Podcasts about Unsupervised learning

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

A secretive AI called Mythos is already finding zero-day exploits that humans missed for decades, but Anthropic claims it is too risky for public release. Hear what leading security experts think could happen if this technology escapes the lab. Claude Mythos Preview System Card - Claude Mythos Preview System Card.pdf Sam Altman May Control Our Future—Can He Be Trusted? Meta Employees Vie for AI 'Token Legend' Status Meta releases new model for Social Media "Muse Spark" Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy How Accurate Are Google's A.I. Overviews? Testing suggests Google's AI Overviews tell millions of lies per hour Google's AI Overviews are correct nine out of ten times, study finds How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company The back story behind the first "$1.8 Billion" dollar "AI Company" It's Called Silicon Sampling, and It's Going to Ruin Public Opinion Polling Cloudflare builds what it calls the successor to WordPress JuliusBrussee/caveman:

A secretive AI called Mythos is already finding zero-day exploits that humans missed for decades, but Anthropic claims it is too risky for public release. Hear what leading security experts think could happen if this technology escapes the lab. Claude Mythos Preview System Card - Claude Mythos Preview System Card.pdf Sam Altman May Control Our Future—Can He Be Trusted? Meta Employees Vie for AI 'Token Legend' Status Meta releases new model for Social Media "Muse Spark" Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy How Accurate Are Google's A.I. Overviews? Testing suggests Google's AI Overviews tell millions of lies per hour Google's AI Overviews are correct nine out of ten times, study finds How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company The back story behind the first "$1.8 Billion" dollar "AI Company" It's Called Silicon Sampling, and It's Going to Ruin Public Opinion Polling Cloudflare builds what it calls the successor to WordPress JuliusBrussee/caveman:

A secretive AI called Mythos is already finding zero-day exploits that humans missed for decades, but Anthropic claims it is too risky for public release. Hear what leading security experts think could happen if this technology escapes the lab. Claude Mythos Preview System Card - Claude Mythos Preview System Card.pdf Sam Altman May Control Our Future—Can He Be Trusted? Meta Employees Vie for AI 'Token Legend' Status Meta releases new model for Social Media "Muse Spark" Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy How Accurate Are Google's A.I. Overviews? Testing suggests Google's AI Overviews tell millions of lies per hour Google's AI Overviews are correct nine out of ten times, study finds How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company The back story behind the first "$1.8 Billion" dollar "AI Company" It's Called Silicon Sampling, and It's Going to Ruin Public Opinion Polling Cloudflare builds what it calls the successor to WordPress JuliusBrussee/caveman:

A secretive AI called Mythos is already finding zero-day exploits that humans missed for decades, but Anthropic claims it is too risky for public release. Hear what leading security experts think could happen if this technology escapes the lab. Claude Mythos Preview System Card - Claude Mythos Preview System Card.pdf Sam Altman May Control Our Future—Can He Be Trusted? Meta Employees Vie for AI 'Token Legend' Status Meta releases new model for Social Media "Muse Spark" Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy How Accurate Are Google's A.I. Overviews? Testing suggests Google's AI Overviews tell millions of lies per hour Google's AI Overviews are correct nine out of ten times, study finds How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company The back story behind the first "$1.8 Billion" dollar "AI Company" It's Called Silicon Sampling, and It's Going to Ruin Public Opinion Polling Cloudflare builds what it calls the successor to WordPress JuliusBrussee/caveman:

A secretive AI called Mythos is already finding zero-day exploits that humans missed for decades, but Anthropic claims it is too risky for public release. Hear what leading security experts think could happen if this technology escapes the lab. Claude Mythos Preview System Card - Claude Mythos Preview System Card.pdf Sam Altman May Control Our Future—Can He Be Trusted? Meta Employees Vie for AI 'Token Legend' Status Meta releases new model for Social Media "Muse Spark" Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy How Accurate Are Google's A.I. Overviews? Testing suggests Google's AI Overviews tell millions of lies per hour Google's AI Overviews are correct nine out of ten times, study finds How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company The back story behind the first "$1.8 Billion" dollar "AI Company" It's Called Silicon Sampling, and It's Going to Ruin Public Opinion Polling Cloudflare builds what it calls the successor to WordPress JuliusBrussee/caveman:

A secretive AI called Mythos is already finding zero-day exploits that humans missed for decades, but Anthropic claims it is too risky for public release. Hear what leading security experts think could happen if this technology escapes the lab. Claude Mythos Preview System Card - Claude Mythos Preview System Card.pdf Sam Altman May Control Our Future—Can He Be Trusted? Meta Employees Vie for AI 'Token Legend' Status Meta releases new model for Social Media "Muse Spark" Why OpenAI's Purchase of a Big Tech Podcast Is So Sleazy How Accurate Are Google's A.I. Overviews? Testing suggests Google's AI Overviews tell millions of lies per hour Google's AI Overviews are correct nine out of ten times, study finds How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company The back story behind the first "$1.8 Billion" dollar "AI Company" It's Called Silicon Sampling, and It's Going to Ruin Public Opinion Polling Cloudflare builds what it calls the successor to WordPress JuliusBrussee/caveman:

On this episode of Unsupervised Learning, Razib talks to Mike White, a Genetics professor at the Washington University in St. Louis. White has a position at the School of Medicine in St. Louis, where he leads a research team focused on understanding the biophysical architecture of regulatory DNA. He earned a B.A. in music before pivoting to the sciences, receiving his Ph.D. in Biochemistry from the University of Rochester in 2006 and completing a postdoctoral fellowship at Wash U under Dr. Barak Cohen. White's work combines functional genomics, synthetic biology, computational biology, and deep learning to decipher how cells interpret regulatory sequences. His lab aims to predict how non-coding genetic variations impact complex human traits and disease risk, while exploring how to apply transcriptional circuits for broader applications in health and agriculture. Razib first talks to White about the cultural, political and social winds moving through academia since 2010. How did academic science become so politically polarized, and what significance does it have for future funding streams? White brings his insights from the viewpoint of someone whose perch is in a medical school, and so somewhat at the margins of the cultural revolution sweeping through academia and even STEM. He notes it seems that the activist high tide peaked around 2020, though the hostility between the Right and institutional academia continues unabated, affecting NIH funding. Then White discusses where we are in terms of understanding gene regulation, and its importance in biological function. Razib and White review how almost 99% of the human genome does not code for proteins, so often it is called "junk DNA," but the reality is that there are other functions in that region, first and foremost, regulating and modifying protein expressing regions. Razib asks White where we are in human genomics more than 25 years after the draft, has it lived up to expectations? And where we are going in the future?

Artificial intelligence often feels mysterious. Machines detect spam, recommend products, analyse customers, and power countless digital tools. But behind all of these systems lies a surprisingly simple question: how do machines actually learn?In this episode of A Beginner's Guide to AI, Prof GePharT breaks down one of the most important concepts in machine learning: the difference between supervised learning and unsupervised learning.You will discover how AI models learn from labelled data when the answers are already known, and how algorithms can explore raw data to uncover hidden patterns without guidance. These two learning strategies power many of the systems shaping modern technology.Using practical examples such as spam filters, customer segmentation, and simple analogies like cake classification, the episode explains how machines learn from data and why the training method makes a huge difference.Key takeaways include how supervised learning works with labelled datasets, how unsupervised learning reveals patterns in complex information, why training data quality matters, and how businesses use both methods to build intelligent systems.

On this episode of Unsupervised Learning, Razib talks to Davide Piffer, whose Substack examines genetic differences between populations. Piffer has been publishing on human genetic variation for a decade, and recently started a Substack, Piffer Pilfer, exploring similar issues in detail over a series of posts. Razib asks Piffer about the difficulties in analyzing polygenic scores from quantitative traits in ancient DNA samples. How does he do in technical terms, from genome quality to imputation to ancient populations from modern ones? Then, they discuss some of Piffer's findings, in particular, his work on pigmentation. Piffer talks about how he discovered that modern European pigmentation, and in particular, light complexion, is the product of both admixture from different populations with different characteristics and natural selection over the millennia. Piffer talks about how he discovered that selection for lighter pigmentation continued into the Iron Age.

On this episode of Unsupervised Learning, Razib again talks to George Washington University archaeologist Eric Cline. The author of 1177 B.C. - The Year Civilization Collapsed and After 1177 B.C. - The Survival of Civilizations, Cline has a new book out, Love, War, and Diplomacy: The Discovery of the Amarna Letters and the Bronze Age World They Revealed. While 1177 B.C. closed with the end of the first global civilization, that of the Eastern Mediterranean at the end of the Bronze Age, and After 1177 B.C. tells the story of those who picked up the pieces, Love, War, and Diplomacy puts the spotlight on the Late Bronze Age at its peak. Razib and Cline discuss the two major threads in Love, War, and Diplomacy: the decipherment of cuneiform and the emergence of the field of Assyriology, and the diplomatic world of Bronze Age Great Powers. Cline addresses the reality that 19th-century archaeology was not an idealized enterprise, and scholars had to compete with treasure hunters, and negotiate difficult nationalist sensitivities. He also explains how they deciphered cuneiform decades after hieroglyphs, providing an alternative view of the earliest antiquity. The discussion then focuses on the intricate and tense relationship between Egypt, Assyria, the Hittites, and the Mitanni. Cline also highlights the reality that the Amarna Letters also shed light on the bickering between the petty states of the Levant and their relationship to their hegemon, Egypt.

On this very special episode, Razib talks to paleoanthroplogists John Hawks and Chris Stringer. Hawks is a paleoanthropologist who has been a researcher and commentator in human evolutionary biology and paleoanthropology for over two decades. With a widely read weblog (now on Substack), a book on Homo naledi, and highly cited scientific papers, Hawks is an essential voice in understanding the origins of our species. He graduated from Kansas State University in 1994 with degrees in French, English, and Anthropology, and received both his M.A. and Ph.D. in Anthropology from the University of Michigan, where he studied under Milford Wolpoff. He is currently working on a textbook on the origins of modern humans in their evolutionary context. Hawks has already been a guest on Unsupervised Learning three times. Chris Stringer is affiliated with the Natural History Museum in London. Stringer is the author of African Exodus. The Origins of Modern Humanity, Lone Survivors: How We Came to Be the Only Humans on Earth and Homo Britannicus - The Incredible Story of Human Life in Britain. A proponent since the 1970's of the recent African origin of modern humans, he has also for decades been at the center of debates around our species' relationship to Neanderthals. In the 1980's, with the rise to prominence of the molecular model of "mtDNA Eve," Stringer came to the fore as a paleoanthropological voice lending support to the genetic insights that pointed to our African origins. Trained as an anatomist, Stringer asserted that the fossil evidence was in alignment with the mtDNA phylogenies, a contention that has been broadly confirmed over the last five decades. Razib, Hawks and Stringer discuss the latest work that has come out of Yuxian, China, and how it updates our understanding of human morphological diversity, and integrate it with the newest findings about Denisovans from whole genome sequencing. They talk about how we exist at a junction, with more and more data, but theories that are becoming more and more rickety in terms of explaining the patterns we see. Hawks talks about the skewing effect of selection on phylogenetic trees, while Stringer addresses the complexity of the fossil record in East Asia.

This week on Unsupervised Learning, Jacob Effron is joined by Jordan Schneider, host of China Talk, who challenges widespread assumptions about US-China AI competition. China's AI development is driven by private capital and market competition—not central government planning—with companies like DeepSeek, Alibaba, and ByteDance operating more like Silicon Valley startups than state projects. The critical bottleneck is compute: the West maintains a 10-15x advantage in advanced chips, and US export controls implemented one month before ChatGPT created a structural edge favoring America for years. Chinese companies aggressively open-source models from strategic necessity—they couldn't establish a quality gap justifying paid access like OpenAI. Jordan explains why the "Goldilocks strategy" of controlled chip dependency fails, why expert consensus opposes selling advanced semiconductors to China despite Nvidia's lobbying, and how Taiwan's invasion risk is driven more by domestic politics than AGI scenarios. China's real advantage may emerge in robotics manufacturing at scale, where they're already deploying while the US debates strategy. Inside the Politburo's AI Study Session: https://www.chinatalk.media/p/xi-takes-an-ai-masterclassSubmit your questions to Jacob here: https://docs.google.com/forms/d/1vHBYv0bTT_EgFWTjbKnLr_sn3pZnFmcFGWYVTltKEco/edit (0:00) Intro(1:45) The Chinese AI Ecosystem: Pre and Post ChatGPT(3:45) Government Influence and Private Sector Dynamics(6:40) Venture Funding and Major Players(8:36) Talent and International Collaboration(11:25) Open Source Models and Market Dynamics(15:24) What Role Does The Chinese Government Play?(31:17) US-China AI Policy and Strategic Competition(36:18) The Argument for Selling AI Accelerators(37:02) Risks of Not Selling to China(43:34) Technological Constraints and Huawei's Challenges(51:18) US-China Relations and Taiwan(1:02:46) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

The real disruption isn't AI replacing humans, it's the shocking possibility that human labor was the economic bubble all along. In this episode, Ron Eddings sits down with Daniel Miessler, founder of Unsupervised Learning and longtime security leader, to break open why companies are hitting record profits with shrinking workforces, and what that means for your future. Daniel shares how AI agents, context management, and his Telos problem-first framework are reshaping what it means to create value in the modern economy. From Apple to Human 3.0, Daniel explains why building in public, learning fast, and solving real problems are the ultimate career edge in an AI-powered world. Impactful Moments: 00:00 - Introduction 02:00 - Jobless profit boom accelerates 05:00 - Daniel's AI journey at Apple 08:00 - Building careers around problems 12:00 - AI bubble or timing problem 15:00 - Nine-year-old codes app in two hours 18:00 - Human labor is the bubble 22:00 - Context management changes everything 26:00 - Adaptation equals survival Links: Daniel's Website: danielmiessler.com/ Daniel's Github: https://github.com/danielmiessler/ Daniel's LinkedIn: https://www.linkedin.com/in/danielmiessler/ Upcoming events: https://www.hackervalley.com/livestreams Love Hacker Valley Studio? Pick up some swag: https://store.hackervalley.com Continue the conversation by joining our Discord: https://hackervalley.com/discord Become a sponsor of the show to amplify your brand: https://hackervalley.com/work-with-us/ Join our creative mastermind and stand out as a cybersecurity professional: https://www.patreon.com/hackervalleystudio  

On this episode of Unsupervised Learning, Razib talks to Alexander Cortes. Cortes is a trainer, fitness influencer and entrepreneur. He is the co-founder, along with his wife, of Ferta, a company that aims to "optimize your reproductive health and conceive naturally." Born and raised in California, Cortes began his career in the fitness industry as a personal trainer in 2010. Over the next few years he expanded his efforts online, writing about fitness and nutrition from a science-informed perspective. Cortes developed a following by offering practical advice on strength training, muscle building, and the psychological aspects of fitness to the interested general public, translating the wisdom-of-the-gym for the person on the street. In the first part of the podcast, Razib and Cortes talk about "broscience," and how it differs from "quantified self" and other movements geared toward self-optimization. They discuss how "bros" arrived on the importance and utility of peptides long before the ozempic revolution, and how the iterative and experimental methods of gym-addicted amateurs predated and anticipated what would later become conventional wisdom. Razib also explores how Cortes' particular style of broscience differs from that of others, with its stronger empirical basis and analytical orientation (and aversion to fads like "raw food"). They discuss the "peptide revolution" and how online fitness and health influencers discovered it earlier, the utility of the macromolecules in health and wellness, and what the online community discovered already that is likely to come down the clinical pipeline. In the second part of the discussion, Cortes introduces his new company, Ferta, and its situates its position in the fertility space. He explains the origin of his firm as he and his wife began to attempt to conceive in their 30s, and how difficult or easy the process was conditional on the optimizations they engaged in. Cortes explains many people struggle because they do things wrong, and don't maximize their chances by being healthy and fertile.

Recently, the new embryo-selection start-up Herasight has been in the news, finally coming out of stealth. Part of the buzz is because of the public involvement of well-known geneticists and academics like Alex Young and Joe Pickrell in Herasight's algorithm development. Additionally, Noor Siddiqi, the CEO of Orchid, a competitor to Herasight (and onetime advertiser on this podcast), was a guest on Ross Douthat's show Interesting Times, triggering another round of conversations around embryo-selection, including in The Wall Street Journal and Breaking Points. To hash out some opposing viewpoints, Unsupervised Learning decided to bring on two guests that stake out very different positions, Dr. James Lee, a psychometrician and behavior geneticist at the University of Minnesota, and Dr. Jonathan Anomaly, a philosopher and Herasight's sales lead. Lee has been on the record with his skepticism of reproductive technology, writing an op-ed in The Wall Street Journal four years ago warning against the consequences of polygenic embryo selection. Meanwhile, Anomaly's last book was Creating Future People: The Science and Ethics of Genetic Enhancement, where he advances the idea that such technologies will unlock human potential.

Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8This week on Unsupervised Learning, Jacob sits down with Nicole Brichtova and Oliver Wang, the Google researchers behind "Nano Banana" - the breakthrough AI image model that achieved unprecedented character consistency and took over social media.The conversation covers how their model fits into creative workflows, why we're still in the early innings of image AI development despite impressive current capabilities, and how image and video generation are converging toward unified models. They also share honest perspectives on current limitations, safety approaches, and why the expectation of going from prompt to production-ready content is fundamentally overhyped.(0:00) Intro(1:42) Early Nano Banana Use Cases and Character Consistency(3:05) Popular Features and User Requests(3:54) Future Frontiers in Image Models(5:26) Personalization and Aesthetic Models(7:39) Model Success and User Engagement(10:59) Product Design for Different Users(19:30) Advanced Use Cases and Future Workflows(23:14) Editing Workflows and Chatbots(25:14) Google's Image Model Applications(27:12) Milestones in Image Generation(29:30) MidJourney's Success(30:54) Future of Image Models(33:55) Image Models vs. Video Models(36:35) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

Bret Taylor is the CEO of Sierra and Chairman of the Board of OpenAI. He previously served as co-CEO of Salesforce. I sat down with Bret to explore how the AI revolution compares to previous platform shifts and what it means for both startups and incumbents navigating this transition. (00:00) Introduction and Recent Milestone (00:38) AI Market and Historical Comparisons (02:30) Competitive Landscape and Business Models (06:02) Outcome-Based Pricing and Value Creation (13:52) Technological Shifts and Business Transitions (26:32) Adoption Challenges and Forward Deployed Engineering (37:21) Early Investment in Snowflake and Cloud Strategy (38:02) Enterprise Software Market Dynamics (38:38) AI Agents and Implementation Costs (41:06) Democratization of Software Development (43:35) The Future of Software Companies and AI Agents (49:36) Consumer Behavior and AI Agents (58:56) The Role of AI in Customer Experience (01:01:25) Career Advice in the Age of AI Executive Producer: Rashad Assir Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

On last week's episode of Unsupervised Learning, Razib spoke with Alex Nowrestah, a vice president at the Cato Institute and a strong advocate for expanding legal immigration. This week, he turned to the other side of the debate with Jason Richwhine, a resident scholar at the Center for Immigration Studies and a vocal supporter of sharply reducing immigration. Richwine earned undergraduate degrees in mathematics and political science from American University, and later a Ph.D. in public policy from Harvard. Before joining CIS, he served as deputy director of the National Institute of Standards and Technology and worked as a senior policy analyst at the Heritage Foundation. The conversation begins with an overview of the dramatic swings in U.S. immigration policy under Biden and Trump. Both note the surge of the foreign-born population in the early 2020s, with the unauthorized share now estimated at 15-16 million. Richwine faults Biden for lax border enforcement and the abuse of parole programs, and points to the comparative effectiveness of Trump's Remain in Mexico policy. He also presses the case for a moratorium, arguing that even legal immigration must be scaled back to sustainable levels. Razib and Richwine weigh the economic and cultural consequences of high-skilled immigration and close by considering whether meaningful reform is politically possible in the years ahead.

Logan is joined by Marc Benioff, the legendary co-founder and CEO of Salesforce, for a wide-ranging conversation on the rise of AI in enterprises. Marc explains how Salesforce has become the testing ground for its own “agentic” technology, using AI agents to handle customer support, boost sales, and transform marketing. He also shares his perspective on what's hype vs. reality in the AI race, the opportunities for startups, and why the future is about humans and agents working together. (00:00) Introduction and Salesforce's Lead Management (00:35) Reflecting on the Last Eight Months (01:14) The Impact of AI on Salesforce Operations (02:15) AI's Role in Customer Support and Sales (03:45) Salesforce's Vision for an Agentic Enterprise (05:00) Public Market Sentiment and AI Adoption (06:15) Salesforce's Data and Application Foundations (08:13) The Future of CRM and ITSM Markets (12:57) Managing Agents and Human Workers (17:45) Salesforce's Growth and AI Product Line (19:38) Pricing Models and Customer Success Stories (23:26) The Role of AI in Different Market Segments (28:51) Salesforce Ventures and Startup Investments (36:05) Advice for Young Professionals and Future Trends (41:04) Dreamforce Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Today on Unsupervised Learning, Razib talks to John Hawks, a paleoanthropologist who has been a researcher and commentator in human evolutionary biology and paleoanthropology for over two decades. With a widely read weblog (now on Substack), a book on Homo naledi, and highly cited scientific papers, Hawks is an essential voice in understanding the origins of our species. He graduated from Kansas State University in 1994 with degrees in French, English, and Anthropology, and received both his M.A. and Ph.D. in Anthropology from the University of Michigan, where he studied under Milford Wolpoff. He is currently working on a textbook on the origins of modern humans in their evolutionary context. Hawks has already been a guest on Unsupervised Learning three times. In this episode, Razib and Hawks focus on a very specific question: What were the different contributions to the heritage of modern humans in a world more than 200,000 years ago that was inhabited by at least half a dozen hominin species? First, Hawks takes us back to the year 2000 and his early work extending a more multiregional framework of human evolution, exploring what could be gleaned from the archaeological and paleontological record. Then Razib and Hawks discuss the ancient DNA revolution and the discovery that modern humans had ancestry from Neanderthals, as well as from an entirely new species, the Denisovans. They also examine the fact that, unlike Neanderthals, Denisovans appear to have been separated into very different regional populations that made distinct contributions to various modern populations. Razib also asks Hawks about the discovery of new pygmy human species in Luzon, as well as the current state of research on Homo naledi in South Africa and the Hobbits of Flores. Hawks contends that DNA will likely be extracted from all these lineages at some point and, if not, protein sequence data may be obtained. This would finally give researchers the statistical power to evaluate the possibility of extremely archaic admixture events. Hawks and Razib also address the potential role of natural selection driven by introgressed genes from sister lineages of humans and how this shaped modern variation.

In this episode of Unsupervised Learning, I sit down with Michael Brown, Principal Security Engineer at Trail of Bits, to dive deep into the design and lessons learned from the AI Cyber Challenge (AIxCC). Michael led the team behind Buttercup, an AI-driven system that secured 2nd place overall. We discuss: -The design philosophy behind Buttercup and how it blended deterministic systems with AI/ML -Why modular architectures and “best of both worlds” approaches outperform pure LLM-heavy -designs -How large language models performed in patch generation and fuzzing support -The risks of compounding errors in AI pipelines — and how to avoid them -Broader lessons for applying AI in cybersecurity and beyond If you’re interested in AI, security engineering, or system design at scale, this conversation breaks down what worked, what didn’t, and where the field is heading. Subscribe to the newsletter at:https://danielmiessler.com/subscribe Join the UL community at:https://danielmiessler.com/upgrade Follow on X:https://x.com/danielmiessler Follow on LinkedIn:https://www.linkedin.com/in/danielmiesslerBecome a Member: https://danielmiessler.com/upgradeSee omnystudio.com/listener for privacy information.

On this episode of Unsupervised Learning, in the wake of Elon Musk's xAI Grok chatbot turning anti-Semitic following a recent update, Razib catches up with Nikolai Yakovenko about the state of AI in the summer of 2025. Nearly three years after their first conversations on the topic, the catch up, covering ChatGPT's release and the anticipation of massive macroeconomic transformations driven by automation of knowledge-work. Yakovenko is a former professional poker player and research scientist at Google, Twitter (now X) and Nvidia (now the first $4 trillion company). With more than a decade on the leading edge computer science, Yakovenko has been at the forefront of the large-language-model revolution that was a necessary precursor to the rise of companies like OpenAI, Anthropic and Perplexity, as well as hundreds of smaller startups. Currently, he is the CEO of DeepNewz, an AI-driven news startup that leverages the latest models to retrieve the ground-truth on news-stories. Disclosure: Razib actively uses and recommends the service and is an advisor to the company. Razib and Yakovenko first tackle why Mark Zuckerberg's Meta is offering individual pay packages north of $200 million, poaching some of OpenAI's top individual contributors. Yakovenko observes that it seems Meta is giving up on its open-source Llama project, their competitor to the models that underpin OpenAI and ChatGPT (he also comments that it seems that engineers at xAI are disappointed in the latest version of Grok). Overall, though the pay-packages of AI engineers and researchers are high; there is now a big shakeout as massive companies with the money and engineering researchers pull away from their competitors. Additionally, in terms of cutting-edge models, the US and China are the only two international players (Yakovenko notes parenthetically that Chinese engineers are also the primary labor base of American AI firms). They also discuss how it is notable that almost three years after the beginning of the current booming repeated hype-cycles of artificial intelligence began to crest, we are still no closer to “artificial general intelligence” and the “intelligence super-explosion” that Ray Kurzweil has been predicting for generations. AI is partially behind the rise of companies like Waymo that are on the verge of transforming the economy, but overall, even though AI is still casting around for its killer app, big-tech has fully bought in and believes that the next decade will determine who wins the future.

On this episode of the Unsupervised Learning podcast, Razib welcomes back Ethan Strauss, a writer who has covered sports and culture for the past decade, including in the book The Victory Machine: The Making and Unmaking of the Warriors Dynasty. More recently his writing is to be found at his Substack, House of Strauss, which is notable for offering a candid take on the cross-pollination between broader culture and athletics, notably in the piece Nike's End of Men: Why Nike no longer wants us to Be Like Mike. Strauss and Razib first discuss professional sports and the different representation of various nationalities. Strauss recounts the generational attempt by the NBA to get Chinese representation to gin up a lucrative rivalry, and how it sputtered due to the reality that 1.4 billion Han Chinese seem to have less basketball talent than small nations like Croatia. Razib also asks about how and why baseball is popular in parts of Latin America and East Asia, and why there are so many more Dominicans in MLB than Mexicans. Strauss says differences between populations are so obvious in sports there's no need for complex social explanations. Then they explore the role of DEI in professional sports, and especially the NBA, and how it might be impacting decisions in the league. They recall the years around 2020, when a drive for minority representation, and in particular of blacks, was prevalent across the corporate world, and how thatimpacted professional sports. Strauss then offers his theory for why the Dallas Mavericks inexplicably traded away a potentially generational talent, Luka Dončić, and Mark Cuban's role in the choice. Finally, he highlights the racism that Jeremy Lin, one of the few Asian American stars in the 2010's, faced from fellow players.

Send us a textToday's episode explores the letter "U" in our ABCs of AI series, representing both yoU (our curious young listeners) and Unsupervised Learning. We break down how artificial intelligence systems can organize photos, music, and data by identifying similarities without being explicitly told what to look for. Through our "Sort It Like a Robot" activity, kids can experience firsthand how machines approach pattern recognition by sorting household objects and discussing the different ways things can be categorized.But beyond the technical concepts, we emphasize something crucial: despite all the amazing capabilities of AI, human qualities remain irreplaceable. Your feelings, imagination, creativity, and kindness are superpowers that no algorithm can duplicate. We discuss why it's essential to have "humans in the loop" checking AI's work, especially when machines might miss context or make incorrect assumptions based on limited information.Whether you're a tech-savvy kid or a parent looking to help your child navigate our increasingly AI-driven world, this episode offers accessible explanations and a fun hands-on activity that brings abstract concepts to life. Subscribe to AI for Kids, have your parents sign up for our newsletter at www.aidigitales.com/newsletter, and join us as we continue our journey through the ABCs of artificial intelligence!Sign up for the weekly newsletter here to get up to date news on AI for Kids: https://aidigitales.com/newsletterSupport the showHelp us become the #1 podcast for AI for Kids.Buy our new book "Let Kids Be Kids, Not Robots!: Embracing Childhood in an Age of AI"Social Media & Contact: Website: www.aidigitales.com Email: contact@aidigitales.com Follow Us: Instagram, YouTube Gift or get our books on Amazon or Free AI Worksheets Listen, rate, and subscribe! Stay updated with our latest episodes by subscribing to AI for Kids on your favorite podcast platform. Apple Podcasts Amazon Music Spotify YouTube Other Like our content, subscribe or feel free to donate to our Patreon here: patreon.com/AiDigiTales...

Chris Degnan is one of the most legendary CROs of this generation. He joined Snowflake as employee #13 and the 1st sales hire. He scaled the sales org from 0 to over $3B in ARR, spanned four CEOs, and retired as CRO after 11 years. In his first podcast post-retirement, Chris opened his CRO playbook, from early enablement to hiring rigor and fending off threats from competitors. He also reflects on lessons from working with leaders like Frank Slootman, John McMahon, and Sridhar Ramaswamy. If you're a founder or running sales at a startup, this one is for you. (00:00) Introduction to Chris's Journey at Snowflake (01:47) Navigating Leadership Changes (04:39) The Importance of Sales Methodology and Enablement (10:22) Near-Death Experiences and Company Resilience (13:39) Building a Strong Sales Organization (27:25) Hiring and Scaling the Sales Team (34:52) Board Dynamics and Mentorship (44:29) The Influence of John McMahon (46:22) Leadership Styles and Intuition (46:56) Launching Snowflake Japan (49:39) Learning from Leaders (55:10) The Importance of Competitive Moats (59:12) Snowflake vs. Databricks (01:07:45) Public vs. Private Markets (01:14:03) Sales and Marketing Synergy (01:26:17) Final Thoughts and Future Plans Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Logan sits down with Bipul Sinha, CEO and co-founder of Rubrik and former VC at Lightspeed and Blumberg Capital. Bipul shares what he learned transitioning from investor to founder, why intuition beats expertise, and how he built Rubrik into a category-defining business by betting on uncool ideas. They talk product-market fit in the AI era, what most VCs get wrong today, and why the enterprise IT market is still just getting started. It's a conversation packed with hard-earned wisdom and bold takes on building lasting companies. (00:00) Intro (01:42) Transitioning from VC to Founder (02:27) The Genesis of Rubrik (03:30) Navigating Uncertainty in Business (06:57) Product Market Fit and Early Success (08:56) Evolving with the Market (13:14) AI and Data Security (18:53) Leadership and Intuition (28:34) Building a Transparent Culture (31:52) Handling Tough Questions in Board Meetings (33:28) Changing Perspectives Over Time (34:57) Traits of Successful Entrepreneurs (36:46) The Future of Venture Capital and Startups (40:38) Balancing Forward and Lateral Motion in Business (42:35) The Impact of AI on Various Industries (01:00:28) The Evolution of Work and Technology (01:02:52) Fostering a Collaborative Company Culture (01:04:56) Looking Ahead: The Future of Rubrik Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Rick Smith (CEO, Axon) joined Logan to share the 30-year journey of building a nearly $50B public company behind the TASER, police body cameras, and now AI-powered tools like Draft One. He talks about taking Axon public in the early 2000s, navigating intense public scrutiny, and evolving from a controversial hardware startup into a software and AI pioneer. Rick also reflects on leadership lessons, regulatory battles, and his long-term mission to make the bullet obsolete. It's a candid and compelling conversation with one of the most unconventional founders in tech. (00:00) Intro (01:31) Axon: Reducing Violence Through Technology (02:12) The Evolution of Axon: From Taser to Body Cameras (04:56) Challenges and Triumphs: Going Public and Beyond (07:17) The Impact of Ferguson and the Rise of Body Cameras (11:16) Navigating Cultural and Business Shifts (17:04) The Role of AI and Future Innovations (25:26) The Taser: Technology and Purpose (34:17) Making the Bullet Obsolete: Future of Law Enforcement (37:10) Consumer Market Evolution (37:59) Proving Taser's Viability (40:17) Targeting Gun Owners (41:45) Taser-Related Deaths and Media Perception (48:07) Employee Taser Experience (50:59) Impact of Body Cameras (52:43) AI Innovations in Law Enforcement (56:15) Challenges in Product Development (01:04:27) Regulatory Hurdles (01:11:31) Leadership and Company Culture (01:14:58) Future Vision for Axon Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

  On this episode of Unsupervised Learning, Razib talks to Bo Winegard and Noah Carl, the editors behind the online publication Aporia Magazine, founded in 2022. Winegard and Carl are both former academics. Winegard has a social psychology Ph.D. from Florida State University, and was an assistant professor at Marietta College. He was an editor at Quillette before moving to Aporia. Carl earned his Ph.D. in sociology from Oxford University. He was a research fellow at St. Edmund's College, Cambridge, before becoming a contributor to The Daily Skeptic and UnHerd, and a managing editor at Aporia. First, Razib asks Winegard and Carl about their respective cancellations, and the recent attacks on Aporia from the British media in particular. Winegard observes that many of the criticisms were muddled, as journalists struggled to get basic facts straight about who did what, as well as mixing up present associations among various editors with past ones. The two also address the change in the culture over the last few years, as cancellations seem to have lost some of their bite. Then Razib asks Winegard about the perception that Aporia is fixated on the third-rail of American culture: race and IQ, and its relevance to social policy and politics. Winegard talks about how he has long since said everything he has to say on the topic, but he still finds that the public conversation fails to address the possibility of cognitive differences between populations, and so keeps finding himself wading back in, to fill a gap in the discourse. Razib also asks the editors about their view of “cold winters theory,” which attempts to explain the higher IQs of temperate zone populations versus tropical ones. Then they discuss the disappointments of the MAGA movement, and its appeal to populist emotion. Winegard had hoped that despite its inchoate nature, it might have been able to pare back the radical excesses of the progressive cultural changes of the 2010's, but now he worries that overreach may up the chances that woke policies make a comeback with the inevitable political backlash in the next few years. Winegard also addresses his personal souring on reflexive anti-wokism, and Carl shares his own views from across the Atlantic, where Britain appears to follow in the US' footsteps, even if from an entirely different social-historical context. Winegard discusses the difficulties of maintaining a consistent heterodoxy in the face of tribalistic demands for conformity. Finally, they discuss the path forward for publications like Aporia that do not toe any particular party line.

  Today Razib talks to Tim Lee, a previous guest on Unsupervised Learning. Lee hosts Understanding AI. Lee covered tech more generally for a decade for Washington Post, Ars Technica, and Vox.com. He has a master's degree in computer science from Princeton. Lee writes extensively about general AI issues, from Deep Research's capabilities to the state of large language models. But one of the major areas he has focused on is self-driving cars. With expansion of Waymo to Austin, and this June's debut of Tesla's robotaxis, Razib wanted to talk to Lee about the state of the industry. They discuss the controversies relating to safety and self-driving cars. Is it true, as some research suggests, that Waymo and self-driving cars are safer than human-driven cars? What about the accidents Waymos have been implicated in? Is it true that they were actually due to human error and recklessness, rather than the self-driving cars themselves? Lee also contrasts the different companies' strategies in the sector, from Waymo to Zoox to Tesla. Razib also asks him about the fact that self-driving cars' imminent arrival seems to have been overhyped five years ago, with Andrew Yang predicting trucker mass unemployment, to the reality that Waymo has now surpassed Lyft in ride volume in San Francisco. They also discuss the limitations of self-driving cars in terms of their ability to navigate cities and regions where snow might be a major impediment, and why there has been a delay in their expansion to freeway routes.

Logan sits down with Jeffrey Katzenberg, Hollywood legend and co-founder of DreamWorks, and Sujay Jaswa, former CFO of Dropbox - together, the duo behind WndrCo. They talk about building enduring companies, bridging tech and media, and what makes a great CEO partnership. The conversation also touches on storytelling as a business superpower and lessons from scaling at different stages. Whether you're a founder or a media nerd, there's something here for you. (00:00) Intro (04:26) The Genesis of the Partnership (13:06) Building and Investing in Companies (20:27) The Team and Their Roles (26:52) Decision-Making Process (33:25) Balancing Dreams and Skepticism (35:06) The Dynamics of Partnerships (37:25) Transitioning to Tech (38:45) Cultural Differences in Industries (41:26) The Value of Failure and Success (44:37) Excitement in Emerging Technologies (48:23) The Venture Capital Game (56:42) The Dropbox Talent Network (01:01:20) AI's Impact on Media and Creativity (01:06:18) Transitioning to CG Animation at DreamWorks (01:08:39) Embracing Change in the Intelligence Revolution (01:11:52) The Role of AI in Enhancing Productivity (01:14:11) Building a Consumer Cybersecurity Business (01:23:49) The Mission to Protect Children Online (01:35:17) Reflections on Partnership and Innovation Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Sholto Douglas, a Member of Technical Staff at Anthropic, joined Unsupervised Learning to break down why coding is the clearest early signal of model progress, how AI agents are already accelerating research, and what it'll take to unlock real-world breakthroughs in fields like biology and robotics. (0:00) Intro(0:48) Claude 4(1:30) Capabilities and Improvements(2:29) Practical Applications and Advice(3:04) Future of AI in Coding(4:38) Managing Multiple AI Models(11:20) The Barrier to Agents is Reliability(16:35) Agents Conducting Research(19:54) Impact of Models on World GDP(25:14) Most Important Metrics in Model Improvement(29:53) Stories of Model Creativity(32:45) How Often Will New Models Be Shipped in the Future?(39:51) Day-to-Day Work of AI Researchers(46:46) The Future of AI and Society(51:26) Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

In this episode, Logan is joined by Zach Weinberg (Co-Founder/CEO @ Curie.Bio) and Derek Thompson (writer at The Atlantic) for a candid discussion on the state of U.S. healthcare and scientific progress. They unpack what went right, and wrong, with COVID vaccine policy, the public backlash against mRNA technology, and the ripple effects on trust in science. The conversation also dives into the real reasons behind NIH budget cuts, the economics of drug discovery, and the business incentives in medical R&D. It's a sharp, thought-provoking look at the intersection of policy, innovation, and public perception. (00:00) Introduction to Drug Pricing in the US (00:23) Broad Healthcare Topics and Open-Ended Discussion (02:37) COVID-19 Vaccines: Successes and Public Perception (06:21) The Evolution of COVID-19 and Vaccine Efficacy (07:59) Public Policy and Vaccine Mandates (13:10) Impact of School Closures and Public Sentiment (19:23) NIH Funding and the Importance of Basic Research (25:04) Challenges in Science Funding and Public Perception (35:19) Government vs. Private Investment in Science (36:40) Operation Warp Speed: A Case Study (39:07) Antibiotic Resistance Crisis (43:22) The Drug Pricing Debate (44:05) Challenges in Drug Discovery (54:06) Regulatory Hurdles in Medical R&D (58:06) The Future of Drug Development (01:04:19) Concluding Thoughts Executive Producer: Rashad Assir Producer: Leah Clapper Mixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Yotam Segev is the co-founder and CEO of Cyera, one of the fastest-growing cybersecurity startups in the world. In this episode, he joins Logan to talk about scaling Cyera from 100 to 550 employees in under two years, what it takes to operate at that speed, and why going slow can actually be riskier. They cover lessons from a tough go-to-market year, the emotional conviction behind choosing data security, and how Yotam thinks about platform expansion, hiring, and staying close to customers. It's a candid look at the mindset and mechanics behind building an elite security company at breakneck pace.(00:00) Intro(01:23) Yotam's Journey in Cybersecurity(02:30) Scaling a Company with Core Values(05:02) Founding Cyera: From Military to Startup(07:59) Entering the Venture Ecosystem(18:19) Early Challenges and Lessons Learned(22:36) Achieving Product-Market Fit(33:01) Ambitious Goals and Rapid Growth(37:39) The Future of Cybersecurity(39:07) The Cybersecurity Paradigm Shift(39:47) Entrepreneurship and Innovation in Cybersecurity(40:25) The Cat and Mouse Game of Cybersecurity(42:47) Traits of Effective CISOs(43:55) Expanding the Cybersecurity Platform(52:36) The Role of AI in Cybersecurity(01:03:25) The Impact of the October 2023 Attack on Israel(01:08:27) Leadership and Company Culture at Cyera(01:12:33) Reflections on Success and Future Goals(01:21:37) Fundraising and Partnerships(01:26:07) Hiring and Company GrowthExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

When Khan Academy launched Khanmigo, Salman Khan thought they might reach 100k users by 2025. Today, they're at 1.4 million. 

Blake Scholl, founder and CEO of Boom Supersonic, is leading the boldest effort in decades to bring back commercial supersonic flight—this time with product-market fit.We talk about what went wrong with the world's first try at supersonic commercial aircraft (launched in the 70s), why Boeing hasn't introduced a new plane in over a decade, and how Blake's startup is building a jet that flies 2x faster than today's aircraft—without the sonic boom. This episode is a crash course in engineering ambition, regulatory dysfunction, and what it takes to defy gravity and incumbents.(00:00) Intro(00:40) The History and Evolution of Aviation(01:12) The Rise and Fall of Concorde(05:25) The Impact of Government and Founders on Innovation(08:57) Regulatory Challenges and Business Models(26:53) Boom's Vision for Supersonic Travel(47:10) Building Trust with Regulators(48:16) Challenges in the Aerospace Startup(49:36) Recruiting Talent from Unlikely Places(55:47) The Importance of Mission Success Events(01:01:52) Developing a Custom Jet Engine(01:22:54) Reindustrialization and Economic Strategy(01:34:42) Conclusion and Final ThoughtsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

College sports are going through massive changes—from athlete pay drama to superconference realignment and transfer portal chaos, not to mention the giant class action lawsuit playing out now.Matt Brown, the publisher behind Extra Points and one of the top experts on the business of college athletics, joined the show to break it all down. We walked through the full history of college sports, the current money dynamics, and where things could be headed. (00:00) Meet Matt Brown: Expert in College Sports Business(03:09) The Origins of College Sports(06:31) The Evolution of College Sports Broadcasting(14:53) Title IX and Its Impact on College Athletics(17:53) The 1984 Supreme Court Decision and Its Aftermath(20:03) The SMU Death Penalty Scandal(22:19) Conference Realignment and the BCS Era(28:22) The Rise of Conference Television Networks(30:23) The Arms Race in College Sports Facilities(34:41) The Role of Boosters in College Sports(36:03) Financial Breakdown of Major College Sports Programs(37:04) Understanding Nonprofit Accounting in College Athletics(38:20) Revenue Generation in College Sports(40:34) Athletics as Enrollment Management(42:04) The Flutie Effect and University Applications(44:37) Conference Realignment and Financial Instability(48:58) The O'Bannon Case and Video Game Licensing(53:59) The Northwestern Unionization Attempt(58:19) The Alston Case and Educational Awards(01:02:11) Name, Image, and Likeness (NIL) Marketplaces(01:05:51) The Role of Collectives in College Sports(01:12:08) Dependability of Young Campaign Partners(01:13:03) Transfer Portal and Its Impact(01:15:56) Rise of NIL Agents and Handlers(01:17:40) Economic Incentives and Transfer Market(01:20:37) Challenges in NIL Enforcement(01:22:48) House Settlement and Future Implications(01:25:38) Allocation of NIL Funds by Universities(01:44:26) Potential Super Leagues and Investment Challenges(01:48:07) Concluding Thoughts on College SportsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Aidan joined this week's Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what's breaking in the foundation model stack. If you're building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere's agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they've made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea) (0:00) Intro(0:32) Enterprise AI(3:23) Custom Integrations and Future of AI Agents(4:33) Enterprise Use Cases for Gen AI(7:02) The Importance of Reasoning in AI Models(10:38) Custom Models and Synthetic Data(17:48) Cohere's Approach to AI Applications(23:24) Future Use Cases and Market Fit(27:11) Building a Unified Automation Platform(27:34) Strategic Decisions in the AI Journey(29:19) International Partnerships and Language Models(31:05) Future of Foundation Models(32:27) AI in Specialized Domains(34:40) Challenges in Data Integration(35:06) Emerging Foundation Model Companies(35:31) Technological Frontiers and Architectures(37:29) Scaling Hypothesis and Model Capabilities(42:26) AI Research Culture and Team Building(44:39) Future of AI and Societal Impact(48:31) Addressing AI Risks With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

In this episode, Derek Thompson (Writer, The Atlantic) delves into the tumultuous nature of Trump's trade policies, especially regarding tariffs, and how they impact American manufacturing and global markets. They discuss the constant changes in policy, the resulting uncertainty for industries like automotive and aerospace, and the mismatch between Trump's ‘madman strategy' and effective industrial policy. The conversation also explores the broader economic consequences, including stock market volatility, housing affordability issues, and the role of government in promoting economic growth and innovation.(00:00) Intro(00:20) Trump's Trade Policy and Its Implications(01:30) The Uncertainty of Tariff Policies(02:12) Impact on American Manufacturing(05:15) Stock Market Reactions(07:00) Debating the Effectiveness of Tariffs(10:02) Wall Street vs. Main Street(18:44) Housing and Healthcare Challenges(34:53) Historical Context of Housing Regulations(41:48) The Reality of Construction Jobs(42:35) The American Dream and Housing Costs(42:57) The 30-Year Mortgage and Its Impact(43:48) Comparing Home Ownership to Stock Market Investments(45:14) Political Reception of the Book 'Abundance'(46:17) Pro-Business Democrats and Government's Role(48:38) The Need for Aggressive Democratic Leaders(51:18) The Importance of Economic Growth(01:01:26) Debating Government's Role in Industrial Policy(01:03:34) Challenges in the Semiconductor Industry(01:13:19) The Housing Problem in New York City(01:15:26) Conclusion and Final ThoughtsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Debate between Keith Rabois and Zach Weinberg on what tariffs are actually trying to accomplish. One core theme: Tariffs aren't fully about “bringing back factories,” but rather a negotiation tool to eliminate foreign trade barriers - ultimately aiming to increase free trade, not restrict it.We also got into:- What each of them would do if they were in charge- Whether the trade deficit is a meaningful metric or just a misunderstood talking point- If tariffs could be part of an initiative to replace income tax — shifting toward a more consumption-based tax system- If tariffs could successfully be used as a non-military tool to reduce drug supply to the US- If there's a major disconnect between the new administration's rhetoric and the actual economic goals behind the policyOne of the deepest economic conversations from the show's recent history — and a rare debate where both sides had real logic behind their views.(00:00) Introduction and Host's Biases(00:46) Keith's Perspective on Tariffs(03:05) Zach's Perspective and Clarifying Questions(05:14) Debating Tariff Strategies(07:45) Economic Implications and Free Trade(13:31) Trump's Tariff Policies and Goals(16:57) Global Trade and Protectionism(25:52) Final Thoughts on Tariffs and Trade(29:16) Discussion on Trade Tariffs and Partners(30:17) Impact of Tariffs on GDP and Debt(31:20) Political Coalitions and Trade Policies(32:00) Tariffs as Consumer Taxes(33:30) Debate on Trade Deficit and Tariff Rates(36:53) Regulatory Reforms and Economic Policies(47:25) Fentanyl Crisis and Trade Negotiations(51:06) Closing Remarks and Future TopicsExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

In this freeform episode, Logan sits down with Zach Weinberg (Co-Founder and CEO of Curie.Bio) to break down two of the biggest storylines in tech: tariffs and AI.They banter through the core arguments for and against tariffs, including national security, domestic employment, and negotiation power. Plus, they revisit what's happened in past trade wars and share predictions on the real economic consequences this time around.Logan and Zach also discuss OpenAI's $40B raise and the broader race for AI dominance—can OpenAI maintain its lead against tech giants like Google and Apple? They debate the limits of product defensibility, the power of platform defaults, and the strategic moves OpenAI might need to make to stay ahead.Topics include:The arguments for and against tariffsWhat happened during past U.S. tariff cycles—and how this one comparesWhether OpenAI can maintain its edge in a world of native AI platformsA possible playbook for OpenAI to build user lock-in beyond utilityWhat this era of AI competition means for the U.S.—and what could derail ithttps://fdra.org/wp-content/uploads/2025/03/Trade-War-Lessons-from-the-Past-2025.pdf?utm_source=newsletter&utm_medium=email&utm_campaign=newsletter_axiosmacro&stream=business00:00 Intro01:35 Liberation Day and Global Trade02:13 Freeform Discussion on Various Topics02:44 Podcasting and VC Life03:32 Debating Tariffs and National Security11:26 Arguments Against Tariffs22:19 Historical Context of Tariffs26:58 Economic Predictions and Stagflation33:39 The Forgotten Lessons of Recessions36:02 The Fixed vs. Growth Mindset in Economics37:17 The Democratic Party's Shift on Economic Policies42:33 The Rise of Populism and Its Impact50:28 OpenAI's Explosive Growth and Challenges54:28 The Competitive Landscape of AI58:33 The Future of AI and Consumer Behavior01:07:20 The Role of Social Networking in AI's Future01:10:43 Wildcard: The Role of XAI and GrokExecutive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin HrabovskyCheck out Unsupervised Learning, Redpoint's AI Podcast: / @redpointai

  On this episode of Unsupervised Learning, Razib talks to Graeme Wood. Wood is a staff writer at The Atlantic, where he usually covers geopolitics and international affairs. His work ranges from a profile of Richard Spencer, the American white nationalist public figure with whom he went to high school with, to the Islamic State. He is the author of The Way of the Strangers: Encounters with the Islamic State. Wood grew up in Dallas, Texas, and graduated from Harvard College. He also studied at the American University in Cairo, Indiana University and Deep Springs College. Today Razib talks to Wood about his piece in The Atlantic, Germany's Anti-Extremist Firewall Is Collapsing. Wood addresses the economic malaise of contemporary Germany, in particular, the former East Germany, and how that is impacting the national cultural climate. More concretely, they consider why the right-wing Alternative For Deutschland (AFD) party is so popular, and its transformation from an anti-EU party to an anti-migrant party. Wood emphasizes that Germany has become a highly polarized society when it comes to ethnicities, with very cosmopolitan cities, but small towns in rural eastern provinces where he recalls feeling like possibly the only non-white face at the local beer hall (his father is a white American while his mother is ethnically Chinese). Razib muses whether German multiculturalism as an ideology has allowed for more, not less racism, while Wood reflects on his multi-decade experience visiting the nation as an outsider.

Two weeks ago, OpenAI released its set of tools to help developers build agentic systems. Today on Unsupervised Learning, Nikunj Handa (Product Lead) and Steve Coffey (Eng Lead) answer some of the biggest questions around how developers should be thinking about building in the agentic paradigm in 2025. [0:00] Intro[0:53] OpenAI's Vision for Consumer Interaction[4:51] Building Multi-Agent Systems for Business Solutions[6:53] Challenges and Innovations in AI Fine-Tuning[13:20] Exploring Computer Use Cases and Applications[17:20] Advanced Use Cases and Developer Insights[25:29] Challenges with Context Storage and Chat Completions[26:09] Introducing the Responses API and MCP[27:16] AI Infrastructure Companies and Their Role[29:35] Building the Tools Ecosystem[30:17] Exploring Computer Use Models[31:47] The Future of AI and Developer Tools[38:36] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq'd by VMWare) @jordan_segall - Partner at Redpoint

As tariff drama continues to heat up, Ryan Petersen, CEO of Flexport (one of the hottest freight forwarders in the world) came on the show to unpack the impact. Ryan also dives deep into the hidden world of US shipping, opportunities for AI automation in logistics, reflections on building Flexport, and some supply chain conspiracy theories.(00:00) Intro(01:16) Flexport's Mission and Operations(02:28) Impact of Tariffs on Businesses(05:15) Navigating New Duties and Regulations(09:19) Flexport's Strategic Response(14:39) Challenges in U.S. Shipping Policies(28:21) Union Influence on Port Automation(40:35) National Security and Trade Negotiations(41:06) Tariffs and Business Planning Challenges(42:16) Investment Opportunities in Ports(44:02) Port Automation and AI Integration(45:09) Flexport's Big Tech Launch(47:02) AI's Role in Supply Chain Management(53:14) Digitizing Freight Contracts(58:18) Lessons from Flexport's Growth(01:09:13) Conspiracy Theories in Shipping Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

  On this episode of Unsupervised Learning, friend of the podcast, Charles Murray returns to chat with Razib again. Murray has been a public intellectual and scholar since the 1970's. He is the author of Losing Ground, The Bell Curve, Human Accomplishment, Real Education, Coming Apart and What it means to be a libertarian and Human Diversity, among others. Born in 1943 in Newton, Iowa, Murray has a BA from Harvard, an MA and PhD from MIT, and did a 1960's stint in the Peace Corps in Thailand. He has held positions at the American Institutions for Research, the Manhattan Institute and the American Enterprise Institute. More than four years after their last conversation, and seven years after his official retirement, Murray reflects with Razib on where he sees America going in the next decade, and what has surprised him about the last 25 years. Razib asks what it is like to be a long-standing “Never Trump conservative” and a libertarian in Trump's populist America. They also discuss the end of the “awokening” that began in the mid-2010s, and whether Murray's long exile from notice and acknowledgement from mainstream opinion-leaders and tastemakers is at an end. Murray also addresses the ideological fractures he sees on the right, and how America will deal with the last generation of mass immigration that has altered the US' demographic balance. They also discuss how taboo it still is to talk about group differences in cognitive performance, and whether America will be able to face the reality of demographics and the social consequences thereof in the 21st century.

On this episode of Unsupervised Learning, Razib talks to Titus Techera, a Romanian living in Budapest, but commenting extensively on American and European culture. He is the Executive Director of the American Cinema Foundation, International Coordinator of the National Conservatism Conference and is a primary contributor to the Substack PostModernConservative. Techera also hosts a podcast for the American Cinema Foundation. Razib first talks to Techera about the 2024 Romanian presidential election that was overturned by the courts over accusations of Russian interference. Techera explains the social and cultural context of the candidate initially declared victorious against a backdrop of Romanian society's typical stock characters. Techera also discusses the tension between having a nation-state with a distinctive character and becoming part of the broader EU project that is attempting to forge unity across 27 countries. He then addresses what a “Postmodern Conservative” is in the context of the arts. Perhaps most importantly, PostModern Conservatives take the 20th century and the modernist period seriously; they are not simply reactionaries who want to return to the 19th century. Conservatives who value the arts and culture cannot simply roll the tape back; they have to engage with what has come before. Razib and Techera also consider how inferences from the sciences, like the rejection of the “blank slate,” might influence the arts. They also discuss their disagreements about the latest Dune films, Techera prefers David Lynch's attempt to adapt the book in 1984 to Denis Villeneuve's 2021 version.

Yamini Rangan, CEO of HubSpot (a $40 billion leader in the CRM space) shares how AI is transforming go-to-market strategies, the key lessons Yamini has learned as a first-time CEO, and the sales tactics she's mastered.She also discusses the challenges of navigating major business pivots, including how companies can successfully transition into AI-first businesses and what it takes to stay competitive in an evolving landscape.(00:00) Intro(00:56) Yamini Rangan's Background and Career Journey(02:33) Joining HubSpot and Early Challenges(03:49) Transition to CEO and Leadership Insights(07:33) Strategic Planning and Long-Term Vision(15:15) AI Transformation and Product Innovation(18:57) AI's Impact on CRM and Future Prospects(28:51) Content Strategy and Customer Engagement(37:34) Contextual AI Features for Better Usage(38:13) Human Expectations and AI(39:36) AI in Daily Productivity(42:54) The Art and Science of Sales(51:05) The Role of Curiosity and Resilience in Sales(53:23) Evolving Company Culture(55:27) Leadership Style and Management Lessons(58:27) Scaling Startups: Lessons from Workday(01:02:54) The Future of AI and Incumbents(01:14:10) Concluding Thoughts Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

Andy is the founder of Artisanal Ventures and Artisanal Talent, one of Silicon Valley's top search firms. He's helped build leadership teams at companies like Databricks, Snowflake, Confluent, Abnormal Security, AcuityMD, and many more.In this episode, he shares…- How founders can differentiate in the talent war today- Maximizing the success rate of executive hires- Why interviews are a waste of time- The best ways to do references- How to choose the right search firm& more (00:00) Intro(02:02) Andy Price's Background and Career Journey(03:20) The Role of Founders in Hiring(04:32) Challenges in Early Stage Hiring(10:08) Importance of Venture Capital Brand(12:14) Effective Search Processes and Candidate Evaluation(23:27) Backchannel References and Networking(29:10) Identifying Key Players in Sales Growth(29:44) The Importance of Minimal Disruption(30:40) Effective Founder-Executive Relationships(30:57) The Role of Soak Time in Differentiation(31:52) Hiring Strategies for Rapid Growth(33:42) Common Failure Modes in Hiring(34:32) Aligning Founder and Executive Expectations(38:26) Building a Strong Talent Acquisition Team(40:51) The Talent Wars and Hiring Choke Points(44:24) Balancing Skill Sets and Company Culture(47:29) Evaluating and Upleveling Team Members(49:59) The Importance of Forecasting and Planning(51:34) Handling Executive Transitions Smoothly(59:09) The Art of Firing: Best Practices(59:32) Handling Employee Terminations with Dignity(01:02:19) Negotiating with Candidates: Tips and Tricks(01:06:31) Understanding Compensation Trends(01:08:18) Avoiding Common Founder Mistakes(01:11:28) Scaling Operations in Hypergrowth(01:15:00) Navigating the Current VC and Talent Ecosystem(01:23:34) The Importance of Specialized Search Firms(01:28:03) Adapting to the New Market Realities(01:30:46) Final Thoughts and Reflections Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA

The recent controversy between WordPress and WP Engine put Matt Mullenweg (Co-Founder of WordPress, CEO of Automattic) under intense online scrutiny. In our conversation, he shared lessons from the controversy and managing through crisis, as well as this thoughts on the future of open source AI and more.(00:00) Intro(01:17) Controversy with WP Engine(03:36) Understanding Open Source and Trademarks(04:36) Automattic's Role and Contributions(08:26) Navigating Legal Battles and Community Relations(18:27) Leadership and Personal Resilience(21:49) The Impact of Social Media on CEOs(31:22) Future Outlook and Reflections(32:42) Exploring the Quinn Model and Open Source Innovations(33:17) The Evolution of AI Interfaces and User Interactions(35:36) AI as a Writing and Coding Partner(38:07) The Power of Open Source in AI Development(40:00) Commoditizing Complements: A Business Strategy(41:39) The Battle with Shopify and Open Source Models(42:33) The Impact of Open Source on Market Dynamics(43:55) USB-C Transition and Gadget Recommendations(47:53) The Benefits of Sabbaticals(53:34) The Future of WordPress and Automattic(59:12) Employee Ownership and Liquidity Programs(01:04:33) Conclusion and Final Thoughts Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA 

Two weeks after Oura CEO Tom Hale started tracking and improving his sleep, he recalls, “It was like walking out of a black-and-white movie into a 4K technicolor movie… I've been missing this for 30 years.” The experience of feeling 20 again every day inspired him to apply for the Oura CEO role. Now, nearly 3 years into the job, he sat down to discuss what all founders and CEOs should consider when it comes to avoiding burnout and maximizing health and productivity. We covered his experiences with tools like continuous glucose monitoring, his thoughts on the future of wearables, and how AI insights will help us take better control of our health. (00:00) Intro(00:53) The Journey with Oura Ring(01:47) Sleep Optimization and Health Trends(05:06) Behavioral Changes for Better Sleep(09:33) Tom Hale's Professional Background(12:47) Challenges and Opportunities at Oura(22:50) The Importance of Sleep(26:05) Health Benefits of Quality Sleep(28:38) Oura's Unique Position in the Market(36:47) Consumer Choice and Healthcare Disruption(40:59) Unexpected Insights from HSA and FSA Spending(41:36) The Future of Insurance and Wearable Data(44:40) Preventative Care and Employer Incentives(47:21) The Impact of Small Choices on Health(48:52) Artificial Intelligence in Healthcare(54:08) The Role of Continuous Glucose Monitors(59:50) Expanding Oura's Market and Product Strategy(01:12:04) Navigating Leadership and Company Culture(01:19:22) Future Opportunities and Global Expansion(01:23:43) Closing Remarks and Reflections  Executive Producer: Rashad AssirProducer: Leah ClapperMixing and editing: Justin Hrabovsky Check out Unsupervised Learning, Redpoint's AI Podcast: https://www.youtube.com/@UCUl-s_Vp-Kkk_XVyDylNwLA