peter [00:25:09]: And then there's sort of the group of people that haven't done that, and that the productivity gap is just enormous. And so we're, we're trying to obviously select for the people that are really into this.Qasar [00:25:20]: I first wrote the my AI engineer piece three years ago, and when I first wrote about it, I was like, “Actually, not everyone should be an AI engineer,” ‘cause I think there's a there's an extremist stance where well, every software is an engineer is an AI engineer. And my actual example of people who should not be adopting AI was embedded systems and operating systems, and database people. Are they adopting AI?Peter [00:25:41]: I think it's the classic bitter lesson, topic, which is the Six months ago I would've said the same thing, but it's, it's becoming super useful for every domain.Qasar [00:25:53]: I'm sure.Peter [00:25:54]: Right? Like,Peter [00:25:56]: there was, I think six months ago, or maybe a year ago, if you tried to use, let's say the latest Claude model for writing shaders, GPU shaders, the results were probably underwhelming. And if you use the latest model now to do that kind of task, you're a little bit blown away, like, “Wow, that actually worked. That's amazing.” And we see the same thing in the embedded realm. No question though, especially when you get into safety critical systems, the human validation isPeter [00:26:25]: is 100% key. Like I You're not gonna trust your life to a an AI written software that's, that's not been very carefully, checked by humans. And so I think now the really the challenge is about that appropriate level of human validation for these safety critical systems.Verifiable Rewards, Evals, and Neural SimulationAlessio [00:26:41]: How do you think about, yeah, touching on the simulation side, I think verifiable reward and reinforcement learning is, like, the hottest thing. What have you done internally to build around that? And like, what gives you What makes you sleep at night? Like, if somebody's like, just web coding something or likeAlessio [00:26:57]: wants to try something new, you have like a good enough system. Because I think the opposite is also true, is like if it's super easy to write anythingAlessio [00:27:04]: then it puts a lot of work on like the verifiableAlessio [00:27:07]: side of it. Like, what does that look like for people?Peter [00:27:10]: Yeah. So verifiability, a broader bucket of like evaluations, right? Like how do you evaluate the results that you're, you're getting? I think this is probably the hardest problem right now, because the As the models get better, it can be harder and harder to find the faults on the system.Peter [00:27:29]: And so like the problem of doing proper eval to find those faults, like that problem also keeps getting harder as the models get better. But it's no less important than it's ever been, right? You still there are still going to be edge cases that are not met and whatnot. And so it's, it's a big area of investment for us. On the reinforcement learning topic, the key thing is there's all these new requirements that come to be in the latest generation of these technologies. So for example, end-to-end is the big thing right now in autonomy and physical AI, which is you can now train these models that can effectively take sensor data in and then put control signals out, and get really good results out of that. But the way that you train and improve those models is really different from the previous generations. And so to do reinforcement learning on an end-to-end model, you now need to actually simulate all the sensor data, right? So then this becomes a we call our, work in this neural simulation, but it'sPeter [00:28:26]: think of it like a hybrid of Gaussian, splatting and diffusion methods, and where you really care about performance. Like performance is everything. If you can't do enough simulation fast enough and cheap enough, you actually can't get results that are worthwhile, in the end. It also gets to a lot of our work in embedded systems, which is like performance critical work, and that performance optimization, performance criticality, it carries over to a lot of the model training work. because, like, the only way to make it affordable is it has to be really fast.Qasar [00:28:58]: I think it's worth a few minutes talking about our own, evolving thoughts on verification and validation withinQasar [00:29:05]: kind of, traditional simulators, which are, you can think of like vehicle dynamics or something like that, which you're just taking textbooks and taking those formulasQasar [00:29:13]: and putting them into software, to like now this neural sim/world model universe. I think that's an interesting topic.Peter [00:29:20]: Yeah. So in more traditional development, right, you oftentimes would have, more black-and-white answers to questions.Peter [00:29:28]: And so the in Europe as an example, there's, a regulatory, system, it's called Euro NCAP. It's the European New Car Assessment Program, and as part of that, the vehicles have to pass a bunch of tests, and those tests actually, include, safety systems. So automatic emergency braking for a child that runs in front of a carPeter [00:29:51]: or let's say an occluded child that runs out and you hit it. And so you have You end up with sort of these binary answers of like, well, did the car under test pass this specific test? And there's a very well-known set of test casesPeter [00:30:05]: that the vehicle has to pass. And that was how the industry worked, let's say, until 10-ish years ago. But what's changed now is with these models, everything is statistics, right? Like you no longer have a black-and-white answer, but it's like, well, how many orders of magnitude or how many nines of reliability can I get in the system, and how can I, how can I prove that to be true? And the big unlock honestly for physical AI as an industry is that these models are just becoming much more reliable. Right? Things like things actually work a lot better. It's like the number of nines you can get out of these systems are now good enough that it actually becomes cost effective to really deploy these things. And so the big shift in, so verification and validation has been from a little bit more of a Again the past it was strictly requirements, and are you meeting or not? And now it's more of a statistical, verification and validation case where it's all about how many nines of reliability and meantime between failures, that sort of thing.Statistical Validation, Regulators, and the Cruise LessonSwyx [00:31:04]: And is the target audience regulators or even the customers are yeah, if you I imagine the customers are bought in, and it's mostly regulators that need to be satisfied.Peter [00:31:15]: We do work with the US government, we do work of course with the European governments and the government of Japan, and the government is not like an AI lab by any means.Peter [00:31:25]: So Swyx [00:31:26]: They just care about the outcome.Peter [00:31:27]: They care about the outcome.Peter [00:31:28]: And so we do education, in that regard, and like so sort of teaching about, “Hey, this is how we think validation should be done, and this is an approach that we think is reasonable,” and how to think about like when is a driverless system actually safe enough to go on the roads and that sort of thing. But I wouldn't say that the government is asking for it. It's like we're more teaching the government in that, in that sense. It's honestly, it's more so for our own, our own comfort, right? Like, we want to build very safe systems, and then of course our customers care deeply about that as well. But in that context we're also typically educating our customers.Qasar [00:32:01]: Yeah. Our first, our first core value is on round safety. So I think we can't underline enough that, us also verifying and validating that the systems that we're deploying are safe to us is probably as important as, like, some regulator or a customer saying,Swyx [00:32:19]: Of course. Okay. Yeah.Swyx [00:32:20]: You have to satisfy yourselves.Peter [00:32:22]: As I say, as a whole across the world, regulation oftentimes it's like a almost lowest common denominator. But like, you really have to substantially exceed what the regulators are expecting to make good products.Swyx [00:32:33]: Yeah. One thing I often talk about, I think and I try to make this relatable to the audience also, is Cruise, where they had an accident that basically ended the company. I wonder if people overreact to single incidents, because incidents are going to happen regardless, right? ‘Cause it's a statistical thing, but as long I don't know if regulators understand that, you cannot extrapolate from a single incident, but we do because that's all we have to go on. And your sample sizes are necessarily gonna be lower than, I don't knowSwyx [00:33:00]: consumer driving.Qasar [00:33:01]: Yeah. I think the Cruise example wasn't a technology failure. there was The real, compounding issue there was just how did the company talk to the regulators and what was their kind of behavior, and I think that became more of the issue. If you look,Peter [00:33:19]: It isn't It definitely was a technology failure, but it was made much worse by theSwyx [00:33:23]: Put the car back on the woman.Qasar [00:33:25]: Yeah. And let me put it another way. There is a version where Cruise still exists.Swyx [00:33:29]: right. Right.Qasar [00:33:30]: Right. It'sSwyx [00:33:30]: It was like the last strawQasar [00:33:31]: ItSwyx [00:33:31]: in like a long chain ofSwyx [00:33:33]: like issues.Qasar [00:33:33]: So do you feel like ATG had that horrific accident or someone actually dying, because, that was a homeless person crossing the street? So yeah, I think we can't understate enough that ultimately, like, statistical validation of something, that's one part of it, but it's not the only part of it. Like, consumer and let's say, mainstream adoption of these technologies is also gonna be part of that conversation. I think companies like Waymo are doing a lot of service positively to the industry in the sense of they're, they're setting a high benchmark and they're showing, kind of in a very responsible way how to, how to deal with these. There have been Waymo incidences as well. They've just not been as significant as the Cruise one that you mentioned. But yeah, so I think you'll just continue to see that. I think probably the long term question is really gonna be, again, around Like it is very clear humans are way worse drivers statistically.Qasar [00:34:29]: Like, there's no, there's no debate. And so at what point But we're emotional animals.Swyx [00:34:34]: Yeah. So my thing is, like, we have to get to a point as a society where we accept horrific accidents that would never happen by a human because statistically we understand that it is safer overall. In the same way that planes, they're safer, than I think they're the safest mode of transport that we have.Qasar [00:34:50]: Yeah. it's more dangerous to drive to the airport than it is to get on a flight.Qasar [00:34:53]: So if you're everQasar [00:34:54]: if you're ever getting nervous about getting on a plane, just think “I just gotta get to the airport.”Swyx [00:34:58]: Yes, we're flying.Qasar [00:34:59]: If I get to the airportQasar [00:35:00]: I'll be good.Swyx [00:35:00]: But then it's, planes also concentrate the tail risk if planesQasar [00:35:03]: Yeah. AndPeter [00:35:04]: And I was, I don't think we honestly have to worry about there ever being, accidents from these systems that are like much worse than what humans would cause, ‘cause humans do terrible things.Peter [00:35:14]: Like, people fall asleep at the wheel all the time.Swyx [00:35:16]: I have.Swyx [00:35:17]: Like, I'll call, I've been a drowsy driver.Peter [00:35:19]: Kinda drunk drivers, and that'sPeter [00:35:20]: that's the extreme end of the example. But these AI systems, you have redundancies, you have fallbacks. Like, there's many things have to go wrong for there to actually be a something catastrophic because there's, there's so many, fallbacks that these systems have.Alessio [00:35:36]: your simulation is like so vast because there's so many use cases. What are, like, maybe things that worked in a simulation and then you put it out and it's like, “F**k, this isAlessio [00:35:45]: this just did not work at all?”Peter [00:35:47]: Yes.Alessio [00:35:47]: IsPeter [00:35:47]: That's maybe a bit of a misconception, about simulation there. So let me go a little bit, more technical on this. So at first go, no simulation is going to represent the real world. There's always a process of this, sim to real matchingPeter [00:36:02]: where you actually, you need the real world feedback to basically feed into the parameters that are being used in the simulator, and you have to do that, it's like this validation flow, a number of times until you can get some confidence that, like I think the simulator is now accurately representingPeter [00:36:19]: what's gonna happen in the real world. Now, if you have a situation where you've done that full validation and you thought that it was accurate and then there's something different, those are much trickier cases, and that's, that absolutely can happen, but really I think the validation process is a really important part. You can never skip the simulation validation process, like where you're actually ensuring that, hey, the actual, my sim to real gap here is small enough that I can trust these simulation results. And there's, there's so many fun things that you can do when you get into it. Like, I'll, I'll give one fun example that came up recently is like in these humanoid robotics, systemsOverheating actuators is a real problem, right? So obviously phenomenal demos. IPeter [00:37:01]: The most amazingAlessio [00:37:02]: For 10 minutes.Peter [00:37:03]: The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that's representedPeter [00:37:18]: in the simulation. And if you're doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it's moving, it's actually beginning to overheat this motor. But if you didn't have that parameter of, let's say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail.Alessio [00:37:43]: I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? WellAlessio [00:37:53]: why did you make my robot worse when it runs in like a freezer?Alessio [00:37:57]: So it actually shouldn't worry about that. it's like, yeah, how do you design these simulations?Peter [00:38:02]: This is honestly the This is what makes simulation so hard, right? it's because you Simulation is fundamentally about you're trying to optimize the development of a system, right? Like, how can I build this system faster and better and cheaper and what are all the levers that I have to actually accomplish that? And because simulation's just a software program, you can, you can change it a lot more easily than you can hardware systems. And then what's particularly awesome about the let's say, world models and using that as a part of simulation is now the simulation doesn't just scale with, let's say, adding new math equations inPeter [00:38:36]: but we can actually scale the simulation environment now with additional real world data and that also unlocks a whole new field of robotics.Qasar [00:38:46]: There is a meniscus line where you cross where still doing real world testing is better. there's, in this, sim-to-real gap, you can reproduce reality at exceedingly expensive costs and this So nothing is free. So really you have to you're finding that line where you're getting great performance, you're getting great feedback, whether it's on the training side or on the eval side, but it's way cheaper than doing it in the real world. At some point it, that doesn't make sense. And so even, from our earliest days in autonomy, our view was you're still gonna do real world testing. You There's, there's not, there's not this, magical land where you're not gonna do that. And maybe even like a more nuanced version of this in like traditional software development is, most of your testing for software in a vehicle, 95% of that can be like traditional CI/CD kind of, flows that you would have in traditional web development. But once you have Now you, let's say you have a truck. Well, you can do like 4% of those in like a rig which has all the components, the electrical and electronics of a truck, but doesn't have, it doesn't have the tires and it doesn't have the And then you have the 1%, which is actually the vehicle. There's something There's a similar analogy in terms of using simulation for intelligent systems. You can do a lot in a simulator, but in using world models, but ultimately it's, it's physical AI. So you're gonna deploy it on physical machines andQasar [00:40:17]: the freezer example comes to, comes to light.Alessio [00:40:20]: The world model thing has been to me the hardest thing toAlessio [00:40:22]: wrap my head around. Like we have Faith Eliyon on the podcast.World Models, Hydroplaning, and Cause-Effect LearningQasar [00:40:25]: We've been doing a small series with like another Intuition company, General Intuition as well.Qasar [00:40:31]: yeah, and I mean, lots of, lots of coverage on NeRFs and yes.Alessio [00:40:34]: Yeah. It feels like we talk with about, the heliocentric system, right? It's like in a world model, if you just feed visual data, the model might learn that the sun spins around the Earth. It makes sense, right? And it's like, well, not really. And I think what are like some of these other things that like hydroplaning is one thing I think about, is like can a world model understand hydroplaning and like what amount of water like causes it to happen? And it's like, yeah, to me it's like I don't understand how you guys do it. I guess it's like the real thing is like when you're doing both cars and the highway in Japan versus the excavator in a mine in,Qasar [00:41:13]: ArizonaAlessio [00:41:13]: wherever you're Arizona, wherever you're deploying them.Alessio [00:41:15]: How much of it are you relying on the world models to like generate the simulations for you and then try and close the gap after versus like giving the world models as a tool to your engineers to like curate the simulations if that makes sense?Peter [00:41:28]: Yeah, totally. So yeah, I can say at a pure engineering level, I think if you're hoping to do real world deploys and you're purely relying on a world model approach, you probably won't get to something that works, before you go bankrupt. So there is just a very practical mindset of like, world models are amazing and they're extremely useful for a lot of use cases, but there are a lot of other things that you need to do to actually get something started and something deployed and working. most fundamentally, world models are all about It's understanding the world, but also understanding what's going to happen. It's like the cause-effect relationship.Peter [00:42:01]: Right? And so like it, right, if you have a take some sort of construction tool, and that construction tool is gonna be doing some work on the Earth in some way, it's gonna be moving earth, the world model needs to understand that cause-effect relationship. Like, okay, when I, when I take this material from here and put it over there and now I have things that are over here and not over there anymore and that cause-effect, relationship. data obviously is a is a big problem. The hydroplaningPeter [00:42:26]: one is actually a really great example because it's actually quite non-obvious sometimes. Right? It's like, well, it's, it's raining and well this road, has, let's say the appropriate curvature to it so the water is running off the road and cars are driving faster here and then you approach a road that's very flat and water is now puddling on that road and all of a sudden cars are driving slower because when they were driving faster they were starting to lose control. And there are a lot of visual nuance, very nuanced visual cues in the scene and so I do think in the world model concept there's a good chance that the model actually would learn that you should just drive slower when these visual cues exist, and that's obviously the beautiful-The beauty of, these kinds of models where they just, they learn these non-obvious things.Swyx [00:43:14]: It doesn't need to know about hydroplaning to know that it needs to drive slower.Peter [00:43:17]: Yes.Swyx [00:43:17]: I guess it's Yeah. I wanna ask questions about, also deploying models. I presume, like, you use a lot of these world models for training data and simulation, but what about deploying it onto the systems in production? Presumably you have you have, like, GPUs on deviceOnboard vs. Offboard: Latency, Embedded ML, and DistillationSwyx [00:43:36]: but they're I keep saying on device. What's the what's the right term for that?Peter [00:43:40]: On machine.Swyx [00:43:41]: On machine.Peter [00:43:41]: Or embedded, yeah.Swyx [00:43:42]: Yeah. What is the embedded world like? because for people who are not used to that world, this is very alien.Peter [00:43:49]: Yeah. So it's actually We call it onboard and off board.Peter [00:43:52]: So like, onboard software and off board software.Peter [00:43:54]: And the great thing about off board software is you don't have to care about time, and you can run really large models, right? So you can, you can say, “Well, this model, I don't care if it takes one second for it to give me a result or 10 seconds for it to give me a result, because we have time.” And the models can be really big, and they can run, in a data center or on a on a huge GPU and you can obviously have distribute to compute, et cetera. But onboard you don't have any of those benefits. You're like, “Well, I need I have this many milliseconds where I need an answer from this model.” And so a lot more of the energy then is about, think of it more like distillation and it's like truly efficiency and like, literally every fraction of a millisecond counts. And you can't have a situation where the model takes too long because then the vehicle can't actually function.Peter [00:44:42]: And so you can, you can still use a lot of the same techniques, and the models themselves you can think of as like a derivative of larger models that you can run offline, and then you're, you're trying to just get a model that is still performs really well but it's, it's a it's smaller, small enough version that you can then run on this embedded system where you care about latency and power.Qasar [00:45:03]: Yeah. And I think like, the broader point I think which, maybe is not obvious but it's worth saying is in physical AI world, we're not really constrained right now by, like, the intelligence of the models. It's actually what Peter's talking about, it's actually deploying them inSwyx [00:45:19]: The hardware they give you.Qasar [00:45:21]: Yeah. On the hardware you give you.Qasar [00:45:22]: And so And there's just a reality is of safety critical systems. So those end up being the your limiting factorsQasar [00:45:29]: rather than, let's say, a limiting factor for, a foundation model companyQasar [00:45:34]: is gonna be just capital maybe or researchers.Qasar [00:45:38]: So we're, we're in that way dealing with, for us as people who kind of come in that realm with like a very interesting Those constraints force creativity.Swyx [00:45:47]: And I imagine, nobody was deploying or giving you the hardware for transformers back in 2018, whatever, but now they are. What's the evolution like? just peel back the curtains a little bit.Peter [00:45:59]: Yeah. Transformers first off, I think the paper was originally published in 2017.Swyx [00:46:02]: 2017.Swyx [00:46:02]: So there's no time.Peter [00:46:04]: And ISwyx [00:46:05]: But I'm just saying I guess I'm saying, like, embedded ML systems usually, like, a lot less parameters, a lot less compute, and now, like, orders of magnitude more.Peter [00:46:14]: Yeah. absolutely. what I was gonna say though was I think in the in the original paper in 2017, maybe it's in the last paragraph, somewhere in the paper they talk about, like, “Oh, by the way, this technique might be useful for, like, images and videos as well.”Peter [00:46:30]: These last subjects.Peter [00:46:31]: And it took a few years for that impact to really hit. But like, now, we're seeing transformers are everywhere.Swyx [00:46:39]: Yeah. Vision transformers.Peter [00:46:40]: And then then the compute just keeps getting better and better. But you do have this fundamental trade-off, right? It's like you have power, you have cost, and performance and like, getting the right, getting the right mix of those things in an embedded package that can also be, like, shaken and baked in all thePeter [00:47:00]: conditions that these things have to have to operate in. But yeah, I think that they're only going to keep getting better and so we also try to plan our strategy understanding that, we know the rate of improvements of these systems.Swyx [00:47:11]: Yeah. So like, Google just released the Gemma 2B modelSwyx [00:47:15]: that effective 2B model. Is that useful to you guys or is that too big?Peter [00:47:18]: You can run that model on an embedded system, definitely.Peter [00:47:21]: the So yes, it's, it's useful in that regard. The bigger question is, like, what do you use it for in an embedded system? Like, you actually need to customize it quite a bit to make it useful for something. But yeah, you could run a two billion parameter model, definitely.Swyx [00:47:35]: It also interesting, like, what percent is a custom ML model that only does that thing versus a generalist LLMSwyx [00:47:41]: which probably is not that useful actually for your context.Peter [00:47:46]: Like, you, like, you can imagine different use cases, right?Peter [00:47:48]: So theSwyx [00:47:49]: The voice stuff, yes.Peter [00:47:49]: Yeah, the voice test. Totally, yes.Peter [00:47:51]: So for the actual, autonomy elements, that's 100% in-house. We do every bit of that, the data simulation, the model, everything. But when you get into the more generic use cases like voice or voice assistant kind of thing, that's where these more generalist models like Gemma actually can be quite, can be quite useful.Swyx [00:48:09]: Yeah. And then there's also obviously a trade-off between, like, what percent must you do on machine, versus just call home.Peter [00:48:16]: Yeah. It's all about latency.Swyx [00:48:17]: Latency.Peter [00:48:17]: It's all about latency. Yeah.Swyx [00:48:18]: Yeah. Well, like, I think actually in a lot of contexts, especially in the US, you can just have a connection to the web.Qasar [00:48:26]: Yeah. I think though most of our universe is everything has to be fairly, embedded and local because just the nature of Even in the US there's a lot of likeSwyx [00:48:39]: PatchinessQasar [00:48:40]: don't haveQasar [00:48:41]: have coverage, right? And if you look at, like, the old world of autonomy within mining, which is, like, long before transformers and kind of, neural networks, in the like CNN and kind of a universe, they were really just hand-coded, systems. They were just like, this machine is gonna run to that place with thisPeter [00:49:03]: That was our GPS, like very accurate GPS.Qasar [00:49:05]: Yeah. And so that worked, and that worked for 20 years, so why would we actually need to use transformers or kind of more modern end-to-end systems? Mainly because you can only really run a path and run backwards. That provided a lot of value, but m-Not as much as you get when the machine is actually intelligent. It's, it's seeing, it's perceiving, it's acting in a dynamic world.Alessio [00:49:28]: I looked up RTK, real-time kinematic, one to two-centimeter accuracy.Qasar [00:49:32]: Yeah. Fantastic. But the and fantastic in faraway lands where there's not gonna be cell phone coverage.Peter [00:49:39]: Yeah, so it's widely used on the legacy mining and agricultural autonomy systems today. So like, for example, a combine that can be precise within one or two centimeters as it's driving down the field, they use RTK.Qasar [00:49:53]: Yes.Peter [00:49:53]: But it's, it's expensive.Qasar [00:49:54]: Yeah. And it's, it's, it's autonomy, but it's not intelligent in the way that I think all of usQasar [00:49:58]: if in twenty-six we'd be talking about intelligence.Alessio [00:50:00]: In one of your blog posts, you mentioned research on large scale transformers that are similar to those doing modern generative AI. What are, like, the big differences other than, “You're absolutely right. I should steer the car, so you probably wanna remove that?”Peter [00:50:14]: We have a diversified bet strategy internally, and the reason we've done that is because we operate in now a bunch of industries, a bunch of geographies, and each of the approaches has, obviously a different risk to them.Peter [00:50:27]: And so like, we're not going to put all of our eggs in a single basket for a single approach because that approach may no

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

AI-generated deepfakes are exploding in volume and quality, posing frightening challenges for public discourse, security, safety, and more. My guest, Henry Ajder, has been mapping the deepfake landscape since before most people had heard the term. In this conversation, he describes the dramatic changes in realism, efficiency, accessibility, and functionality of synthetic media tools since he published the first comprehensive census of deepfakes in 2019. Ajder describes the current moment as one of "epistemic nihilism," where people cannot reliably distinguish real from synthetic content and the available technological responses are not yet at a level of categorical trust. He introduces a framework of "deception, doubt, and degradation" for understanding deepfake harms, and draws a distinction between the clearly malicious, the clearly beneficial, and a vast unsettling middle ground of uses that society has not yet figured out how to evaluate. On the response side, Ajder warns that media literacy advice is not just outdated but actively harmful, because it gives people false confidence in their ability to spot fakes. Detection tools, watermarking, and content provenance standards like C2PA, while valuable, each have real limitations. Ajder's practical advice for organizations centers on red-teaming, understanding what your tool is actually for and who it serves, and recognizing that authenticity is a strategic asset in a synthetic age. Henry Ajder is the founder of Latent Space Advisory and one of the world's foremost experts on deepfakes and generative AI. He authored the landmark 2019 State of Deepfakes report, and has since advised organizations including Meta, Adobe, the UK Government, the EU Commission, the US FTC, and the World Economic Forum. He co-leads the University of Cambridge's Generative AI in Business programme, and sits on Meta's Reality Labs Advisory Council. Transcript Latent Space Advisory The State of Deepfakes: Landscape, Threats, and Impact (2019) The Future Will Be Synthesised (BBC Radio 4 Documentary Series, 2022)  

For all those who missed out on London, see you in Miami next week!Notion, the knowledge work decacorn, has been building AI tooling since before ChatGPT, with many hits from Q&A in 2023 and unified AI in 2024 and Meeting Notes in 2025. At the end of their last Make user conference, Ryan Nystrom teased Notion 3.0's Custom Agents - and they are finally embracing the Agent Lab playbook!Sarah Sachs and Simon Last of Notion join us for a deep dive into how Notion built Custom Agents, why it took years and multiple rebuilds to get right, and what it means to turn a productivity tool into an agent-native system of record for enterprise work.We go inside the product, engineering, evals, pricing, and org design decisions behind one of the most ambitious AI product efforts in software today — from early failed tool-calling experiments in 2022 to agent harnesses, progressive tool disclosure, meeting notes as data capture, and the long-term vision for software factories and agentic work.We discuss:* Sarah and Simon's path to launching Notion Custom Agents, and why the feature was rebuilt four or five times before it was ready for production* Why early agent attempts failed: no tool-calling standard, short context windows, unreliable models, and too much complexity exposed to the model* The “Agent Lab” thesis: not just wrapping a model, but understanding how people collaborate and building the right product system around frontier capabilities* How Notion thinks about roadmap timing: not swimming upstream against model limitations, but also building early enough that the product is ready when the models are* Why coding agents feel like the kernel of AGI, and how Notion is thinking about “software factories” made up of agents that spec, code, test, debug, review, and maintain codebases together* How Sarah runs AI engineering at Notion (“notes from Token Town”): objective-setting over idea ownership, low-ego teams comfortable deleting their own work, and a culture designed to swarm around fast-changing opportunities* The “Simon Vortex,” company hackathons, and why security gets pulled in early rather than late* How Notion organizes AI: core AI capabilities and infrastructure, product packaging teams, and a broader company mandate that every product surface must increasingly work for both humans and agents* Why prototypes have become much easier to build internally, and how “demos over memos” changes product development inside a tool the whole company already uses every day* Notion's eval philosophy: regression tests, launch-quality evals, and “frontier/headroom” evals that intentionally only pass ~30% of the time so the company can see where model capabilities are going* What a “Model Behavior Engineer” is, and why Notion treats eval writing, failure analysis, and model understanding as a distinct function rather than just software engineering* The changing role of software engineers in the age of coding agents, and why the new job looks less like typing code and more like supervising a rigorous outer system of agents, PRs, and verification loops* How the “software factory” should work: specs, self-verification, bug flows, subagents, and minimizing human intervention while preserving the invariants that matter* A live walkthrough of a Notion Custom Agent handling coworking space tenant applications by triaging email, enriching applicants with web search, and writing structured data into a Notion database* How agents compose inside Notion: shared databases as primitives, agents invoking other agents, “manager agents” supervising dozens of specialized agents, and memory implemented simply as pages and databases* Notion's take on MCP vs CLI: why Simon is bullish on CLI's self-debugging nature, where MCP still makes sense, and how Sarah thinks about capability, determinism, permissioning, and pricing alignment* The evolution of Notion's internal agent harness: from early JavaScript coding agents, to custom XML, to Markdown and SQL-like abstractions, to tool definitions, progressive disclosure, and a much shorter system prompt* Why Notion cares about teaching “the top of the class,” building for sophisticated operators rather than abstracting away too much capability for everyone* How agent setup works today: agents that can configure themselves, inspect their own failures, and edit their own instructions — with guardrails around permissions* How Notion prices Custom Agents: credits as an abstraction over tokens, model type, serving tier, web search, and future sandbox costs; why usage-based pricing was necessary; and how “auto” tries to match the right model to the right task* Why Notion is not eager to train a foundation model, where they do fine-tune and optimize today, and why retrieval/ranking is one of the most important investment areas as more searches come from agents rather than humans* Why Meeting Notes became one of Notion's strongest growth loops: not just as transcription, but as high-signal data capture that powers search, custom agents, follow-up workflows, and the broader system of record for company collaboration* Why Notion is more interested in being the place where collaboration data lives than in building hardware themselves — and how wearables or other capture devices may eventually feed into that systemSarah SachsLinkedIn: https://www.linkedin.com/in/sarahmsachsX: https://x.com/sarahmsachsSimon LastLinkedIn: https://www.linkedin.com/in/simon-last-41404140X: https://x.com/simonlastFull Video EpisodeTimestamps* 00:00:00 Introduction and launching Notion Custom Agents* 00:01:17 Why Notion rebuilt agents four or five times* 00:03:35 Building for where models are going, not just where they are* 00:05:32 The Agent Lab thesis, wrappers, and product intuition* 00:08:07 User journeys, leadership, and low-ego AI teams* 00:13:16 The Simon Vortex, hackathons, and bringing security in early* 00:16:39 Team structure, demos over memos, and building for agents* 00:20:25 Evals, Notion's Last Exam, and the Model Behavior Engineer role* 00:27:37 Evals as an agent harness and the changing role of software engineers* 00:30:42 The software factory: specs, verification, and agent workflows* 00:32:18 Live demo: a custom agent for coworking space applications* 00:35:08 Composing agents, manager agents, and memory as pages* 00:38:15 Notion Mail, Gmail, native integrations, and tools* 00:39:43 MCP vs CLI and the cost of capability* 00:44:13 When Notion uses MCP vs building its own integrations* 00:47:43 The history of Notion's agent harness rebuilds* 00:55:35 Power users, public tools, and the setup agent* 00:58:01 Self-fixing agents, permissions, and “flippy”* 01:01:13 Pricing, credits, and choosing the right model automatically* 01:09:01 Why Notion isn't training its own frontier model* 01:14:07 Retrieval, ranking, and search built for agents* 01:17:27 Meeting Notes as data capture and workflow automation* 01:21:18 Wearables, hardware, and Notion as the system of record* 01:23:45 OutroTranscript[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast. This is Alessio founder of Kernel Labs and I'm joined by swyx, editor of the Latent Space.[00:00:11] swyx: Hello. Hello. We're back in the beautiful studio that, uh, Alessio has set up for us with Simon and Sarah from Notion. Welcome.[00:00:18] Sarah Sachs: Thanks for having us.[00:00:19] Alessio: Thanks for having us. Yeah.[00:00:20] swyx: Congrats on the launch recently the custom agents, finally it's here. How's it feel?[00:00:26] Sarah Sachs: We ship things slowly. So it had been in Alpha for a little bit and at the point at which is it's an alpha, um, there's a group of people that are making sure it's ready for prod, and then there's a group of people working on the next thing.So sometimes some of these launches are a bit delayed satisfaction, so it's quite nice to remind yourself all the work you did because we do have a habit of like. Being two or three milestones ahead. Uh, just ‘cause you have to be, you know, you can't get complacent. Um, but it's been great that people understood how this is helpful.And I think that's just easier in general building AI tools today than it was two, three years ago. People kind of get it and so that user education, um, there's just, it was our most successful launch in terms of free trials and converting people and things like that. It was really successful, so yeah.But there's a lot to build.[00:01:12] swyx: Making it free for three months helps.[00:01:16] Sarah Sachs: Yep.[00:01:17] Simon Last: It was definitely super exciting for me because it's probably the fourth or fifth time that we rebuilt that.[00:01:22] swyx: Yes.[00:01:23] Simon Last: And I mean,[00:01:24] swyx: you've been building this since like 20, 22.[00:01:26] Simon Last: Yeah, I mean, like, it was even right when we got access to like GPT four in late 20 22, 1 of the first ideas we had is like, oh, okay, let's make an agent that I, we used the word assistant at the time, there wasn't really the word, the word agent yet, but, oh, we'll give an access to all the tools the notion can do, and then it, we run in the background like, like do work for us.And then we just tried that many times and it just. Was too early. Um,[00:01:48] swyx: I need to force you to like double click on that. What is too early? What didn't work?[00:01:52] Sarah Sachs: We were fine to, like, before function calling came out. We were trying to fine tune with the Frontier Labs and with fireworks, like a function calling model on notion functions.This is right when I joined. I joined because, um, we needed a manager as Simon was needed to be able to go on vacation. So, uh, that's, that's around when I joined, so you can speak much more to it.[00:02:11] Simon Last: Yeah, we did partnerships with both philanthropic and open AI at different times, uh, to try to, at the time the, I mean, when we first tried, there wasn't even a constant of like tools yet.We, we sort of designed our own like, like tool calling framework and then we tried to fine tune the models to, uh, to use it over multiple turns. Um, and because it, it didn't work well out the box, I think. Yeah. The models are just too dumb and the context thing was also way too short.[00:02:37] Alsesio: Yeah.[00:02:37] Simon Last: Um, and yeah, we just kind of banged our head against it for a long time.Uh, unfortunately it was always like, there was always like sort of. Glimmers that it was working, but um, it never felt quite robust enough to be like a useful, delightful thing. Um, until I would say, uh, the big unlock was probably like Sonic 3.6 or seven, uh, early last year. And that's when we started working on our agent, which we shipped last year.Um, and then, and then uh, uh, custom agents, kinda a similar capability and that, that one just took longer because we, we just wanted to get the reliability up a lot higher. ‘cause it's actually running in the background.[00:03:14] Sarah Sachs: And the product interface of like permissions and understanding, you know, this custom agent is shared in a Slack channel with X group of people and has access to documents that are surfaced to Y group of people.And the intersect experts, Y might not be whole. And so how do you build the product around making sure administrators understand that permissioning took multiple swings.[00:03:35] Alsesio: Everything is hard back at the end of the day. Yeah. I'm curious, like when the models are not working, how do you inform the product roadmap of like, okay, we should probably build, expecting the models to be better at some reasonable pace, but at the same time we need to, you know, you had a lot of customers in 2022.It's not like you were a new company or like no user base.[00:03:54] Simon Last: Yeah, I mean I think there's always the balance of, you know, like you want to be a GI pilled and thinking ahead and building for where things are going. Uh, but also you wanna be like shipping useful things. And so we always try to like, like keep a balance there.You know, we. We try to take clear, like a portfolio approach. You know, we're always working on multiple projects and, and we're always trying to work on, you know, maintaining things where that have already shipped, like, like shipping new things that are like eminently working well and make them really good.And, and then we wanna always have a few projects that are a little bit crazy. Um,[00:04:23] Alsesio: and what are the a GI peel projects that you have today? I'm curious about, uh, you don't have to share exactly what you're working on, but I'm curious what are things today that maybe in 18 months people will be like, oh, obviously this was gonna work[00:04:35] Sarah Sachs: 18 months.[00:04:37] Alsesio: Yeah, 18 months is, you know,[00:04:37] Sarah Sachs: it's a long time and Yeah. Yeah.[00:04:39] Simon Last: I mean, there's a number of things happening. I think one thing that's becoming more clear is I think like, like, uh, coding agents are the kernel of EGI, sort of, everything is a coding agent. Mm-hmm. I think that's, that's sort of one, one direction.Um, and then, yeah, the exciting thing about that is sort of your agent can sort of bootstrap its own software and capabilities and actually debug and maintain them. And so yeah, we're, we're, we're thinking a lot about that. And then, yeah, like, like another category of things that I'm, I'm really excited about is like, uh, we call the software factory also.People are using this, uh, this, this sort of word. Um, basically it just means can you create sort of like a, as automated as possible, a workflow for developing debugging. Mm-hmm. Merging, reviewing, and maintaining a code base and a service where there's a bunch of agents working together inside, and like, like how does that work?[00:05:28] Sarah Sachs: If you think back to your initial question, like, why did this take so long? I think something,[00:05:32] swyx: I didn't say that, but Yes. Okay. Go ahead.[00:05:34] Sarah Sachs: Why, what, what changed over the three and half years of trying[00:05:37] swyx: it? Exactly. Right. Because most people always say like, it didn't work yet. Then reasoning models came, then it worked.I was like, okay, let's go a little[00:05:43] Sarah Sachs: bit. That's, I mean, that's part of it, but I think the other part of it that I actually think is really what will set notion apart for every new capability is we have like. Two skills that are crucial when it comes to frontier capabilities. One is not letting yourself swim upstream.So like quickly realizing if you're just pressing against model capabilities versus not exposing the model to the right information, not having the right infrastructure set up. That and of itself is the skill of intuition. And the second is to see, okay, you're not swimming upstream. Which direction is the river flowing and what is like, how do we think ahead about the product and start building it even if it's not great yet, so that when it is there, we're ready for it.Right? And like those can sometimes feel like counterintuitive things. Like we can be trying to fine tune a tool calling model when they don't exist yet. And that the trick is to not do that for too long, but realize that there was something there. And we've had a lot of things which like, um, we're just like not swimming in the right direction with the streams.I think we had multiple versions of transcription before we got meeting notes, right? Oh, I gotta talk[00:06:39] swyx: about that. Yeah.[00:06:40] Sarah Sachs: Yeah. Um, and so. I, I, I think that like we, we really closely partner with the Frontier Labs on capabilities and we also have to have strong conviction on, as those capabilities move.Notion is about being the best place for you to collaborate and do your work. And how does that narrative change if the way that we work changes?Yeah.[00:06:58] swyx: Yeah. You told me you were a fan of the Agent Lab thesis, and this is, this is kind of it, right?[00:07:02] Sarah Sachs: Right. I show that thesis to so many candidates. Like I have it as like micro chrome autofill.Um, at this point, like it's one of my most visitations[00:07:10] swyx: because like, is this the, here's why you should work in notion and not open, open eye. I, it's like,[00:07:14] Sarah Sachs: here's, here's what's different about it.[00:07:16] swyx: Yeah.[00:07:16] Sarah Sachs: And here's why. It's not just a rapper. I actually think more and more people understand it's not just a wrapper.[00:07:21] swyx: Yeah.[00:07:22] Sarah Sachs: Um, and by the way, like in the beginning, parts of what we build are wrappers on functionality. That works well, of course, but that's not really the most, um. I would say that's not the product that, that drives revenue. And that's not necessarily always what users need.[00:07:35] swyx: I mean, you know, notion is the AWS wrapper, but like the, the wrapper is very beautiful and like very, very well polished.So[00:07:40] Sarah Sachs: like the analogy,[00:07:41] swyx: like[00:07:42] Sarah Sachs: the analogy that I've been coming back to his Datadog in AWS[00:07:45] swyx: Yeah.[00:07:46] Sarah Sachs: So, uh, Datadog could not exist with, without cloud storage. Right. That it's kind of fundamental that that works. Um, and AWS has like a CloudWatch product, but Datadog is an expert on understanding how people want observability on the products they launch.And we're experts in understanding how people wanna collaborate, and that's really where our expertise lies.[00:08:04] swyx: Totally.[00:08:04] Sarah Sachs: Um, regardless of the tools that we use,[00:08:07] Alsesio: I'm kind of curious how you think about implicit versus explicit expertise. I feel like Datadog is half and half implicit and explicit. It's like they understand across markets and industries what engineering teams usually look for.With notion, it's almost like more of the expertise is at the edge because you as a platform, you're like so horizontal that the end user is not really the same. Mm-hmm. Like with Datadog, the end user is always like, yeah, an engineering lead, a kinda like SRE related person with notion. It can be anything.So I'm curious how you put that expertise into a product versus, you know, obviously it, WS cannot build notion. It's, that doesn't quite work in this case, but[00:08:44] Simon Last: it's, it's a little bit differently shaped. I think, you know, a classic vertical SaaS, like the data is kind of like that. They understand their individual customer very deeply.It's kinda a narrow slice, um, notion has always been super horizontal. And our, our task has always been to sort of balance these two somewhat opposing forces of like, we're listening to our customers and what they want us to build. It's a broad slice. And then also we're thinking about like, okay, how do we decompose what they want into, uh, nice primitives that are, that are really nice to use and we'll, we'll get us like as much bang for the buck as possible.And then, you know. Maintain the whole system, make it all like, like super clean and nice to use.[00:09:22] Sarah Sachs: We still have user journeys. I mean, we still focus on like core. I actually think the failure of our team is when we focus too much on what are cools that are, what are tools that are[00:09:31] Simon Last: mm-hmm.[00:09:31] Sarah Sachs: Cool tools. I actually think that's when we make have the least velocity because you still need some sort of focus on a user journey.So like for instance, we'll all sit down every Friday and look at the P 99 of like the most token exhaustive custom agent transcript and just look at why it didn't do well and cut a bunch of tasks. Like we still focus on like, this has, like this should work. Email triaging should work. Mm-hmm. Right. And similarly, like when we're talking about before building, um, chatting, um, before we started filming about, okay, how can I do PDF export?Well that's functionality that then merits. Maybe we should build a tool that has access to a computer sandbox in a file system and the ability to write code. Right? Right. Um, but it's because we're thinking about the fact that our users to do their, to do their daily work, need to export PDFs, not because we're like, Hmm, I think a computer tool could be cool.Like, let's just see what happens. Mm-hmm. Like we, we have to focus on some user journeys, otherwise we just don't have like, enough strategy to, to prioritize.[00:10:29] swyx: I think there's a lot of like really strong opinions that you've had. Do you have like sort of like a towel of Sarah Sachs? Like, you know, like what, how do you run your team?Like I feel like you just have accumulated all these strong opinions. Obviously part, part of this is your, your token town thing.[00:10:43] Sarah Sachs: I think the TAs working with Service X is, um, you'd have to, it depends who you ask. Um, I think it depends if you're on my team or a partner Right. Or a vendor.[00:10:54] swyx: Yeah. There other people want to run their teams the way that you're Yeah.You're like bringing these things. And then also similarly, uh, Simon, when you did the custom agents demo, you had like, well, we've been using custom agents and here's the super long list of everything that we do. No humans ever read it. Right? That's what you said. I was like,[00:11:07] Sarah Sachs: yeah. So I think for, for me, um, something that I learned very quickly and became very comfortable with was that my job was not to be the ideas per person or the technical expert.My job was to make it so that everybody understood the objective, had a resource to help prioritize what they should work on, and had an avenue to prioritize what they thought was important. And I think that's true with all, all leadership, but I think especially on the AI team. Almost all of our best ideas come from prototypes, from people that have a cool idea because they saw a user problem, and it's a huge disservice if all of those ideas have to pass, like the sniff test of what me and a product partner or Simon and Ivan decided were the direction, right?Because a lot of what we're doing is leaning into capabilities, so. I think that's the first thing is like, I don't really view like the role of engineering leadership as like, uh, hierarchical, nor has it ever been, but especially now, like very willing to change direction based on, um, like proof is in the pudding.Yeah. And like, and I think we have rebuilt our harness three or four times. And when you do that, then the second rule of engineering leadership is like you need to build a team that's comfortable deleting their own code and is very low ego and is driven by what's best for the company. And, um, doesn't write design docs because they think it's their promotion packet.Right. And that's a culture that notion had long before I joined, but like our willingness to just swarm on different problems and um, redo things that we've built before because something has changed. Like, there's a lot of friction that can happen at companies when you do that. And it doesn't happen at Notion.And because it doesn't happen when new people join. Like they don't wanna be the ones that are saying, we shouldn't do this. I wrote that code. So then it's, you know, you, you create a culture that everyone thoughts and that culture comes directly, I think from Simon and Ivan though, um, because they're very open-minded.[00:12:50] swyx: Anything that you,[00:12:50] Simon Last: you'd add? I'm not a manager, like, like, like Sarah is. Um, a lot of my role is really to try to think a little bit ahead, make sure that we're, we're building on the right capabilities and then like the prototyping stuff. And yeah, it's really, really critical to always just be starting again.It's like, okay, this is new thing. What does this mean? What if we just rethought everything or wrote everything? And so I, I'm, I'm basically just doing that in a loop every six months.[00:13:16] swyx: Yeah. Do you believe in internal hackathons for this stuff?[00:13:19] Sarah Sachs: I think there's like two different versions. So one is like, we just have a, a, a solid bench of senior engineers that come and go on what we call the Simon Vortex and Productionizing what we built, right?Because when you're in the Simon Vortex, the velocity is super high. The direction changes daily, and it's meant to be like the equivalent of a SC Works lab. We don't need to do hackathons for that. We need to have senior engineers that we trust to come in and out of those projects. For instance, like management boundaries are really loose.Like you report to him, but you work for her right now. Yeah. That's something that when we hire managers, it's important they don't care about because we tend to form more structures. Yeah. Don't be too[00:13:54] swyx: territorial.[00:13:55] Sarah Sachs: We form more. It's after we ship things, not not before, just historically. Um, the second thing is we do have companywide hackathons.Actually we just had our demos day for the hackathon we had last week this morning. That's more for people that aren't directly working on the project, feeling like they have the time to pause and learn how to make themselves more productive or how they would use notion custom agents to build something.Or part of the hackathon was actually encouraging everyone across the company to build their own agentic tool loop, calling from scratch. Follow like an every blog post on how to do what I think because we want[00:14:26] swyx: just with the compound engineering one. Yeah.[00:14:28] Sarah Sachs: We want everyone to use cloud code in the company or whatever the coding agent they please and understand that fundamental.So we set aside a day and a half. We're all leadership, encourage everyone on their teams across the company to do it. So we have hackathons like that. I would say like kind of facetiously, like everything we build is a little bit like a hackathon until it graduates and puts on big boy pants and as a product ops rollout leader and has a assigned data scientists and stuff like that,[00:14:54] swyx: security review enterprise stuff,[00:14:56] Sarah Sachs: actually security reviews one of the things that we bring in first because it just slows us down way more and, um, causes a lot of tension and they build better product if they're involved early.So, um, that is probably the first person to get involved in something that's the[00:15:09] swyx: right PR approved answer.[00:15:10] Sarah Sachs: No, but it's not just PR approved. It like, um, um, it's[00:15:13] swyx: actually real. It's actually real. It's like, um, I'm just saying scar[00:15:15] Sarah Sachs: tissue.[00:15:15] swyx: Yeah,[00:15:16] Sarah Sachs: because like, you know, my background's also, I worked at Robinhood for a number of years.Yes. So like, uh, compliance and things like that, um, are a little bit more, you learn the hard way when it doesn't come naturally.[00:15:26] Simon Last: Yeah. I think the. The hackathon is really important for uplifting the general population, but like, if that's the only way you can build new things, you're kind of toast. I mean, it, it has to be like the daily processes, like, you know, building these new things.Um, and it has to be about, I think like, I think in the AI era a lot more leverage accumulates to the most curious and excited people. And so it's like we're all about just like activating that energy. You know, like if someone's protesting something on the weekend that they're excited about and it's important, that should be the main thing that we're doing.Yeah. Um, it's not a hackathon that we schedule once a quarter, it's just like, yeah. Daily process. Part of the culture.[00:16:02] Sarah Sachs: I mean, that's how we shift image generation and notion now. It was always this thing that would be kind of nice to have, but it wasn't really clear where that was necessarily aligned in product priorities.It'd be a lot of work. And we had someone on the database collections team, Jimmy, who was like. I really wanna do image generation for cover photos and inside notion. And we're like, if you wanna build it, like it's, do it please. Like we encourage you. We gave ‘em all the resources of working directly with Gemini and being able to like track the token usage and it working through endpoints.We gave them eval, support, everything, and then became a, a full project.[00:16:34] Alsesio: Yeah.[00:16:35] Sarah Sachs: That's why you can't have like ego as a, a leader. Like that's, that's how we work.[00:16:39] Alsesio: What's the size of the team today, both engineering and overall?[00:16:43] Sarah Sachs: I manage, uh, the team. That's what we'll call it. Core AI capabilities and infrastructure.That's about 50 people. But then we have per i partner teams that do packaging. So how it shows up in the corner chat versus custom agents versus meeting notes, that's another 30, 40 people. And, and then every team that has a product service at Notion that a user can interface with owns the tool that the agent interfaces with the editor team.The team that did CRDT for offline mode is the same team that handles how two agents, um, edit competing blocks. Mm-hmm. Right? It's the same problem. The team that built the underlying SQL engine is the same team that owns how the agent asks it to run a SQL query, and it does it performantly. And so from that regard, anyone working on product engineering is tasked with making them work for customers that are humans and agents because over time the majority of our traffic will be coming from agencies using in our interface, not humans.And so. Our objective is to make it so that the whole product org is building for agents.[00:17:40] Alsesio: Yeah. How has it changed internally? The activation bar is kind of lowered a lot. Like anybody can kind of create a prototype very, somewhat easily, especially if you're like an existing code base. Have you raised the bar on like what type of prototype people need to bring forward to gonna be taken?Not like seriously, but like, you know what I[00:17:58] Simon Last: mean? Yeah. I think the bar is lowered in many ways. Be like, one thing our, uh, our team built that is really cool is our, uh, our, our design team made a whole separate GitHub repo, uh, called the, the design Playground. And it's basically just to create a bunch of like, like helper components and you, uh, for, for quickly a throwing together UIs.And it's become like actually quite sophisticated. Like it has like an agent in there and like, uh, that's pretty fun. So like, we pretty much, like, they don't do mocks, they just make like, like full, full prototypes.[00:18:27] swyx: Here it is. It works.[00:18:28] Simon Last: They give you like a u rl. They're like, okay, all right. So we have to make the, like the real production version of that.Um, and then for engineers. A prototype looks like just making it a feature flag that actually works. Like that's sort of the bar.[00:18:39] Sarah Sachs: Something to understand that's really unique about notion. One of the reasons I joined we're super lucky is no one uses Notion in their job as much as people that work at Notion.[00:18:46] Simon Last: Of course.[00:18:47] Sarah Sachs: So I think there's very few companies, maybe if you worked on Chrome I guess, but like everything that we ship, we ship internally first and get a lot of really quick feedback. And also sometimes our dev instance is totally borked and you have to change a bunch of flags to get things done. And that's kind of like, but everyone, so people that do it ticketing, people that do supply chain procurement, recruiting, everyone is using the same instance of notion with like a lot of flags on for these prototypes people build.Um, and so we have this, Brian Levin, one of the designers on our team, I think evangelize this concept of demos over memos.[00:19:18] swyx: Ooh, too[00:19:20] Sarah Sachs: good. Um, which has been, uh, very good for building demos, and I think it's put a big pressure point on us to have really strong product conviction, because if anything can be demoed, you really need a strong filter of making sure that if you know, you're doing X amount of work, you're making the, you're, you're focusing on one tower, you're not just building a really flat hill.Right. That's actually where I think there has to be more conviction from our PMs, um, and our designers and, and well, the company really to have conviction of what journey we're going on.[00:19:52] Simon Last: But overall, I feel like it works pretty well. Like people, almost all the engineers have good enough taste to realize that like, this prototype doesn't actually make sense in the product, or, or it does.So it's not that common that I would see a prototype. It's like, oh, this makes no sense. Mm-hmm. It's like, you know, people are doing reasonable things and, and, and then it's just a matter of. Which things we build first and then often just, just figuring out how to turn it on and off. There's our, in the, in our like experimental chat ui, there's this, there's probably like, like a hundred check boxes in there.[00:20:22] Sarah Sachs: Kills me[00:20:23] Simon Last: the things you could turn on and off.[00:20:25] Sarah Sachs: Uh, but I think that, okay, so that is kind of true, Simon, but like being the person that manages the evals team, like there is a level of intensity that it adds to the platform team. So, you know, if we're gonna do image generation and notion, all of a sudden the way that we do attachments and the way that we, um, our LLM completion like cortex talks and expects tokens back and now it's getting images back.Like there's a lot of platform work that we do need to, like solidify a little bit. So sometimes it'll be in dev for a couple weeks before it makes it to prod just because we still have to like, make it robust, make it HIPAA compliant, ZDR compliant, figure out the right contracting with the vendor, whatever it is.And we need to eval it because we want the team. To still maintain what they build. That's the one thing is like if we have a bunch of prototypes, it can't just be like a small group of people that then maintain whatever end prototypes. So we have invested a lot of people in an eval and model behavior understanding teams that, we call it agent dev velocity.So your dev velocity building agents can be faster if we invest in that platform. And so we have a whole org dedicated to Asian, um, platform velocity so that you can build your own eval and then maintain it once you ship it. So if a new model release comes out and we, every[00:21:38] swyx: team maintains their own eval,[00:21:40] Sarah Sachs: we maintain the eval framework.Every team owns their own evals and a lot of them we've integrated to Optin, to ci, or we run them nightly and we have a team, uh, a custom agent that triggers to a team to look at the major failures. That's really critical because if we have like all these different surfaces now, a lot of it's on the same agent harness, so it's easier to maintain.It's just packaging of different agent harnesses, but new functionality of the agent. Let's say that like we wanna update like. Uh, you know, they deprecated, sonnet, um, four or whatever it is and we need to auto update. Are[00:22:11] swyx: they already? That's so, okay. Yeah. Actually wasn't that long ago.[00:22:14] Alsesio: Theywere[00:22:14] Alsesio: just 3.5.[00:22:15] Sarah Sachs: 3.537. Just got deprecated.[00:22:18] swyx: 3 7, 5 0.2 or, yeah. No,[00:22:20] Sarah Sachs: it's not. 5.2 is five point. Five point no. Yeah, five four is 40% more expensive than five two. So if they deprecated five two, you would hear they can, you would hear from me about that one. Um, but, uh, another conversation to have.[00:22:35] swyx: I have a cheeky evals question for you.Have you noticed any secret degradation from any of the major model providers?[00:22:40] Sarah Sachs: Secret degradation,[00:22:42] swyx: like. During the War Bay, when it's high traffic, it suddenly gets dumber.[00:22:47] Sarah Sachs: Yeah. I mean, not just between the, I mean, we definitely notice flakiness, we've definitely noticed, particularly for some providers, that things are slower during working hours and[00:22:57] swyx: there's a latency argument.Yes. Not a quality argument.[00:22:59] Sarah Sachs: No. I think the quality difference that's interesting is, um, even though companies that say they're selling the same, a, it's really into like quanti quantization, but like companies that say they're selling the same model through different vendors, whether it be through first party or Bedrock, Azure, et cetera.We do see different qualities sometimes, and that's not necessarily what's advertised.[00:23:21] swyx: Yeah. Kidney went to the point of like, if we, they shipped like this, like eval across all the providers and it was like very obvious we were secret equalizing and it was very,[00:23:28] Sarah Sachs: yeah. But[00:23:29] swyx: that's very embarrassing.[00:23:30] Sarah Sachs: You know, um, we hire Subprocess to figure that out for us.So we just wanna understand where it's regressing or where it's optimized. And sometimes we're okay with regressions that optimize latency if they're the appropriate regressions. Our job is to make sure we have the evals to understand the changes that are important to us. And even like when we're partnering with labs on pre-releasees of models, they'll send us multiple snapshots.And this is less about quantization, but more just regressions. Like they have shipped models that were not the snapshots that we wanted, and they have changed the snapshots that they shipped based on the feedback that we give. Because our feedback tends to be more enterprise work focused and not coding agent focused.And definitely those can be bummers, like, you know, uh, we know that this wasn't the version you wanted, but we'll help you make it work. I mean, we always make it work, but that definitely happens.[00:24:16] Alsesio: Yeah. Do you have, um, failing evals that you're just hoping, oh, that will have success eventually when a good model comes out?[00:24:23] Sarah Sachs: Uh, I mean, yeah. So I think. I mean, I could talk about this for 60 minutes, so I will limit myself. I think it's a real issue when people say evals and it's just like, that's quality, that's like unit, I mean, it's like saying testing. It's not just unit tests, right? So. We have the equivalent of unit test.Regression test. Those live in ci, those have to pass a certain percent, you know, within some stochastic error rate. Then we have, as you're building a product, evals of these aren't passing right now, and this is launch quality. So we have a report card and we need to, on these categories, you know, be it 80 or 90% of all of these user journeys to launch, and then what we have what we call frontier or headroom evals, where we actively wanna be at 30% pass rate.And that's actually been a effort that we took in partnership with philanthropic and OpenAI in the past maybe two or three months, because we actually hit a point where our evals were saturated and we weren't able to really give insightful feedback other than it wasn't worse. And not only is that not helpful for our partners, it's not helpful for us to understand where the stream is going.You know, going back to that analogy. And so we spent a lot of time thinking about. What notions last exam looks like, right? Mm-hmm. Not just humanities, last exam. Ooh, notions last exam. Mm-hmm. And, um, there's a lot of, you know, dreams about what that would look like. I know we've talked a lot about benchmarking, um, swix, but, uh, yeah.Notions last exam is a big thing inside the company and we have people, full-time staff to it exclusively. Mm. We have a data scientist, a model behavior engineer, and an full-time, um, evals engineer just dedicated to the evals that we pass 30% of the time.[00:25:56] swyx: What you're hiring for[00:25:57] Sarah Sachs: MBEs? I am hiring[00:25:58] swyx: What is an MBEA[00:25:59] Sarah Sachs: model?Behavior Engineer Model. Behavior engineers started with a title data specialist before I joined when they were working with Simon on like, uh, Google Sheets and like Simon just needed someone to look through Google Sheets and say, yes, no, this looks bad. This looks good. Right? And so we hired people with kind of diverse linguistics background.We had like a linguistics PhD dropout. Mm-hmm. And a Stanford ate new grad. And they're amazing. And they formed a new function basically. And over time we've built a whole team, um, with a manager who's now kind of reinventing what that role is with coding agents. So they used to be kind of manually inspecting code.Now they're primarily building agents that can write evals for themselves or LLM judges. There's a really funny day I can send you the picture where Simon, about a year and a half ago, was teaching them how to use GitHub. Um, and they're on the whiteboard and it was like, okay, I think it would be so much faster if our data specialists learned how to use GitHub and like learned how to commit these things in Dakota.And, and that was then and now I think, you know, coding has been a lot more accessible. Um, but moving forward it's this mix of like data scientist PM and prompt engineer because there's craft in understanding like even like what models can and can't do things. How do we define like that headroom? How do we define like what a good journey is?Um, is this model better or not? Why is this failing? There's some qualitative work, but then there's also like a lot of instinct and taste to it, and that's not necessarily software engineering. And so we have like very firm conviction and we have had for a number of years now that that is its own career path and we have always welcomed the misfits, so to speak.So we really firmly believe that you don't need an engineering background to be the best at this job. And that's what's quite unique about this particular role.[00:27:37] Simon Last: Yeah, this is something that I've been pretty excited about recently is we made an effort basically to treat the eval system as like an agent harness.So if you think about it, like, you know, you should be able to have an agent end-to-end, download a dataset, run an eval, iterate on a failure, debug, and, and then implement a fix. And ultimately you should be able to, you know, drive the full time process with a human sort of observing the, you know, the outer uh, system.So yeah, we went, went pretty hard on that. And that's, that's worked extremely well so far. It's like basically just to turn it into a coding agent, uh, uh, problem.[00:28:11] swyx: Your coding agent or just whatever[00:28:13] Simon Last: harness No coding agent. Yeah, code, cloud code. It should be totally general. Yeah. I think if it would be a mistake to like, like fix it on any, any particular coding agent.At the end of the day, it's just like CLI tools.[00:28:21] Sarah Sachs: It's like the same way that you would've a coding agent write the unit test. You should have a coding agent write the eval.[00:28:26] swyx: Yeah.[00:28:26] Sarah Sachs: But there's a lot of supervision in that still. We just don't believe that supervision has to come from software engineers because a lot of it is like, um, kind of you XREE and whatever, and these are the people that also triage failures and tell us where we should be investing next.[00:28:40] swyx: Yeah. I'm gonna go ahead and ask a spicy question. Is there a data, there are no software engineers at Notion.[00:28:46] Simon Last: Um,[00:28:46] Sarah Sachs: what does it mean to be a software engineer?[00:28:47] swyx: Exactly.[00:28:48] Simon Last: I mean, I think the way things are going is like we're on some continuum where. If, if you look back three years ago, humans were typing all the code and then we had auto complete, you're typing list of the code.Then we had sort of like filling agents, filling lines, and now we're getting into like agents doing longer range tasks where you can debug and implement a fix and then verify it works and you know, get your, get your PR even like, like Merion deployed. I think we're sort of just moving up the abstraction ladder and then the human role becomes more about observing and maintaining the outer system.There's a string of agents flowing through, like me prs what's going off the rails. Like what do I need to approve? Is there like a learning or memory mechanism that that works? So it's kind of a hard engineering problem. There's a, you know, there's, there's a lot to do there. I think we're just sort of moving up stack[00:29:34] Sarah Sachs: the same transition machine learning engineers have made, right?Like I haven't looked at a PR curve in a while.[00:29:39] swyx: Yeah. You used to do this stuff and now, um, auto research can do it,[00:29:42] Sarah Sachs: right? Like I think it depends on what you define as a software engineer.[00:29:46] swyx: Yes. It's, that's changing for sure.[00:29:49] Sarah Sachs: I think every software engineer in notion this summer went through like this, um, sheer, um, one of our engineering leads of the company called it, like every software engineer is going through the, the, uh, identity crisis that every manager goes through, where all of a sudden they realize their ability to write code is less important than their ability to delegate in context switch.And I think that is a transition out of being a software engineer. But[00:30:12] Simon Last: yeah. Yeah, there's a critical difference to being a manager, which is that like, it is actually very deeply technical. The problem, you know, humans are very like, like, like fuzzy and you can't like treat a team of humans like a, like a rigorous system where like, you know, prs like, like flow through and can be in like a block status and then what happens when they're blocked, right.With a set of agents, you actually can do that. And, and, and I think it's actually, there's a lot of interesting technical rigor that that goes into that it's like it's a technical design problem. Ultimately.[00:30:42] Alsesio: What is the design of the software factory that you're building?[00:30:46] Simon Last: Yeah, I mean, I think we're. Trying a lot of different things.I mean, ultimately you want to design a system that requires as little human intervention as possible, but like still maintaining the in variance that, that you care about. So yeah, we're exploring a lot different ideas there. I mean, I think I could talk about a few things I think are important there.Like, one thing I think is really important is, um, having some kind of like specification layer you can just commit marked on files. Mm-hmm. That works pretty well, but[00:31:15] swyx: it's nice to be notion man. I'm just saying like the spec, like Yeah. The natural home for specs is notion.[00:31:21] Simon Last: Yeah. Right. It can be a database of pages.Yeah. I mean, it needs to be something that is, you know, human readable and I viewable and I think that's pretty key. Another really key component is like the, the self verification loop. Yes. You need really, really good testing layers, basically. And that's a really deep, uh, uh, problem. But by getting that right, you know, and then, and then it's kinda like the workflow of like.What happens when there's a bug? How does it flow into the system? Like, is it like a subagent working on it? How does it make a PR and how does that get reviewed? And me, and then, you know, so there's like the, the flow or process.[00:31:56] swyx: Yeah. Cool. Uh, you know, one thing we did work out before you guys came in was this demo or this[00:32:01] Simon Last: agents[00:32:02] swyx: agent demo.Uh,[00:32:03] Simon Last: so every,[00:32:04] Alsesio: every time we do an episode, we try the product. Right. I don't think there's ever been an episode that I haven't tried. Yeah. Um,[00:32:11] swyx: and we, we try, try is a, a big word. Like since day one lane space has been on Notion, but this is the, this is the net new thing. Yes.[00:32:18] Alsesio: So this is for Nel Labs, which is the space we're in.So next week we're opening applications for tenants. So there's a web form, let me, we got this form done here. Uh, so, uh, before. Uh, the workflow would be I get an email, then I look at the person. It was like, should I spend time talking to this person? Then I respond, they respond back. So I build this. So the name it came up for on its own.Can you maybe h how do, how does it come up with its own name?[00:32:43] Simon Last: Yeah, that's a pretty app name. It's, it, it is just a random, it's a random, a name generator.[00:32:47] Alsesio: Oh, that's funny. It just came,[00:32:49] Simon Last: the fact that it picked that is, is kind of hilarious. I'm pretty sure it's just determined,[00:32:54] Sarah Sachs: resilient collector. I, I think I've never looked at the code for that.I've never second guessed it. I think it's kind of like a madlib situation.[00:33:00] Simon Last: Yeah, I think you're right. Yeah. It's, it's totally a, a deterministic. Oh, I thought it was great. Yes. Although, although when the, if you use the AI to set itself up, it can update its own name, so. Okay. Um,[00:33:11] Sarah Sachs: how did you create it? It, did you just do[00:33:12] Alsesio: classroom?I,[00:33:13] Sarah Sachs: okay.[00:33:13] Alsesio: I did, yeah. I'll say just check my inbox for applications for a coworking space. Keep a people, so it created the database for me. Which I have here. And I guess database is like an notion table because everything is notion. Um, and then whenever um, an email comes in, like here, it just creates a new role for the person.Mm-hmm. And then it uses web search to enrich the mm-hmm. The profile. So it kind of like searches the web and it's like, this is who this person is, this is when they say they wanna move in and kind of updates everything else. This is, I mean, it's not a GI, but to me, I don't wanna do this work. So it feels like, I mean, it took me maybe like 15 minutes to set up the whole thing.Um, and I really like that most of the information should live here. You know, it is not like some other tool asking me[00:34:01] Sarah Sachs: Yeah.[00:34:01] Alsesio: To like, bring my stuff there. It's like I would've probably already created an ocean thing.[00:34:06] Sarah Sachs: Mm-hmm.[00:34:06] Alsesio: So[00:34:07] Sarah Sachs: most of our biggest use cases and gains are from. That extra layer of human involvement in the process to make it so right.And so like one of our biggest use cases is bug triaging. So if someone posts something in Slack, can you just have a custom agent that lives there that has its own routing constitution of what team this belongs to, creates a task in your task database and then posts in that Slack channel, right? Like that's like one of the first things that we built internally, I think.And it's completely changed the way that notion functions as a company. Nothing falls through, well, most things don't fall through the crack. We don't know what we don't know. But it's not replacing people, it's replacing processes.[00:34:44] Alsesio: Yeah.[00:34:44] Sarah Sachs: Right.[00:34:45] Alsesio: And I'm curious how you think about composability of these things.So the other one I was working on is like a. These filler. So whenever somebody signs up as a tenant, kind of he'll sell the lease for them. There should probably some agent that is like office manager agent mm-hmm. That can handle the request, make the lease, and then, uh, give them a ADA access to the office and all of that.How do you think about that feature?[00:35:08] Simon Last: Yeah, so I mean, there's, there's two ways you can compose. One way is by using like the data primitives. So you can, you know, you, you could give, you have one agent, uh, be writing to the database and there's another agent that's walked in the database. So that's, that's one way that they, they can coordinate that's like a little bit more decoupled and mm-hmm.Works really well. Or you, you can couple them. So I, I think it's actually not released yet. Releasing it like next week is, uh, in the settings for an agent, you can give access to invoke any other agent.[00:35:34] swyx: Hmm.[00:35:34] Simon Last: So you can have them just. Just, uh, uh, talk directly. So[00:35:37] swyx: you, was there a limit on like, number of recursions or just,[00:35:40] Simon Last: um, probably,[00:35:42] swyx: you know what I mean?Like, you can just get an infinite loop that way there's[00:35:45] Simon Last: some kind of Yeah,[00:35:46] Sarah Sachs: I think it's, there is actually a number somewhere.[00:35:49] swyx: I believe I'm just, you know, like, you're, you're, someone's gonna screw up. You[00:35:51] Simon Last: should you try to see[00:35:53] swyx: Yeah. I mean, everything's gonna be paperclips.[00:35:55] Simon Last: Oh, yeah. Yeah. But, uh, but, but that's really useful.Yeah. So we, you know, like I just, I, I helped, uh, someone internally the other day, they had, they had built like over 30 custom agents for, uh, for our go to market team doing all kinds of different things. You know, for example, like researching, you know, like, like filling information about, about a customer or like, like triaging customer feedback or like, uh, something like that.Literally over 30 of them. And, and then he, and then he even made like a database of all the agents and then he is like, okay, and, and now I'm getting 70, over 70 notifications per day with just the agents are blocked on various things. Uh, and then I was like, oh, okay, cool. You know, the obvious thing to do there is to make a manager agent,[00:36:32] Sarah Sachs: right?[00:36:33] Simon Last: That's gonna sort of blocks be another abstraction layer in between your, your, uh, uh, 30 agents. Uh, so yeah, we, we send out with like a manager agent and then has access to invoke all the other agents and it's sort of like, like watching and observing them and then it sort of, it just creates a layer of abstraction.So instead of 70 notifications per day, it's like, like five. And then, and then the manager agent can help like, uh, debug and fix any problems with the,[00:36:54] swyx: does this is a concept of like an inbox or something like piece, you're basically saying that they can message each other?[00:37:00] Simon Last: Yeah.[00:37:01] Sarah Sachs: Well[00:37:01] swyx: they use the system of record, which, which is[00:37:02] Sarah Sachs: notion, so we[00:37:03] Simon Last: actually, yeah, we didn't make any special concepts at all.[00:37:06] swyx: They're interested to the motion notifications that I would've got,[00:37:09] Sarah Sachs: they can just like write a task to a database that the other agent's task to listening to, or they can actually call a web book to the agent, like they can just add the agent. Okay.[00:37:17] Simon Last: Yeah, I mean, this is something that, that we're still working on.I, I think we, you know, like, like generally, generally the way we do these things is, you know, you first make it possible, maybe like a sort of janky way. So I, I, I think the way I set ‘em up is like, you know, we created like a new database that was sort of like issues mm-hmm. That the custom agents were, were experiencing, and then gave them all access to file an issue and then the manager has access to, to read the issues.Um, and that works pretty well, essentially like, like give it its own like internal issue tracker just for the agents. And then, you know, if that becomes a, a concept that seems useful, generally maybe we will think of how to package it in. But I mean, generally we try to just keep it to composing the primitive if we can.You know, another example of this is we have no built-in memory concept. Memory is, is just pages and databases. And so if you wanna give a memory, just give it a page and give it. Edit access to that page and the[00:38:03] swyx: human can edit it. Agent can edit[00:38:04] Simon Last: it. Yeah. And so that works, that pattern works extremely well on it.And you know, depending this case, you can have it be just a page or it could be an entire database with, you know, or, you know, I can have sub pages is is pretty on what you can do with that.[00:38:15] Alsesio: So when I was setting this up, uh, I connected my inbox and it was like, do you wanna use Gmail or Notion Mail? And I'm like, I don't wanna use Eater, I just want you to do it.I'm curious how you think about, you know, notion, mail, notion, calendar, all of these kind of ui ux interfaces, full stack[00:38:29] Simon Last: notion.[00:38:30] Alsesio: Yeah. When like at the same time you have the agents abstracting them away from you in a way, you know, how do you spend like the product calories so to speak?[00:38:37] Simon Last: Yeah, I mean, I think it's pretty important that you don't have to use, not your mail to connect to the mail capability.So we can just connect to Gmail or, or whatever you want, uh, to use. And we're thinking of the mail service as being really great to the extent that it's really agent built, right? So maybe the mail app is just sort of a prepackaged agent that helps you automate your, your inbox.[00:39:00] Alsesio: Yeah, the auto labeling is great.Think[00:39:03] Sarah Sachs: the, when we, um, integrate with Gmail for instance, we have a series of tools available that are available via MCP or API to Gmail. When we integrate with Notion Mail, we have the Notion Mail engineering team to build us the, um, exact right tools that optimize latency, optimize performance and quality.They own that quality. Um, there's product leads there. They're directly thinking about the user problems that happen in mail. So it tends to be when we build integrations and connections, we build natively first. Um, and then think about, um, extending them generally just because it's also easier. Mm-hmm. Um, um, to build natively first.Um, so that tends to be how we phase things out.[00:39:43] swyx: Talking about integrations, you prompted me, so I gotta ask. M-C-P-C-L-I. What's going on? What's the[00:39:48] Simon Last: Yeah. Opinion. I think, I mean, I'm, I'm definitely bullish and excited about cli. I think there's a few really cool things about cli. So one really cool thing is like, um, is that it's in the terminal environment, so it gets a bunch of extra power.So it, you know, for example, it can like, like paginating and cursor through like long outputs. Um, and it has a progressive disclosure inherently. Uh, so, you know, you don't see all the tools at once. It's just, you see the CLI wrapper and you can like use the, the help commands and, and, and read files. And then I think the most important thing that's, that's super cool is that there, it's also inherently a, a bootstrapped.So if there's an issue, uh, the agent can debug and fix itself within the same environment that it uses the tool.[00:40:30] swyx: Mm.[00:40:30] Simon Last: Right. Like, you know, I think I saw a tweet this morning. Someone said, you know, my agent didn't have a browser, so I asked it to make all a browser tool and within a hundred lines of code, it gave itself a little browser, like, like wrapping the, the, the chromium API, um.That's pretty incredible. And then if there was a bug, it would just immediately try to fix it. Mm-hmm. Right. On the other hand, if you use an, you know, if you use like of, of the Chrome dev tools, MCP, I've had this issue where like, like sometimes the transport gets like messed up. If it gets messed up, the agent has no way to fix itself.It, it no longer has a browser, it's, it's not broken. Right. I think that's, that's pretty fundamental, but I would say like a lot of the, the bad things about it can be fixed. Uh, so I think like, as a progressive disclosure, that can be fixed with, with right harness. Like, it, it obviously doesn't make sense to show it all the tools all the time.That's not really inherent to the MCP protocol. It's just like how you wrap it and use it.[00:41:16] swyx: There's many poorly built MCPs because we didn't know.[00:41:19] Simon Last: Yeah, yeah. I mean it was just early, like, like the obvious thing is, uh, you know, to start with is, is to just show it all the tools and it's like, okay, now we have a hundred tools.Yeah. And like the tool calling actually works. So let's of[00:41:28] swyx: your success[00:41:29] Simon Last: give it a way to like, like filter to source the tools. So yeah, I would say like broadly speaking, I'm really bullish on cli. I'm still bullish on CPS and in a certain environment. I think in, in particular, CP is really great for when you want sort of like a narrow, lightweight agent.I think there's, there's definitely a lot of use cases where, where you don't want like a full coding agent with a compute run time. And also you want it to be like more tightly permissioned. MCP inherently has a really strong permission model, like all you can do is call the tools. A CLI is a little bit murkier.It's like, can I access the, if PI token are you, like, properly sort of like re-encrypt the token so it can't like exfiltrate it, it introduce a lot of like, like new issues, which are. Real and hard to solve. And MCP is just like the dumb simple thing that works and it that it's pretty good.[00:42:12] Sarah Sachs: I'll add two more perspectives, not from it working well for Notion, but how notion like commits to both platforms.Notion is dedicated to being the best system of record for where people do their enterprise work. So we will always support our MCP and so far as other people are using cps, right? So regardless of our perspective, we've put a lot of effort into our MCP and we have a fantastic team that we're building, um, to do more there.And the second thing I'll say, I think, um, we all think a lot, but lately I've been thinking a lot about making sure there's a value alignment and pricing, um, with capability.[00:42:43] swyx: Literally our next question[00:42:44] Sarah Sachs: and. Needing language to execute deterministic tasks feels wasteful and requiring on a language model to interface with third party providers seems wasteful for tasks that don't require it.And particularly because our custom agents are using usage-based pricing. We think of pricing as like the barrier of entry for use of our product, and we're quite committed to making sure that it's not wasteful. Um, not just because it's a bad deal for our customers, but it's also bad business. We wanna have as many buyers, like there's a, there's an elasticity of demand and so if we can have our agents properly execute code that calls on CLI deterministically, it's a one-time cost, right?Versus constantly having a language model integrate with an MCP over and over and over and paying those like repeated token fees and it's happening outside the cash window, then you're paying for it over and over and over and it's just kind of unnecessary and less deterministic when it doesn't have to be.[00:43:36] Alessio: Yeah, the open-endedness I think is like, the main thing is like, well, if I go write code to just call an API, I would never use an MCP. But then you need an NCP sometimes when you know what to call, but you don't want it to restart versus like, I think the it built a browser from scratch is like, it's great when you're doing it on your own, but like if your customers were having your AI write a browser from scratch every time and you had to pay the token cost of that, yeah.You'd be like, no, no. The Chrome dev tools CP is actually pretty great. Just use that. I'm curious, how do you make that decision? Like should it be. Just straight API call very narrow. Should it be an MCP? Should it be super open-ended?[00:44:10] Sarah Sachs: Do you mean for when we ship notion capabilities or when we add capabilities to[00:44:13] Alessio: notion[00:44:14] Sarah Sachs: AI or,[00:44:14] Alessio: I mean, you might have a capability that the only way to do is an open-ended agent, like an agent with a coding sandbox.[00:44:21] Sarah Sachs: Yeah. In Notion ai they're not explicit, not We also ship an MCP.[00:44:24] Alsesio: Yeah. Yeah. In B,[00:44:25] Sarah Sachs: yeah.[00:44:26] Alsesio: Internally. Okay. Like is there ever a discussion of like, we're not gonna ship it because we're not able to tie it down? Or are you happy to just like,[00:44:33] Sarah Sachs: um, no. I mean, there are a lot of things where we choose not to use MCP because we wanna add more high touch to quality.I think search an agent to find is like the largest instance of that, where we have. Um, slack and linear and Jira search and notion that is not using necessarily the search MCP functionality that is provided by those companies. And that's because it's quite critical we think, to how our agent trajectories work is for us to have a little bit more control on the functionality of the search journey.And so it usually comes from quality and there's a long tail of things and that's why we built an MCP client or an MCP server, excuse me, so that people can connect whatever they want. There's that long tail, right. But we, for search particularly, I would say that's like the primary entry point, but there are other connections as well that it's a little bit of secret sauce a

Hey yall, Alex here, writing this from sunny London, at the first ever AI Engineer conference in Europe!What a show we have for you today! First, let me catch you up on what's important: Anthropic, this week announced a whopping $30B ARR up from 19B in Feb, while also telling us about Claude Mythos Preview their next gen HUGE model that they won't release to the public (yet?) that finds crazy vulnerabilities in existing code bases. Apparently OpenAI will follow up with a similar non-public model soon.The Meta Superintelligence Lab led by Alex Wang finally showed what they were working on, Muse Spark, the smaller of their upcoming models on a complete new infrastructure (MSL announcement, Simon Willison's deep dive on the 16 hidden tools).In other news:Z.AI released GLM 5.1 in OSS finally (HF weights), Seedance 2.0 finally available in US on Replicate, OpenAI testing out GPT-image-2 on LM Arena under codenames, HappyHorse from Alibaba takes the video crown, and Mila Jovovich (5th Element, Resident Evil) releases agentic memory plugin called MemPalace (Ben Sigman's transparent correction thread is worth reading).We had 5 guests today on the show, we kick off with @swyx the founder of AI Engineer and host of Latent Space. We then chatted with @petergostev from Arena (formerly LMArena) about Mythos and the compute wars, then Vincent Koc, the second most prolific contributor to OpenClaw, then our friends VB from OpenAI and Omar from DeepMind, both previously at HuggingFace. This is a busy busy show, and given the time-zones, I unfortunately don't have time for a full weekly writeup, but as always, I will share the raw notes and post the video (lightly edited).ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.AI Engineer - LondonThursdAI came a long way since the first AI Engineer conference, but many who read this don't know, that was my big break. Swyx invited me to cover the first AIE in San Francisco in 2023, and I remember, I was in an Uber to the airport, the driver asked me what I do, and I, for the first time said “I host a podcast”. I (and ThursdAI) owe a lot to Swyx, and AIE team, and it's been incredible to see how big they've grown and how many great speakers this event hosts! The term AI Engineer has drifted in those 3 years, but also has the term Software Engineer. Swyx predicted this nearly 3 years ago, what I don't think he predicted, is that all engineers are now AI Engineers, and this includes domains like Agens (OpenClaw), Context and Harness Engineering, Evals and Observability, Voice & Vision all of which are tracks in this conference. I was really surprised to see how many of the talks/speakers here are native to London (after all, Deepmind is from here, OAI, Anthropic, Meta have offices here) and the latest boom in agents, OpenClaw, Pi were all Europe based as well, and they are joined the AI Engineer stage. Oh, and there's also a Giant Inflatable Claw at the entrance, yup, for pictures and vibes, and to show off how quickly the OpenClaw took over the mind-share. Anthropic announces $30B ARR and Mythos, their next model, will not be released to the public. The thing that everyone will tell you, is that Anthropic is on a roll, this is obviously connected to their upcoming IPO this year. We've been covering many issues on their part, but this week we saw them posting about a HUGE increase in ARR, from 19B in February to 30B in April, passing OpenAI at $25B. That last fact though, is kind of disproven because they report on ARR differently, OpenAI apparently only counts their cloud revenue from Microsoft per the information. The growth is undeniable though, and so is the most unprecedented release announcement, Claude Mythos Preview, which was rumored for a bit and now was announced proper. With project Project GlassWing, Anthropic has announced that this model is SO good at cyber security and finding bugs in code, that they cannot share it with the public, and through GlassWing they will share it with companies like Microsoft, Linux, CrowdStrike and a bunch of others, to harden their security. This is it folks, this is the first time, where a model was “announced” but deemed too risky to release. Now, is it truly “too risky”? Previously, folks thought that DALL-E is too risky, or cloning voice tech is too risky, and now it's everywhere. The capabilities catch up even in OpenSource. But the facts are, Anthropic says they've found a 27-year old bug in OpenBSD (famously very secure), and that this model is very very good at connecting the dots between several, seemingly inacuous bugs, to string them together into one coheren exploit. This is, indeed scary. Just last week, one of the top security researchers in the world, Nicolas Carlini, now at Anthropic, gave a talk at Black Hat, showing off these results, and saying that these models since December and definitely recently have passed him as a security engineer. If you haven't seen this talk, watch it, then try to estimate if Anthropic did the right thing by only releasing this model to enterprises first. But on the show, Peter Gostev from Arena gave me a take on this that I haven't been able to shake. Peter pulled up his Compute Wars chart live on the show — and the picture is that OpenAI is way ahead of Anthropic on compute, with Anthropic only recently getting a noticeable bump (which lines up suspiciously well with Mythos being trainable in the first place). His read: “it sounds cooler to say it's too risky to release than ‘we can't serve it.'” The official partner pricing is $25 / $125 per million tokens — 5x Opus 4.6 — but if you don't have the GPUs to serve it broadly, the price doesn't matter. In the year of the IPO, the company that cannot serve a model says the model is too dangerous to serve. Make of that what you will.This also reframes the whole rate-limit drama with OpenClaw. Anthropic didn't ban OpenClaw — I want to be very clear about this because the discourse went sideways. What they did is they made it significantly more expensive for Max-tier subscribers to use Opus through OpenClaw, which pushed a lot of people over to GPT-5.4 via Codex. Same root cause: they're out of compute. The freshly announced Anthropic + Google TPU deal (Google already owns ~10% of Anthropic) is them trying to fix this — though as Peter noted, it's pretty wild that Google is propping up a direct competitor to their own DeepMind team. Same pattern as their original $2B Anthropic investment ending up propping AWS Bedrock against Google Cloud. Big Google contains multitudes.Meta Superintelligence Labs ships Muse Spark — Llama is dead, long live MuseLlama is dead, long live Muse. This week Meta finally showed what the very expensive Meta Superintelligence Labs under Alexandr Wang has been cooking, and the answer is Muse Spark — the smaller of their new model family, built on a fully rebuilt AI stack from scratch in just 9 months. Nine months is wild for that kind of overhaul, and the headline number people are quoting is that they reach Llama 4 Maverick capability with over 10x less compute.Spark is intentionally small and latency-optimized — it's not trying to be the biggest, it's trying to be the first step on Meta's new scaling ladder. But the benchmarks in certain areas are nuts: 86.4 on CharXiv Reasoning (beats Opus, Gemini, GPT-5.4), and the one that really got me — 42.8 on HealthBench Hard vs Opus at 14.8 and Gemini at 20.6. They trained it with data curated by over 1,000 physicians and it shows. They also shipped a Contemplating mode which is parallel multi-agent reasoning, hitting 58.4% on Humanity's Last Exam with tools. Coding is the acknowledged weak point (77.4 on SWE-Bench Verified vs Opus 80.8) but for v1 from a brand new stack, this is extremely respectable.Meta is Back!The real story isn't any single benchmark though, it's distribution. Spark is rolling out across meta.ai, WhatsApp, Instagram, Threads, Messenger, and Ray-Ban Meta glasses — billions of users. Meta went from open Llama to a closed consumer model and they're clearly playing a different game now (though Wang says future Muse versions might be open-sourced).The deep-dive that's really worth your time is Simon Willison's post where he poked at the meta.ai chat UI and got the model to spit out descriptions of 16 hidden tools behind the scenes — full Code Interpreter with persistent Python 3.9, a visual grounding tool that does pixel-precise object detection (bounding boxes, point coordinates, counting — it located 8 objects including individual whiskers and claws on a generated raccoon), sub-agent spawning, file editing, and semantic search across Instagram/Threads/Facebook posts. It's basically an entire agentic harness baked into the chat UI. Jack Wu from MSL confirmed the tools are part of a new harness built specifically for Spark's launch. Meta stock went up 7% on this. They are very much back in the frontier game.Guest highlights We had an unprecedented packed show with 5 guests (also this is the shortest show we've everSwyx kicked us off with vibes from the AI Engineer floor — harness engineering as the dominant theme (gains are coming from the harness, not the weights), the rise of skills (English-as-programming-language) absorbing more of that harness work, and his thesis that supply-chain attacks like the recent light LLM and Axios incidents mean you should basically vendor everything — pip fork instead of pip install. We also chatted about how MCP has gone from “the most exciting protocol” to “settled and stable, therefore less interesting,” which is a great problem to have.Peter Gostev from Arena (you saw a lot of him in the Mythos section above) also dropped a bonus on us: Arena just released 3 years of historical leaderboard data and actual prompt datasets on Hugging Face. He used to literally scrape the arena website by hand into Google sheets to make those overtime leaderboards we all loved — now it's all public. Also: he confirmed that Seedance 2.0 jumped ~80 ELO points above the next video model on Arena, which is unprecedented — video models normally cluster within 10 points of each other.Vincent Koc — the #2 OpenClaw maintainer after Peter Steinberger — joined us fresh off the OpenClaw track stage. The OpenClaw codebase is now ~1.5 million lines of code including unreleased iOS and Android native apps. GitHub literally caps the issue/PR counter at “5K+” and they hit the ceiling. We talked about OpenClaw 2026.4.5 which ships /dreaming GA (Light/Deep/REM phases that defrag agent memory and write a human-readable Dream Diary to DREAMS.md), built-in video and music generation across 4 backends, GPT-5.4 as the new default, prompt-cache reuse improvements, and Control UI + docs in 12 new languages. Vincent's framing of dreaming was beautiful — “how do you explain agent memory to a mom? You call it dreaming.” He also gave my favorite line of the show on the GPT-5.4 personality problem: incredible at coding, but soulless. (For what it's worth, I came home after watching Project Hail Mary, cloned the Rocky voice, dropped it into my OpenClaw, and it was magical. That's the kind of thing you can only do when the harness and the model are decoupled.)VB from OpenAI told us Codex just hit 3 million weekly active users — up from 2 million last month. We talked plugins (the Stripe / Supabase / shadcn ones that ship as packages), sub-agents (yes, one is named Jason), and Guardian Approvals — an experimental mode that classifies each tool call by risk and only escalates the dangerous ones to you, so you don't have to YOLO-mode everything. The story that stuck with me though is his 9 AM Codex automation: every morning it reads his Slack mentions, cross-references Gmail and Calendar, and creates 5-minute pre-brief calendar events for upcoming meetings. None of that is “coding.” That's the super-app future hiding inside a “developer tool.” I'm stealing this workflow.Omar Sanseviero from Google DeepMind came on to celebrate Gemma 4 crossing 10M+ downloads with 1,000+ Gemma-4-based fine-tunes already on HF (and Gemma family total is now over 500M downloads). Gemma 4 is also the foundation for the next generation of Gemini Nano on Pixel/Samsung devices. Lama.cpp vision capability fixes are landing. Gemma 4 is also live on W&B Inference if you want to play. Wolfram (whose entire household runs on Pixel + Google AI Studio, including his 70-year-old mother on voice unlock) was in heaven.This Week's BuzzA short but spicy week from Weights & Biases:* W&B Automations are LIVE. You can now wire event triggers from your training runs (completion, eval thresholds, drift) into notifications, GitHub Actions, deployments, infra shutdowns — closing the loop from experiment to production. Pairs really well with the iOS app we recently shipped, so you can get a ping on your phone the moment something interesting happens on a run.* GLM 5.1 is live on W&B Inference (alongside Gemma 4 from last week) — the team is moving fast to host the best open models the moment they drop.* Wolfram published a deep dive on “more reasoning is not always better” on the W&B blog — the research behind his finding that giving models more thinking tokens can actually make them dumber on certain tasks. It's the in-depth version of what we discussed on the show last week, with all the data. Go read it on wandb.com.Also: shout out to everyone who came up to me at AI Engineer and said hi. The Wolf Bench mentions in particular made my day. If you're listening to this and you're at AIE — come find us, we'll be around tomorrow too.That's it for this week — newsletter is short because the show was long and London is calling. As always, thanks for reading and listening

This episode originally aired on the Latent Space Podcast. swyx and Alessio Fanelli speak with Marc Andreessen about the arc of AI from its origins in 1943 to today's breakthroughs in reasoning, coding agents, and self-improvement. They cover the parallels between AI scaling laws and Moore's Law, the architectural insight behind Claude Code and the Unix shell, the coming supply crunch in compute, and why the messy reality of 8 billion people means both AI utopians and doomers are too optimistic about the pace of change. Follow Marc Andreessen on X: https://twitter.com/pmarca Follow Shawn "swyx" Wang on X:  https://twitter.com/swyx Follow Alessio Fanelli on X: https://twitter.com/FanaHOVA Listen to Latent Space. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mistral has been on an absolute tear - with frequent successful model launches it is easy to forget that they raised the largest European AI round in history last year. We were long overdue for a Mistral episode, and we were very fortunate to work with Sophia and Howard to catch up with Pavan (Voxtral lead) and Guillaume (Chief Scientist, Co-founder) on the occasion of this week's Voxtral TTS launch:Mistral can't directly say it, but the benchmarks do imply, that this is basically an open-weights ElevenLabs-level TTS model (Technically, it is a 4B Ministral based multilingual low-latency TTS open weights model that has a 68.4% win rate vs ElevenLabs Flash v2.5). The contributions are not just in the open weights but also in open research: We also spend a decent amount of the pod talking about their architecture that combines auto-regressive generation of semantic speech tokens with flow-matching for acoustic tokens (typically only applied in the Image Generation space, as seen in the Flow Matching NeurIPS workshop from the principal authors that we reference in the pod).You can catch up on the paper here and the full episode is live on youtube!Timestamps00:00 Welcome and Guests00:22 Announcing Voxtral TTS01:41 Architecture and Codec02:53 Understanding vs Generation05:39 Flow Matching for Audio07:27 Real Time Voice Agents13:40 Efficiency and Model Strategy14:53 Voice Agents Vision17:56 Enterprise Deployment and Privacy23:39 Fine Tuning and Personalization25:22 Enterprise Voice Personalization26:09 Long-Form Speech Models26:58 Real-Time Encoder Advances27:45 Scaling Context for TTS28:53 What Makes Small Models30:37 Merging Modalities Tradeoffs33:05 Open Source Mission35:51 Lean and Formal Proofs38:40 Reasoning Transfer and Agents40:25 Next Frontiers in Training42:20 Hiring and AI for Science44:19 Forward Deployed Engineering46:22 Customer Feedback Loop48:29 Wrap Up and ThanksTranscriptswyx: Okay, welcome to Latent Space. We're here in the studio with our gues co-host Vibh u. Welcome. Thanks. Excited for this one as well as Guillaume and Pavan from Mistral. Welcome. Excited to be here.Guillaume: Thank you.swyx: Pavan, you are leading audio research at Mistral and Guillaume, you're Chief Scientist,Announcing Voxtral TTSswyxHost(00:05) Okay. (00:05) Welcome to Lean Space. (00:06) We're here in the studio with trustee co-hosts, Vibhu. (00:09) Welcome.VibhuHost(00:11) Very excited for this one.swyxHost(00:12) As well as Guillaume and Pavan from Mistral. (00:15) Welcome. (00:16) Excited to be here. (00:17) Thank you for having us.(00:18) Pavan, you are leading audio research at Mistral and Guillaume, you're a chief scientist. (00:23) What are we announcing today where we're coordinating this release with you guys?GuillaumeGuest(00:26) Yeah, so we are releasing Voxtral TTS. So it's our first audio model that generates speech. It's not our first audio model. We had a couple of releases before.(00:35) We had one in the summer that was Voxtral, our first audio model, but it was like a transcription model, ASR. Like a few months later, we released some update on top of this, supporting more languages. Also a lot of table stack features for our customers, context biasing, precision, timestamping and transcription. We also have some real-time model that can transcribe not just at the end of the level.(00:56) You don't need to fill your entire audio file, but that can also come in real-time. And here, this is a natural extension in the audio, so basically speech generation. So yeah, so we support nine languages, and this is a pretty small model, 3D model, so very fast, and also state of the art. Performed at the same level as the base model, but it's much more efficient in terms of cost, and also much, in terms of cost, it's also much cheaper, only a fraction of the cost of our competitors.(01:22) And we are also releasing the work that this model is running.swyx What's the decision factor?Guillaume It's a good question.swyxThere will be more. Yeah, Pavan, any sort of research notes to add on?Architecture and CodecPavan: But it's a novel architecture that we develop inhouse.We traded on several internal architectures and ended up with a auto aggressive flow matching architecture. And also have a new in-house neural audio codec. Which, converts this audio into all point by herds latent [00:02:00] tokens, semantic and acoustic tokens. And yeah, that's that's their new part about this model and we're pretty excited that it's, it came out with such good quality and Jim was mentioning. Yeah, it's a three B model. It's based off of the TAL model that we actually released just a few months back and insert trunk and mainly meant for like the TTS stuff, but they need text capabilities are also there. Yeah.swyx: So there's a lot to cover.I always I love any, anything to do with novel encodings and all those things because I think that's obviously I creates a lot of efficiency, but also maybe bugs that sometimes happen. You were previously a Gemini and you worked on post training for language models, and maybe a lot of people will have less experience with audio models just in general compared to pure language.What did you find that you have to revisit from scratch as you joined this trial and started doing this? At leastUnderstanding vs GenerationPavan: when it comes to, for, I think the, there are two buckets, I guess the audio understanding and audio [00:03:00] generation. The audio understanding, like the walkthrough models that Kim was mentioning that we released earlier.The walkthrough chat that we released I think July last year, and the follow up transcription only, models family that we released in January, that would be one bucket, and the generation is another bucket. I think. You can also treat them as a unified set of models, but currently the approaches are a little different between these two.To your question on how audio is fed to the model? In the understanding model, it's very similar to actually Pixar models that we also released,swyx: yes.Pavan: That'sswyx: amazing.Pavan: It was pretty, I, that was the first project I worked on after joined Misra. It was pretty, pretty nice. And Wtu was very similar in spirit.I guess So we feed audio through an audio encoder similar to images through a vision encoder, and it produces continuous embeddings and which are fed as tokens to the main transformer decoded transformer model. Yeah. On the model output is just text. So on the output side, there is nothing that needs to be done in these kinds of mode.I [00:04:00] guess the interesting part of what the generation stuff is, the output now has to produce audio and. The approach that we have is this neural audio codec, which converts audio into these latent tokens. There is a lot of existing attrition and a lot of models which are based off of this kind of approach.And we took a slightly. A different, design decisions around this. But at the end of the day, the neural audio product converts audio into a 12.5 herdz set of latents. And each latent is, has a semantic token and a set of acoustic tokens. And the idea is that you take these discrete tokens and then feed it on the input side.There's several ways to use this at each frame, but we just sum the embedding. So it's like having key different vocabularies. Combine all of them because they all correspond to one audio frame on the input side. The output side is the interesting part on the output side, the, it's not the, I don't know if it's the most popular, but one.Popular technique is to have a depth transformer [00:05:00] because you have K tokens at each time step, like with a text, you just have one token at each time step. So you just do predict the token from the vocabulary with, yeah, with just, you get probabilityswyx: This's a very straightforward text. VeryPavan: straightforward.swyx: Yeah.Pavan: But if you have K tokens, then the name thing would be to predict all of them in paddle. That doesn't work. At least that doesn't work that well because audio has more entropy. And the, one of the techniques people use is this depth transformer where you you almost have a small transformer, or it can be L-S-T-M-R in as well, but people use transformers and you predict the K tokens in auto aggressive fashion in that.So you have two auto reive things going on.Flow Matching for AudioPavan: So the thing we did differently is in, instead of having this auto aggressive K step prediction, we have a flow matching model. Instead of modeling this as a discrete token set we trained the codec to be both discrete and continuous to have this flexibility.So we did try the discrete stuff too, and which it works well, but the continuous stuff works just better. So yeah, we took this flow matching, so the, it's a flow [00:06:00] matching head, which takes the latent from the main transformer and like kind in fusion, it's denoising, but in this flow matching itself, velocity estimate.So you go from this noise t all the way to there. Audio latent, which corresponds to the 80 millisecond audio and then, which is sent through the work order to get back the 80 millisecond audio frame.swyx: Yeah. Is this the first application of flow matching in audio? Because usually I come across this in the image.Pavan: Yeah. Actually, in some sense there are models flow matching models in audio, but I think this specific combination I could be wrong. There could be somewhat. No. I haven't seen. I haven't seen much work in this, so I think it's novel and a lot of it's just a way bigger community, so they, I think they pioneer a lot of these diffusion flow matching work, and it's interesting to adopt some of the ideas there into audio and,swyx: yeah.Pavan: Yeah, I'm, personally that's the think part which is trying out about. One of more meta point is unlike text, even in vision, I think this is true, but in [00:07:00] audio step literature that there is no.Winner model, yet there is no, okay, this is the way you do things. It's it's still by, I think people are still iterating and figuring out like what's the best overall recipe. I guess the idea. Pretty sure there are models which are also completely end-to-end, like NATO audio. NATO audio, but it's still not come to a convergence point where this, the right way to think that.That also makes. A space pretty exciting to explore.Real Time Voice AgentsVibhu: What are some of the ways to look at it?Vibhu: There are ways where you can do diffusion for audio generation, but if you want like real time generation, that's a big thing with the approach I'm assuming that you took. Yeah. And also like how do you go about evaluating different axes of what you care about, yeah,Pavan: good point. I think we so you can do just flow matching diffusion for the whole audio. We didn't even go down that path because one of the main applications is voice agents and we want real time streaming, and that's the use case. That's not the only use case, but that's one of the primary use cases we want to get to.So we [00:08:00] picked the auto aggressive approach for that. And within the auto aggressive space, again, you can do chunk by chunk or you can do so we picked the. I think at least personally prefer the operations, which are the simplest, and so we try to see, can we just add audio as just another head to our regular transformer decode model because that kind of makes it easier for eventual end-to-end modeling of audio text native modeling.Yeah. And it works pretty well. So I guess we went with that and we tried a little bit, but the flow matching head itself, like we had a discreet. Diffusion kind of approach, which also works well, but the flow matching work better.swyx: I was just curious about how you also think about this overall direction of research.Do you basically, when you work with the audio team, do you set some high level parameters and then let them explore whatever, or how does it work between you guys?Guillaume: No I think the way it works is that we are the, we are prioritizing together, I think, what are the most important features because there are many things we can do [00:09:00] in audio.Yeah, I think we try to. These are like how we should do things, for instance. Ultimately what we want to do is to build this through duplex model, but we are not going to start this start there directly, I think is. Some of the project people are doing, butswyx: just to confirm, full effects means it can speak while I'm speaking or,Guillaume: yeah.Okay. Audio. Yeah. Yeah. So intimately we're going to get there, but for us it was, we decided to take it like a step by step. So we start with whatever is the most important. I think support customers, which is the transcription is the most popular use case. Then the speech generation, Soviet time, just a bit before that.And then actually to be like more, but try combining everything all together. But but yeah, we thought it was also important to like separate things and optimize each capability one by one before weswyx: measure of that together. And the super omni model. ButGuillaume: very interesting because as Par said, it's when you work on some other domains of this airline and everything, there are many areas where I think it's not as interesting.For instance. Many places, it's essentially just around data or like creating new environments on a lot of kind [00:10:00] of easy things. But things were, I think the research is maybe not as interesting. Were in audio. There are so many ways to actually build this model. So many ways to go around it. That's the sense I think is really interesting.And what we also tried for speed generation is that we tried multiple approaches. What was interesting that even though they were extremely different, they under the big know the particles but the for matching turned out to be quite more natural. So we are happy with this.swyx: Is there intuition why it maybe like flow matching is just models speech better in some natural fundamental, latent dimension?Pavan: No, I think the main thing is e even at a particular time step, there is a distribution of things.swyx: Yes.Pavan: To be predicted like the way you inflate. So you already know the word that you're speaking and Yeah. The intake space, let's say the word maps register a single token for simplicity.In most cases it does. So there is not a lot of so you just pick the word, but with within audio, even the same word could, even with your own voice, could be inflicted in so many different ways. And I think [00:11:00] any approach which like models this distribution and. And flow matching is one, one of the take.It's not the only one at all, but it's a one which works pretty reasonably well. I think that's better. So you have to pick across several different, the intuition I have is it's, there are some, several different clusters each corresponding to some specific way you would inflict, pronounce that thing.And you can't predict the mean of it because that corresponds to some blurred out speech or something like that. But you have to pick one. And then like sharpswyx: conditional inference.Pavan: Yeah, exactly.swyx: Is that all covered under disfluencies, which is I think the normal term of art. Pauses intonations. By the way, I have to thank Sophia for setting all this up, including like some of these really good notes becausePavan: Yeah.swyx: I'm less familiar with the audios for me.Pavan: No. I think dis dismisses are definitely one such Eno defenses is more likeswyx: which is arms are.Pavan: Yeah, arms. And also repeat like you like,swyx: yeah.Pavan: You do this full of words, your thinking, so you repeat the word.swyx: Okay. Whereas intonation is like a diff, it's up up [00:12:00] speak and all this.Okay.Pavan: Yeah. So I think there is a lot of like entropy. And modeling it as a distribution. And a, any technique which helps with it and the depth transformer is a conditional way of modeling this. And Transformers actually really good at it, even though that's a mini transformers. So I think that worked pretty well too for us too.It's just that the main concentration is when you have a depth transformer. If you have K tokens, you need to do K auto steps, right? Even though it's a small thing, it's K steps, which is very vacant, say heavy, but flow matching. We were able to cut it down significantly. So we are able to do the inference in quad steps or 16 steps and it works pretty well.And there are more normal techniques to bring it down even further to like, in extreme case, one step like we're not doing it yet, but it at least the framework, LEDs itself to more efficient and Yes.swyx: And the image guys have done.Pavan: Yeah.swyx: Incredible work guys. Yeah.Pavan: It now you just. Send a prompt and you get an image.swyx: Yeah. Surprisingly not enough. I think image model labs use those techniques in production. I think it's, I feel like it's a lot of research demos, but [00:13:00] nothing I can use on my phone today.Guillaume: The thing, there's a thing that would be interesting here is that since, indeed I've been so much sure that has been done in the vision community compared to radio dys, stomach, I think there are so many long infra Yeah.And there are so many things we can do to actually improve this further. So it's our first version, but we have so many ways to exist, much better and much more efficient, cost efficient, soswyx: yeah.Guillaume: So really it's not a new field at all, of course, but there are still so many things that can be done.Perfect. It'sswyx: nice. I should also mention for those who are newer to flow matching, I think the creator, this guy's name is Alex, he's done I think in Europe's maybe two Europes as ago. There was, there's a very good workshop. There's one hour on like this matching is I would recommend people look that up.That's the other thing, right?Efficiency and Model Strategyswyx: The efficiency wise, like I, I imagine like the reason is open weights the reason you pick 3.6 B backbone it you are 3.4 B you are, try to fit to some kinda hardware constraints. You kinda fits some kinda basic constraints. What are they?Guillaume: Not necessarily, I think something we care about in our model that they're efficient.So we have a [00:14:00] lot of separate model, for instance. So we have this that is very small, very efficient. We also have a small OCR model that is available. Good, highly efficient as well. And I think on a project maybe there, I think companies are going to take is to have a coverage general model that will do a bit of everything.But that is also going to be expensive. On here. What want say is if you care about this specific use case, if you can actually use this model, it just does that. It's extremely good at it. Survey, very efficient. That's why we can actually add. We do, but also OCR that are like really good at that.And that would be much more cost effective factors and the general model that will contain a lot of capabilities you don't really need. So yeah. So we're doing like general model, but also like more customized model. This,Open Weights and BenchmarksVibhu: how does it compare to other TTS models? It's, we are going follow open wave.We're just dropping it. I think it's pretty good.Pavan: Yeah, I think it's pretty good. Like it, it's definitely one of the best. For sure. It's probably I would say it's the best open source model, butVibhu: decipher themselves.swyx: Yeah.Voice Agents VisionVibhu: Why now? How does it fit into broader ral vision? How do you see voice agents?How do you see voice? I think every year I've heard, okay, you're a [00:15:00] voice. You're a voice. There's a lot of architectural stuff. There's a lot of end time that see it, your solving, but where do you see voice setting?Guillaume: We had so many customers asking for voice. That's also why we wanted to build it.What's interesting in this domain is that. In a sense, if you take something simple like transcription it doesn't seem like something that should be very hard to do for a model. It's essentially, it's pattern recognition. It's classification on this. Models are very good at classifying, right?Or nonetheless, when you talk to them it's not there yet, right? It's not, you don't talk to them the same way you talk to a person. On something, maybe people don't realize it. It's in English it's still much better than in any user language, even compared to French instance. If you talk to this million in French, when you see people talking to this they'll talk very slow.They'll articulate as much as they can. So it's not natural, right? We're not yet to this. And I think, yeah, maybe the next generation will not know this, but yeah, I think people that. But our edge will actually always keep this bias speaking very slowly when they talk to this model. Even if maybe, probably in a couple of years, maybe next year it'll not be necessary anymore.But yeah. But what's interesting is to see that yeah, even for like languages [00:16:00] like yeah, French and Spanish Germans that are not no, no resource on religion. You have a lot of audios there on still it's not as good. And I think a consequence. Because then for this, I suppose just is not as much energy, as much effort that has been put done in some other mod that for some vision or like coding.But but yeah, there's still a lot of progress to be done. I think it's just a question of doing the work and it's clear path I think to get there.Pavan: It's a little fascinating because I worked on Google Assistant I think while back at this point, but it's, I think it's, it like when you take a step back, it's fascinating.It's not that long ago. It was like four years ago or five years ago, and it's now it's completely audio in, audio out and the function calling and the whole thing happens completely end to end. And in a very natural,swyx: yeah,Pavan: natural way and still ways to go. Kim was telling, even despite all the previous, it's not like you're speaking to a person.When you talk to any of these agents, bots, or voice mode kind of situation, it's still like a gap. I think that's the great part and I feel like with even the existing [00:17:00] stack, we should be able to get to this very natural speech conversational abilities soon enough I guess.And we'll also hope. I get thatGuillaume: on this kind of the next step, right? Because when you talk to these agents, like usually people are just writing to them and sometimes they'll this very clear, for instance, you are, you want to write code, but you are, you have a very clear idea of how you want the model to implement what you in mind.But so here you are able to spend a lot of time writing. So it's not really efficient on audio is really like a natural interface that is just not there yet, but I think it's just gonna be the place.Vibhu: How's it like building, serving, inferencing, like we see a lot about, it's very easy to take LMS off the shelf, serve them.Fine tuning, deploying. I know you guys have a whole you have Ford, you have a whole stack of customizing, deploying. Is there a lag in getting that. Like distribution channel. Are you helping? There is. So like prompting, lms, you can have them be concise, verbose, all that.They're built on LM backbones, these models. How do you see all that?Enterprise Deployment and PrivacyGuillaume: Yeah, I think this is a lot of what we're doing with our own customers. Very [00:18:00] often they come to us, so it's for different reasons. I think one reason is sometimes they have this lot of privacy concerns.They have this data that it's very sensitive. They don't want data to leave. The companies, they wanted to stay. Inside the company. So we have them deploy model in-house. So either on a, either on premise or on private cloud. So they're not worried that it's given to a third party on the there some leakage.Sometimes they have this kind of many companies have this different, sensitivity of data they have like sometimes channel chat can send it to the cloud has to stay there. So then it creates some kind of heterogeneous workflows where it's annoying. You cannot send some data to the cloud.This one you can, so here, when we actually deploy the model for them, they don't have this consideration. They are like not worried that, this is going to leak. Everything is much easier. So we help them basically do this on the, so it's one of the very proposition. But but the other is very often, when customers use this off the shelf close model, but very sad is that they are not leveraging, these data that have been collecting for four years or something for decades.So much data. Sometimes it's trillions of tokens of [00:19:00] data in a very specific domain. Their domain, which is data that you'll not find in the public, on the public internet. So data on which, like close model, we actually not have access to one, which that's going to be really good. So if they're using like closed source models are basically not benefiting from all these insights.All these data they have collected three years, they can always give it into the context that in France, but is never as good as if you actually train the modern analysis. So yes, that's basically what we help them to do. We actually provide them some purchase, basically what we announced at GTC this week.So we provide them with this, it's basically like a platform with a lot of tools to actually help them process data. Trained on that. Yeah, it's actually the same thing that we're using in the science team. So it's actually very better tested infrastructure, like a lot of efficient training cut base.For a quality pre-training like a fine tuning, even doing S-F-T-I-L. So we help them do this using the same tools as what our science team is building is using. So since it's tools that we've been using for two years now, it's really better tested. It's really sophisticated.So it's the same thing. We are giving to them, giving the company the same thing [00:20:00] that what are same still using internally actually build their own ai and it makes a really big difference. I think sometimes customers. And many in general don't realize how much better the model becomes when you fine tune it on your own data.And you can have a, your model is here. You start from there. You have a cross source model, which is sort here, but if you actually fine tune it can actually really go much further than this. And then you have a very big advantage. The model is trained on your entire company knowledge, so it knows everything.You don't have to feed like 10 K tokens of contact at every query. So it's it's much easier. It's a bit, I think using a closed source model is really sad because it basically puts. You are not leveraging all this data and you are going to be using the same model as all your old competitors when you're actually using, everything you have been collected for years, which is really valuable.So yeah. So we help basically customers do this. We have a lot of solution I mean deployed for engineers that go in the company that basically look at the problem customers are facing to look at what they're struggling to do what we should do to solve it. So we help them solve them together.So it's I think our approach is a bit different, but here. [00:21:00] Some of their companies and competitors, it's, we don't just release an endpoint on sale, do some stuff on top of that, or we don't just give a checkpoint. We really look very closely with customers. We look at the issues they have, we had them solve them.We really make some tailored solution for the client are facing. Some example are also going to be, sometime we have some customers. They really wanted to have a really good model, really performance on some, like Asian languages on the, if you take some of the shelf models, they can speak it, they can write in this language, but it's not amazing.This language would be like maybe zero 1% of the mixture. So it has been included during training, but very little. So what we did here is upgrade. We trained a new model for them, but so this language was 50% of the mix, so it's much, much stronger. It knows of the dialects, it knows the, so it's yeah.So it's some example of things we can do and it's really arbitrary, custom. I think you had some of their customers, for instance, they wanted some. They wanted some 3D model that can do audio with a very good function cable. So something you wanted to put in the car in particular, they wanted this to be offline because in a car you don't necessarily have access to internet.So [00:22:00] yeah. So here we can actually build the solutions. There is no like model out of the box on this. In the internet you have this very, you have this very general model generalist, like he's strong model. But for things like this, they always want at specific solutions and on some other reasons.Sometimes they come to us is because, like they, they experiment with some closed source model. They get some prototype. They're happy with what they build. They, it works well. They're happy with the performance, and then they want to go to production and then they analyze. But it's extremely expensive.You cannot push this. It's so then they come back to us on this. They can help us build the same thing as this, but using something much cheaper on here. And here we can sometime be something 10 x cheaper by just functioning a model and it'll be better OnPrem on their old server and also much cheaper as well.So yeah,swyx: that's the drop pitch right there. Take all themoney.Vibhu: And outside of that you do, we do put open wave models so people can do this themselves. I feel like not enough people go outta their way.swyx: They're not going to, they're gonna ask them to do it as the expert. IGuillaume: think initially we didn't know, [00:23:00] we wanted completely short at the beginning of the company because, I think our study was not exactly the same as what it is today, but what we underestimated initially is the complexity of deploying this model and connecting them to everything to be sure it has access to the company knowledge on the, and it was, yeah, on, we were seeing customers struggling with this, but it was even, that was three years ago and no, things are much more complicated because now you don't just have, text on SFT on a simple instruction following.You have reasoning like your agents, you have like tools. You have a multimodal audio, so it's much more complicated than before. And even back then it was hard for customers. So they really need, have some support and this is why actually providing like always some four D position as well. The processFine Tuning and Personalizationswyx: I'm curious is there also voice fine tuning that people do?Pavan: So in this forge we also have a say unified framework. And the hope is like the er speech to text that we released earlier this year. And even the ER chart that we released last year. And I think a big people, I think there's a big, rich ecosystem [00:24:00] of people fine tuning whisper, and people want the same thing with w so it's much stronger than Whisper.And yeah, the the platform offers that kind of fine tuning yeah, which could be any kind of fine tuning. Like for instance, even sometimes people want to support new languages to this, which are tail languages, which we hope to cover. Certain natively, but if there is a language where you data and you want to frank you, I think this is a good use case.Or the other use cases, you, it's the same language, like even English but it's in a very domain specific way.swyx: Yeah. Terminology, jargon, medical stuff.Pavan: Exactly. And also there's specific acoustic conditions like there's a lot of noise or the, and. The model will do decently in most conditions, but you can always make it better.And that those are some of the use cases where you can improve it e even further. And that's one good use case for this and for text to speech. We're just releasing it so we'll have support for that soon too. I think it's similar use case.Voice Personalization Pavan: It's little different the kind of things that you want to extend a [00:25:00] text to speech model to, which could be like voice personalization, voice adaptation for enterprises.Many enterprises need very specific kind of tone, very specific kind of like personality for this kind of voice. And all of those are like good use cases for fine tuning.swyx: This one I was gonna ask you, we never talked about cloning voice clothing here. How important is it, right?Like I can clone a famous person's voice. Okay. ButPavan: the main use case would be like for enterprise personalization, like enterprises need like a lot of customization. You don't want the same. Voice for all the enterprises. Each enterprise want a customized, specialized something which is representative both their brand and also their, I guess safety considerations and the use case I think the kind of thing that you would deploy as a empathetic assistant in the context of a healthcare domain would be very different from the kind of thing that would be in a customer support bot and would be different from like more conversational aspects.I think those are the. [00:26:00] Customizations you would expect from enterprise. And that's the main use case, at least from our side.Vibhu: My, my basic example is you don't want to call to customer services and have the same exact voice. It's just, it's gonna be weird.Long-Form Speech ModelsLong-Form Speech ModelsVibhu: But also on the technical side of this, so there's like a few things in TRO that I thought were pretty interesting.He's a big fan of this paper. Oh, he said very good paper. He said this is the best SR paper he's ever read. Yeah. I've hyped up this voice paper enough. We covered it. Somewhere, but a big thing. So Whisper is known for 32nd generation a 32nd processing. You extended this to 40 minutes. There was a lot of good detail in the paper about how this was done.Even little niches of how the padding is. So it's very much needed. You need to have that padding in there, the synthetic data generation around this. I'm wondering if you can share the same about the new speech to text, right? Text to speech. So how do you. How do you generate long form, coherent?How do you generate, how do you do that? And then any gems? Is there gonna be a paper?Pavan: Yeah. Yeah. They would be a technical report. Okay. Yeah. I think I could have a lot of details.Real-Time Encoder AdvancesPavan: But me I think the [00:27:00] summary of it, actually, some of the considerations in this paper were, because we started with the wipa encoder as the starting point, and now we have in-house encoders, like the bigger time model, for instance, which we released in January.Also release a technical report for that real time model as well, which is this dual stream architecture. It's an interesting architecture. You should check it out. And there we have a causal encoder and I don't think there's any strong, multilingual causal encoder out in the community. So we thought it's a good contribution.So that's one nice encoder there. Other people want to adapt. That's a good end code. And we train it from scratch. I think her. Post stack is now mature enough that we are able to train super strong ENC codes. And some of these considerations, like spatting and stuff, is a function of the Whisper ENC code.And now that we train encoders, inhouse the design concentrations are different.Scaling Context for TTSPavan: And for the question on text to speech, I think that's also leans onto the original auto aggressive decoder backbone. I think, it says very, almost identical considerations. I think the long context in it's not even long con, [00:28:00] so the model processes audio at 12.5 herds, so one second maps to like 12.5 tokens.So I think one minute is like 7.8 tokens. You can get like up to 10 minutes in eight K context window and get half an hour and 30 K context window. So that's and 30 2K context is something that's we are very comfortable training on. We can extend it even much longer. 1 48 K. Okay. You can naturally see how it can extend to even our long generations.Yeah. We need the. Like data recipe and the whole algorithm to work coherently enough through such long context. But the techniques are some way very similar to the text, long context modeling. And the key differences, it's just doing flow matching order regressively instead of a text open prediction.swyx: Okay. I think that was most, most of the sort of voice questions that we had. ButWhat Makes a Model SmallVibhu: I have a big question on Mr. Al, Mr. Small. So what is small? How do we define [00:29:00] small? What is this? What is this? I remember the days of Misal seven B on my laptop. The snuff fitting on my laptop. I could run it on the big laptop, butGuillaume: it's just additional.Question of terminology, like here what we did, baseball is north active parameters, but it's true. Really not give it another name, but yeah, we could have called it medium, but only, I,I suppose it's a model that we released mixture of experts. It's a model that combines different model before which we were doing the same, is that we had one model, general model for Israel. Doing instruction following, were like a separate model that was Devrel trial. So qu coding specify specific to code with another model for Reason Maal.So this were separate artifacts built by different team at trial on what we're doing is basically merging all of this. It was, you had pixel trial was the first vision model. We was like a separate model on the way we do things internally is that we have one team focus on one capability, build one model.On the means mature, mature enough, we decide to merge this into the [00:30:00] matrix. But here it was the first time we basically match all of this into one. But there are some other things we did at first time to merge time, for instance, like more capabilities or function coding I think would be, are, it's going to be much, much better in this trial, small platform.But but yeah, so it's our latest model on the working is,Vibhu: and yeah, key things is it's very sparse. Six, be active pretty efficient to serve. 2 56 K context. Yeah,Merging Capabilities vs Specialistsswyx: I think what's interesting is just this general theory of developing individual capabilities in different teams and then merging them.Where is this going gonna end up?Vibhu: Like we've seen the five things put together in this. Yeah. What are the next five teams?swyx: I think actually OpenAI has gone away from the original four Oh. Vision of the Omni model. This was what they were selling. All modalities and all modalities out.But I feel like you might do it.Guillaume: I think there's some mod where it's not competitive use, for instance for audio. For audio here, if you want to do transcription, I think it makes no sense to use a model. If you just want to trans tech it, it'll be very inefficient. If you want to do audio, you probably just want to be the [00:31:00] one VR 3D model performance essentiallyswyx: the same.It's going to be incredibly cheaper. So here, that's why we wantGuillaume: to have a separate but just does this. Yeah, I think the question is just, yeah. If you are to, to your model. By speech and you asking like a very complex questions on how you do this on the, just to cascade things. Do you want to put a d in a model that has like a one key around it?It's like a, not a competitive discussion, I think unaware if you doing into the direction, but that's possible. Of course. But yeah. But I think for us, the next capabilities we want to try to integrate into these models when we are going to be yes, like marketing or no reasoning better, I think more capabilities that people don't talk too much about, but at high bottom, I think for our customers in our, on different industries, for instance, things are around like a legal computer.I design all these things that is this males out of the box are to put at that. Because people, if you don't prioritize this, there is not like too benchmark on that. Butswyx: this done how toGuillaume: make this good and this just start to do the work. Extracting some that processing it [00:32:00] expression. So yeah.But we are offering the imagine to this.swyx: I think for voice. Yeah. The key thing I think over maybe like the last year or so with VO and gr Imagine and all these things is joining voice with video, right? Which people don't understand spatial audio because like most TTS is just oh, I'm speaking to a microphone in perfect studio quality.But when you have video, like the voice moves around.Pavan: That's true. The constitution was a little different in the sense that there it's like a a standalone artifact where you get the whole thing and you consume it. But in a conversational setting, it's a, you need the extreme low latency.swyx: Yeah,Pavan: streaming would be one of the primary concentrations.swyx: You can build a giant company just doing that, right? So you don't need to do the voice, but I was just know on the theme of merging modalities, that is something I, I am like, wow. Like I didn't, everyone up till, let's say mid last year was just doing these like pipelines of okay, we'll stitch a TTS model with a voice thing and a lip sync [00:33:00] thing and what have you.Nope. Just giant model. Yeah.Open Source MissionVibhu: I have a two part question. So one is, it's still open. It seems like open source is still very core to what you guys do and I just have to plug your paper. Jan 2024. This is the one trial of experts like. Very fundamental research on how to do good.Moes paper comes out very good paper for anyone. That's just side tangent. No.swyx: This thing caused, we bring back, eight by 22 was like the nuclear bomb for open source. I think it takes Shouldn be more seven B more. Yeah. Yeah. But this is a bigger opposite than me.Yeah. Yeah I don't remember this. I remember, I don't think it was January, right? It was like new reps it was, it dropped during new reps and everyone in Europes was December of 25th, I think. Yeah. The model was did as well.Vibhu: It's just a little update probably.swyx: Yeah. No, but you have a point to make.Vibhu: No, you gotta check that. But then, I just want to hear more broadly on open source for you guys, and when you had asked earlier [00:34:00] about what's next, what are the other, side tapes working on you. You put out Lean straw. This,swyx: it's not necessarily surprise. I was like, I don't, this doesn't fit my mental model or Misra.Guillaume: Yeah. First for open source in general, I think it's really something which looks to the January of the company. I think we started it per once, is we so we have open sourcing with, since the beginning and even before this. So before this, so me and Tim were at Meta, we released LA and I think what was really nice.To see that before this, for most researchers like universities, it was impossible to work on elements. There was no alien outside. And if you look at many of the techniques that were developed after, for instance, was open source all this post-training approaches like even DPOD, like preference optimization, all of this were done by people that had access to this portal.And it'll have been impossible to do without this. So it's really making sense, move faster. So we really want to contribute to this ecosystem. I think like the deep and also like very lot of impact. All these papers that are I think in the open source community are really helping the science community as a whole to move faster.So [00:35:00] we want contribute to this ecosystem. That's why we're releasing very detailed technical reports. So ma trial and our first reason model, and ation, lot of results, things that work, things that did not work as well. Think helpful on the, yeah, so for the audio model also to share a lot of details, share of them for real time model.And the, yeah, so we really want to continue this, basically belong to this community of people who share science. I think we really don't want to be, leading in a world where the smartest model, the best models are only behind, close doors. Only accessible to a shoe companies that we, as a power to decide we can use them on it.I think it's a scary future. We don't want to live in, we really want this model to be accessible to anyone that want. Intelligence to be used unaccessible by anyone who can use it. So yeah, so that's why we are pushing this mission and source model. Yeah. So not, so yeah, no strategy. So it's open source, not the first model, so not the best on the Yeah.Lean and Formal ProofsGuillaume: LIN trial I think is also one step into this direction. So it's yeah, a bit different than what we are usually releasing. But we have a small team internally [00:36:00] working on them. Formal proofing, formal math. So I think a subject we care about in general and we were working on reasoning. I think we started too early before doing reasoning without LMD is very hard, especially when you work with formal systems because the amount of data you have is negligible.It's addressable community of people writing like formal proofs. But the reason why we like it is because I think there is if you look at what people are doing with reasoning, is there, the problems that you can use. Are usually going to be problems where you can verify the output. So for instance, all this ai ME problem where the solution is a number between 100, like a thousand.So you can verify, compare this with a reference or it's an expression. You can actually compare the output expression generic with the reference. But there are many, most of them have problem and most of the reason problem. There is no like way to easily verify the solution. If the question is show that F is continuous, cannot compare in the reference, right?If it's a probe that this is true or probes is properties, there is no way to. You cannot act, simply verify the correctness of your proof. So it's hard to apply the, there is no referable reward here. So [00:37:00] what you could provide is of course, like a judge and judge that will look at your proof. But it's very hard and it's very, you could do certain, some reward hacking happening there.So it's difficult. You could provide like a reference proof, but then there are also many ways to prove the same thing. So if the model says give negative reward because it's a different poop, maybe it was still digit proof, just different. So it's not going to work well. What's nice with lean and with formal probing is that you don't have to worry about this whatsoever.We just,swyx: they're all function is largely compiles in lean is functionally the same. Exactly.Guillaume: It's like a problem if it compiles it's correct. It's very easy. And you can apply this and then you can,swyx: it's just way too small. So no human will actually go and do it.Guillaume: Yeah, that's exactly.It's the only people can do it. It's like a very small committee of people doing a PhD on that. So it's super small. And it's sad because it's actually very useful on not just mat, but also in software verification. So for instance, software verification today. So tiny market. Very few industries work on this and we need that.It's usually going to be like companies like building airplanes, air robotics,swyx: likeGuillaume: things [00:38:00] where they absolutely want to be sure. Life depend on this, but it's very rare that people formally verify the correctness of their software. But I think one of the reasons for this is simply that it's just hard to do.swyx: Are you think of TLA plus? It's the language that some people do for software verification? No. That people use in a ference, but but yeah, it's the reason I think why people don't use it more and why this industry is not as big as could be is because it's very hard. But now with cutting edges that are there, it's going to be very different.Guillaume: We're going to see much more of this. So I think yes, industry there is going to be much larger in the future that we, these models. So yeah. Here also anticipating this a little bit, we wanted to work on that because it's proving like a math theory and like a, essentially the same tools.swyx: Yeah.Reasoning Transfer and Agentsswyx: One of my theories is that because the proofs takes so long, it's actually just a proxy for long horizon reasoning and coherence and planning. Maybe a lot of people will say okay, it's for people who like math. It's for being okay. It's like a niche math language. Who cares? But actually, and you use this as part of your data mixture for [00:39:00] post-training and reasoning, actually, it might spike everywhere else.Yeah. And I think that's un under explored or no one's like really put out a definitive paper on how this generalizes.Guillaume: Yeah, absolutely. AndPavan: I think evenGuillaume: that's what we're seeing already. For instance, you should do some reasoning on math as then the American should do reason even.Yeah. In the early stage. So we, the, there is some transfer, some sort of emergence that happens. And I think some, it's also interesting, it's not just I think the topic in general, but it's, there is a lot of connection with this on including agents because. Sometimes the model can see like a three that it has to prove it's very complex, but then it can take the initiative to say, I'm going to prove this three lr.I'm going to suggest three Rs, and I'm going to in parallel prove each R. So three of them in parallel with sub agents, but I'm also going to prove them in theory and the three tool so you can do this also. Pretty interesting. You can, even if you fail to put one of the LeMar, you can actually, maybe you succeed to put the normal lema too, so you get some possible reward here.So it's a bit less Spartan issue, just get to zero one for the entire thing. [00:40:00] So it's pretty interesting. I think we can actually,Vibhu: yeah, it's also an interesting case just for specialized models in general, right? Like the cost thing you show is pretty interesting yeah, similar score wise, you are, thirty, seventy, a hundred fifty, three hundred bucks.Smaller.swyx: I think cost is a bit unfair, right? ‘cause this one is at like inference cost. It's always there on top with their margins on top of it. But, we don't know anything else, so we gotta figure it out.Vibhu: Okay.Next Frontiers in TrainingVibhu: I did wanna actually push on that more. Not on cost, but you mentioned about, okay, it's a great way to have verifiable long context reasoning.What are other frontiers that, I'm sure you guys are working on internally, there's a lot of push of people pushing back on pre-training. Scaling, RL pushing, compute towards having more than half of your training budget. All on rl. Where are you guys seeing the frontier of research in that?Guillaume: You mean theVibhu: just in foundation model training in the next, one thing that you guys do actually is you do fundamental research from the ground up, right? So you probably have a really good look at where you can [00:41:00] forecast this out.Guillaume: Yeah. I think for us we're still working a lot on the pre-training side.I think we are very far from situational, the pre-training. I think ML four preprinting will be like big step compared to everything we have done before. So we are pretty excited about this. And I think on the other side, I think now we have more and more to think about this algorithm that will actually support this very long trajectories.I think when it was, for instance, GRPO for it doesn't really work this any bit of policy. Which was okay initially because you are solving math problem that can be solved in like a few thousand tokens. So the model can alize them pretty quickly. So when you do your update, the model is never too far off.It's never too far off. But now when you are moving towards this kind of problems where certain takes hours, like six hours to get a reward, then your model is co pick places. So you have bi new infrastructure that supports this, but also new A, so now everything we're doing internally, we're trying to. Build some infra that we actually anticipate is what we have in six months, one now, which is this extremely no scenarios on the, I think when we started Missal, part of me and [00:42:00] we wanted to, is very nice under element where people are there, they can do research, they like with a lot of resources.So it was nice. I think things changed a lot when I think when J Pity came out. I think after that I think was. This one is same again. But but yeah, but it was nice. And I think we also want to work part of this descrip beforeswyx: coming to the end.Hiring and Team Footprintswyx: We're just, obviously, I think you guys are doing incredible work.You've, they are a very impressive vision for open source and for voice. What are you hiring for? What's the what are you looking for that you are trying to join the company?Guillaume: Yeah, so we are hiring a lot of people in our sense team. We're hiring, in all our offices. So we have a, our H two is in France in Paris.We have a small team in London. We like a team in Pato as well. Co we open some offices in in SAU, in Poland. So one in Zurich. We also like some presence in New York as well on Sooner one in San Francisco. So we all bit either way also like hiring remotely. So we're going the team trying to hire like very strong people.I think we want to stay, so the team is not. Instead of fairly small team. [00:43:00] But I think we want to keep it that way. ‘Cause we we find it quite efficient. So like a small team they agile so yeah.swyx: Okay.AI for Science Partnershipsswyx: Let's focus on science and the forward deployed. We actually are strong believers in science.We started the our new science pod that focuses specifically on the air for science. What areas do you think are the most promis.Guillaume: What we're pretty excited about right now, and something we have already started doing or that we'd probably be able to share more about this in a couple of months, is that we are exploring AI for science.And there are a lot of areas where we think that you could get some extremely promising buzz. If you were to apply AI in these domains. There are a lot of long inputs. You just have to find these domains where actually AI has not been yet applied, and it's usually hard to do because the people working in those domains don't necessarily know the capability of these models.They don't know. How I would just have to pair them with Yeah, exactly. Your researcher slashing, which is actually hard to do. But this matching, we're doing it naturally with our customers. So we have some company we are very closely with. So for instance, ISM Andreesen are one of our partners, so we're doing some research with them on their other, like tons of extremely interesting problems.Columns in physics, in [00:44:00] science matter science that they're essentially the only ones to work on. ‘cause they're doing something No, no one else is doing on the, yeah. So there are many domains where AI can actually revolutionize things. Just you have to think about it on you familiar with what can do or to apply it.So yeah, it's something where more modeling with our partners, with our customers sort AI for s, but.swyx: Yeah. Okay.Forward Deployed Skillsswyx: And then for deployed what it makes a good four deployed engineer, what do they need? Where do people fail?Guillaume: I think it's usually you need people that are very familiar with the tech and not necessarily with a lot of research expertise, but that are actually pretty good at using this model that can actually like that know how to do functioning, that know how to like, start some error pipeline.And it's it's not easy. It's something that mucus. Majority of companies will not be able to do this on their own. So here I think we need people that are, that like to solve problems that are accept solving some complex, very concrete problem. It's applied science basically.And yeah, so I think it's not too different. I think from the case you need in research because it's essentially you are trying to find solutions to problems that in [00:45:00] customers have not yet. So sometimes it's easy. Sometimes you're here to do the work. You have to like create synthetic data.Find some edge case. So it can be, yeah. Depends on the problem. But but yeah, you have to, I think it also a bit of patience on the be creative. I think very similar skill is Asian,Pavan: the diversity of the work they do. It always surprises me. It's it's, it goes all the way from the kind of stuff they encounter in industries.It's just very interesting. I think.swyx: Any fun like success anecdotes.Guillaume: Yeah, it can be actually training this small model on edge that just we do one specific thing can be like training some very large model without some specific languages as well. Making models really good at some tube use, like for instance, computer ID design, these kind of things.Is that pairing with vision as well? Yeah,Pavan: and the fact detection for chips or like in, in factories identifying things like it, the. Diversity could be anything where you can deploy these foundation models. So yeah the work to make it work in that specific setting, basically whatever it takes to make it like add value in that, by the way, workflow.Vibhu: Yeah. [00:46:00] And it goes across the stack, right? Like even just pulling up the website like.swyx: It's so broad on compute. It is so broad.Vibhu: We didn't even touch on if you have a coding CLI tool. One thing you guys were actually like, I think the first tool was agents, ral agents. You had the agent builder, you can serve it via API and all that.And I'm guessing forward deploy people.Guillaume: Yeah.Vibhu: Help build that out and stuff.Customer Feedback LoopGuillaume: It is also why we are, so we're doing many things, but I think that's also part of the value proposition that sometime know customers. They're always very. Extremely careful about their data and they don't want to, they don't like, trusting so many partners, trusting one partner for code, giving the data to another third party for like audios and another one.So they don't like this here. What they really like with our approach that we can help them on anything so they don't have to send the data to so many clouds. So yeah,swyx: I think that there can be many orders of magnitude more. F Ds then research scientists and they don't need your full experience, but they're still super variable to customersGuillaume: in practice.These two teams [00:47:00] are still quite intertwine, very often. Yeah. So first of all, they're using the same tools, the same data pipeline and everything on the, it's it's very helpful for the science team to get the feedback and the solution team ‘cause they can. Look at these customers are trying to do this.This is not working. It can really be show in the next version. Yeah. But this is basically a real world eval. Yeah, it's real world eval and it's not something, for instance, if you're just working in the lab, it's just ships model. But you don't do this work of for customers. You have no idea for whether your model is good at this H case.For instance, you even in year found this, right? So yeah, there is a very gap, big gap between the public benchmarks that are very like academic. OnPavan: the rare cases are just very diverse and in the specific concept of a customer, you can fine tune and make it like first evaluate, create a solid eval, benchmark, and then measure in the context of their, the kind of audio.Like for instance, one use case is literally just, there's the word for kids and they have to just say it out. It's a very specific thing. You're just saying one word and then you have to you, you'll grade the kid whether they did it right or not. It's [00:48:00] like R for, but so there're very diverse use cases and the idea is that they, the.Applied scientist engineer will go and make it better. And then from the learnings we incorporate it into the base model itself. So it's it's just better out of the box.Vibhu: Yeah. It's a good full circle system. Like the foundation model evals are all just proxies of what you really, you're never gonna have one that says it, it doesn't make sense for there to be, a one word transcription like that.It's not something you wanna fit on. Perfect.Wrap Up and Thanksswyx: Everyone should go check out everything that Michelle has to offer and try the TTS model, which will link in the show notes. But thank you so much for coming tha thanks. Such a stretch. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Claude Cowork came out of an accident.Felix and the Anthropic team noticed something interesting with Claude Code: many users were using it primarily for all kinds of messy knowledge work instead of coding. Even technical builders would use it for lots of non-technical work.Even more shocking, Claude cowork wrote itself. With a team of humans simply orchestrating multiple claude code instances, the tool was ready after a brief week and a half.This isn't Felix's first rodeo with impactful and playful desktop apps. He's helped ship the Slack desktop app and is a core maintainer of Electron the open-source software framework used for building cross-platform desktop applications, even putting Windows 95 into an Electron app that runs on macOS, Windows, and Linux.In this episode, Felix joins us to unpack why execution has suddenly become cheap enough that teams can “just build all the candidates” and why the real frontier in AI products is no longer better chat, but trusted task execution.He also shares why Anthropic is betting on local-first agent workflows, why skills may matter more than most people realize, and how the hardest questions ahead are about autonomy, safety, portability, and the changing shape of knowledge work itself.We discuss* Felix's path: Slack desktop app, Electron, Windows 95 in JavaScript, and now building Claude Cowork at Anthropic* What Claude Cowork actually is: a more user-friendly, VM-based version of Claude Code designed to bring agentic workflows to non-terminal-native users* Why “user-friendly” does not mean “less powerful”: Cowork as a superset product, much like how VS Code initially looked simpler than Visual Studio but became more hackable and extensible* Anthropic's prototype-first culture: why Cowork was built in 10 days using many pre-existing internal pieces, and how internal prototypes shaped the final product* Why execution is getting cheap: the shift from long memos, specs, and debate toward rapidly building multiple candidates and choosing based on reality instead of theory* The local debate: why Felix thinks Silicon Valley is undervaluing the local computer, and why putting Claude “where you work” is often more powerful* Why Claude gets its own computer: the VM as both a safety boundary and a capability unlock, letting Claude install tools, run scripts, and work more independently without constant approval* Safety through sandboxing: why “approve every command” is not a real long-term UX, and how virtual machines create a middle ground between uselessly safe and dangerously autonomous* How Cowork differs from Claude Code: coding evals vs. knowledge-work evals, different system-prompt tradeoffs, longer planning horizons, and heavier use of planning and clarification tools* Why skills matter: simple markdown-based instructions as a lightweight abstraction layer for reusable workflows, personalized automation, and portable agent behavior* Skills vs. MCPs: why Felix is increasingly interested in file-based, text-native interfaces that tell the model what to do, rather than forcing everything through rigid tool schemas* The portability problem: why personal skills should move across agent products, and the unresolved tension between public reusable workflows and private user-specific context* Real use cases already happening today: uploading videos, organizing files, handling taxes, managing calendars, debugging internal crashes, analyzing finances, and automating repetitive browser workflows* Why AI products should work with your existing stack: Anthropic's bias toward integrating with Chrome, Office, and existing workflows instead of rebuilding every app from scratch* Computer use one year later: how much better it has gotten, why vision plus browser context is such a superpower, and why letting Claude see the thing it is working on changes everything* Why many “AI verticals” may get compressed: specialized wrappers may matter in the short term, but better general models and stronger primitives could absorb a lot of narrow use cases* The future of junior work: Felix's concerns about entry-level roles, labor-market disruption, and whether AI can compress early-career learning into denser simulated experience* Why Waterloo grads stand out: internships, shipping experience, and learning how real teams build products versus purely theoretical academic preparation* The agentic future of the desktop: what it means for Claude to have its own computer, whether AI should act on your machine or a remote one, and how intimacy with personal data changes the product design space* Why Electron still mattered: shipping Chromium as a controlled rendering stack, the limits of OS-native webviews, and why browser engines remain one of the great software abstractions* Anthropic's Labs mentality: wild internal experiments, half-broken future-looking prototypes, and the broader effort to move users from asking questions to delegating increasingly long and valuable tasks* Why the endgame is not just more capability, but more independence: teaching users to trust AI with bigger scopes of work, for longer durations, with fewer interventionsFelix Rieseberg* X: https://x.com/felixrieseberg* LinkedIn: https://www.linkedin.com/in/felixrieseberg* Website: https://felixrieseberg.com/Anthropic* Website: http://anthropic.comFull Video PodTimestamps00:00 — Cheap execution and building all the candidates00:44 — Intro in the new Kernel studio02:47 — What Claude Cowork is04:18 — Why user-friendly can be more powerful05:33 — How Anthropic built Cowork07:09 — Prototype-first product development08:00 — Why local computers still matter09:20 — Skills, primitives, and platform leverage12:13 — Cowork's architecture: VM + Chrome + system prompt15:38 — Felix's own bug-fixing Cowork workflows17:38 — Local-first agents20:16 — Evals, planning, and knowledge-work optimization23:14 — What Anthropic means by evals24:21 — Scaffolding, tools, and why skills matter27:44 — Demo: YouTube uploads and self-generated skills31:03 — Calendar automation and cleaning your desktop34:47 — Browser context and why DOM access matters37:47 — Skills portability and plugins44:36 — Which AI categories survive?46:19 — Junior jobs, simulated work, and labor disruption52:00 — Gradual takeoff vs big-bang takeoff53:42 — Finance, taxes, and enterprise verticals56:24 — Vision and the improvement in computer use57:31 — Why Claude writes its own scripts58:06 — Should Claude have its own computer?1:01:26 — Windows 95 in JavaScript1:03:19 — VM tradeoffs and sandbox design1:07:23 — Approval fatigue and safe delegation1:11:18 — The future of Cowork1:12:27 — What comes next for agentic knowledge work1:15:13 — Electron, Chromium, and desktop software lessons1:22:16 — Multiplayer agents and coworker-to-coworker workflows1:26:05 — Anthropic Labs and closing thoughtsTranscriptAlessio: Hey everyone. Welcome to the Latent Space Podcast, our first one in the new studio. This is Alessio, founder of Kernel Labs, and I'm joined by swyx, editor of Latent Space.swyx: Yeah, so nice to be here. Thanks to, uh, TJ, Alessio, Allen helping to set everything up. It looks beautiful. We even have the logo outside.Yeah, kind.Felix: It's like really nice, right? When you walk in here as a guest, you're like, ah, this is a serious production. You're like, feel it immediately.swyx: Yeah. Felix, you've been, you're, you're currently a product manager of Cowork or,Felix: uh, really Technicswyx: Eng. Yeah. The, the identities are kind of vague member technical staff.Felix: I know member staff is like, the official title will carry around forever.swyx: Yeah. I basically kind of wanted, like we've been. Kinda obsessed. I, I've been using it a lot, even for managing latent space. Like, uh, cowork helps me upload videos and like title things and like edit and everything. It's, it's like really amazing.Alessio: Cool. He said multiple times Cowork has said gi in the group track.swyx: Yeah, yeah, yeah. So, so we have a second, uh, we have a second channel, uh, for latent space tv. Uh, and I, uh, and uh, we basically, this is our Discord meetup. Um, and I I, we have like Claude Coworks, it might be a GI, I don't know if we, we have, uh, uploaded it yet, but one of the sessions was like a, like a Claude cowork thing.Felix: I, you have to see, I would love to see it. Like, I'm so curious, like one of the most fun parts of my job is like constantly see the weird things people use Cowork for because it's obviously like very hard for us to actually design for specific use cases we do. But like every single person who's like most amazed is usually amazed about a thing that I didn't even expect cowork would be good at.Um, we have a new designer and it's one of the first small tasks. I was like, Hey, we need like a new emoji for cowork for our internal stock. It's like a pretty small thing. I like, can you please do it? And he drew an SVG and just gave it to coworker was like, can you animate this emoji? And now it has like this beautiful loopy animation.Um, and I mean, I think obviously this goes down to like, it turns out you can do more things with code than you expected, but it, it's like that kind of stuff that is really fun to me. So, long story short, I would love to see like, the kind of things you're doing.swyx: I'll pull it up. I'll pull it up.Felix: Yeah. Yeah.swyx: Uh, but before we get into it, I, I think always wanna start with like a top level. What is Claude Cowork for people who haven't heard of it? Haven't tried it out.Felix: Okay. Uh, real quick, Claude Cowork is a user friendly version of Claude Code. So the way it basically works is we have Claude Code and for us, fairly impressive agent harness that over December we noticed more and more people are using either, even though they're not technical, they, they're not at home in the terminal or they are at home in the terminal, but they started using Claude Code for non-coding workloads, right?Like managing expenses or like filling out receipts or organizing a knowledge base. Like there was a big obsidian moment that a lot of people liked and we wanted to capitalize on that, but also bring, bring this capability to people who are not terminal native and who might not know how to like brew and store something.So cowork is Claude Code running in original machine with a little bit of padding, a little bit more guardrails, making it a little safer and a little bit more convenient for people who don't wanna first open up the terminal when they go to work.swyx: It's interesting, uh, that is kind of. Pitch that way as a more user friendly thing because I always feel like it, it, to me, I I treat it as like why I'm familiar with Claude Code.Like we, we did a Claude Code episode Yeah. A year ago. But this one is like even more power user tools ‘cause it, uh, it kind of integrates much better with like clotting Chrome and, uh, in all the, all the other tooling. But like, maybe, maybe that's like a perception thing, right? LikeFelix: No, honestly, I don't think you're wrong.This is like a, a thing I've been thinking a lot about for like the last two weeks. So,swyx: but when they say user friendly, it's like, oh, it's the dumb down version. But no, actually this is the superset.Felix: Yeah. Like, I think a similar thing happened, A similar thing happened to me about 10 years ago, like maybe 12 years ago when I was at Microsoft and we started working on, on Electron and like browser-based technologies and cross-platform stuff.And one of the first use cases was Visual Studio Code, which used to be a website. And the initial narrative was, or Visual Studio Code is, is like a more user-friendly version of Visual Studio. But in a similar vein, I think there was some voices saying, oh, this is. For serious developers, like, we're not gonna use this.Right? For like anything. And I think in the end what happened is people have different stories about why Visual Studio Code became such a big thing. But my personal, my personal belief is that the Hackability and the extendability has like played a pretty big role, right? You can hook in Visual Studio Code that like almost any workload, it's so easy to hack on, so easy to put extensions for it.And I think cowork might be hitting a similar thing where it's very easy to extend and it's very easy to bring into your workflows. Uh, so the convenience I think is a bit of a, it's obviously the thing we strive for as developers, but I think the way people find value in it then is by probably mapping it onto whatever they actually have to do in their job.Alessio: So end of last year, you see the spike of like non-technical usage and clock code. What's the design process to say we should make clock code work? Because I mean, you built it in only 10 days. Um, I'm sure there was some discussion before on whether it's easier to use mean. You know, like making, making like a desktop GUI is obviously one way to do it, but like there's a lot of nuance in the product.Like maybe talk people through what was like the trigger of like, we should build a separate thing. We should not build like a different plot code thing. And then maybe some of the more interesting design decisions that maybe you didn't take.Felix: Yeah, I think philanthropic, we've been thinking about ways to move people who are comfortable with using Claude to answer questions and bring more of the power of like this thing to now like, execute tasks for you.I can like solve problems for you can like build things for you. How do we bring that capability to people who are currently mostly comfortable with like a like question answer paradigm within the chat. And we've had a lot of prototypes around that. Just going back as far as like easily a year and a half.Like we had a lot of people working on that. Um, and internally philanthropic is a very prototype demo, first culture. We have a lot of like internal prototypes that don't reach the public. What Cowork actually became is like we sort of picked the right pieces out of the many prototypes that we had.Right. And that's, that's maybe also like, I think an important qualifier whenever people mention this like 10 day number. I do think it's important to me to mention that within Double Scratch there was like a lot of stuff already happening, right? Like, and I think it's important for people to remember that when you build a website, you use React, you use like a bunch of other things.And this is like a similar scenario with like a lot of pieces we already had. Um, and in terms of decision path, I think we live in like an interesting new world where execution is actually quite cheap.swyx: Mm-hmm.Felix: So maybe, maybe what you would do That's so crazy. The year. I know it's wild.swyx: You should be, ideas are cheap.Execution is the hard part. IFelix: know. And like the, we, we used to live in this world maybe where you would take a product manager and the product manager would go to a number of potential customers and in this like very low bandwidth way, would try to. Try to like tease out what are the problems they're having, what are they willing to buy?Um, and then maybe what can you build to like drive out that need and then you go back and you like draft a spec and you think about it and then like you make a design and you execute it. We internally philanthropic app, not pretty much closer to the point where we're like, don't even write a memo, just like build, like let's build all the candidates very quickly.Let's just build all of them and then pick the best ones. I think the, the decision that is most impactful both for the product as well for the users right now is like the way we put value on your local computer. I think that's a big decision point a lot of people have thought about. Should this thing, whatever it is, should it ultimately run into computer or should it run in the cloud?‘cause they're big trade offs, right?Alessio: I guess like if we solve auth, it would be easy to do in the cloud. But I think like the fact that I can just download any file from anywhere and then put it and cowork there, it's like a big unlock. Um, I mean it's interesting you mentioned reusing certain pieces. I think this is something I've been thinking about even with Claude Code, right?The price of like writing code is going to zero, blah, blah, blah. But it actually seems like the value of having some sort of platform substrate is like increasing because as you build these new things, you can kind of plug them together.Felix: Yeah.Alessio: So I almost feel like when people are saying, oh, the value of a lot of software is gonna zero because you can recreate it, to me it's almost like the opposite.It's like having an existing platform to build on top of. It's like even more valuable because you can kind of bolt things on.Felix: Yeah.Alessio: You have obviously mcps, you have skills, you have like obviously the models, which is a big part. All these things kind of come together. Do you feel like that's a valid way to think about it, where people should invest even more in kind of like primitives.To rebuild on or are you like recreating a lot of it each time because like things change and it's easier to rewrite than reuse?Felix: You know, I think, I think you're right. I think you're right that the holistic platform is really useful. And this is maybe a whole like a somewhat contrarian view to a lot of people in ai.I actually don't think that the future is going to be hyper personalized software down to the point where everyone is running their own version. Like, I actually think it's going to be quite hard for all of us to have our own internal chat tool and like, if I wanna talk to you, likeswyx: howFelix: is that gonna work, right?In the, in the context of cowork and how we build it, I think it's a bit of a combination. Like what the, the execution that gets cheap is not necessarily rebuilding all the primitives. I think our priori, there's also not a lot of value in it. So for instance, my team did not think about rebuilding clock code.We're like very much started with the. The core thesis of this should be Claude Code.Mm-hmm.Felix: And then we'll like build things on top of it. The part of the execution that gets a little cheaper is like, how do you take all of these Lego pieces and put them together in a way that makes sense for users?It's like actually valuable. You have so many different approaches now in terms of what kind of, what kind of things do you actually elevate to a primitive, do you strongly believe that all your products should be built by just combining primitive that the public also has available? Do you keep some things internal?Um, and I think that's still evolving, but I think what's probably gonna go away is like, I'm not sure if it's gonna fully go away, but I'm gonna say, I think for me personally, I will probably no longer try to come up with a really good product without testing up with people. This is not a new concept, but wherever you used to have to make costly decisions around, do we pick technology A or technology B, or do we like, um, build it this way, build it the other way.I really strongly believe now you just build all of them and try them out with a small focus group and then whatever, whatever is better is what you go with. Right. And that, that is probably quite different even from how we maybe worked a year ago. Right. Like, I think, I think this happened very recently.Alessio: Yeah. I started building something in on Electron since you're here. Coincidence. Uh, but then Electron and like SQL Light are like, there's like some issues that like between development and like, uh, building anyway. And I was like, let's just rebuild the whole thing in Swift and just recreated the whole thing in Swift.And it's like, I. It's done.swyx: You know, I didn't take any effort. I, I, I don't even know Swift.Alessio: Yeah, exactly. I was like, I'm the, I'm not reviewing it anyway, whatever. You can write in whatever language you pick, but the important stuff that I did was not write the electron bindings. Yeah. It was like the logic of what happens in the app, you know, and then the model is like, yeah, I can just recreate the same thing as withswyx: Yeah.I, I think you still want, especially for people who are doing like high performance software or like very complex software, uh, you still want like, some view of the architecture. Uh, but you can use markdown for that,Felix: right? Yeah.swyx: Uh, you don't actually have to read the code again. I, I'm still like on a sort of like a definitional thing.Um, can we build a good mental model of Claude Cowork? Um, this is what I have, right? Like you you said it's like fundamentally cloud co. We don't wanna touch it. There's the cloud app, there's clouding Chrome. I think you guys do something different in planning, but, uh, I've been talking with Tariq who is on the cloud co team, and you guys are, he's like, no, we just exposed planning.Maybe we can clarify like, what are the major pieces. That people should be aware. It goes into cowork, like,Felix: okay, I think you basically have them. So really, um, you can, you can take planning more or less out. I think there's a few things that are really valuable in cowork. Um, the virtual machine is probably the most powerful thing.So we currently run like a, we currently run like a lightweight VM and we put clocked out into the vm and we do that for, for, um, a number of reasons. Safety and security is a big one, but even if you, even if you ignore for a second safety and security and you're just like, okay, Yolo, I want this thing to do whatever.It is quite powerful to give Claus on computer that is like generally a good idea. And in terms of architecture and UX and everything else that we've been working on, philanthropic, it often is quite useful for you to like anthropomorphize, um, clot aggressively and just be like, this is a person. What will you do if you give a, if you had a person, right?Yeah. And the analogy I've given my dad this morning who is still like quite insistent on using chat even for like coding things, is if you were a developer and your employer told you that you don't need a computer, they're just gonna like, send you emails with a code and you send emails with code back like that, maybe work for Patrick Miles in the back, but that it's not very effective.Um, so what we can do with the VM is because it's a, it's a Linux system, Claude Code has more or less free reign to install whatever needs to install. It can install Python, it can install no js. We do have strict network ingress and egress controls. So you can still, as, as a user in like plain human language, make it clear to, to the entire system what you're okay with and what you're not okay with.But at no point do we have to ask a real person, like a, like a person who might be in marketing or a lawyer. I'd have to go to a lawyer and be like, are you okay with me installing Homebrew?Alessio: Yeah, yeah.Felix: Right. Because the implications of the question and the answer are complex and nuanced and like, not, not easy to reason about.This gives us a lot of distraction that makes Cloud very powerful. Now then around it, we, we do probably have a number of things that also keeps growing almost every single week that you're probably noticing that make cowork maybe better for certain tasks than just cloud. Cloud on its own. Yeah. But most of those actually live in the system prompt.They're about like, what can we infer about the work that you do? What can we, what can we intru in the system prompt to make that more effective? It's of course the like very tight integration with Cloud and Chrome. You're noticing that a lot of people, especially as the models get better, a lot of people throw up their hands when it comes to MCP connectors in this area.I'm not gonna, I'm not gonna go through like 25 M CCP connectors, click off everywhere and then like half of them don't let me do the things anyway. So Cloud and Chrome is quite powerful because we can just talk to the cloud and Chrome sub agent and that will just do things for you.swyx: Yeah, so, so one example right in MCPI, honestly, I think that the state of MCP is kind of, kind of.Really hard to integrate. Um, I need to, I needed to add, uh, Figma MCP to the coding agent that I use.Felix: Yeah.swyx: Uh, and, but I didn't wanna read the docs, so I just had caught to it. And it's, it's great at reading docs and the same, same way I had to set up like a Google Cloud, um, account for some project I was working on and get some API keys somewhere.And Google Cloud is famously super hard to navigate, so I just didn't wanna deal with any of it. I just used Claude CoworkFelix: within the first week of developing on Core. This happened very, very quickly. Um, I caught myself by starting to use cowork for coding tasks, which is not ostensibly what we built it for, right?We don't need to. But I found myself, um, I found myself like on our internal, internal tool that we have for, to collect crashes and just like debugging information and I found myself sort like picking out the ones that I think we can easily fix versus the ones that might be like kernel corruption or something else on the operating system.And I found myself sort of picking these out and then just telling Clark, go fix this bug. I was like, what am I doing here? Go one level up, tell a cowork, I want you to go to all these crash tools. I want you to find all the bugs that you think are fixable and not like an operating system crash. And then I want you to tell another cloud to like fix all of that.Um, and that's, that's, that's sort of another cloud,swyx: just so it can spin up another instance or,Felix: uh, it, currently what I do is, um, and this is a bit of a hack, but I tell it to use clockwork remote to which website itself? Yeah, that's interesting. So you basically take, if you, if you imagine like a dashboard with like 20 bucks, you, this is remote control or clock or remote, or, sorry, I just wanted to confirm what, the way I'm using it is.I have cowork running and I'm telling cowork, here's where I normally go every morning to find the latest bugs. Go read the entire bug list, separate out which ones are fixable, which ones are, are fixable, and then for the fixable ones, four is this almost loop. For each bug, write a markdown file with a prompt.And then for each markdown v, that is a prompt. Start of a cloud set. So natively Claude Code hasswyx: this concept of subagents. Mm-hmm. And this is basically a subagent, but you're not using the subagent functionality.Felix: I'm not using the subagent functionality. And the reason I'm not is because I'm firing that off as a Claude Code remoteswyx: task.Felix: Yes. That's kind of nice. ‘cause then I can just fire it off. I can go to my next meeting and in Claude Code remote. Now the work is happening.swyx: Mm-hmm. Yeah. You, you see like you're already starting to use the cloud over your local machine. And I think this is one of those things where like. Shouldn't just everything just be cloud first, right?Felix: Ah, this is such a good group. I'm like solely bad about this. I have so many thoughts about that. Okay. So I generally believe that Silicon Valley overall is undervaluing the local computer. And my default argument for that is always how come we're all using MacBooks and not like an iPad or a Chromebook?Um, that there is like still value in, in having a local machine. And now when I think about Clot, it's this entity that is supposed to be very useful to you, like it tremendously useful to you. I think that entity needs to have access to all the same tools you have access to. Otherwise it's gonna be hamstrung in like all these complex ways.And there's, there's sort of two approaches we could take. We could say, okay, we're gonna like one by one chip away at everything that is at your computer and move it into the cloud. That's, that's one way to do it. Um, and I think other products have taken that path. I personally, this is a very personal opinion, but I personally, for the amount of tools that I use.Just don't have the patience to give another tool like permissions to every single thing and keep those permissions up to date. The second thing that I'm still grappling with, and I don't have a good answer for anyone just yet, but the second thing I'm still grappling with is what does it look like for someone to slurp up your entire work and put that in the cloud?Like if I, just as an example, like if you could click a button and it just clone your entire computer into the cloud, is that something that you would want? I'm not totally convinced yet that all everyone will. Mm-hmm. And that is sort of like upstream of all the technical issues we're gonna have. ‘cause like in general, I think the world is not ready for this kind of stuff.Like, I'll give you one quick example that would probably be very easy for us. So as a desktop app, we in theory with your permission, can do a lot of things on your computer, including reading your Chrome cookies. If we really want to do right, we could take your Chrome cookies, you would have to decrypt them for us.We could put those on the cloud if we really felt like it. Pretty easy solution. That would be super cool. We could just be like, oh, we can do all your tasks in the cloud now. Um, a lot of websites, thanks, include it. If, if they see the same authentication from like two different locations, we'll just lock down your account and now you have to go to the branch and be like, okay, I, I'm here with my passport.You actually know that. Wow. Yeah. As tired as well are of the term agent for the age agent future, I think there's a lot of stuff that sort of slowly needs to catch up and until that's the case, the way I, as someone's working on clock and make Cloud most effective is to like put it where you are working.swyx: Anything else? I thought with our mental model, so like, basically like, uh, part of me also just want, like the more I understand how it works, the more I can use it to its full potential. Right?Felix: Yeah.swyx: And so what I'm get hearing from you is you told me to delete the planning thing. You're not doing anything special on, on the, that's only exclusive to Qua cowork.Felix: We have some tricks for this sort of like change week over week. We eval cowork maybe against different use cases than he would evil clock code, right? If you think about it this way. Okay, so like clock code is our eval clock cowork. Yeah. So clock code is like quite optimized for coding tasks and we mostly value it whether or not we're getting better or worse depending on how good it is at like a typical suite job.And Clark Cowork on the other hand, we evaluate more against typical knowledge work, the kind of stuff he would find in finance or in like maybe a, like in like a legal office. Um, my personal use case is always like managing my things, like managing my personal mortgage or something like that, right? Or like wealth planning for me and my family.Those are the kinds of use cases we eval, clock cowork on. And what you might be picking up on is like the subtle changes we make to the system. Prompt what we put in the system, prompt how we steer, clot with the tools we give it. Um, like either it'd be better in one or the other direction and whether there's a trade off, try us exist a lot.CLO code will be better of a code and Claude Cowork will be better. For non-coding tasks, will those gaps still exist in the next three generations of models? It's like a little unclear to me though.swyx: Yeah,Felix: because right now these like hyper optimizations we make, I'm not sure for how long they're still be relevant.swyx: I think what I was referring to was also, it, it just, uh, it qualitatively felt different when I probably, it's just all prompting and I'm reading too much into it, but like the, the fact that it comes out with like a nine step plan, I can edit the plan and give feedback and, and, and see it execute the plan.Yeah. It felt more long range than in Claude Code, but maybe that already existed in Claude Code and you just build a nicer UI for it.Felix: It's kind of both. Um, like if the Clark Code people who build the planning functionalities would city, they probably say yes, we have all of those things in Clark code and they do.Um, I think people tend to give cowork. Tasks that are maybe of longer time horizon, I thought isswyx: so long. Yeah.Felix: That's like one thing, right? It's just like that the, the chunk of work tends to be maybe a little bigger. And then the second thing is that because the work, when it gets longer, it gets a little bit more ambiguous.We do tell co-work to make heavy use of the planning tool or to make heavy use of the ask user question tool, right? We do want it to come up with like. Different scenarios of, okay, tease out what the user actually wants. Don't go off to work for like four hours and then come back with the wrong thing.And you're probably picking up on that.swyx: Yeah.Felix: Um, I wish I could tell you I like built this magical thing and it's like, there's some secret sauce,swyx: but No, no, no. I mean, it's, it's just clarity is good that, you know, engineers just want to know. Yeah. They can, they can plan around it. And then I think also for me, um, I am realizing I have to switch to my, my other machine because this is a new machine that doesn't have my session.But, uh, yeah, the, the, the planning is really important for, for me to like approve or like to see whether it's like, it's right. The ask is, the question is so beautifully presented. I mean, it also, it also available in like cursor and, and in Claude Code. But like, I, I think like it's so nice to see that it, like it's kind of for me like to understand that it gets me, it gets what I want to do.Felix: Yeah.swyx: Yeah.Felix: It probably very hardswyx: just on the topical evals. Mm-hmm. When you say eval, I think people are very vague about what it means. Is it just like vibe testing or do you have like automated programmatic evals of Claude Cowork?Felix: When we say eval, uh, what we really mean is that we essentially take the entire transcript, including all the tools that clot has available ultimately to it, and we then measure what are the outputs, depending on what we tweak, right?So we do run that a lot. We use that in training. Um, we use that in, in like, if you sort of separate out post training from like the scaffolding around it. Cowork sort of exists in the scaffolding space, but obviously we also train on it a little bit. Um, so when we say eval, we mean given the certain transcript, what do the outputs look like?Including the file outputs as well as like the actual token outputs, like the ones that you see in the chat window.Alessio: I'm curious, um, how much of the failure modes are the model intelligence versus like the usage of the end tool to put the intelligence in? Like the well planning is like a good example, right?It's like one thing is to come up with a plan. The other thing is like make a nice spreadsheet. Yeah. That kind of runs you through the plan. Like how have you seen that? Well,Felix: the thing that I grapple with a lot is that whatever scaffolding you come up with, I think we still have a bit of sort of like model overhang where the model is dramatically more capable than right.Users end up using it for. And I think part of that is that we're just not getting the model all the tools to do all the things that's theory capable of, right? There's like one thing, um, however, whenever you do build the scaffolding, I'm sort of wondering at what point, at what point will that scaffolding go away and like how much you invest in figuring out what the right scaffolding is.It's kind of up to, it's a little bit of a bet. And one thing that I as an NJ quite enjoy is that like working in philanthropic and working at a frontier lab, I maybe have a little bit more insight into what's coming, coming down the chute in terms of like, what's the next model, what is the model capable of?What is good at, what is it bad at? And I'm, I'm increasingly wondering, is the right thing for us to like really invest too much in sort of these like scaffolding corrections where the model might otherwise not misbehave, but just not do the thing that you want?Alessio: Yeah.Felix: Or is it to just like give it as many capabilities as possible, try to make those safe so there's the worst case scenarios, likeno status might be otherwise.And then just simply wait a second for the next model drop. I'm personally, currently more leaning into the ladder. I think we're gonna see a lot of like applications and companies that do very impressive things with ai that in the short term might seem very effective ‘cause they're very specialized to individual use cases.But I think once models get better generalization and get better at like those specific use cases without being super guided on those, I'm not sure how long that's gonna stick around. And you can kind of, kind of already see this in like skills and NCP servers, right? Mm-hmm. We've, we've already seen sort of this like slow shift from MCP service to skills.And like, maybe a good example is Barry who made skills. He was initially hacking on something that honestly looked a lot, looked, looked a lot like what Cowork does today. It was sort of thinking about what if cowork, but for like people who don't wanna build code. Mm-hmm. And, um, he too did that as a prototype inside the desktop app.One of the first use cases we thought of were, okay, what, what are like coding like use cases that could really benefit from graphical interfaces and like from being a little separated from the actual underlying code. And everyone comes with the same answers. Data analysis,Alessio: right?Felix: Yeah. Or saying how many users do we have today?How many, like, it's always data analysis. And I think the thing that ultimately led to skills is that we wanted to connect this little prototype to our data warehouse and. The team very quickly discovered that like instead of building a custom tool for the thing to talk our data warehouse, they just like meet and embarked on follow like mm-hmm.Dear Claude, if you want to get data, here's the end point. Here's what the API looks like. You'll figure it out.swyx: Ah.Felix: And then it be hand over control. Yeah, yeah. Also just like maybe go one step up in the layer of abstractions, right. Just, yeah. Instead of, instead of telling the thing, here's ACL I, please call the CLI, or here's an MCP.Please call this ECT shape. Just like this is the end point. If you wanna know something, if you post here, maybe you can do post sql. It's gonna be okay. And that ended up being so effective that they started trying the same pattern of like just giving the model a markdown file that describes whatever it needs to do.That the whole thing eventually became skills and we're like. We should package this up. This is a good idea.swyx: Yeah. Um, we've had Barry Mahesh, uh, on, on our conference and uh, he's uh, definitely got a good idea there.Felix: Yeah.swyx: I wanted to show you the, how I've been using Claude Cowork.Felix: Uh, this is was my favorite part.swyx: This is this. So this is like me, uh, this is how we run the Discord. Uh, we literally, uh, at first I didn't trust Cloud Core. This was my very first usage.Felix: Okay.swyx: Right. So then I was like, okay, I will just try to manually download from Zoom all my recordings and upload it to YouTube. Yeah. Because this is a very laborious process.I got a click, click, click YouTube, um, isn't super user friendly. Uh, and it just did it. And then I was like, actually, you know, even the download from Zoom part, I should also. Put into Claude Cowork, and then I did it right. Here's a bunch of, and it starts compacting here, and it, and it, it starts to even be able to do things like look through the individual frames of the video to name the video so I can upload it auto automatically.Oh, that is, and this replaces my job as a YouTuber. We will forever appreciate your creative Yes. You know, and so that's great. Uh, but then by the way, it compacts and makes, makes like a new thing, right? So I, I don't, I don't have the initial, initial thing, but then I asked it to make its own skills so that it, so that something that's repetitive and one-off and human guided becomes more automated and I can use the skills independently and reuse them.Uh, and it obviously you can write skills and that goes into context and skills at the bottom here, which is, which is so nice. Um, so I have all these skills that, that I now sort of do on a weekly basis. Uh, I know you've released scheduled Coworks, which I haven't done yet, butFelix: course I should try them. I, I think this is like so wonderful and fun for me to see because.One thing that is very fun for me about skills in particular is that they're so easy to make. Like anyone can make a skill, like a text message, could be a skill, and they can be so hyper personalized to you. And this is like sort of the subtraction layer, right? Like, um, I, I'm just guessing, but I assume, heck, you are very good at your job.You're probably given this thing some guidance about how to do it, right? I,swyx: I just said, wrap everything up into, into a skill, right?Felix: Yeah.swyx: And then, uh, and then I was like, actually, sometimes I might need to break, uh, things apart because some parts fail or some parts might be needed in individually. So I told it to split one skill into three skills.So it's like a skill splitting thing, and then there's like a parent skill that just orchestrates all of them if I want to use that. You know, like, um, I think that's, that's like really good. Uh, and, and, uh, there's, there's one more part, which is the, uh, Google Chrome thing that I told you about.Felix: Yeah.swyx: Where I'm like, okay, you know, what's better than uploading, using Claude Coworks to YouTube?Like actually. Looking at the docs to like programmatically upload to YouTube and then putting that in a skill. And I've never done that before. I don't want to deal with Google Cloud. Yeah. So Claude Cowork does it for me.Felix: That is really cool.swyx: So, so I, I just, I don't care. I just, like, I do a thing. I don't, it doesn't really matter.Felix: That is really cool. And then you've, I assume paired the skill just with the script that it's built.swyx: Yeah, no, I just update, update the skills.Felix: Oh, that is beautiful. Yeah. That's wonderful.swyx: It's kind of like a skill, like, uh, uh, basically I think like the way that people ease into Claude Cowork is like take a knowledge work task that you would normally be clicking around for and then, uh, try to turn, turn that, and then you do the, okay, well what if you went further?Okay. And then when, if you went further, when, if you, and it sort of expand the scope of cowork as you gain trust with it and, and also teach it how to replace you.Felix: Yeah. It's like a little bit like playing factorial, but for your own life. Uh, like you say, you start really small.swyx: Yeah.Felix: You start automating something really tiny and like.Once it clicks, you keep adding onto this like automation empire. Just like make your life easier and easier. My favorite skill has been, um, every single morning Kohlberg starts looking at my calendar and make sure that there's conflicts because people tend to schedule a lot of meetings, sometimes last minute, sometimes miss it soft and painful.And a lot of products have existed like that A lot. I've written in the custom prompt there. I haven't made it a skill, um, honestly should.swyx: Yeah.Felix: But I've given it like pretty clear instructions about okay, here are some people, if they book over other meetings, I'm probably gonna go to their meeting. Like if Dario schedules a meeting.swyx: Right.Felix: Not try to reschedule down. Right. Um, and I think there's some other rules in there about like what kind of meetings I care more about what kind of meetings I care less about. What is okay to like, maybe pun like when I want to be, when I want to be working, when I don't want to be working. And it's those really small things that I can think kind of click with people.Right. When we launch co-work, I think one of the US races that went most viral on Twitter. X was clean up your desktop, which is stuff, because silly, that's such a smart thing, right? Like you don't need to model to clean up your desktop. Not really. Um,swyx: like this, like clean up my desktop.Felix: Yeah, exactly. Yeah.swyx: I need to, I need to choose my desktop, right? I guess give it access to my desktop.Felix: Yeah.swyx: Okay. Uh, okay. This is very scary. Oh, we'll do it.Alessio: I did, I did it with my downloads folder. It was like, you have so many term sheets and there's like eight copies of your rental lease for your office. I was like, all right.Like, don't yell at me.Felix: It's like, it's not such a small task. And then like, I, I would never go out there and normally otherwise and tell people I've pulled a product. It can organize your folder. Right. Um, because it feels small. But I think to your point like,swyx: oh, here's, here's the, here's the ask user questions.Felix: Yeah.swyx: Uh,Felix: beautiful. Right. Elite obvious junk. You probably shouldn't click that.Alessio: No.Felix: If he's not done right.swyx: As long as it's reversible, I don'tAlessio: make up blend to,swyx: yeah. Uh, yeah. No, I, I have a, I have a typical, everything is super messy folder. So, yes. I think this, this is super helpful. So this is a pretty simple task.Mm-hmm. But I've, okay, here it is. Right. Here's the progress. I don't see this in, that's why I'm like, this gotta be something different than, uh, than Claude Code, because I'm like, weFelix: do. Yeah. That's, we do system prompt that. We're like, all right. We want you to think about like, this task Yeah. Methodology.Yeah.swyx: And then I can, I can, I can do like little suggestions for, for, for these things. It's beautiful. Look at this. I, I can, I can like say like, oh, don't do that. Don't do this. It's amazing.Felix: I'm so happy. You like it. Um, I mean, the other way around, like we're part of the Clark core team, if you would like this in Clark COVID.swyx: Yeah. Yeah. Yeah. Uh, so, so yeah, I mean, uh, this is really good. Obviously I, I'm like kind of raving about it. Uh, you know, I have other things like sign up for pg e so if you can do phone calls for me, that'd be great. Um, I, I do, peopleFelix: have done that. Obviously you can't do that natively, but people have done that with like, various other providers.swyx: Yeah. Uh, and then this is like signing up for the Figma MCP. Um, I, I really am trying to do like everything, um, data analysis as well. I do think, um, oh, design to code, uh, very, very good. Right? So like, here's a Figma file, take it. And then this is where like a lot of other tasks is like knowledge work, like replace my manual clicking, but this is no, I would normally use Claude Code or uh, Claude Code for this, but because I perceive that you have better Chrome integrationFelix: mm-hmm.swyx: I, I think you can actually do a better job of this. And I, this, this is one shot at my, uh, conference website.Felix: That's pretty cool. Like at some point I would love to like, hear how you feel about code. In the desktop apps, which is like I never use, which is the, the same team. Same team.swyx: So I use the call code in terminal, which I, I perceive to be the default way of cloud coding.Felix: So one thing this has,swyx: sorry, I'm just like, I'm notFelix: here, I'm not here. All products. Can I talk about other stuff? Like I, I'm not sure if people out there wanna like hear me advertise my stuff for like an hour. Please do that. Um, this thing is like a builtin browser, which is a thing a lot of products have said.Yeah, it's a builtin browser. And I think giving cloud eyes into like what you're actually working on makes it so much more effective. And that's probably what you've seen in cohort because it can see Chrome, it can like debug the dom, it can like see things. Um, that does make it more powerful.swyx: Yeah. So, so I think, uh, my mental model was kind broken.‘cause I only use this cowork because I thought it had a, a browser thing in it. But I understand that the Claude Code app. The app version of Claude Code does have a built-in browser. I've seen, I've seen this preview thing.Felix: Yeah.swyx: I just, I've never used it.Felix: But in the end, in the end, you sort of have it by hard.Yeah. You basically get the same thing. Right? Like the, the, the additional skill that you're describing is chart is better if we can see what it's working on. Right. That's, that's sort of like the summary here and like whether it's using your Chromeswyx: Yeah.Felix: Or it's just like making up its own little like browser.It doesn't really make a big difference because either way it's gonna see what it's working on and that just makes it much better. And then you don't have to run QA for your cloud.swyx: Why doesn't it pick up my existing Claude Code sessions? ‘cause I, I mean, obviously I've used Claude Code, but Excellent question.Um, don't have a good answer other than like, we're honest. Just haven't Yeah. This is what the Open AI team does. Okay. Uh, cool. I I I don't have other, like, I, I just, I, I do wanna expand people's minds and also maybe show people if they haven't really done it, but like, I, I think it's very interesting how I sometimes use this more than I use, I mean, I use dia, right?Yeah. Um, I, and I use, uh, I've used like all the other agentic browsers and philanthropic didn't have to build an agentic browser because you just had Claude Cowork and that's enough.Felix: Yeah. I also think like maybe integrating with number of excellent browsers out there, it's like currently on my personal priority list, a little higher than like trying to rebuild a browser from scratch.Yeah. You know, never say never, but I think going back to this idea of like, we wanna plug this into an entire existing workflow, I think our goal is actually to not replace any of the applications we have in your computer. But instead of like, work really well within a new workflow,Alessio: make the new one. Yeah.Are, it seems that nowadays, especially on the browser, most of the innovation is like user ergonomics. It's not really like the underlying browser engine. So I feel like to call it, it doesn't really matter if it's like the, uh, or Chrome or Alice, whatever.Felix: Yeah. We wanna, we wanna meet you wherever you are.Which is like, like obviously I would say that, but it's also just generally true because I don't wanna shrink my potential user base artificially by saying, okay, like, I'm gonna start building for the people who are willing to switch browsers.Alessio: Right.Felix: That's such a, like, you know, like many lawsuits have been filed over who gets to review the browser and like a lot of money has switched hands over the question of like, which browser is default and which search engine is default within the browser.Um, I just wanna build for, yeah, I wanna build for swyx essentially. Like, I wanna, I wanna, I wanna build for people who have a number of annoying tasks that they feel like. Maybe clock could do it. Could do it for them.Alessio: Yeah. What do you think about skills portability? I think there's been one thing, I use another thing called zo, which is kinda like a cloud computer plus agent.And I have a skill to add visitors to the office. Yeah. So whenever somebody has to come in after hours, they need to check in downstairs. Um, but I wanna like text the thing, so it doesn't really work in, in cowork, but now that skill is in the zone harness and it's not in my cowork thing. And then if I make a change, it's gotta, I gotta sync them.How do you see that going? Like I see memory as like. Cloud personal, kinda like, I don't necessarily want my memories to be cross thing.Felix: Yeah.Alessio: But I do want my skills to be cross agent that I use. I think with MTPs, people do the same thing. It's like, oh, Mt. P Gateway. Mt P registry. I don't really know if that's like a business.So I'm curious like if you've had any thoughts in the area.Felix: I think for me, this is sort of where I go back to the really basic primitives for our skills are file-based instead of like this complicated thing that exists inside a place somewhere that is like super proprietary. I'm really leaning into the idea of like, it's all just files and vultures, and that makes it very portable on its own.Right. We do have skills as part of this container format, which was just called plugins.Alessio: Mm-hmm.Felix: And plugins are available both for Claude Code and Claude Code work the same format, and you can install plugins. This works in cowork today. You can basically say, I'm gonna add a whole, like just a GitHub repo as a.Skills marketplace or like a plugin marketplace. And that's how we're doing portability. I think we have a lot of room left to grow in. How do we make it easy for people to know that they can write skills? How do we make it easy for them to just like, share a skill with you? Because obviously all the words I just said, right?Like I'm losing most of the knowledge worker base out there, right. And start by saying, oh, you can connect to GitHub repo. It's not exactly how most people will end up working in like a general knowledge worker space. Um, but I think there's something there. And another thing that's there that I think has not really been properly explored is the, the, the combination of which part of the skill is very portable and then which part of the skill is like very personal to you.Right. And I think that's something we haven't really solved as an industry. Hmm.swyx: It's like, which, how you wanna introduce more structure to the skill or have always have like. Public skill, private skill, you know, pair. Yeah, yeah. Kind of. I think there'sFelix: like a, like the easiest way to do this, which is we do like use string interpolation or something.Right, right. Yeah, yeah. Insert username here, insert like phone number, insert, like known folder, locations, that kind of stuff. Um, that's probably clunky. That's why we haven't built it. Um, but I do think someone is going to come up with like an interesting way to keep everything we like about skills. The portability is just a file, it's just marked down.It's just text, honestly. Right. Like a text file words. The complete lack of structure, which means you don't need any kind of tutorial to write a skill. Just like explain it to Claude the way he would explain it to me and Claude will probably get it before I work. Mm-hmm. Right? You're just like, for booking a flight, tell Claude how to book a flight the same way we tell him somewhere.I just started working here today. But combine that with a very like, personal thing. Um, maybe we'll stick with a booking a flight example. I don't actually think. AI should be booking flights. I think the tools we have is yes.swyx: Yeah. Finally, somebody says it. It's the default demo that everyone's making.Felix: I'mswyx: like, I even against like booking demos, it is not a good showcase.Felix: Yeah. I'm like, I just wanna book my flight myself. But, um, I think there's a lot of things that have a personal and a non-personal component and that's maybe why people reach for flight booking because some things are very universal. Yeah. Super flight is usually better, right? Like few people try to book the most expensive flight.And then some things are quite personal about like what times you prefer, which seat you prefer, which airports you prefer. Combining that and like a skill format that is actually portable, compatible, easy to understand for people. I think that would be very exciting. We just haven't figured it out yet.Alessio: Yeah, I think the text part every, I think everybody by now has some sort of like cloud file thing. Either Dropbox, Google Drive, whatever. So it feels like in a way it should basically like sim link. My skills into all my agent harnesses. Yeah. Just keep those ing like we have internally this like valuable tokens repo, which is like all the commands sub agents.It's good. Uh, and then I build like a TUI where you can start it and be like, you know, install this command and this three sub agents into this agent in this folder and just copy paste this. It doesn't do anything. It literally cp the file into that. But I feel like there should be something similar where like whenever I go into a new thing, it's like, hey, here's like the link to exactly the cloud folder and just bring down these skills into this.Yeah. Like today it doesn't quite work like that. Like if I install a new agent, I cannot, I have to like copy paste all the skills and I don't even know where they are.Felix: Yeah.Alessio: That's like the big problem. It's like where do I find them?Felix: Yeah.Alessio: Um, so I'm curious like in the future like that, that almost feels like my personal productivity thing will be my skills.Felix: Yeah.Alessio: Is not really the product that I use. Everybody has access to the same product. But today there's, that just looks like copy pasting ME files, IFelix: think so many things I, I really like thinking about agents and LLMs just as like another coworker. So many attempts have made to build documentation companies that are like, oh, we're gonna solve oil documentation problems.Um, I myself, like spend a little bit of time working in notion, right? I'm like deeply familiar with the concept of let's get everyone on the same page. Mm-hmm. Right? And what you're basically saying here is you want all your agents to be on the same page about your preferences, about the skills, about the way they ought to work and like how they ought to execute.And I'm not sure what the right thing is going to be if it's going to be some, some company that can say, all right, we're as an independent body, we're not trying to like, push into any particular product. It's our job to be like the skill authority, and we provide, I don't know, we're gonna be the Dropbox of skills and we can just sim link us into all the products we want to use.I'm not sure that's gonna be viable business, but as, as an idea, it would be cool.Alessio: Yeah. Yeah. I think so many things are just going away as businesses. It's like, how am I supposed to do it? I'm not even asking somebody to make a product about it. Like yeah. I wanna personally know. And there's things like you said, it's like you almost wanna skill and then interpolate it between personal and work.So if I'm booking a fly for work, it's different than I'm booking a flight personally.Felix: Yeah.Alessio: In some ways, yeah. But like a lot of the scaffolding is the same, you know? Cool.Felix: I mean, as an engineer I will tell you like, you know, technic a person to technic a person. I will just be like siblings.Alessio: Well that's what, that's what I do.We call that MD and agents that MD's just the same how sim length. And so it is like, that works, but it feels like, yeah, I don't know. MaybeFelix: you can always go one, you can always tell cowork problem and then cowork will solve it for you. Just make the siblings. That's like one way to do it.Alessio: That's true.That's true. All right. Everything is called cowork.Felix: Uh, potentially spicy. Question for both of you.swyx: Uh, which of these industries will go away?Alessio: Okay, so what Felix was saying before is interesting. There's busy like. The short term pressure of like, we need to turn these tokens into valuable things, which is I should build the last mile product that harness the model.And then there's the question of like, long term, which ones are gonna still be valuable? And I think you're kind of seeing this today with like, uh, you know, the coding space in a way is kind of like everybody's moving up and up in stack because you need more than just turning tokens into code. I think search, like enterprise search is kind of saying the same thing.Like with G Clean and like all these different companies is like, at the end of the day, if Cowork is the one doing all the work, the search itself is like such a small part that like, I don't know if I'm really gonna pay that much money just to do search. It's almost like everything is like a cowork vertical.So like how much can cowork first party support?swyx: Mm-hmm.Alessio: And how much can it not? I think for a lot of these things, the planning thing that you were showing do Which one? The planning. The planning.swyx: Okay. Yeah. Yeah.Alessio: That's one thing where like most of the value that these agents provide is like they're better at planning for specific tasks.Yeah. And have better tools for it.swyx: Yeah.Alessio: But I think the models are now moving in that direction and they have the right harnesses and they're on your computer. So for me it's almost like if for the end customer trusts your startup to be the provider of that task result, then I think that works. This is, uh, something that, this is a shortswyx: spike that we're, we're working on.Uh, yeah.Felix: I think, look, I'll, I'll, I'll tell you this, like I don't think I'm the best person to like actually estimate which industry is going to be hit the hardest. But I do think that at philanthropic as a group of people, we're deeply worried about the impact. That the tools are going to have on the labor market, especially for like junior employees that, because I think, I think it's only honest to say that when we talk about automating a lot away, a lot of the work that we personally find annoying that we maybe think's not the best use of our time.In a lot of industries, that kind of work would've been given to a junior entry level employee. Yeah. Right. And I think it's, it's only, it's only right to be really worried about that and like worry what that's going to do in particular to people like enter the shop market.Alessio: Mm-hmm. I have a solution for that.Which you make them, you create simulative jobs for them.Felix: Okay.Alessio: So this is, this is like half joke, half true. So if you think about software engineering, when you're like a junior engineer, you work like 1, 2, 3 years. And in those three years there's like maybe like a handful of moments where like you really learn something.And then a bunch of other days where like you're not really progressing.Felix: Yeah.Alessio: I think now we can use AI and these models to actually like shortcut these careers and almost like simulate the early years of your work and like just make them like super dense and like these learnings, it's like, hey, we're working on this feature, which is like a distributed system and you need to learn this thing that might take three months at a company.And so you take three months here, it's like we're just simulating the whole thing. It's actually not a real thing. And in one week we kind of speed run through the whole thing and you kind of learn your lesson from there. And we kind of repeat that in like one year. You basically get like three years worth of like projects and experience.Yeah. I think it's harder for like things like sales or for things like, you know, marketing because you don't really have a way to get the feedback loop. But I think a lot of it, it sounds kind of silly, it's like you're making the new effect job, but it's almost like you go to college, right? People pay to learn how to do it, and this might feel similar where it's like, hey, we have the.Jane Street Simulator is like, you wanna come work at Jane Street? We'll just put you in the simulator for like three months.Felix: Wow.Alessio: And you'll come out of it. It's like, you know, I'm ready.Felix: So there, there is an aspect here. I'm not an expert enough to like actually know what, what is going to happen to marketing or legal or finance, right?Like, I don't work in those jobs and I, I don't think I should talk about them, but I am an engineer and I think I have a pretty good idea of what engineering is like. And I think one thing we're sort of seeing is that as a company and also as, as the public, we're like deeply worried about entry level, but we're also seeing more senior engineers accelerate it.If like they're more productive. They, they actually increase the value they provide. And the thing that I'm thinking about a lot is the fact that even before all of this happened, um, I've always had a lot of respect for the University of Waterloo and the, the new grads that have joined my teams as from coming from the University of Waterloo always felt like.More ready than new grads will like literally spend their entire time at the university regardless of how good, but never actually had to work inside an environment where you have to ship things that eventually will be used by users. And I'm, I'm, I'm German. I like initially went to German University and I think the, the, the like information systems programs, there tend to be very theoretical, right?Like I often give people the example of like trying

An artist asked AI for the opposite of Marlon Brando. A woman with blood-red cheeks appeared — and no matter what anyone did next, she kept coming back. This is Loab.

This is a free preview of a paid episode. To hear more, visit www.latent.spaceAIE Europe CFP and AIE World's Fair paper submissions for CAIS peer review are due TODAY - do not delay! Last call ever.We're excited to welcome METR for their first LS Pod, hopefully the first of many:METR are keepers of currently the single most infamous chart in AI:But every Latent Space reader should be sophisticated enough to know that the details matter and that hype and hyperbole go hand in hand in AI social media, because the millions of impressions that got, by people who don't understand or care about the nuances, disclaimers, and error bars, far outreaches the 69k views on the corrections by the people who actually made the chart:There's a lot of nuance both in making benchmarks (as we discovered with OpenAI on our SWE-Bench Verified podcast) and in extrapolating results from them, especially where exponentials and sigmoids are concerned. METR's Long Horizons work itself has known biases that the authors have responsibly disclosed, but go far too underappreciated in the pursuit of doomer chart porn.If you're interested in a short, sharable TED talk version of this pod, over at AIE CODE we were blessed to feature Joel twice, as a stage talk and with a longer form small workshop with Q&A:We also make sure cover some of METR's lesser known work on Threat Evaluation but also Developer Productivity, where 2x friend of the pod and now Zyphra founder Quentin Anthony was the ONLY productive participant!Finally, if you're the sort to read these show notes to the end, then you definitely deserve some pictures of Joel shredding the guitar at Love Band Karaoke which we mention at the end: Full Video PodTimestamps00:00 What METR Means00:39 Podcast Intro With Joel01:39 ME vs TR03:33 Time Horizon Origin Story04:56 Picking Tasks And Biases09:13 Time Horizon Misconceptions11:37 Opus 4.5 And Trendlines14:27 Productivity Studies And Explosions29:50 Compute Slows Progress30:47 Algorithms Need Compute32:45 Industry Spend and Data34:57 Clusters and Shipping Timelines36:44 Prediction Markets for Models38:10 Manifold Alpha Story43:04 Beyond Benchmarks Evals51:39 METR Roadmap and FarewellTranscript

a16z's Martin Casado and Sarah Wang join Latent Space hosts Alessio Fanelli and Swyx to discuss what makes this AI investment cycle unlike anything in the history of venture capital. They cover why the lines between venture and growth, apps and infrastructure are blurring, how frontier model companies can raise more than the aggregate of everyone built on top of them, and why the industry-wide gap between perception and reality has never been wider. Follow Alessio Fanelli on X: https://x.com/FanaHOVA Follow Swyx (Shawn Wang) on X: https://twitter.com/FanaHOVA Follow Martin Casado on X: https://twitter.com/martin_casado Follow Sarah Wang on X: https://twitter.com/sarahdingwang   Listen to more from Latent Space: https://www.youtube.com/@LatentSpacePod Stay Updated: Find a16z on YouTube: YouTube Find a16z on X Find a16z on LinkedIn Listen to the a16z Show on Spotify Listen to the a16z Show on Apple Podcasts Follow our host: https://twitter.com/eriktorenberg   Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

a16z's Martin Casado and Sarah Wang join Latent Space hosts Alessio Fanelli and Swyx to discuss what makes this AI investment cycle unlike anything in the history of venture capital. They cover why the lines between venture and growth, apps and infrastructure are blurring, how frontier model companies can raise more than the aggregate of everyone built on top of them, and why the industry-wide gap between perception and reality has never been wider.   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

This crossover episode from the Latent Space podcast features Mark Zuckerberg and Priscilla Chan on the 10-year anniversary of the Chan Zuckerberg Initiative and their expanded Biohub vision. They discuss how a “Frontier Biology Lab” working in sync with a “Frontier AI Lab” could enable breakthroughs like a Virtual Cell and true N-of-1 precision medicine. The conversation covers the acquisition of Evolutionary Scale and ESM3, new biological data collection at scale, and how AI-powered biology might transform drug discovery and disease prevention. Sponsors: Blitzy: Blitzy is the autonomous code generation platform that ingests millions of lines of code to accelerate enterprise software development by up to 5x with premium, spec-driven output. Schedule a strategy session with their AI solutions consultants at https://blitzy.com Framer: Framer is an enterprise-grade website builder that lets business teams design, launch, and optimize their.com with AI-powered wireframing, real-time collaboration, and built-in analytics. Start building for free and get 30% off a Framer Pro annual plan at https://framer.com/cognitive Serval: Serval uses AI-powered automations to cut IT help desk tickets by more than 50%, freeing your team from repetitive tasks like password resets and onboarding. Book your free pilot and guarantee 50% help desk automation by week four at https://serval.com/cognitive Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (04:27) CZI origins and focus (08:29) Why tools over cures (14:43) Virtual cells and imaging (Part 1) (20:19) Sponsors: Blitzy | Framer (23:24) Virtual cells and imaging (Part 2) (25:22) Data diversity and grounding (32:30) Evaluating models and Biohub (Part 1) (37:53) Sponsors: Serval | Tasklet (40:42) Evaluating models and Biohub (Part 2) (41:06) Future healthcare and aging (53:39) Modeling scales and immunity (58:53) Timelines, data, and collaboration (01:04:01) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

What makes something truly *intelligent?* Is a rock an agent? Could a perfect simulation of your brain actually *be* you? In this fascinating conversation, Dr. Jeff Beck takes us on a journey through the philosophical and technical foundations of agency, intelligence, and the future of AI.Jeff doesn't hold back on the big questions. He argues that from a purely mathematical perspective, there's no structural difference between an agent and a rock – both execute policies that map inputs to outputs. The real distinction lies in *sophistication* – how complex are the internal computations? Does the system engage in planning and counterfactual reasoning, or is it just a lookup table that happens to give the right answers?*Key topics explored in this conversation:**The Black Box Problem of Agency* – How can we tell if something is truly planning versus just executing a pre-computed response? Jeff explains why this question is nearly impossible to answer from the outside, and why the best we can do is ask which model gives us the simplest explanation.*Energy-Based Models Explained* – A masterclass on how EBMs differ from standard neural networks. The key insight: traditional networks only optimize weights, while energy-based models optimize *both* weights and internal states – a subtle but profound distinction that connects to Bayesian inference.*Why Your Brain Might Have Evolved from Your Nose* – One of the most surprising moments in the conversation. Jeff proposes that the complex, non-smooth nature of olfactory space may have driven the evolution of our associative cortex and planning abilities.*The JEPA Revolution* – A deep dive into Yann LeCun's Joint Embedding Prediction Architecture and why learning in latent space (rather than predicting every pixel) might be the key to more robust AI representations.*AI Safety Without Skynet Fears* – Jeff takes a refreshingly grounded stance on AI risk. He's less worried about rogue superintelligences and more concerned about humans becoming "reward function selectors" – couch potatoes who just approve or reject AI outputs. His proposed solution? Use inverse reinforcement learning to derive AI goals from observed human behavior, then make *small* perturbations rather than naive commands like "end world hunger."Whether you're interested in the philosophy of mind, the technical details of modern machine learning, or just want to understand what makes intelligence *tick,* this conversation delivers insights you won't find anywhere else.---TIMESTAMPS:00:00:00 Geometric Deep Learning & Physical Symmetries00:00:56 Defining Agency: From Rocks to Planning00:05:25 The Black Box Problem & Counterfactuals00:08:45 Simulated Agency vs. Physical Reality00:12:55 Energy-Based Models & Test-Time Training00:17:30 Bayesian Inference & Free Energy00:20:07 JEPA, Latent Space, & Non-Contrastive Learning00:27:07 Evolution of Intelligence & Modular Brains00:34:00 Scientific Discovery & Automated Experimentation00:38:04 AI Safety, Enfeeblement & The Future of Work---REFERENCES:Concept:[00:00:58] Free Energy Principle (FEP)https://en.wikipedia.org/wiki/Free_energy_principle[00:06:00] Monte Carlo Tree Searchhttps://en.wikipedia.org/wiki/Monte_Carlo_tree_searchBook:[00:09:00] The Intentional Stancehttps://mitpress.mit.edu/9780262540537/the-intentional-stance/Paper:[00:13:00] A Tutorial on Energy-Based Learning (LeCun 2006)http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf[00:15:00] Auto-Encoding Variational Bayes (VAE)https://arxiv.org/abs/1312.6114[00:20:15] JEPA (Joint Embedding Prediction Architecture)https://openreview.net/forum?id=BZ5a1r-kVsf[00:22:30] The Wake-Sleep Algorithmhttps://www.cs.toronto.edu/~hinton/absps/ws.pdf---RESCRIPT:https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_EoPDF:https://app.rescript.info/api/public/sessions/0efec296b9b6e905/pdf

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

We are reupping this episode after LMArena announced their fresh Series A (https://www.theinformation.com/articles/ai-evaluation-startup-lmarena-valued-1-7-billion-new-funding-round?rc=luxwz4), raising $150m at a $1.7B valuation, with $30M annualized consumption revenue (aka $2.5m MRR) after their September evals product launch.—-From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users.We discuss:* The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent* The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in* The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves)* Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities* The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science* New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soonFull Video EpisodeTimestamps00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup00:02:47 The Decision to Start a Company: Scaling Beyond Academia00:03:38 The $100M Raise: Use of Funds and Platform Economics00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation00:08:12 Educational Value and Learning from the Community00:08:41 Technical Migration: From Gradio to React and Platform Evolution00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity00:12:29 Nano Banana Moment: How Preview Models Create Market Impact00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals00:19:10 API Strategy and Focus: Doing One Thing Well00:19:51 Community Management and Retention: Sign-In, History, and Daily Value00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses00:21:49 Hiring and Building a High-Performance Team Get full access to Latent.Space at www.latent.space/subscribe

From building LMArena in a Berkeley basement to raising $100M and becoming the de facto leaderboard for frontier AI, Anastasios Angelopoulos returns to Latent Space to recap 2025 in one of the most influential platforms in AI—trusted by millions of users, every major lab, and the entire industry to answer one question: which model is actually best for real-world use cases? We caught up with Anastasios live at NeurIPS 2025 to dig into the origin story (spoiler: it started as an academic project incubated by Anjney Midha at a16z, who formed an entity and gave grants before they even committed to starting a company), why they decided to spin out instead of staying academic or nonprofit (the only way to scale was to build a company), how they're spending that $100M (inference costs, React migration off Gradio, and hiring world-class talent across ML, product, and go-to-market), the leaderboard delusion controversy and why their response demolished the paper's claims (factual errors, misrepresentation of open vs. closed source sampling, and ignoring the transparency of preview testing that the community loves), why platform integrity comes first (the public leaderboard is a charity, not a pay-to-play system—models can't pay to get on, can't pay to get off, and scores reflect millions of real votes), how they're expanding into occupational verticals (medicine, legal, finance, creative marketing) and multimodal arenas (video coming soon), why consumer retention is earned every single day (sign-in and persistent history were the unlock, but users are fickle and can leave at any moment), the Gemini Nano Banana moment that changed Google's market share overnight (and why multimodal models are becoming economically critical for marketing, design, and AI-for-science), how they're thinking about agents and harnesses (Code Arena evaluates models, but maybe it should evaluate full agents like Devin), and his vision for Arena as the central evaluation platform that provides the North Star for the industry—constantly fresh, immune to overfitting, and grounded in millions of real-world conversations from real users. We discuss: The $100M raise: use of funds is primarily inference costs (funding free usage for tens of millions of monthly conversations), React migration off Gradio (custom loading icons, better developer hiring, more flexibility), and hiring world-class talent The scale: 250M+ conversations on the platform, tens of millions per month, 25% of users do software for a living, and half of users are now logged in The leaderboard illusion controversy: Cohere researchers claimed undisclosed private testing created inequities, but Arena's response demolished the paper's factual errors (misrepresented open vs. closed source sampling, ignored transparency of preview testing that the community loves) Why preview testing is loved by the community: secret codenames (Gemini Nano Banana, named after PM Naina's nickname), early access to unreleased models, and the thrill of being first to vote on frontier capabilities The Nano Banana moment: changed Google's market share overnight, billions of dollars in stock movement, and validated that multimodal models (image generation, video) are economically critical for marketing, design, and AI-for-science New categories: occupational and expert arenas (medicine, legal, finance, creative marketing), Code Arena, and video arena coming soon Consumer retention: sign-in and persistent history were the unlock, but users are fickle and earned every single day—"every user is earned, they can leave at any moment" — Anastasios Angelopoulos Arena: https://lmarena.ai X: https://x.com/arena Chapters 00:00:00 Introduction: Anastasios from Arena and the LM Arena Journey 00:01:36 The Anjney Midha Incubation: From Berkeley Basement to Startup 00:02:47 The Decision to Start a Company: Scaling Beyond Academia 00:03:38 The $100M Raise: Use of Funds and Platform Economics 00:05:10 Arena's User Base: 5M+ Users and Diverse Demographics 00:06:02 The Competitive Landscape: Artificial Analysis, AI.xyz, and Arena's Differentiation 00:08:12 Educational Value and Learning from the Community 00:08:41 Technical Migration: From Gradio to React and Platform Evolution 00:10:18 Leaderboard Delusion Paper: Addressing Critiques and Maintaining Integrity 00:12:29 Nano Banana Moment: How Preview Models Create Market Impact 00:13:41 Multimodal AI and Image Generation: From Skepticism to Economic Value 00:15:37 Core Principles: Platform Integrity and the Public Leaderboard as Charity 00:18:29 Future Roadmap: Expert Categories, Multimodal, Video, and Occupational Verticals 00:19:10 API Strategy and Focus: Doing One Thing Well 00:19:51 Community Management and Retention: Sign-In, History, and Daily Value 00:22:21 Partnerships and Agent Evaluation: From Devon to Full-Featured Harnesses 00:21:49 Hiring and Building a High-Performance Team

Deedy Das, Partner at Menlo Ventures, returns to Latent Space to discuss his journey from Glean to venture capital, the explosive rise of Anthropic, and how AI is reshaping enterprise software and coding. From investing in Anthropic early on when they had no revenue to managing the $100M Ontology Fund, Das shares insider perspectives on the fastest-growing software company in history and what's next for AI infrastructure, research investing, and the future of engineering.We cover Glean's rise from “boring” enterprise search to a $7B AI-native company, Anthropic's meteoric rise, the strategic decisions behind products like Claude Code, and why market share in enterprise AI is shifting dramatically. Das explains his investment thesis on research companies like Goodfire, Prime Intellect, and OpenRouter and how the Anthology Fund is quietly seeding the next wave of AI infra, research, and devtools.Full Video EpisodeTimestamps* 00:00:00 Introduction and Deedy's Return to Latent Space* 00:01:20 Glean's Journey: From Boring Enterprise Search to Valuation* 00:15:37 Anthropic's Meteoric Rise and Market Share Dynamics* 00:17:50 Claude Artifacts and Product Innovation* 00:41:20 The Anthology Fund: Investing in the Anthropic Ecosystem* 00:48:01 Goodfire and Mechanistic Interpretability* 00:51:25 Prime Intellect and Distributed AI Training* 00:53:40 OpenRouter: Building the AI Model Gateway* 01:13:36 The Stargate Project and Infrastructure Arms Race* 01:18:14 The Future of Software Engineering and AI Coding Get full access to Latent.Space at www.latent.space/subscribe

Deedy Das, Partner at Menlo Ventures, returns to Latent Space to discuss his journey from Glean to venture capital, the explosive rise of Anthropic, and how AI is reshaping enterprise software and coding. From investing in Anthropic early on when they had no revenue to managing the $100M Ontology Fund, Das shares insider perspectives on the fastest-growing software company in history and what's next for AI infrastructure, research investing, and the future of engineering. We cover Glean's rise from “boring” enterprise search to a $7B AI-native company, Anthropic's meteoric rise, the strategic decisions behind products like Claude Code, and why market share in enterprise AI is shifting dramatically. Das explains his investment thesis on research companies like Goodfire, Prime Intellect, and OpenRouter and how the Anthology Fund is quietly seeding the next wave of AI infra, research, and devtools.

Jared Palmer, SVP at GitHub and VP of CoreAI at Microsoft, joins Latent Space for an in-depth look at the evolution of coding agents and modern developer tools. Recently joining after leading AI initiatives at Vercel, Palmer shares firsthand insights from behind the scenes at GitHub Universe, including the launch of Agent HQ which is a new collaboration hub for coding agents and developers.This episode traces Palmer's journey from building Copilot inspired tools to pioneering the focused Next.js coding agent, v0, and explores how platform constraints fostered rapid experimentation and a breakout success in AI-powered frontend development. Palmer explains the unique advantages of GitHub's massive developer network, the challenges of scaling agent-based workflows, and why integrating seamless AI into developer experiences is now a top priority for both Microsoft and GitHub.Full Video EpisodeTimestamps00:00:00 Introduction and Jared's New Role at GitHub00:01:00 From V0 to Agent HQ: The Evolution of Coding Agents00:02:51 The V0 Origin Story: From ChatGPT to AI Playground00:05:40 Building the AI SDK and ShadCN Collaboration00:07:08 The Birth of V0: Prompt to UI Revolution00:09:18 V0's Growth Journey and Model Evolution00:11:05 Model Strategy: Composite Models vs User Choice00:13:16 GitHub's Agent HQ and Model Marketplace00:15:51 The Future of Agent Abstraction and Standards00:16:33 Microsoft Core AI Integration and Workflow Vision00:18:37 Dev Containers and Repo Setup Challenges00:24:10 Agent Quality and Infrastructure Reliability00:27:05 Using Coding Agents for Non-Coding Tasks00:29:11 GitHub Homepage Redesign and Community Feedback00:30:27 Stacked Diffs: GitHub's Most Requested Feature Get full access to Latent.Space at www.latent.space/subscribe

Jed Borovik, Product Lead at Google Labs, joins Latent Space to unpack how Google is building the future of AI-powered software development with Jules. From his journey discovering GenAI through Stable Diffusion to leading one of the most ambitious coding agent projects in tech, Borovik shares behind-the-scenes insights into how Google Labs operates at the intersection of DeepMind's model development and product innovation. We explore Jules' approach to autonomous coding agents and why they run on their own infrastructure, how Google simplified their agent scaffolding as models improved, and why embeddings-based RAG is giving way to attention-based search. Borovik reveals how developers are using Jules for hours or even days at a time, the challenges of managing context windows that push 2 million tokens, and why coding agents represent both the most important AI application and the clearest path to AGI. This conversation reveals Google's positioning in the coding agent race, the evolution from internal tools to public products, and what founders, developers, and AI engineers should understand about building for a future where AI becomes the new brush for software engineering. Chapters 00:00:00 Introduction and GitHub Universe Recap 00:00:57 New York Tech Scene and East Coast Hackathons 00:02:19 From Google Search to AI Coding: Jed's Journey 00:04:19 Google Labs Mission and DeepMind Collaboration 00:06:41 Jules: Autonomous Coding Agents Explained 00:09:39 The Evolution of Agent Scaffolding and Model Quality 00:11:30 RAG vs Attention: The Shift in Code Understanding 00:13:49 Jules' Journey from Preview to Production 00:15:05 AI Engineer Summit: Community Building and Networking 00:25:06 Context Management in Long-Running Agents 00:29:02 The Future of Software Engineering with AI 00:36:26 Beyond Vibe Coding: Spec Development and Verification 00:40:20 Multimodal Input and Computer Use for Coding Agents

Jed Borovik, Product Lead at Google Labs, joins Latent Space to unpack how Google is building the future of AI-powered software development with Jules. From his journey discovering GenAI through Stable Diffusion to leading one of the most ambitious coding agent projects in tech, Borovik shares behind-the-scenes insights into how Google Labs operates at the intersection of DeepMind's model development and product innovation.We explore Jules' approach to autonomous coding agents and why they run on their own infrastructure, how Google simplified their agent scaffolding as models improved, and why embeddings-based RAG is giving way to attention-based search. Borovik reveals how developers are using Jules for hours or even days at a time, the challenges of managing context windows that push 2 million tokens, and why coding agents represent both the most important AI application and the clearest path to AGI.This conversation reveals Google's positioning in the coding agent race, the evolution from internal tools to public products, and what founders, developers, and AI engineers should understand about building for a future where AI becomes the new brush for software engineering.Full Video EpisodeTimestamps00:00:00 Introduction and GitHub Universe Recap00:00:57 New York Tech Scene and East Coast Hackathons00:02:19 From Google Search to AI Coding: Jed's Journey00:04:19 Google Labs Mission and DeepMind Collaboration00:06:41 Jules: Autonomous Coding Agents Explained00:09:39 The Evolution of Agent Scaffolding and Model Quality00:11:30 RAG vs Attention: The Shift in Code Understanding00:13:49 Jules' Journey from Preview to Production00:15:05 AI Engineer Summit: Community Building and Networking00:25:06 Context Management in Long-Running Agents00:29:02 The Future of Software Engineering with AI00:36:26 Beyond Vibe Coding: Spec Development and Verification00:40:20 Multimodal Input and Computer Use for Coding Agents Get full access to Latent.Space at www.latent.space/subscribe

Jared Palmer, SVP at GitHub and VP of CoreAI at Microsoft, joins Latent Space for an in-depth look at the evolution of coding agents and modern developer tools. Recently joining after leading AI initiatives at Vercel, Palmer shares firsthand insights from behind the scenes at GitHub Universe, including the launch of Agent HQ which is a new collaboration hub for coding agents and developers. This episode traces Palmer's journey from building Copilot inspired tools to pioneering the focused Next.js coding agent, v0, and explores how platform constraints fostered rapid experimentation and a breakout success in AI-powered frontend development. Palmer explains the unique advantages of GitHub's massive developer network, the challenges of scaling agent-based workflows, and why integrating seamless AI into developer experiences is now a top priority for both Microsoft and GitHub.

With NLW currently on the road, he's joined in this conversation by Sean “Swyx” Wang — developer, writer, Latent Space host and newly joined member of Cognition. They explore how AI coding became 2025's defining story, why “vibe coding” is ending (sort of), what comes next for developers, and how “Agent Labs” are reshaping the balance between model makers and product builders. Swyx also previews the upcoming AI Engineer Code Summit in New York and shares why “code AGI” could deliver 80% of AGI's value long before full AGI arrives.Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

Artificial Intelligence and the potential productivity benefits continue to be an area of focus across industries, yet AI's influence now extends into our culture. From deepfakes and cloned voices to AI-generated art and virtual influencers, a new wave of synthetic content is transforming media and testing our ability to trust what we see and hear.In this episode of The Reboot Chronicles,  Henry Ajder,founder of Latent Space Advisory and program lead of Generative AI and Business at the University of Cambridge joins us to discuss this shift. Henry has spent the better part of a decade decoding how synthetic media will alter trust and creativity. He is with us today to help understand what a synthetic age of media really means for business, leaders, governments, and everyday people. 

Quinn Slack (CEO) and Thorsten Ball (Amp Dictator) from SourceGraph join the show to talk about Amp Code, how they ship 15x/day with no code reviews, and why subagents and prompt optimizers aren't a promising direction for coding agents. Amp Code: https://ampcode.com/ Latent Space: https://latent.space/ 00:00 Introduction 00:41 Transition from Cody to Amp 03:18 The Importance of Building the Best Coding Agent 06:43 Adapting to a Rapidly Evolving AI Tooling Landscape 09:36 Dogfooding at Sourcegraph 12:35 CLI vs. VS Code Extension 21:08 Positioning Amp in Coding Agent Market 24:10 The Diminishing Importance of Model Selectors 32:39 Tooling vs. Harness 37:19 Common Failure Modes of Coding Agents 47:33 Agent-Friendly Logging and Tooling 52:31 Are Subagents Real? 56:52 New Frameworks and Agent-Integrated Developer Tools 1:00:25 How Agents Are Encouraging Codebase and Workflow Changes 1:03:13 Evolving Outer Loop Tasks 1:07:09 Version Control and Merge Conflicts in an AI-First World 1:10:36 Rise of User-Generated Enterprise Software 1:14:39 Empowering Technical Leaders with AI 1:17:11 Evaluating Product Without Traditional Evals 1:20:58 Hiring

Quinn Slack (CEO) and Thorsten Ball (Amp Dictator) from SourceGraph join the show to talk about Amp Code, how they ship 15x/day with no code reviews, and why subagents and prompt optimizers aren't a promising direction for coding agents.Amp Code: https://ampcode.com/Latent Space: https://latent.space/Full Video EpisodeTimestamps00:00 Introduction00:41 Transition from Cody to Amp03:18 The Importance of Building the Best Coding Agent06:43 Adapting to a Rapidly Evolving AI Tooling Landscape09:36 Dogfooding at Sourcegraph12:35 CLI vs. VS Code Extension21:08 Positioning Amp in Coding Agent Market24:10 The Diminishing Importance of Model Selectors32:39 Tooling vs. Harness37:19 Common Failure Modes of Coding Agents47:33 Agent-Friendly Logging and Tooling52:31 Are Subagents Real?56:52 New Frameworks and Agent-Integrated Developer Tools1:00:25 How Agents Are Encouraging Codebase and Workflow Changes1:03:13 Evolving Outer Loop Tasks1:07:09 Version Control and Merge Conflicts in an AI-First World1:10:36 Rise of User-Generated Enterprise Software1:14:39 Empowering Technical Leaders with AI1:17:11 Evaluating Product Without Traditional Evals1:20:58 Hiring Get full access to Latent.Space at www.latent.space/subscribe

When the first video diffusion models started emerging, they were little more than just “moving pictures” - still frames extended a few seconds in either direction in time. There was a ton of excitement about OpenAI's Sora on release through 2024, but so far only Sora-lite has been widely released. Meanwhile, other good videogen models like Genmo Mochi, Pika, MiniMax T2V, Tencent Hunyuan Video, and Kuaishou's Kling have emerged, but the reigning king this year seems to be Google's Veo 3, which for the first time has added native audio generation into their model capabilities, eliminating the need for a whole class of lipsynching tooling and SFX editing. The rise of Veo 3 unlocks a whole new category of AI Video creators that many of our audience may not have been exposed to, but is undeniably effective and important particularly in the “kids” and “brainrot” segments of the global consumer internet platforms like Tiktok, YouTube and Instagram. By far the best documentarians of these trends for laypeople are Olivia and Justine Moore, both partners at a16z, who not only collate the best examples from all over the web, but dabble in video creation themselves to put theory into practice. We've been thinking of dabbling in AI brainrot on a secondary channel for Latent Space, so we wanted to get the braindump from the Moore twins on how to make a Latent Space Brainrot channel. Jump on in! Chapters 00:00:00 Introductions & Guest Welcome 00:00:49 The Rise of Generative Media 00:02:24 AI Video Trends: Italian Brain Rot & Viral Characters 00:05:00 Following Trends & Creating AI Content 00:07:17 Hands-On with AI Video Creation 00:18:36 Monetization & Business of AI Content 00:23:34 Platforms, Models, and the Creator Stack 00:37:22 Native Content vs. Clipping & Going Viral 00:41:52 Prompt Theory & Meta-Trends in AI Creativity 00:47:42 Professional, Commercial, and Platform-Specific AI Video 00:48:57 Wrap-Up & Final Thoughts