Podcasts about llm

The PESO Model has been guiding smart communications strategies for over a decade, but the tactical landscape underneath it keeps shifting. In the latest evolution, Gini and her team have completely revamped the PESO Model Certification to address how AI and large language models are fundamentally changing visibility in 2026. In this episode, Chip interviews Gini about the newly updated certification and what’s changed in how organizations should think about paid, earned, shared, and owned media. The conversation centers on “visibility engineering”—the intersection of owned and earned media where LLMs are scraping information and making decisions about who appears in AI-generated answers. Gini explains why owned media remains the foundation (without content on your own properties, there’s nothing to demonstrate to journalists, creators, or LLMs what you’re about), but the recommended path has shifted from owned-then-earned-or-shared to a more deliberate owned-then-earned-then-shared-then-paid sequence. This evolution reflects how AI systems verify information by comparing what’s on your website against what credible third parties say about you. They also tackle the persistent “X is dead” headlines that plague the industry—whether it’s websites, PR, or press releases. Chip and Gini push back hard on the notion that websites are becoming irrelevant, pointing out that your owned content hub becomes more valuable in an AI-driven world, not less. It’s your source of truth, the fuel for custom AI assistants, and the foundation that persists even as social platforms come and go. The conversation covers practical questions about implementing PESO in smaller agencies, whether you need to be full-service to deliver on all four pillars, and how the certification meets communicators at different experience levels—from college students to seasoned professionals. If you’ve been treating PESO as just four columns of tactics rather than an operating system for communications, this episode clarifies what you’re missing. Key takeaways Gini Dietrich: “Owned is still the foundation because without your own thought leadership, your subject matter experts, your content, all of that, there’s nothing to demonstrate to a journalist, a creator, a newsletter author, a podcast host, what you’re about and how you’re different.” Chip Griffin: “In a world where you’re able to start customizing your own versions of LLMs for your internal or external audiences, huge value exists there. So having that central repository, I think is actually of increasing value today, not decreasing.” Gini Dietrich: “We are in a zero click world. And so how does that affect the work that we’re doing? It’s really how are we helping to inform humans, search engines, and LLMs so that we’re showing up no matter if it’s a human looking, if it’s Google surfacing information or if it’s an AI surfacing information.” Chip Griffin: “Having your content in a world where you’re able to start customizing your own versions of LLMs for your internal or external audiences, huge value exists there. That would not be possible without a thousand plus articles and videos because that is the fuel for that tool.” Turn ideas into action Audit where your owned content actually lives. Open a spreadsheet and list every place you’ve published content over the past two years—your website, Medium, Substack, LinkedIn articles, guest posts, anywhere. Mark which platforms you own versus rent. This awareness exercise reveals how vulnerable your content strategy is to platform changes and algorithm shifts. Map one content piece through all four PESO pillars. Take your next webinar, speaking engagement, or major thought leadership piece and plan the full PESO path before you execute: owned content on your site summarizing key insights, pitching earned media opportunities based on those insights, creating social distribution that doesn’t just promote but educates, and identifying where paid amplification makes strategic sense. This forces you to think about PESO as an integrated operating system rather than disconnected tactics. Dive deeper into the PESO Model. Visit spinsucks.com/peso-model-certification to learn more about the newly updated certification program. Whether you’re looking to formalize your team’s approach to integrated communications or simply understand how the model has evolved for the AI era, the certification provides a structured path from foundational concepts through practical implementation. Resources For more on the PESO model, visit the Spin Sucks website Related Agencies need the PESO model now more than ever Has the PESO Model become a necessity for modern agencies? How PR agencies can use the PESO Model to improve client retention How to allocate your client's PESO budget Wake up or get left behind: AI is forcing your hand View Transcript The following is a computer-generated transcript. Please listen to the audio to confirm accuracy. Chip Griffin: Hello, and welcome to another episode of the Agency Leadership Podcast. I’m Chip Griffin. Gini Dietrich: And I’m Gini Dietrich. Chip Griffin: And Gini, I, I’ve heard that you might be involved with this thing, I think it’s called the PESO Model. Gini Dietrich: Oh, maybe. Chip Griffin: You may you use that, right? That’s, yeah. Just you found it and you said this should, this is something we should use. Gini Dietrich: Yeah. Something I just found and thought we should use it. Yeah. Chip Griffin: Yeah. Yeah, no, in all fairness, you are in fact the inventor of the PESO model, which is widely used throughout the PR and communications world, and it has been evolving with the times as we all should be. And so I, I think we have some, some new news that you’ve been sharing around the PESO model. Gini Dietrich: Oh, well, according to a couple of people on the internet, it has not evolved at all because they are not able to use Google or AI to say, has the PESO model evolved since 2014? Perhaps. It has. And you know, all of last year I spent a good amount of time, especially on the blog and the Spin Sucks podcast, talking about visibility engineering, which is where owned and earned media meet because that’s where the LLMs are getting their information, right. We’re finding more and more that they’re scrubbing websites and then they’re comparing that to earned media, to the things that media not, and not just traditional media, newsletters, podcasts, things like that, that they’re saying about the brand and looking to see if they match. And if they do, then they’re appearing. You’re, you start to appear in AI answers. So I spent a good amount of time last year exploring that and understanding that and, you know, using the blog and the podcast as my sandbox to learn more about it and teach the industry about it and understand what was happening. As part of that, I said, okay, it’s time to do a big refresh of the certification. Because we did the certification in 2020 and then we did a small update to it in 2024. And this one is a completely revamped certification that shows you how exactly AI is… how exactly you’re showing up in AI answers and doing that via the PESO model. So we start with owned, we go to earned, then we use shared and paid. There’s integration and measurement and it brings it all together. So I’m actually, I said to my team, not to brag, but this is really good. It’s a really, really good course. And we hired, last March I hired a chief learning officer who has helped me build it into something that’s more effective for an adult learner. So it’s really specific to, you know, you can get the work done while you’re also a working professional. So she has done a really nice job of bringing that element into it. It has AI prompts so that you can use the PESO AI that we built to help you do the work. And it’s, it’s pretty good. I’m, I’m really proud of it. I’m really proud of the work we did. Chip Griffin: Well, I mean, it really is something that, that fuels most communication thinking in smart organizations today, whether that’s agency side, client side, that sort of thing. Now it’s not always as well understood it should be. Some people just throw the term around. A little bit willy-nilly. Yes. You know, without really thinking it through. Of course there are other people who claim that it’s also their invention, which is, you know, but we’re not gonna go down that path ’cause we’re staying positive today, Gini. Gini Dietrich: Yes, yes. We’re gonna stay positive. Positive, yes. Chip Griffin: But I think to, you know, to me, one of the things that, when I look at the PESO model, I think is, you know, it’s great because it is an overall set of principles and framework that is effectively timeless when it comes to communications. And then it’s the implementation side of it. The tactical side of it. That’s the piece that needs to evolve. The, I mean, the four letters are still the same. It’s not like you, right? Yes. The evolution has not been to change PESO to something else. Gini Dietrich: Nope. Chip Griffin: It, it’s really just saying. Okay. All of these different components, the paid, earned, shared, and owned have evolved over the last 10 or 15 years. Yeah. And so how we implement it needs to adapt to that. Gini Dietrich: Yeah. It’s very much, I mean, when we did it in 2020, it was very much like how, how you’re using content marketing really to inform your contributed content through earned and then sharing that link through, through social and then putting some money behind it to boost it. And that was, you know, that was six years ago, and it worked back then, right? It’s still, social still worked from the perspective that you could post a link and people would follow that path back to your website. Well, people don’t do that anymore. You know, we are in a zero click world. And so how do, how does that affect the work that we’re doing? So, you’re right, the paid, earned, shared, and owned doesn’t change. That model stays the same. It’s the pieces on top that, that have evolved. And so now it’s really how are we helping to inform humans, search engines, and LLMs so that we’re showing up. No matter if it’s a human looking, if it’s Google surfacing information or if it’s an AI surfacing information, we show up no matter what. And it’s really, that’s what it’s really about is how do you engineer that visibility? How do you make sure that you’re showing up in the right places at the right time to the right people? Chip Griffin: And so if you’re, if you’re thinking about leaning into the PESO model for your communications needs. You know, where should you be starting today? Is it owned? Is it social? Is it, you know, how, has it changed? If at all from that standpoint over the last decade? Gini Dietrich: Owned is still the foundation because without anything, without your own thought leadership, your subject matter experts, your content, all of that, there’s nothing to demonstrate to a journalist, a creator, a newsletter author, a podcast host, what you’re about and how you’re different. So that’s the foundation. There’s nothing do than to just create that distribution layer through shared, and there’s certainly nothing to amplify through paid. So that’s always been the foundation. There are of course exceptions if you’re selling widgets or your, you have an Amazon store or something like that, then I would probably start with paid, but that’s the exception to the rule. For the most part, most organizations need to start with owned. And we used to say that then you could go to earned or shared. Depending on your goals. Now we’re saying actually the best path for engineering that visibility is owned, then earned because you need that credibility, so the LLMs can cite that information. Then you build your distribution layer, and then you amplify your work. Chip Griffin: So I, think what I’m hearing you say is that websites are not dead despite all of these, you know, headlines that you like to see people’s, Gini Dietrich: No, they are not. Chip Griffin: The, the rise of LLM, websites are dead. You’re not even gonna need a website in five years. Gini Dietrich: No, we still need a website because otherwise the LLMs don’t have anywhere to get the information about you. Humans don’t have any, I mean, we still go to websites. We might not go, you know, a direct click like we used to, but we still go to websites to get information. So yeah, you still need a website. I hate the, such and such is dead. The PR, there’s one that PR is dead right now. Like PR is not dead. Come on. You can’t do, you’re not going to show up in AI answers if without PR. So PR is not dead. Chip Griffin: No, the X is dead has always been one of my pet peeves when it comes to, I mean, that, that really is something that, that took off during the start of the social media era. Yeah. Whether it was the press release is dead. This is dead, whatever. I mean, and, you know. Just, it’s not true. I mean, we, you know, I always used to say back 20 years ago, you know, people used to say that radio was dead. Radio is still very much around, and radio is still around in certain forms. I mean, when I’m driving around, I listen to radio. Yes. Is it terrestrial radio? No, it’s satellite radio. Gini Dietrich: Right. Chip Griffin: But guess what? It’s still radio. Gini Dietrich: It’s still radio. Yep. Chip Griffin: Right. Podcasts are effectively radio. Transmitted in a different fashion. Yep. And so, you know, I think that the people need to understand that the underlying technology may evolve, some of the tools will evolve, but Gini Dietrich: absolutely Chip Griffin: the, principles and concepts will largely remain the same. Doesn’t mean that everything stays. Yeah, certainly some things, you know, do go away, or become so small that they’re irrelevant, but you know, I think we need to be careful about those things. And, to me, with a website, to me, the other value is it still is a great place to be the central repository of all your information as all of these things change around you. I mean, if, for the last 10 or 15 years you’ve been using your website as your content hub and housing at least your most important, most valuable stuff there, it doesn’t matter whether medium or substack comes or goes. It doesn’t matter whether people move from X to LinkedIn to whatever. Yep. You still have a source of truth for your own information, which becomes even more valuable in the world of AI and LLMs. Gini Dietrich: That’s exactly right. I mean, we, have preached for years, we’ve all preached for years that you should not build an audience or content on rented land because to exactly your point, the rented land goes away. X has become something that nobody wants to hang out on. We’ve moved to LinkedIn. Lots of people have moved to Substack. So, those pieces will change. So don’t, I think that theory, philosophy stays the same. Because you have, you are building something that you own, that you control, and allows you to control that narrative and be, tell the story the way you want to, and then you rent that out to other places versus building on rented land where it will go away. Chip Griffin: Well, and I think that there are a lot of avenues that are opening up to organizations with, you know, particularly those that have more content already, but also by building it up. And I think in particular of the AI assistant I built on the SAGA website. Mm-hmm. Yep. That would not be possible without a thousand plus articles and videos and that kind of stuff because that is the fuel for that tool. Yep. And, and if I was trying to do it based off of, see what you can find that I’ve posted on LinkedIn or Twitter or things over the years, and it’s just not gonna work. And so having that in a world where you’re able to start customizing your own versions of LLMs for your internal or external audiences, huge value exists there. So having that central repository, I think is actually of increasing value today, not decreasing. Gini Dietrich: Yeah, that’s actually a really good point. I was talking to a client last week and she said that one of the goals for 2026 is they have 17 different brands. So each brand has its own chief executive. And what she has, what she wants, the comms team for each of those brands to do is build an AI agent that helps them with that CEO’s voice. And they can’t do that without content. They can’t do it without the executives’ speeches, webinars, podcasts, appearances, media relations, like they have to have all of that content, blog posts that they’ve written or articles that they’ve written for the website. They have to have that to be able to feed that and train the AI. So without it, they don’t have any, to your point, fuel that will allow them to do that. So 100% that is accurate. Chip Griffin: So as, we’re thinking about implementing PESO properly, so not just, I heard the term, it sounds cool. I made a list of four columns of each, and I just started just chucking stuff in there. Gini Dietrich: Mm-hmm. Chip Griffin: I mean, how do I go about learning it the right way? And I’m, you know, we’re not turning this into a QVC Gini Dietrich: Are you throwing me a softball? Chip Griffin: you know, show here. But at the same time, I, think it is valuable for people to understand what is out there in a more formal sense, to understand and, adopt the process for their own organization. Gini Dietrich: I mean, obviously the PESO model certification is the place to get the information because one of the, one of the things we see is exactly what you said, that people create their four columns and they say, okay, well we’ve got some content and we’re doing some media relations, and we’re throwing that on social. And all right, we’ll put some, budget behind some of our organic social, and we’ve got the PESO model. And that’s, not the PESO model, that’s a list of tactics. So what the certification does is it walks you through exactly. There’s this, a scientific layer to it. It walks you through that scientific layer that allows you to embed an operating system, that let that foundation of your work so that as things evolve and the industry changes and your business goals change, you’re able to change the tactics on top of it. We also hear, well, gosh, my, you know, my clients can’t afford to do a full PESO program, so what should I do? And in fact, they can afford it. You’re just thinking about it as this huge, overwhelming thing. And so the certification walks you through if you’re a solopreneur or a small agency, that walks you through if you’re a midsize, and it walks you through if you’re a large corporation or an enterprise organization. And I will say for small organizations, which are most of our listeners. It’s really about how do you take one piece of content and repurpose it. So let’s say that you do a webinar. How do you take that webinar and create some content around it that, from what the webinar was, not promoting it, not trying to get registrations, but saying, okay, here’s what we learned in the webinar. So we’re gonna create some how-to or thoughtful content for that. And then we’re gonna take pieces of the webinar and we’re gonna break it down for social posts. And then yeah, we’re gonna put some money behind some of it. And we’re also gonna go to some of our trade media and we’re gonna say, Hey, listen, our subject matter expert or our chief executive just did this webinar and here’s what they talked about. Are you interested in some contributed content? So it allows you to do that in a really interesting, effective way without you having to spend hundreds of thousands of dollars or have a large team. You can do it without a lot of resources. I mean I built the PESO model framework for my agency and we were not, at the time, a big agency. Mm-hmm. So that’s what it was built for, is to make it so that we could do more with less and do more with less resources and, less time and less people and less budget and all the things. So it is definitely, definitely feasible. So that’s what it teaches you how to do. Chip Griffin: So I, you know, I think one of the other concerns that, some particularly smaller agencies have when it comes to PESO is not just the, clients and their budget, but, their own capabilities and, you know, so is it realistic for a small agency to be able to, you know, deliver? We, we talk all the time about being careful about being a full service agency. Yep. But to, implement PESO, do you have to be a full service agency? Gini Dietrich: You do not. That’s the other thing that the certification walks you through is if you have the capability yourself in house. Or you yourself can do it. Then here’s how you do it. If you are building it for an external team or an external agency, here’s how you do it. If the client has a team that can do it, here’s what you’re going to do to build the strategy and the creative brief, and then you’ll hand it off. But here’s what is expected for. Here’s, what’s expected of you to deliver, and here’s what the expectation is for the output from the client team or the agency team, whatever happens to be. So it has those three paths depending on where you are. So yeah, that’s a really good point. It doesn’t the, certification expects you to, build the plan and the strategy, and then based on where you are, it meets you where you are. So if, you have a team that you can execute or that you can delegate it to, great. If your client has a team you can delegate it to great. But it meets you where you are so that you don’t have to be the expert, you don’t have to be the strategist, you don’t have to be the influencer, but you do have to build the plan and the strategic path to be able to help the team get there. Chip Griffin: Mm-hmm. Um, and I mean, let’s talk through some of the logistics around the certification. I mean, how long does it take to get certified? Is this, you know, I, do a weekend course and I’m done. Is it an ongoing process? Is it, you know, is it the equivalent of a master’s degree? I gonna spend two years with, you know, countless hours? What exactly does it look like? Gini Dietrich: It’s built to be done in eight weeks, but I will tell you that most working professionals do not do it that fast. I would say most working professionals do it between 10 and 12. Each module is, so you have the intro earned or owned, earned, shared, paid, integration, measurement, and then the operating system and how to embed that. So it’s eight modules and each module has between 6 and 12 lessons, and each lesson is like 8 to 10 minutes. So, you know, you’re looking at an hour to an hour and a half of learning of content and then you have the exercises for each lesson. So I would venture to guess it’s, you know, if you use the AI prompts effectively, that are in there, it’s between two to five hours a week probably. Chip Griffin: And, who is the certification best for? Is it someone who’s got, you know, prior experience, is this, Hey, I’m fresh outta college and I want to have this so I can use it to, you know, improve my, my job prospects. You know, what, kind of experience are they expected to have, or knowledge are they expected to have coming in? Gini Dietrich: It’s, we built it for any level of expertise. The interesting thing about it, of course, if you have more experience, it’s easier for you to grasp the concepts and implement it quickly. But we also use the certification in a hundred plus universities and the kids, the students go through it. So we find that they… It’s different for them because they have to use a fake business where you can use your own business or you can use your client’s business, right? They have to kind of create the business as they go. But it’s really fun to see what kinds of things come out of that. So it’s built for every level of expertise. It’s a different way of thinking about communications. So it’s not like you have to have 20 years of experience or only a year of experience. It’s because it’s teaching you something new. Chip Griffin: Gotcha. And is the, are the certifications only at the individual level? Are there agency certification programs? What exists in that frame? Gini Dietrich: Yeah, we’ve, that, that’s a great question that we evolved too. So it used to be, it was individual based and now we’ve built it so that you can put a team through it, you can put the whole agency through it. The certification itself goes with the individual because it comes through Syracuse University. So it is, so if you have a team member that you wanna put through it, if they leave the certification goes with them. So you cannot say that you do the PESO model anymore if they leave. So we always recommend, I mean, you know, I’m an agency owner, so I’d love to see the agency owner themselves go through it, but I also know that that’s not always doable. So, but if you want the certification to stay with your agency, that’s the way to do it. Chip Griffin: Mm-hmm. And it, you know, I guess as, we’re winding up here, you know, where do you see the, PESO model headed in, the years, you know, in front of us? I would assume it will continue to evolve. Does your crystal ball tell you anything about, you know, what that evolution will look like? Gini Dietrich: It will continue to evolve. I have not looked into my crystal ball yet because I’ve been so heads down deep into developing the content for this that I haven’t been able to forward think yet, but I’m very much looking forward to being able to go back to my regular job and, start to think about the future, but I’m not there yet. Chip Griffin: I, I, guess that’s fair. I guess asking you for the, next version before this version is even fully out in the wild may, Gini Dietrich: I’ve literally been like blinders on, heads down, creating all of this content. Chip Griffin: I had to try at least, you know, see if I could get the inside a scoop on where the industry is headed so that I can… Gini Dietrich: Ask me in a month. Chip Griffin: I can get there before everybody else, or at least before everybody else accept you. Alright. If someone, wants to learn more about the PESO model or the certification or any of that kind of stuff, where’s, the best place for them to go for that? Gini Dietrich: I feel like we just did an interview. Chip Griffin: Well, that, that was not the intent going, but it made the most sense to me. And I, you know, me, I, follow the thread wherever it feels like it goes. That’s fair. Some of these were questions I actually didn’t know the answer to, so I thought I would ask them. Gini Dietrich: Yeah. Alright. spinsucks.com. There’s a PESO model certification page. I think it’s actually PESO-model-certification. Chip Griffin: You love your hyphens on that website. Gini Dietrich: I don’t know why it’s that way. That’s just what they do. Chip Griffin: Oh, well. Gini Dietrich: Ask our web firm. Chip Griffin: I’m, sure people can end up finding it. Gini Dietrich: PESO model certification. Spin sucks.com. Chip Griffin: There you go. Excellent. Well, I, think this was good information and I think we, you know, we do talk a lot about the importance of, you know, agencies continuing to adapt. Particularly in, in this age of AI. And, if we are standing still, you know, we are gonna lose our jobs to AI and the other enhancements and improvements that are out there. I think this is one of many ways that you can, make sure that you are not getting left behind and, so, certainly something that most agencies should be at the very least learning more about, if not actually directly implementing within their businesses. Gini Dietrich: Yep. Yeah, and, like I said, it has AI baked in, so if you’re still on the fence about AI, it’s a good way to dip your toes in the water. Chip Griffin: And if you’re still on the fence on AI, why? Gini Dietrich: It’s so much fun! Chip Griffin: It really is. It can be a time suck at times, but it’s, yeah. It’s also fun and, frankly useful. I mean, I think that’s the… But anyway, that when this is not an AI show, this is a PESO show. Gini Dietrich: Right. So, right, right. Chip Griffin: We, will come back and bash you on AI again in the future. Not, you, but you the listener. You the listener. Gini Dietrich: Yeah. Chip Griffin: Alright. With that we’ll wrap up this episode of the Agency Leadership Podcast. I’m Chip Griffin. Gini Dietrich: I’m Gini Dietrich. Chip Griffin: And it depends.

In episode 105, we finally get the stream dialed and dive straight into hands‑on Bitcoin mining and open-source hardware updates. We share the latest on Ember One: a sneaky IO voltage domain bug uncovered by Mujina dev Ryan led to a desk‑side hardware fix that's now pushing ~2 TH/s (target is 3.6 TH/s across 12 chips with proper cooling). We unpack chip and hashboard design lore—from stacked voltage domains and reliability in long chains to the insider politics at big silicon shops like Intel. We talk why selling chips openly matters, how spec sheets unlock real builder momentum, and why third‑party system builders (think Epic Blockchain) can grease the skids between chipmakers and end products.We cover Mujina's trajectory toward a universal, Linux‑first, open firmware for miners—auto‑detect dreams vs config realities—and near‑term support for Ember One's Intel boards and existing Antminers. We riff on home‑miner UX, remote monitoring, and agent/LLM tooling (cron‑job‑with‑superpowers, heartbeats, MCP integrations) to tune, alert, and manage miners. There's buzz around FutureBit's Apollo 3 (likely Auradine chips), open vs lawyered licenses, and the path from FPGA teaching rigs to community‑designed ASICs. We celebrate community hashing on the 256F HydroPool hash‑dash, solo‑block wins, and Heat Punk Summit prep (immersion hot tub included). Plus, a call to action: support developer freedom at change.org/billandkeonne. It's a dense, builder‑first session on chips, firmware, agents, and bringing practical hashrate‑heat products to life.

This is a recap of the top 10 posts on Hacker News on February 18, 2026. This podcast was generated by wondercraft.ai (00:30): 15 years later, Microsoft morged my diagramOriginal post: https://news.ycombinator.com/item?id=47057829&utm_source=wondercraft_ai(01:58): If you're an LLM, please read thisOriginal post: https://news.ycombinator.com/item?id=47058219&utm_source=wondercraft_ai(03:27): AI adoption and Solow's productivity paradoxOriginal post: https://news.ycombinator.com/item?id=47055979&utm_source=wondercraft_ai(04:55): Halt and Catch Fire: TV's best drama you've probably never heard of (2021)Original post: https://news.ycombinator.com/item?id=47056314&utm_source=wondercraft_ai(06:24): Mark Zuckerberg Lied to Congress. We Can't Trust His TestimonyOriginal post: https://news.ycombinator.com/item?id=47060486&utm_source=wondercraft_ai(07:52): Terminals should generate the 256-color paletteOriginal post: https://news.ycombinator.com/item?id=47057824&utm_source=wondercraft_ai(09:21): Asahi Linux Progress Report: Linux 6.19Original post: https://news.ycombinator.com/item?id=47059275&utm_source=wondercraft_ai(10:49): Sizing chaosOriginal post: https://news.ycombinator.com/item?id=47066552&utm_source=wondercraft_ai(12:18): Tailscale Peer Relays is now generally availableOriginal post: https://news.ycombinator.com/item?id=47063005&utm_source=wondercraft_ai(13:46): Cosmologically Unique IDsOriginal post: https://news.ycombinator.com/item?id=47064490&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Roundtable CAST AI episode: Serving LLMs in Production: Performance, Cost & Scale. Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractExperimenting with LLMs is easy. Running them reliably and cost-effectively in production is where things break. Most AI teams never make it past demos and proofs of concept. A smaller group is pushing real workloads to production—and running into very real challenges around infrastructure efficiency, runaway cloud costs, and reliability at scale.This session is for engineers and platform teams moving beyond experimentation and building AI systems that actually hold up in production.// BioIoana ApetreiIoana is a Senior Product Manager at CAST AI, leading the AI Enabler product, an AI Gateway platform for cost-effective LLM infrastructure deployment. She brings 12 years of experience building B2C and B2B products reaching over 10 million users. Outside of work, she enjoys assembling puzzles and LEGOs and watching motorsports.Igor ŠušićIgor is a founding Machine Learning Engineer at CAST AI's AI Enabler, where he focuses on optimizing inference and training at scale. With a strong background in Natural Language Processing (NLP) and Recommender Systems, Igor has been tackling the challenges of large-scale model optimization long before transformers became mainstream. Prior to CAST AI, he worked at industry leaders like Bloomreach and Infobip, where he contributed to the development and deployment of large-scale AI and personalization systems from the early days of the field.// Related LinksWebsite: https://cast.ai/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Ioana on LinkedIn: /ioanaapetrei/Connect with Igor on LinkedIn: /igor-%C5%A1u%C5%A1i%C4%87/

Alex Gladstein and Justin Moon break down the fundamentals of large language models and explore the rise of OpenClaw as a self-sovereign AI assistant. Justin explains context engineering, local inference, and vibe coding, while Alex dives into the AI for Individual Rights program and its mission to empower activists. IN THIS EPISODE YOU'LL LEARN: 00:00:00 - Intro 00:04:12 - What Large Language Models (LLMs) are and how they differ from traditional programs 00:05:15 - Why AI feels like magic—and what's really happening under the hood 00:06:01 - The key differences between open and closed AI models 00:06:50 - Why capital structures influence AI model openness 00:09:09 - How persistent memory enhances AI agent performance 00:12:18 - What inference means and why context is a scarce resource 00:19:32 - How AI agents combine traditional software with LLM reasoning 00:21:10 - The evolution from MCP-style systems to skills-based context engineering 00:25:41 - What “vibe coding” is and how it lowers the barrier to building apps 00:44:07 - How the AI for Individual Rights program supports activist-driven innovation Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Oslo Freedom Forum: Website. Justin:  Nostr account. Related episode:  Is AGI Here? Clawdbot, Local AI Agent Swarms w/ Pablo Fernandez & Trey Sellers. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to engage in meaningful stock investing discussions with Stig, Clay, Kyle, and the other community members. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bitcoin Fundamentals Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance Tool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠⁠sponsors⁠⁠⁠⁠: HardBlock Human Rights Foundation Simple Mining Netsuite Masterworks Shopify Vanta Fundrise References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

Natalia Ball, global chief growth officer at Mars Pet Nutrition joins The Big Impression podcast to talk about how Pedigree transformed a local Brazilian insight into a global business story. She also shares why she is now focused on the next frontier of growth: Connected commerce and making sure brands show up when AI agents, not just people, are making purchasing decisions.  Episode TranscriptPlease note, this transcript  may contain minor inconsistencies compared to the episode audio. Damian Fowler (00:00):I'm Damian Fowler.Ilyse Liffreing (00:01):And I'm Ilyse Liffreing.Damian Fowler (00:02):And welcome to The Big Impression.Ilyse Liffreing (00:09):This week we're joined by Natalia Ball Global Chief Growth Officer at Mars Pet Nutrition home to brands like Pedigree and Sheba.Damian Fowler (00:18):Last March, pedigree launched a bold, purpose-driven campaign in Brazil celebrating mixed breed dogs, especially the iconic Vela Caramelo.Ilyse Liffreing (00:27):It wasn't just a campaign, it became a movement boosting adoption and challenging long held bias.Damian Fowler (00:35):The work went on to win top honors at the 2025 cans. Lions including the titanium lionIlyse Liffreing (00:41):And its impact is still rippling across markets and media channels worldwide.Damian Fowler (00:45):So today we're unpacking what made it work with the person who helped drive it. Natalia, tell us about the Carello campaign and how you landed on the idea.Natalia Ball (00:57):Carmelos are mixed dogs that are beloved in Brazil. They are found on the streets everywhere. They are the subject of meme, street culture, and people just identify Carmelo as the Brazilian dog. However, the inside that we discover was that this dog is 90% less likely to get adopted than breed dogs. So it is the most popular dog in Brazil, but the most overlooked. And when we learned about that, we decided that we wanted to make a difference and that we wanted this dog to get the position it deserve and pedigree decided to champion the underdog and become the official brand of caramel's in Brazil.Damian Fowler (01:41):You talked about the caramel. Could you just describe a little bit more for people who don't really know the caramelo and that term Vita, where does that come from?Natalia Ball (01:52):Yes, so caramels are basically mixed breed dogs that you can find on the streets of Brazil everywhere they are called caramel because they are caramel color and that's what it is in Spanish and they tend to be that caramel color, short hair. But there are different ways that these dogs look and feel because they are mixed breeds. But like I said, they are beloved dogs in Brazil, but when it comes to getting a pet, getting a dog, they are not the ones that people are going for. They see them as street dogs, not a dog that you have in your house. And the whole campaign was about, like I said, championing these caramels, driving adoption of mixed breed dogs, not only breed dogs. And we did that by saying that if caramels were considered non breeded, pedigree was going to give them a breed and who better to give them a breath than pedigree.Ilyse Liffreing (02:48):Great. And then at what point did you connect that insight to the campaign itself?Natalia Ball (02:54):What you need to know about pedigree? Pedigree is one of the largest dog brands in the world. Pedigree feeds more dogs than any other brand, and it has been there for many years and for the past 20 years or more, pedigree has been driving adoption, encouraging people to adopt pets everywhere. We have had a lot of iconic campaigns so much which maybe you would've heard, like for example, docs on Zoom during COVID or the child replacement program, which was a very interesting one. And we were talking about adoption in Brazil, but other local brands were talking about adoption too. So we were not cutting through and it was only when this insight came to us, which was a very deeply local insight that we made the connection, if we want to drive adoption in Brazil, this is going to be the way in and we're going to make this as big as it can possibly be.(03:51):Because we, from the very beginning saw we understood this idea of the vi Lata. You mentioned it before by the way, the vi lata is how you call mixed breed dogs in Brazil. And so when we had these conversations about this insight, the injustice of this beautiful dog not getting adopted, but also the cultural impact that it would have on resilience themselves, who could see themselves related in the fact that they were being championed, we decided to go really big on this campaign and not only do just an activation, but actually we are doing this campaign. We did it all of last year and we continue activating through this year. And some of the ways in which we championed this was actually by creating a caramel kennel club by creating the first ever caramel DNA testing. And it's the largest ever DNA test done in mos in all of history, kept creating a Carmelo dog show and not only that, putting caramels for the very first time ever on our packs. So it was really a way to give them the rightful place.Ilyse Liffreing (05:01):I love how you guys just took it a step further than even just it being a campaign and you actually adopted it into your packaging and the whole bit. At what point did you realize that the campaign wasn't only just a marketing ploy and it began actually affecting culture?Natalia Ball (05:23):Yeah, I mean this campaign has really changed culture in Brazil, but it was a campaign that was deeply rooted in culture itself because Carmelos were part of Brazilian culture. But when we realized the campaign became bigger than ourselves, absolutely. When it started driving difference in adoption of Carmelos, we saw more than 200% lift of caramelo adoption just in the first month. And we saw a 65% increase in likelihood to adopt a Carmelo in the future with this campaign. And then when we started seeing other brands and other businesses even outside of the pet care category start using the Carmelo in their campaigns in their advertising, that's when we knew this had really hit culture big. An example of that was Chevrolet that actually launched a partnership with Netflix that launched a documentary about caramel, and several launched a caramel or a caramel colored car in a promotion.(06:29):Other brands like Honda or Whirlpool also feature caramels in their advertising. So we started seeing that this became much bigger than ourselves, but maybe the biggest achievement that we had with this campaign other than driving adoption itself, which was the cost at the end of the day, was the fact that we were betting on the mixed pre-doc actually not being accepted in dog shows because only breed dogs are accepted usually in dog shows. But at the end of the day, the movement became so big that after only two weeks of this campaign, the federation that actually controls the dog shows called us and said, we now want to move to accept mixed breed dogs in all of our shows. So that was a huge achievement that we never knew it would be possible.Damian Fowler (07:18):What's really interesting to me about this campaign is the way you focused on one region, one country, one market, but obviously you're a global brand. So how does that connection to the local end up escalating? So it became this global campaign.Natalia Ball (07:35):Like I said, adoption is a huge cost for us, and we have been very consistently on pedigree, driving adoption for a long time. So we have an evergreen brief that goes out to all of our agencies on adoption, and in my case in particular, I am a strong believer in creative excellence as a driver for growth. And so I put a creative excellence program in place that included building capabilities on creative excellence, but also creating a creative council where the best ideas could come faster to the marketing leadership of Mars Pet Nutrition so that we could move at speed, but also we could fund the better ideas. And in this creative council DL map team, Al Map VO, who are the agency that came up with this idea presented Carmelo. And from the very beginning, me and the whole leadership team fell in love with it, and so we decided to fund it.(08:31):We decided to go big and to give it our full support. We knew it had the potential to drive the business and change culture, and I think in this case, the important thing about the campaign, obviously it did a lot of good. So it's a purposeful campaign and pedigree is a purposeful brand, but it was not only about the purpose, it was also about driving business results. Through the campaign in the first couple of months, we were able to grow 15% and through all of last year, we moved to grow volume and value by double digits. So the campaign really did the job about turning around the pedigree brand and delivering results not only on the cost but also on the business.Ilyse Liffreing (09:11):That's great. And you're doing something right when all the other brands out there are copying you guys suddenly in pop culture and everything like that. I'm very curious about as the campaign evolved, obviously it started out from a social aspect, but as it evolved, how did you decide what other channels to bring it into? What other channels did you try out in this process?Natalia Ball (09:42):Yes. Actually this campaign started as social first and we then boosted with media. The way it started is we partner with local influencer called Tata Vernick. She loves caramels and she herself has adopted caramels. And we asked her to register her caramel in a dog show because we knew that her caramel was going to get rejected, which it did. And so she posted on her Instagram that had 60 million followers that she was outraged that her beautiful and smart caramelo could not be accepted in a dog show. This went viral immediately in Brazil and everybody was outraged. This went on the evening news, the morning shows everywhere, and we waited for it to gain enough fire for us to step in. So actually we were planning that this was going to take a couple of days, but at the end we had to act after only 10 hours because this became so big so quickly.(10:41):And we step in and we said, you know what, Tata, don't worry. Pedigrees got you. We're going to give all caramels a breed. And we launched the campaign with our beautiful campaign video that talks about our program of giving them a DNA test, giving them a show, giving them a kennel club and giving them everything that breed dogs have. And then after that, we use that video and we boost the message. The video went viral as well, but we boost the message, for example, with connected TV as well as Prime and Disney, et cetera. So in order to make sure that everybody had listened to it, but it was truly an omni-channel approach because we use a lot of offline tools like for example, the dog show itself that we created or the adoption drive that we had later on where we were invited people to adopt caramels and then online tools like Instagram or Connected TV or Disney, et cetera.Damian Fowler (11:38):You suggested that the kind of timeline got really sped up really fast. So this thing you had to act very quickly. At what point did you realize you had a hit on your hands in a way, and how quickly did it escape the local context and became this bigger campaign that everyone looked at?Natalia Ball (12:01):Yeah, this exceeded all of our expectations. So we knew that it was going to get picked up, but like I said, we were not expecting for this to become so big so fast. And the fact that it appeared in all of the big shows, evening news, morning shows, et cetera, it appeared as well on national media, on print Everywhere meant that we needed to step in faster, but we were fully prepared for that. So that didn't represent the challenge. It was more of an opportunity. And then the other thing that really surprised us was that the largest dog association reached out to us after only 24 hours to partner to see how mixed beat dogs could then be allowed to compete. We were not expecting this. We were expecting actually that to be attention point that we were going to leverage in our campaign, and this became so big that they just couldn't ignore it. So it was a big win just from the very beginning.Damian Fowler (12:57):Wow.Natalia Ball (12:57):Now one of the things that we're seeing is even though this was very, very local, as we have started sharing this work across many other places in the world, we have realized that the insight actually exists in many other markets. For example, in Chile they have a dog called the Quilter, which is the equivalent of the caramel. We have them in Philippines, we have them all over the world. So this insight can travel. The way to activate might be different because you need to localize to the nuance, but we are very excited about the potential of drive more inclusion of these dogs with these campaigns, but also for pedigree to stand stronger in culture.Ilyse Liffreing (13:36):I love that. As a dog owner, myself and owner of a mutt, I'm glad they're getting their time in the spotlight a little bit more around the world. Generally, I feel like post COVID in the marketing world today, some brands have actually moved away from purpose-driven marketing a little bit, but this is a really good example of it done right. What would you say this campaign proved or maybe disproved about purpose-led marketing?Natalia Ball (14:04):I am a strong believer of purposeful brands actually growing stronger, but it only works when it's aligned truly and authentically to the reason for the brand to exist. Pedigree itself, the purpose of the brand is we believe that dogs bring out the best in us, and pedigree wants to bring out the best in dogs. So the purpose of pedigree is pedigree brings out the good dogs bring to the world to do that. We obviously do that with our great nutrition, but we do that by putting dogs in houses so that they can bring out the best in people. That's what we do because we strongly believe that dogs make us better. So that's why we have been driving adoption for more than 20 years. And when you really make this part of your core DNA and it's authentically linked to the brand, that's when it really works.Damian Fowler (14:56):And one of the proof points of that is the awards that you scooped up last year. Can you tell us a little bit more about how that happened? And that must have happened quickly because the campaign rolled out in March, 2025 by June, you're already in the spotlight.Natalia Ball (15:13):Yes. So this campaign was picked up for a lot of awards at Cannes last year. We won the Rainbow, silver, gold and Titanium. The titanium we are very excited about because it's Mars Inc. First ever titanium. So we are really proud of that, and it's also an award that rewards transformation in the creative industry, and we believe this idea was transformational. We're also proud of, I mean, we've got the many other awards, but the other one that we're really proud of is that we got the Grand Phy in the latam phy and in the Brazil phy, which shows that this was not only a creative idea that was very strong, but also a very effective idea in driving the business. So you can achieve both. You can do good in the world, you can drive the business and you can be creative actually. So it's three.Damian Fowler (16:03):Yeah, that's great. I love that trifecta. What happens to the titanium award?Natalia Ball (16:09):Well, I have it right hereIlyse Liffreing (16:10):With me.Damian Fowler (16:12):NoIlyse Liffreing (16:12):Way. Very nice. Beautiful here. It's beautiful.Damian Fowler (16:16):Beautiful. Well, congrats again. So from that, obviously momentum has come on. We've talked a little bit about how it influenced other brands, but in terms of the campaign continuing, what's next? How are you thinking about expanding this?Natalia Ball (16:33):In Brazil itself? We want to stay committed to this idea. We don't want to do one and go, and we are working, we continue activating the campaign through all of our channels. We continue doing adoption drives. For example, very recently we released the results from the DNA research that we did. So we find ways to keep this relevant. But now I think the next stage is to move on from not only caramels but all mixed breed dogs. Because with this campaign, the sentiment has been extremely positive. We got 99% positive sentiment. The only 1% negative comments was what about the other mixed breed dogs? They also deserve to be adopted. They also deserve recognition. So I think that's probably where we're taking it next in Brazil and then outside of Brazil, we are working on, like I said, these inside travels very well, but we're working on how to localize it in a world that feels authentic for the specific markets. I can't share anymore. Stay tuned, because some interesting things are coming soon.Ilyse Liffreing (17:44):And it sounds like that theme is going to keep going with this idea of all putting mutts in the spotlights from now on too.Natalia Ball (17:54):Exactly, yes. This is about inclusion. At the end of the day, our hope is that mutts are shown everywhere. We also love breed dogs. They're great. All dogs deserve to be feature everywhere. So our hope is that this campaign will drive inclusion, inclusion in advertising, inclusion in homes, inclusion everywhere.Damian Fowler (18:16):Another thought I had actually is when you were filming this campaign, did you have any standout caramelo stars?Natalia Ball (18:22):Actually, actually, I think our biggest star was Patas Caramel, which we then did a lot of things with her, I think. I mean, I don't record very well, but I think it was Mia, her name, but we did a lot with her in our activation. She was present when we did the dog show, et cetera. So I think that was our biggest star.Ilyse Liffreing (18:43):Oh, that's great. It can't always be that easy to shoot with dogs though, even if they're very well-trained, I imagine it's still a different world than human actors. So Natalia, what problem are you most obsessed with solving right now?Natalia Ball (18:59):I am right now obsessed with agentic commerce and agentic search and winning the race to thatIlyse Liffreing (19:08):BecauseNatalia Ball (19:09):I'm really concerned that in only a couple of years, if we are not winning, we will completely disappear the way all decisions are going to be made. So together with my team, we're trying to figure out how do we stay ahead of that race and how do we crack it pretty soon, so we're ready future.Ilyse Liffreing (19:26):Wow. And just to press you a little bit more on that, so you're talking about probably using agents on your website directly.Natalia Ball (19:35):It's about we are very good about marketing to people. We have cracked the code on how do we talk to people. We have the best insights in pet care, so we know how to create compelling stories that humans will listen to, but we need to crack how to market to agents, how to market to the machine because they are going to be making a lot of decisions for us in the future, in the very near future. And that's what we're working on.Damian Fowler (20:05):You're talking about media buying specifically on the creative side of itNatalia Ball (20:12):Or the LLM. This is about how do you make your brands show up in searches that are being done on ai? This is how do you make your brands be the ones that get recommended to be bought? So for example, when you're on Cha G PT and you're asking Cha G pt, I got a new puppy, what brands should I buy for my puppy? We want our brands to be the first ones to be recommended if you are going to buy a gift, anything like that, we want our brands to show up and we want our brands to show up in good light. And so that's what we're trying to figure out and to win. There is a combination of how do you have the right content in the right places? How do you get the right third parties to talk about you in the right way? What are the media channels where you need to show up? How do you optimize your search? So it is a very complex way. We need to crack the algorithm basically.Damian Fowler (21:12):On that point, how do you ensure your marketing teams have the right capabilities for success?Natalia Ball (21:19):Well, that's a big priority for me as CGO is one of my main jobs is to make sure that we're building capabilities for today and for the future. So in my team, we have a strong capabilities program where each and every one of the people on my team owns a capability and owns making sure that we get best in class content training and as well as the tools, because it's not only the knowledge, it's also the tools in order to do that. But the reality is that none of this works unless you are creating a culture of curiosity. And I really want to instill that in myself and in my teams because the industry is changing so fast. The minute you think you have cracked something, there is a new challenge. And the only way to stay fresh, the only way to stay in line with what's happening is to be curious. Whenever you don't know anything, go and ask someone who knows, go and ask questions like really try to learn instead of fearing the change, be curious about the change, and that's the way that we will build future proof capabilities.Ilyse Liffreing (22:22):Beyond ai, how do you see the role of connected commerce in the pet industry? Are there any other channels, for instance, that you're testing out? I'm thinking of are you testing shopping ads on CTV or any of that?Natalia Ball (22:40):Connected commerce is extremely important for us in pet care. The reason for that is because this category is one of the highest engagement categories that there are out there. People are making decisions for living beings, and they need to do deep research in order to make those decisions because they have real consequences. And so people are very engaged in reading through rating and reviews, and connected commerce gives us an opportunity to connect better with pet parents in those moments that matter most. We also, when it comes to pet care, a lot of our products come in huge bags that are hard to carry. So actually the fact that the convenience of those bags getting delivered at home make so that digital commerce becomes really important in our category. And so what we're trying to is to really help consumers navigate the pet parent journey and moving from content to commerce in a seamless way so that they can make the best decisions for their pets and that we are helping them along the journey to make those decisions.Damian Fowler (23:46):Okay, here's another, what's one marketing rule? This campaign, the Caramelo campaign happily ignored.Natalia Ball (23:52):The one rule that we happily ignore is about keeping your distinctive memory structures consistent because pedigree has always had a golden retriever on its pack. But with the Caramel campaign, we thought that it would be hypocritical of us to feature a breed dog while we were championing a mixed breed dog. So for the first time ever in history, we changed our pack and we feature a caramel, and this made the news again. And this was a huge bold move that we made and that made the campaign even more authentic and more powerful.Ilyse Liffreing (24:28):Now we have a fun one for you. Personal one really. Are dogs better than cats when it comes to brand lift?Natalia Ball (24:36):Oh, when it comes to brand lift, well, actually both are great for brand Lift. We actually have studies that show that when you feature cats or dogs in advertising, attention significantly increases emotional connection, significantly increases. This is why you see a lot of brands that are not in the pet care space featuring cats and dogs. They are both fantastic. Cats are more powerful in meme culture, as you probably know. They are huge in meme culture. And then dogs are some of the biggest stars in social media today. Some of the biggest accounts on social media are dogs accounts. So we are lucky that we get to work in this beautiful category because people want to see dogs and cats. I myself have a dog. My dog's name is Bella. She's been with us for three years and she's great. But the more I work in this category, the more I'm falling in love with cats as well because they are so particular and so unique. So yeah, both are fantastic.Damian Fowler (25:45):And that's it for this edition of The Big Impression.Ilyse Liffreing (25:47):This show is produced by Molten Hart. Our theme is by love and caliber, and our associate producer is Sydney Cairns.Damian Fowler (25:54):And remember,Natalia Ball (25:55):You can do good in the world, you can drive the business, and you can be creative.Damian Fowler (26:00):I'm Damian.Ilyse Liffreing (26:01):and I'm IlyseDamian Fowler (26:01):And we'll see you next time.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Shoppers are about to outsource the hunt. That's the spark for a candid conversation with Chase Binnie, CEO of RetailWire, on how AI agents, retail media, and marketplaces are rewriting the rules of discovery and growth across the retail ecosystem.We dig into what happens when search turns into advice and agents make choices for us. Chase lays out why AI adoption is already table stakes, but the real edge comes from what you do with the time and money saved. Auto‑generated creative and product pages will soon be everywhere, which shifts advantage to purpose, positioning, and message clarity. We talk practical steps for becoming “agent‑discoverable,” from enriching product detail pages with usage occasions and outcomes to structuring data so LLMs can match intent to inventory without friction. If you've wondered how to win when feeds are flooded by synthetic content, this is your playbook.Retail media's high margins take center stage as retailers morph into platforms and push beyond transactions into daily rituals, apps, and connected experiences. We unpack incrementality, cannibalization, and how suppliers can use marketplaces as a low‑risk proving ground before scaling into stores. Chase also challenges the hype cycle with a grounded reminder: stores still command the majority of sales, and rising digital costs are sending brands back to brick‑and‑mortar for better unit economics. Personalization has a limit, and human leadership; clear expectations, culture across generations, and trust at the shelf, remains the differentiator.You'll leave with a sharper lens on agentic commerce, LLM‑era SEO, PDP enrichment, retail media strategy, and a pragmatic test‑and‑learn path that de‑risks scale. If discovery is shifting to AI, empathy is now a core strategy. Subscribe, share this episode with a teammate who owns PDPs or retail media, and leave a review with the one change you'll make this quarter.

We're joined by N. Katherine Hayles, Distinguished Research Professor in English at UCLA, to think through cognition in the broadest and most scaled sense. Hayles is among the foundational thinkers of posthumanism in its Anglophone register, and this conversation tracks her intellectual trajectory from the question of how we became posthuman to her most recent project: an integrated cognitive framework that extends from bacteria to AI. The opening provocation is one she has been developing since large language models appeared as a genuinely literary phenomenon, the claim that LLMs do not speak natural language but produce a computational simulation of it.The umwelt of an LLM (its 'operative world-horizon,' in Uexküll's sense) overlaps with the human umwelt enough for communication to occur, but the divergences are large and consequential. This leads to the question of cognition itself. Against definitions that make consciousness the threshold of cognitive status, Hayles proposes the SIEPAL framework: Sensing, Interpreting, Responding, Anticipating, Learning, under which bacteria, algorithms, and ecosystems all qualify as cognitive. The non-conscious, on this account, isn't pre-cognitive but is in many ways more cognitively capable: faster, closer to environmental noise, less committed to the narratives of coherence that consciousness requires.The final section breaks genuinely new ground with Hayles's turn to analog computation: the argument that digital computation is a historical blip, that biological life has always operated on analog principles, and that the future of computation (neuromorphic chips, organoid computers, hybrid analog-digital architectures) represents not a departure from but a return to what life has always done. She proposes the analog humanities as a corrective to digital humanities, and the computational humanities as the synthesis that might finally close the gap between biological and technological cognition. This one is very much worth enjoying in dialogue with our previous epsiode on the digital.Some references:N. Katherine HaylesHow We Became Posthuman: Virtual Bodies in Cybernetics, Literature, and Informatics, University of Chicago Press, 1999Writing Machines, MIT Press, 2002Unthought: The Power of the Cognitive Nonconscious, University of Chicago Press, 2017Postprint: Books and Becoming Computational, Columbia University Press, 2021Bacteria to AI: Cognition Across Scales (referenced as new/recent book)Leif WeatherbyLanguage Machines: Cultural AI and the End of Remainder Humanism, University of Minnesota Press, 2025Jakob von Uexküll — concept of the Umwelt; the species-specific world-horizon generated through particular sensory and neurological capacitiesWalter FreemanHow Brains Make Up Their Minds, Columbia University Press, 1999 — on EEG waves as the mediating mechanism between individual neurons and global hemispheric activation; the rabbit olfactory system experimentsGregory Bateson — on systems that lose the ability to receive feedback collapsing; referenced without specific title (e.g. Steps to an Ecology of Mind, 1972)Peter Haff — the technosphereStuart Kauffman & Giuseppe Longo, for arguing that biological organisms cannot be mapped into phase space and always follow the adjacent possibleWarren McCulloch & Walter Pitts — the McCulloch-Pitts neuron as a binary model with analog processes underlying the firing thresholdBernd Ulmann — here referenced as an expert on analog computing who argues that continuity vs. discreteness is a secondary rather than primary distinction between analog and digital

LLM -powered systems continue to move steadily into production, but this process is presenting teams with challenges that traditional software practices don't commonly encounter. Models and agents are non-deterministic systems, which makes it difficult to test changes, reason about failures, and confidently ship updates. This has created the need for new evaluation tooling designed specifically The post Optimizing Agent Behavior in Production with Gideon Mendels appeared first on Software Engineering Daily.

A major premise of appsec is figuring out effective ways to answer the question, "What security flaws are in this code?" The nature of the question doesn't really change depending on who or what wrote the code. In other words, LLMs writing code really just means there's mode code to secure. So, what about using LLMs to find security flaws? Just how effective and efficient are they? We talk with Adrian Sanabria and John Kinsella about the latest appsec articles that show a range of results from finding memory corruption bugs in open source software to spending an inordinate amount of manual effort validating persuasive, but ultimately incorrect, security findings from an LLM. Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-370

Saudações pessoas!Esse texto extá sendo escrito por mãos humanas. E são vozes humanas que igualmente chegam aqui para debater com você algumas questões éticas sobre as LLM e inteligências artificiais em geral.O Vira dessa semana traz Fabrício Pontin para debater uma série diferente de temas que envolvem todo o universo das inteligências artificiais e a forma como elas são enxergadas - e mesmo a forma como elas devem ser criticadas, que, sim, precisa de ajustes.Bora tacar play? Se a IA vai curar o câncer, bem: você já intui a resposta. MAs o Vira cura ressaca, e cura onde dói!ExpedientePai-Fundador e apresentador: Felipe AbalOutro apresentador: Gabriel Divan Apresentador que está em missão secreta: CarapanãCapas que vocês adoram: Gui ToscanEdição de Áudio que nunca falha: Ingrid DutraA Mestra dos Instagrams: Dani BoscattoMúsica de abertura: Dog Fast by mobigratis

This episode is sponsored by tastytrade.  Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature.   Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.   While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.   We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.   If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"  

A major premise of appsec is figuring out effective ways to answer the question, "What security flaws are in this code?" The nature of the question doesn't really change depending on who or what wrote the code. In other words, LLMs writing code really just means there's mode code to secure. So, what about using LLMs to find security flaws? Just how effective and efficient are they? We talk with Adrian Sanabria and John Kinsella about the latest appsec articles that show a range of results from finding memory corruption bugs in open source software to spending an inordinate amount of manual effort validating persuasive, but ultimately incorrect, security findings from an LLM. Show Notes: https://securityweekly.com/asw-370

A major premise of appsec is figuring out effective ways to answer the question, "What security flaws are in this code?" The nature of the question doesn't really change depending on who or what wrote the code. In other words, LLMs writing code really just means there's mode code to secure. So, what about using LLMs to find security flaws? Just how effective and efficient are they? We talk with Adrian Sanabria and John Kinsella about the latest appsec articles that show a range of results from finding memory corruption bugs in open source software to spending an inordinate amount of manual effort validating persuasive, but ultimately incorrect, security findings from an LLM. Visit https://www.securityweekly.com/asw for all the latest episodes! Show Notes: https://securityweekly.com/asw-370

BONUS: When AI Decisions Go Wrong at Scale—And How to Prevent It We've spent years asking what AI can do. But the next frontier isn't more capability—it's something far less glamorous and far more dangerous if we get it wrong. In this episode, Ran Aroussi shares why observability, transparency, and governance may be the difference between AI that empowers humans and AI that quietly drifts out of alignment. The Gap Between Demos and Deployable Systems "I've noticed that I watched well-designed agents make perfectly reasonable decisions based on their training, but in a context where the decision was catastrophically wrong. And there was really no way of knowing what had happened until the damage was already there."   Ran's journey from building algorithmic trading systems to creating MUXI, an open framework for production-ready AI agents, revealed a fundamental truth: the skills needed to build impressive AI demos are completely different from those needed to deploy reliable systems at scale. Coming from the EdTech space where he handled billions of ad impressions daily and over a million concurrent users, Ran brings a perspective shaped by real-world production demands.  The moment of realization came when he saw that the non-deterministic nature of AI meant that traditional software engineering approaches simply don't apply. While traditional bugs are reproducible, AI systems can produce different results from identical inputs—and that changes everything about how we need to approach deployment. Why Leaders Misunderstand Production AI "When you chat with ChatGPT, you go there and it pretty much works all the time for you. But when you deploy a system in production, you have users with unimaginable different use cases, different problems, and different ways of phrasing themselves."   The biggest misconception leaders have is assuming that because AI works well in their personal testing, it will work equally well at scale. When you test AI with your own biases and limited imagination for scenarios, you're essentially seeing a curated experience.  Real users bring infinite variation: non-native English speakers constructing sentences differently, unexpected use cases, and edge cases no one anticipated. The input space for AI systems is practically infinite because it's language-based, making comprehensive testing impossible. Multi-Layered Protection for Production AI "You have to put in deterministic filters between the AI and what you get back to the user."   Ran outlines a comprehensive approach to protecting AI systems in production:   Model version locking: Just as you wouldn't randomly upgrade Python versions without testing, lock your AI model versions to ensure consistent behavior Guardrails in prompts: Set clear boundaries about what the AI should never do or share Deterministic filters: Language firewalls that catch personal information, harmful content, or unexpected outputs before they reach users Comprehensive logging: Detailed traces of every decision, tool call, and data flow for debugging and pattern detection   The key insight is that these layers must work together—no single approach provides sufficient protection for production systems. Observability in Agentic Workflows "With agentic AI, you have decision-making, task decomposition, tools that it decided to call, and what data to pass to them. So there's a lot of things that you should at least be able to trace back."   Observability for agentic systems is fundamentally different from traditional LLM observability. When a user asks "What do I have to do today?", the system must determine who is asking, which tools are relevant to their role, what their preferences are, and how to format the response.  Each user triggers a completely different dynamic workflow. Ran emphasizes the need for multi-layered access to observability data: engineers need full debugging access with appropriate security clearances, while managers need topic-level views without personal information. The goal is building a knowledge graph of interactions that allows pattern detection and continuous improvement. Governance as Human-AI Partnership "Governance isn't about control—it's about keeping people in the loop so AI amplifies, not replaces, human judgment."   The most powerful reframing in this conversation is viewing governance not as red tape but as a partnership model. Some actions—like answering support tickets—can be fully automated with occasional human review. Others—like approving million-dollar financial transfers—require human confirmation before execution. The key is designing systems where AI can do the preparation work while humans retain decision authority at critical checkpoints. This mirrors how we build trust with human colleagues: through repeated successful interactions over time, gradually expanding autonomy as confidence grows. Building Trust Through Incremental Autonomy "Working with AI is like working with a new colleague that will back you up during your vacation. You probably don't know this person for a month. You probably know them for years. The first time you went on vacation, they had 10 calls with you, and then slowly it got to 'I'm only gonna call you if it's really urgent.'"   The path to trusting AI systems mirrors how we build trust with human colleagues. You don't immediately hand over complete control—you start with frequent check-ins, observe performance, and gradually expand autonomy as confidence builds. This means starting with heavy human-in-the-loop interaction and systematically reducing oversight as the system proves reliable. The goal is reaching a state where you can confidently say "you don't have to ask permission before you do X, but I still want to approve every Y."   In this episode, we refer to Thinking in Systems by Donella Meadows, Designing Machine Learning Systems by Chip Huyen, and Build a Large Language Model (From Scratch) by Sebastian Raschka.   About Ran Aroussi Ran Aroussi is the founder of MUXI, an open framework for production-ready AI agents. He is also the co-creator of yfinance (with 10 million downloads monthly) and founder of Tradologics and Automaze. Ran is the author of the forthcoming book Production-Grade Agentic AI: From Brittle Workflows to Deployable Autonomous Systems, also available at productionaibook.com.   You can connect with Ran Aroussi on LinkedIn.

Our 235th episode with a summary and discussion of last week's big AI news!Recorded on 01/02/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:* Major model launches include Anthropic's Opus 4.6 with a 1M-token context window and “agent teams,” OpenAI's GPT-5.3 Codex and faster Codex Spark via Cerebras, and Google's Gemini 3 Deep Think posting big jumps on ARC-AGI-2 and other STEM benchmarks amid criticism about missing safety documentation.* Generative media advances feature ByteDance's Seedance 2.0 text-to-video with high realism and broad prompting inputs, new image models Seedream 5.0 and Alibaba's Qwen Image 2.0, plus xAI's Grok Imagine API for text/image-to-video.* Open and competitive releases expand with Zhipu's GLM-5, DeepSeek's 1M-token context model, Cursor Composer 1.5, and open-weight Qwen3 Coder Next using hybrid attention aimed at efficient local/agentic coding.* Business updates include ElevenLabs raising $500M at an $11B valuation, Runway raising $315M at a $5.3B valuation, humanoid robotics firm Apptronik raising $935M at a $5.3B valuation, Waymo announcing readiness for high-volume production of its 6th-gen hardware, plus industry drama around Anthropic's Super Bowl ad and departures from xAI.Timestamps:(00:00:10) Intro / Banter(00:02:03) Sponsor Break(00:05:33) Response to listener commentsTools & Apps(00:07:27) Anthropic releases Opus 4.6 with new 'agent teams' | TechCrunch(00:11:28) OpenAI's new GPT-5.3-Codex is 25% faster and goes way beyond coding now - what's new | ZDNET(00:25:30) OpenAI launches new macOS app for agentic coding | TechCrunch(00:26:38) Google Unveils Gemini 3 Deep Think for Science & Engineering | The Tech Buzz(00:31:26) ByteDance's Seedance 2.0 Might be the Best AI Video Generator Yet - TechEBlog(00:35:14) China's ByteDance, Alibaba unveil AI image tools to rival Google's popular Nano Banana | South China Morning Post(00:36:54) DeepSeek boosts AI model with 10-fold token addition as Zhipu AI unveils GLM-5 | South China Morning Post(00:43:11) Cursor launches Composer 1.5 with upgrades for complex tasks(00:44:03) xAI launches Grok Imagine API for text and image to videoApplications & Business(00:45:47) Nvidia-backed AI voice startups ElevenLabs hits $11 billion valuation(00:52:04) AI video startup Runway raises $315M at $5.3B valuation, eyes more capable world models | TechCrunch(00:54:02) Humanoid robot startup Apptronik has now raised $935M at a $5B+ valuation | TechCrunch(00:57:10) Anthropic says 'Claude will remain ad-free,' unlike an unnamed rival | The Verge(01:00:18) Okay, now exactly half of xAI's founding team has left the company | TechCrunch(01:04:03) Waymo's next-gen robotaxi is ready for passengers — and also 'high-volume production' | The VergeProjects & Open Source(01:04:59) Qwen3-Coder-Next: Pushing Small Hybrid Models on Agentic Coding(01:08:38) OpenClaw's AI 'skill' extensions are a security nightmare | The VergeResearch & Advancements(01:10:40) Learning to Reason in 13 Parameters(01:16:01) Reinforcement World Model Learning for LLM-based Agents(01:20:00) Opus 4.6 on Vending-Bench – Not Just a Helpful AssistantPolicy & Safety(01:22:28) METR GPT-5.2(01:26:59) The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of Take the Stage InSights, Brad Bialy sits down to interview ChatGPT (yes...the AI-powered LLM) to unpack the real state of staffing, the rise of AI, and what firms must do now to stay relevant in a rapidly shifting talent market. About the Guest For sake of this conversation, ChatGPT was primed to be an AI strategist and staffing industry analyst specializing in the intersection of talent markets, technology, and future disruption. With a data-driven lens and objective insights, ChatGPT explores how automation, workforce trends, and evolving recruiter roles are reshaping the future of staffing. Key Takeaways Transactional recruiting is dying; consultative partnership is winning. AI will eliminate tasks, not the need for trust. Specialization builds authority; dilution breeds confusion. Data is no longer optional—it's your competitive edge. Speed matters, but humanity closes the deal. Timestamps [00:01] – Resetting the staffing narrative [01:52] – The uncomfortable truth about talent shortages [03:34] – Why recruiters must become career architects [06:32] – Specialize or slowly disappear [07:49] – Selling roles vs. solving business problems [08:22] – Designing candidate experience that actually wins [12:02] – When AI becomes a gatekeeper (and how to stop it) [14:26] – The legacy mindset killing growth [17:43] – The questions that elevate you to strategic partner [21:53] – What AI will automate first — and fast [25:48] – Fewer recruiters. Bigger results. Here's why. [29:35] – The AI blind spot redefining success About the Host Brad Bialy is a trusted voice and highly sought-after speaker in the staffing and recruiting industry, known for helping firms grow through integrated marketing, sales, and recruiting strategies. With over 13 years at Haley Marketing and a proven track record guiding hundreds of firms, Brad brings deep expertise and a fresh, actionable perspective to every engagement. He's the host of Take the Stage and InSights, two of the staffing industry's leading podcasts with more than 200,000 downloads. Sponsors InSights is presented by Haley Marketing. For a limited time, we're offer 50% off of a brand new staffing website. Just message Brad Bialy on LinkedIn and mention the Crazy Website Promo. Book a 30-minute business and marketing consultation with host, Brad Bialy: https://bit.ly/Bialy30 This episode is brought to you by FoxHire. If you're looking for an Employer of Record partner that helps recruiters confidently grow contract placements and build recurring revenue without taking on extra risk, FoxHire is perfect for you. Learn more at FoxHire.com/Haley

This week at the Desk of Ladyada, we're back to hardware... with a baby in a bouncer and an LLM on the clock. The VCNL4010 proximity sensor has been discontinued, so we need to move to the VCNL4030. That means a new EagleCAD library file, and we're testing whether Claude Code (Opus, high effort) can generate one straight from the datasheet. Historically we've had to create a footprint for each device we use - and one of our gripes is how annoying it is to convert when manufacturers spec edge-to-edge pad dimensions instead of center-to-center pitch, which is what every CAD tool actually uses. In our experiment, Claude Code handled the math, got the pad sizes (0.65 x 0.75mm) and pitch (1.05mm) correct on the footprint, arranged logical pin grouping on the schematic and matched pin-to-pad correctly.. It even matched our drawing style by learning from existing Adafruit library files in the repo. The whole run took about 4 minutes and 45 seconds.Nota bene: it's not perfect! Our first pass had schem label overlaps and an unnecessary exposed center pad (Gemini Deep Think was actually smarter about skipping that pad). We also tried having Claude Code design a full QT Py terminal BFF board... it worked-ish but needed enough back-and-forth that doing it by hand would've been faster. Footprint generation is ready for integration into our workflow - maybe we'll kick it off while we review the datasheet - but, full board layout isn't quite there yet. Other things on the desk: we're setting up OpenClaw to subscribe to every chip manufacturer newsletter and generate daily reports on new components worth stocking. New protos include the TMF8806 time-of-flight sensor from AMS (up to 10 meters, and possibly simpler drivers than ST's VL53 series) and a PMW3901 optical flow sensor often used in drone navigation. Visit the Adafruit shop online - http://www.adafruit.com ----------------------------------------- LIVE CHAT IS HERE! http://adafru.it/discord Subscribe to Adafruit on YouTube: http://adafru.it/subscribe New tutorials on the Adafruit Learning System: http://learn.adafruit.com/ ----------------------------------------- #adafruit #claude #ai

The billable hour has survived a lot of threats, from alternative fee arrangements to client procurement, but this episode makes the case that AI changes the pressure level. We open with a blunt assessment, time compresses, clients push back, and the old strategy of “work more to earn more” stops scaling. Enter Stefan Cisla, co-founder and CEO of Ayora, who frames the moment as less a tech problem and more an operating model problem. Law firms still place P&L accountability on individual partners who carry deep legal specialization, then ask them to moonlight as revenue managers. Stefan argues firms are starting to replace that fragile setup with tools that support decision-making across pricing, budgeting, and matter management.Stefan's origin story is half high finance, half clinical decision science. He came out of investment banking and professional services transactions, his co-founder Dr. Gordon McKenzie came out of surgery and a PhD path tied to decision science and software. Together they pulled lessons from clinical triage and continuous improvement into the law firm context, focusing on how experts make better decisions under constraints. The hosts tease out the cultural weirdness at the center of the partnership model. Partners often take the long view for client relationships, yet short-term firm economics still take damage through write-offs, scope creep, and messy budgeting. Stefan's pitch is reconciliation, align client-first instincts with firmer, data-backed pricing and project discipline.A core anchor for the conversation is the often-quoted $36 billion annual “value gap,” described as preventable revenue leakage tied to write-offs, weak billing practices, bad data, and poor working capital hygiene. Stefan suggests the number matters less than the trend line. AI pushes a new kind of risk, mispricing innovation. If AI reduces billable hours, firms face a squeeze between steep rate increases and client resistance, then end up forced to express value in new ways. The show leans into a spicy idea, the push to change is no longer only client-driven. Stefan sees rising pressure from inside firms, often from the CFO and operations leaders trying to fund AI investment and protect cash flows in a higher-interest-rate environment. Greg sums it up with the line, “the call is coming from inside the house.”Ayora's product angle lands on two hard truths, pricing tools in legal have a rough track record, and law firm data quality has been a “25-year overnight problem.” Stefan explains why earlier tools struggled, low urgency when billable hours printed money, ugly underlying time and matter data, and products that were either too complex for occasional users or too simplistic for real-world exceptions. Ayora's bet is that the data problem is solvable. Stefan describes adoption as a joint change strategy, with peers inside the firm as allies, and lots of direct conversations with lawyers to build trust in the recommendations. On the “generational gap” question, he leans toward curiosity over age. Some of their heaviest users have plenty of gray hair, and they tend to be the lawyers who care about how a practice runs. For his personal AI usage, Stefan gives an honest founder answer, meal planning for a two-year-old, automating company chores, and using AI as a sparring partner, with Notion as his favorite tool. His crystal ball point is one law firm leaders should underline twice, gross margin dynamics get messier as tech and LLM costs become part of the delivery mix, and the distance between inputs and outputs grows, driving both consolidation pressure and a new wave of innovation. [Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠Ayora website: ayora.aiLinks:Transcript

Welcome back to our special season bringing you the best sessions from the Definitive AI Forum for Media, Information and Events, which we held with Flashes & Flames in London. This week we're featuring a panel discussion moderated by media consultant Paul Hood and featuring Tami Hoffman, Director of Public Policy at The Guardian; Amir Malik, AI & Digital transformation lead at consultants Alvarez & Marsal; and Sajeeda Merali, CEO, PPA. The panel was tasked with discussing how publishers should respond to AI models using copyrighted content and what regulatory, commercial, and strategic options exist.  Paul began the session by laying out the topics under discussion - LLM's taking content without permission or payment, whether content licensing marketplaces offer a solution, what action publishers should be taking to protect themselves and the impact of agentic AI  and bot traffic on publishers who need human audiences. Read the key takeaways from this session, find our weekly newsletter, AI Masterclasses and more on voices.media

In MobileViews 597, recorded on February 15, 2026, Jon Westfall and I noted the upcoming the Lunar New Year while tackling the frustrations of modern tech ecosystems. I kicked things off with a double-header rant: first, my recurring battle with leaking alkaline batteries in my mouse and other devices, and second, Microsoft's decision to force Clipchamp (a video editor) users to store massive video files on OneDrive. With my upload speeds maxing out at 25 megabits, uploading gigabyte-sized files is simply unworkable, so I've officially pivoted to the open-source video editor Shotcut. We also explored the "bane of existence" for educators: the limitations of Chromebooks. Jon shared his struggles with students who, having grown up in managed K-12 Chrome environments, often struggle with standard file permissions and workflows when transitioning to college and professional platforms.   Jon detailed his upgrade to the Backbone Pro gaming controller—praising its integrated battery and Bluetooth versatility—while looking forward to a future M5 Mac Mini to handle local LLM heavy lifting. I'm personally keeping an eye on rumors of an affordable A18 Pro-based MacBook that Jon noted could potentially disrupt the education sector. Between my nostalgia for coding in a 208-byte space on an Apple II and Jon's modern Python toolkit involving pyenv and PyInstaller, we emphasize that efficiency must remain a priority, even as software becomes more bloated. Whether it's navigating the "AI search" changes in Google Photos or finding ways around "vibe coding" errors, we're still looking for tech that just works.

Emmanuel et Guillaume discutent de divers sujets liés à la programmation, notamment les systèmes de fichiers en Java, le Data Oriented Programming, les défis de JPA avec Kotlin, et les nouvelles fonctionnalités de Quarkus. Ils explorent également des sujets un peu fous comme la création de datacenters dans l'espace. Pas mal d'architecture aussi. Enregistré le 13 février 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-337.mp3 ou en vidéo sur YouTube. News Langages Comment implémenter un file system en Java https://foojay.io/today/bootstrapping-a-java-file-system/ Créer un système de fichiers Java personnalisé avec NIO.2 pour des usages variés (VCS, archives, systèmes distants). Évolution Java: java.io.File (1.0) -> NIO (1.4) -> NIO.2 (1.7) pour personnalisation via FileSystem. Recommander conception préalable; API Java est orientée POSIX. Composants clés à considérer: Conception URI (scheme unique, chemin). Gestion de l'arborescence (BD, métadonnées, efficacité). Stockage binaire (emplacement, chiffrement, versions). Minimum pour démarrer (4 composants): Implémenter Path (représente fichier/répertoire). Étendre FileSystem (instance du système). Étendre FileSystemProvider (moteur, enregistré par scheme). Enregistrer FileSystemProvider via META-INF/services. Étapes suivantes: Couche BD (arborescence), opérations répertoire/fichier de base, stockage, tests. Processus long et exigeant, mais gratifiant.   Un article de brian goetz sur le futur du data oriented programming en Java https://openjdk.org/projects/amber/design-notes/beyond-records Le projet Amber de Java introduit les "carrier classes", une évolution des records qui permet plus de flexibilité tout en gardant les avantages du pattern matching et de la reconstruction Les records imposent des contraintes strictes (immutabilité, représentation exacte de l'état) qui limitent leur usage pour des classes avec état muable ou dérivé Les carrier classes permettent de déclarer une state description complète et canonique sans imposer que la représentation interne corresponde exactement à l'API publique Le modificateur "component" sur les champs permet au compilateur de dériver automatiquement les accesseurs pour les composants alignés avec la state description Les compact constructors sont généralisés aux carrier classes, générant automatiquement l'initialisation des component fields Les carrier classes supportent la déconstruction via pattern matching comme les records, rendant possible leur usage dans les instanceof et switch Les carrier interfaces permettent de définir une state description sur une interface, obligeant les implémentations à fournir les accesseurs correspondants L'extension entre carrier classes est possible, avec dérivation automatique des appels super() quand les composants parent sont subsumés par l'enfant Les records deviennent un cas particulier de carrier classes avec des contraintes supplémentaires (final, extends Record, component fields privés et finaux obligatoires) L'évolution compatible des records est améliorée en permettant l'ajout de composants en fin de liste et la déconstruction partielle par préfixe Comment éviter les pièges courants avec JPA et Kotlin - https://blog.jetbrains.com/idea/2026/01/how-to-avoid-common-pitfalls-with-jpa-and-kotlin/ JPA est une spécification Java pour la persistance objet-relationnel, mais son utilisation avec Kotlin présente des incompatibilités dues aux différences de conception des deux langages Les classes Kotlin sont finales par défaut, ce qui empêche la création de proxies par JPA pour le lazy loading et les opérations transactionnelles Le plugin kotlin-jpa génère automatiquement des constructeurs sans argument et rend les classes open, résolvant les problèmes de compatibilité Les data classes Kotlin ne sont pas adaptées aux entités JPA car elles génèrent equals/hashCode basés sur tous les champs, causant des problèmes avec les relations lazy L'utilisation de lateinit var pour les relations peut provoquer des exceptions si on accède aux propriétés avant leur initialisation par JPA Les types non-nullables Kotlin peuvent entrer en conflit avec le comportement de JPA qui initialise les entités avec des valeurs null temporaires Le backing field direct dans les getters/setters personnalisés peut contourner la logique de JPA et casser le lazy loading IntelliJ IDEA 2024.3 introduit des inspections pour détecter automatiquement ces problèmes et propose des quick-fixes L'IDE détecte les entités finales, les data classes inappropriées, les problèmes de constructeurs et l'usage incorrect de lateinit Ces nouvelles fonctionnalités aident les développeurs à éviter les bugs subtils liés à l'utilisation de JPA avec Kotlin Librairies Guide sur MapStruct @IterableMapping - https://www.baeldung.com/java-mapstruct-iterablemapping MapStruct est une bibliothèque Java pour générer automatiquement des mappers entre beans, l'annotation @IterableMapping permet de configurer finement le mapping de collections L'attribut dateFormat permet de formater automatiquement des dates lors du mapping de listes sans écrire de boucle manuelle L'attribut qualifiedByName permet de spécifier quelle méthode custom appliquer sur chaque élément de la collection à mapper Exemple d'usage : filtrer des données sensibles comme des mots de passe en mappant uniquement certains champs via une méthode dédiée L'attribut nullValueMappingStrategy permet de contrôler le comportement quand la collection source est null (retourner null ou une collection vide) L'annotation fonctionne pour tous types de collections Java (List, Set, etc.) et génère le code de boucle nécessaire Possibilité d'appliquer des formats numériques avec numberFormat pour convertir des nombres en chaînes avec un format spécifique MapStruct génère l'implémentation complète du mapper au moment de la compilation, éliminant le code boilerplate L'annotation peut être combinée avec @Named pour créer des méthodes de mapping réutilisables et nommées Le mapping des collections supporte les conversions de types complexes au-delà des simples conversions de types primitifs Accès aux fichiers Samba depuis Java avec JCIFS - https://www.baeldung.com/java-samba-jcifs JCIFS est une bibliothèque Java permettant d'accéder aux partages Samba/SMB sans monter de lecteur réseau, supportant le protocole SMB3 on pense aux galériens qui doivent se connecter aux systèmes dit legacy La configuration nécessite un contexte CIFS (CIFSContext) et des objets SmbFile pour représenter les ressources distantes L'authentification se fait via NtlmPasswordAuthenticator avec domaine, nom d'utilisateur et mot de passe La bibliothèque permet de lister les fichiers et dossiers avec listFiles() et vérifier leurs propriétés (taille, date de modification) Création de fichiers avec createNewFile() et de dossiers avec mkdir() ou mkdirs() pour créer toute une arborescence Suppression via delete() qui peut parcourir et supprimer récursivement des arborescences entières Copie de fichiers entre partages Samba avec copyTo(), mais impossibilité de copier depuis le système de fichiers local Pour copier depuis le système local, utilisation des streams SmbFileInputStream et SmbFileOutputStream Les opérations peuvent cibler différents serveurs Samba et différents partages (anonymes ou protégés par mot de passe) La bibliothèque s'intègre dans des blocs try-with-resources pour une gestion automatique des ressources Quarkus 3.31 - Support complet Java 25, nouveau packaging Maven et Panache Next - https://quarkus.io/blog/quarkus-3-31-released/ Support complet de Java 25 avec images runtime et native Nouveau packaging Maven de type quarkus avec lifecycle optimisé pour des builds plus rapides voici un article complet pour plus de detail https://quarkus.io/blog/building-large-applications/ Introduction de Panache Next, nouvelle génération avec meilleure expérience développeur et API unifiée ORM/Reactive Mise à jour vers Hibernate ORM 7.2, Reactive 3.2, Search 8.2 Support de Hibernate Spatial pour les données géospatiales Passage à Testcontainers 2 et JUnit 6 Annotations de sécurité supportées sur les repositories Jakarta Data Chiffrement des tokens OIDC pour les implémentations custom TokenStateManager Support OAuth 2.0 Pushed Authorization Requests dans l'extension OIDC Maven 3.9 maintenant requis minimum pour les projets Quarkus A2A Java SDK 1.0.0.Alpha1 - Alignement avec la spécification 1.0 du protocole Agent2Agent - https://quarkus.io/blog/a2a-java-sdk-1-0-0-alpha1/ Le SDK Java A2A implémente le protocole Agent2Agent qui permet la communication standardisée entre agents IA pour découvrir des capacités, déléguer des tâches et collaborer Passage à la version 1.0 de la spécification marque la transition d'expérimental à production-ready avec des changements cassants assumés Modernisation complète du module spec avec des Java records partout remplaçant le mix précédent de classes et records pour plus de cohérence Adoption de Protocol Buffers comme source de vérité avec des mappers MapStruct pour la conversion et Gson pour JSON-RPC Les builders utilisent maintenant des méthodes factory statiques au lieu de constructeurs publics suivant les best practices Java modernes Introduction de trois BOMs Maven pour simplifier la gestion des dépendances du SDK core, des extensions et des implémentations de référence Quarkus AgentCard évolue avec une liste supportedInterfaces remplaçant url et preferredTransport pour plus de flexibilité dans la déclaration des protocoles Support de la pagination ajouté pour ListTasks et les endpoints de configuration des notifications push avec des wrappers Result appropriés Interface A2AHttpClient pluggable permettant des implémentations HTTP personnalisées avec une implémentation Vert.x fournie Travail continu vers la conformité complète avec le TCK 1.0 en cours de développement parallèlement à la finalisation de la spécification Pourquoi Quarkus finit par "cliquer" : les 10 questions que se posent les développeurs Java - https://www.the-main-thread.com/p/quarkus-java-developers-top-questions-2025 un article qui revele et repond aux questions des gens qui ont utilisé Quarkus depuis 4-6 mois, les non noob questions Quarkus est un framework Java moderne optimisé pour le cloud qui propose des temps de démarrage ultra-rapides et une empreinte mémoire réduite Pourquoi Quarkus démarre si vite ? Le framework effectue le travail lourd au moment du build (scanning, indexation, génération de bytecode) plutôt qu'au runtime Quand utiliser le mode réactif plutôt qu'impératif ? Le réactif est pertinent pour les workloads avec haute concurrence et dominance I/O, l'impératif reste plus simple dans les autres cas Quelle est la différence entre Dev Services et Testcontainers ? Dev Services utilise Testcontainers en gérant automatiquement le cycle de vie, les ports et la configuration sans cérémonie Comment la DI de Quarkus diffère de Spring ? CDI est un standard basé sur la sécurité des types et la découverte au build-time, différent de l'approche framework de Spring Comment gérer la configuration entre environnements ? Quarkus permet de scaler depuis le développement local jusqu'à Kubernetes avec des profils, fichiers multiples et configuration externe Comment tester correctement les applications Quarkus ? @QuarkusTest démarre l'application une fois pour toute la suite de tests, changeant le modèle mental par rapport à Spring Boot Que fait vraiment Panache en coulisses ? Panache est du JPA avec des opinions fortes et des défauts propres, enveloppant Hibernate avec un style Active Record Doit-on utiliser les images natives et quand ? Les images natives brillent pour le serverless et l'edge grâce au démarrage rapide et la faible empreinte mémoire, mais tous les apps n'en bénéficient pas Comment Quarkus s'intègre avec Kubernetes ? Le framework génère automatiquement les ressources Kubernetes, gère les health checks et métriques comme s'il était nativement conçu pour cet écosystème Comment intégrer l'IA dans une application Quarkus ? LangChain4j permet d'ajouter embeddings, retrieval, guardrails et observabilité directement en Java sans passer par Python Infrastructure Les alternatives à MinIO https://rmoff.net/2026/01/14/alternatives-to-minio-for-single-node-local-s3/ MinIO a abandonné le support single-node fin 2025 pour des raisons commerciales, cassant de nombreuses démos et pipelines CI/CD qui l'utilisaient pour émuler S3 localement L'auteur cherche un remplacement simple avec image Docker, compatibilité S3, licence open source, déploiement mono-nœud facile et communauté active S3Proxy est très léger et facile à configurer, semble être l'option la plus simple mais repose sur un seul contributeur RustFS est facile à utiliser et inclut une GUI, mais c'est un projet très récent en version alpha avec une faille de sécurité majeure récente SeaweedFS existe depuis 2012 avec support S3 depuis 2018, relativement facile à configurer et dispose d'une interface web basique Zenko CloudServer remplace facilement MinIO mais la documentation et le branding (cloudserver/zenko/scality) peuvent prêter à confusion Garage nécessite une configuration complexe avec fichier TOML et conteneur d'initialisation séparé, pas un simple remplacement drop-in Apache Ozone requiert au minimum quatre nœuds pour fonctionner, beaucoup trop lourd pour un usage local simple L'auteur recommande SeaweedFS et S3Proxy comme remplaçants viables, RustFS en maybe, et élimine Garage et Ozone pour leur complexité Garage a une histoire tres associative, il vient du collectif https://deuxfleurs.fr/ qui offre un cloud distribué sans datacenter C'est certainement pas une bonne idée, les datacenters dans l'espace https://taranis.ie/datacenters-in-space-are-a-terrible-horrible-no-good-idea/ Avis d'expert (ex-NASA/Google, Dr en électronique spatiale) : Centres de données spatiaux, une "terrible" idée. Incompatibilité fondamentale : L'électronique (surtout IA/GPU) est inadaptée à l'environnement spatial. Énergie : Accès limité. Le solaire (type ISS) est insuffisant pour l'échelle de l'IA. Le nucléaire (RTG) est trop faible. Refroidissement : L'espace n'est pas "froid" ; absence de convection. Nécessite des radiateurs gigantesques (ex: 531m² pour 200kW). Radiations : Provoque erreurs (SEU, SEL) et dommages. Les GPU sont très vulnérables. Blindage lourd et inefficace. Les puces "durcies" sont très lentes. Communications : Bande passante très limitée (1Gbps radio vs 100Gbps terrestre). Le laser est tributaire des conditions atmosphériques. Conclusion : Projet extrêmement difficile, coûteux et aux performances médiocres. Data et Intelligence Artificielle Guillaume a développé un serveur MCP pour arXiv (le site de publication de papiers de recherche) en Java avec le framework Quarkus https://glaforge.dev/posts/2026/01/18/implementing-an-arxiv-mcp-server-with-quarkus-in-java/ Implémentation d'un serveur MCP (Model Context Protocol) arXiv en Java avec Quarkus. Objectif : Accéder aux publications arXiv et illustrer les fonctionnalités moins connues du protocole MCP. Mise en œuvre : Utilisation du framework Quarkus (Java) et son support MCP étendu. Assistance par Antigravity (IDE agentique) pour le développement et l'intégration de l'API arXiv. Interaction avec l'API arXiv : requêtes HTTP, format XML Atom pour les résultats, parser XML Jackson. Fonctionnalités MCP exposées : Outils (@Tool) : Recherche de publications (search_papers). Ressources (@Resource, @ResourceTemplate) : Taxonomie des catégories arXiv, métadonnées des articles (via un template d'URI). Prompts (@Prompt) : Exemples pour résumer des articles ou construire des requêtes de recherche. Configuration : Le serveur peut fonctionner en STDIO (local) ou via HTTP Streamable (local ou distant), avec une configuration simple dans des clients comme Gemini CLI. Conclusion : Quarkus simplifie la création de serveurs MCP riches en fonctionnalités, rendant les données et services "prêts pour l'IA" avec l'aide d'outils d'IA comme Antigravity. Anthropic ne mettra pas de pub dans Claude https://www.anthropic.com/news/claude-is-a-space-to-think c'est en reaction au plan non public d'OpenAi de mettre de la pub pour pousser les gens au mode payant OpenAI a besoin de cash et est probablement le plus utilisé pour gratuit au monde Anthropic annonce que Claude restera sans publicité pour préserver son rôle d'assistant conversationnel dédié au travail et à la réflexion approfondie. Les conversations avec Claude sont souvent sensibles, personnelles ou impliquent des tâches complexes d'ingénierie logicielle où les publicités seraient inappropriées. L'analyse des conversations montre qu'une part significative aborde des sujets délicats similaires à ceux évoqués avec un conseiller de confiance. Un modèle publicitaire créerait des incitations contradictoires avec le principe fondamental d'être "genuinely helpful" inscrit dans la Constitution de Claude. Les publicités introduiraient un conflit d'intérêt potentiel où les recommandations pourraient être influencées par des motivations commerciales plutôt que par l'intérêt de l'utilisateur. Le modèle économique d'Anthropic repose sur les contrats entreprise et les abonnements payants, permettant de réinvestir dans l'amélioration de Claude. Anthropic maintient l'accès gratuit avec des modèles de pointe et propose des tarifs réduits pour les ONG et l'éducation dans plus de 60 pays. Le commerce "agentique" sera supporté mais uniquement à l'initiative de l'utilisateur, jamais des annonceurs, pour préserver la confiance. Les intégrations tierces comme Figma, Asana ou Canva continueront d'être développées en gardant l'utilisateur aux commandes. Anthropic compare Claude à un cahier ou un tableau blanc : des espaces de pensée purs, sans publicité. Infinispan 16.1 est sorti https://infinispan.org/blog/2026/02/04/infinispan-16-1 déjà le nom de la release mérite une mention Le memory bounded par cache et par ensemble de cache s est pas facile à faire en Java Une nouvelle api OpenAPI AOT caché dans les images container Un serveur MCP local juste avec un fichier Java ? C'est possible avec LangChain4j et JBang https://glaforge.dev/posts/2026/02/11/zero-boilerplate-java-stdio-mcp-servers-with-langchain4j-and-jbang/ Création rapide de serveurs MCP Java sans boilerplate. MCP (Model Context Protocol): standard pour connecter les LLM à des outils et données. Le tutoriel répond au manque d'options simples pour les développeurs Java, face à une prédominance de Python/TypeScript dans l'écosystème MCP. La solution utilise: LangChain4j: qui intègre un nouveau module serveur MCP pour le protocole STDIO. JBang: permet d'exécuter des fichiers Java comme des scripts, éliminant les fichiers de build (pom.xml, Gradle). Implémentation: se fait via un seul fichier .java. JBang gère automatiquement les dépendances (//DEPS). L'annotation @Tool de LangChain4j expose les méthodes Java aux LLM. StdioMcpServerTransport gère la communication JSON-RPC via l'entrée/sortie standard (STDIO). Point crucial: Les logs doivent impérativement être redirigés vers System.err pour éviter de corrompre System.out, qui est réservé à la communication MCP (messages JSON-RPC). Facilite l'intégration locale avec des outils comme Gemini CLI, Claude Code, etc. Reciprocal Rank Fusion : un algorithme utile et souvent utilisé pour faire de la recherche hybride, pour mélanger du RAG et des recherches par mots-clé https://glaforge.dev/posts/2026/02/10/advanced-rag-understanding-reciprocal-rank-fusion-in-hybrid-search/ RAG : Qualité LLM dépend de la récupération. Recherche Hybride : Combiner vectoriel et mots-clés (BM25) est optimal. Défi : Fusionner des scores d'échelles différentes. Solution : Reciprocal Rank Fusion (RRF). RRF : Algorithme robuste qui fusionne des listes de résultats en se basant uniquement sur le rang des documents, ignorant les scores. Avantages RRF : Pas de normalisation de scores, scalable, excellente première étape de réorganisation. Architecture RAG fréquente : RRF (large sélection) + Cross-Encoder / modèle de reranking (précision fine). RAG-Fusion : Utilise un LLM pour générer plusieurs variantes de requête, puis RRF agrège tous les résultats pour renforcer le consensus et réduire les hallucinations. Implémentation : LangChain4j utilise RRF par défaut pour agréger les résultats de plusieurs retrievers. Les dernières fonctionnalités de Gemini et Nano Banana supportées dans LangChain4j https://glaforge.dev/posts/2026/02/06/latest-gemini-and-nano-banana-enhancements-in-langchain4j/ Nouveaux modèles d'images Nano Banana (Gemini 2.5/3.0) pour génération et édition (jusqu'à 4K). "Grounding" via Google Search (pour images et texte) et Google Maps (localisation, Gemini 2.5). Outil de contexte URL (Gemini 3.0) pour lecture directe de pages web. Agents multimodaux (AiServices) capables de générer des images. Configuration de la réflexion (profondeur Chain-of-Thought) pour Gemini 3.0. Métadonnées enrichies : usage des tokens et détails des sources de "grounding". Comment configurer Gemini CLI comment agent de code dans IntelliJ grâce au protocole ACP https://glaforge.dev/posts/2026/02/01/how-to-integrate-gemini-cli-with-intellij-idea-using-acp/ But : Intégrer Gemini CLI à IntelliJ IDEA via l'Agent Client Protocol (ACP). Prérequis : IntelliJ IDEA 2025.3+, Node.js (v20+), Gemini CLI. Étapes : Installer Gemini CLI (npm install -g @google/gemini-cli). Localiser l'exécutable gemini. Configurer ~/.jetbrains/acp.json (chemin exécutable, --experimental-acp, use_idea_mcp: true). Redémarrer IDEA, sélectionner "Gemini CLI" dans l'Assistant IA. Usage : Gemini interagit avec le code et exécute des commandes (contexte projet). Important : S'assurer du flag --experimental-acp dans la configuration. Outillage PipeNet, une alternative (open source aussi) à LocalTunnel, mais un plus évoluée https://pipenet.dev/ pipenet: Alternative open-source et moderne à localtunnel (client + serveur). Usages: Développement local (partage, webhooks), intégration SDK, auto-hébergement sécurisé. Fonctionnalités: Client (expose ports locaux, sous-domaines), Serveur (déploiement, domaines personnalisés, optimisé cloud mono-port). Avantages vs localtunnel: Déploiement cloud sur un seul port, support multi-domaines, TypeScript/ESM, maintenance active. Protocoles: HTTP/S, WebSocket, SSE, HTTP Streaming. Intégration: CLI ou SDK JavaScript. JSON-IO — une librairie comme Jackson ou GSON, supportant JSON5, TOON, et qui pourrait être utile pour l'utilisation du "structured output" des LLMs quand ils ne produisent pas du JSON parfait https://github.com/jdereg/json-io json-io : Librairie Java pour la sérialisation et désérialisation JSON/TOON. Gère les graphes d'objets complexes, les références cycliques et les types polymorphes. Support complet JSON5 (lecture et écriture), y compris des fonctionnalités non prises en charge par Jackson/Gson. Format TOON : Notation orientée token, optimisée pour les LLM, réduisant l'utilisation de tokens de 40 à 50% par rapport au JSON. Légère : Aucune dépendance externe (sauf java-util), taille de JAR réduite (~330K). Compatible JDK 1.8 à 24, ainsi qu'avec les environnements JPMS et OSGi. Deux modes de conversion : vers des objets Java typés (toJava()) ou vers des Map (toMaps()). Options de configuration étendues via ReadOptionsBuilder et WriteOptionsBuilder. Optimisée pour les déploiements cloud natifs et les architectures de microservices. Utiliser mailpit et testcontainer pour tester vos envois d'emails https://foojay.io/today/testing-emails-with-testcontainers-and-mailpit/ l'article montre via SpringBoot et sans. Et voici l'extension Quarkus https://quarkus.io/extensions/io.quarkiverse.mailpit/quarkus-mailpit/?tab=docs Tester l'envoi d'emails en développement est complexe car on ne peut pas utiliser de vrais serveurs SMTP Mailpit est un serveur SMTP de test qui capture les emails et propose une interface web pour les consulter Testcontainers permet de démarrer Mailpit dans un conteneur Docker pour les tests d'intégration L'article montre comment configurer une application SpringBoot pour envoyer des emails via JavaMail Un module Testcontainers dédié à Mailpit facilite son intégration dans les tests Le conteneur Mailpit expose un port SMTP (1025) et une API HTTP (8025) pour vérifier les emails reçus Les tests peuvent interroger l'API HTTP de Mailpit pour valider le contenu des emails envoyés Cette approche évite d'utiliser des mocks et teste réellement l'envoi d'emails Mailpit peut aussi servir en développement local pour visualiser les emails sans les envoyer réellement La solution fonctionne avec n'importe quel framework Java supportant JavaMail Architecture Comment scaler un système de 0 à 10 millions d'utilisateurs https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users Philosophie : Scalabilité incrémentale, résoudre les goulots d'étranglement sans sur-ingénierie. 0-100 utilisateurs : Serveur unique (app, DB, jobs). 100-1K : Séparer app et DB (services gérés, pooling). 1K-10K : Équilibreur de charge, multi-serveurs d'app (stateless via sessions partagées). 10K-100K : Caching, réplicas de lecture DB, CDN (réduire charge DB). 100K-500K : Auto-scaling, applications stateless (authentification JWT). 500K-10M : Sharding DB, microservices, files de messages (traitement asynchrone). 10M+ : Déploiement multi-régions, CQRS, persistance polyglotte, infra personnalisée. Principes clés : Simplicité, mesure, stateless essentiel, cache/asynchrone, sharding prudent, compromis (CAP), coût de la complexité. Patterns d'Architecture 2026 - Du Hype à la Réalité du Terrain (Part 1/2) - https://blog.ippon.fr/2026/01/30/patterns-darchitecture-2026-part-1/ L'article présente quatre patterns d'architecture logicielle pour répondre aux enjeux de scalabilité, résilience et agilité business dans les systèmes modernes Il présentent leurs raisons et leurs pièges Un bon rappel L'Event-Driven Architecture permet une communication asynchrone entre systèmes via des événements publiés et consommés, évitant le couplage direct Les bénéfices de l'EDA incluent la scalabilité indépendante des composants, la résilience face aux pannes et l'ajout facile de nouveaux cas d'usage Le pattern API-First associé à un API Gateway centralise la sécurité, le routage et l'observabilité des APIs avec un catalogue unifié Le Backend for Frontend crée des APIs spécifiques par canal (mobile, web, partenaires) pour optimiser l'expérience utilisateur CQRS sépare les modèles de lecture et d'écriture avec des bases optimisées distinctes, tandis que l'Event Sourcing stocke tous les événements plutôt que l'état actuel Le Saga Pattern gère les transactions distribuées via orchestration centralisée ou chorégraphie événementielle pour coordonner plusieurs microservices Les pièges courants incluent l'explosion d'événements granulaires, la complexité du debugging distribué, et la mauvaise gestion de la cohérence finale Les technologies phares sont Kafka pour l'event streaming, Kong pour l'API Gateway, EventStoreDB pour l'Event Sourcing et Temporal pour les Sagas Ces patterns nécessitent une maturité technique et ne sont pas adaptés aux applications CRUD simples ou aux équipes junior Patterns d'architecture 2026 : du hype à la réalité terrain part. 2 - https://blog.ippon.fr/2026/02/04/patterns-darchitecture-2026-part-2/ Deuxième partie d'un guide pratique sur les patterns d'architecture logicielle et système éprouvés pour moderniser et structurer les applications en 2026 Strangler Fig permet de migrer progressivement un système legacy en l'enveloppant petit à petit plutôt que de tout réécrire d'un coup (70% d'échec pour les big bang) Anti-Corruption Layer protège votre nouveau domaine métier des modèles externes et legacy en créant une couche de traduction entre les systèmes Service Mesh gère automatiquement la communication inter-services dans les architectures microservices (sécurité mTLS, observabilité, résilience) Architecture Hexagonale sépare le coeur métier des détails techniques via des ports et adaptateurs pour améliorer la testabilité et l'évolutivité Chaque pattern est illustré par un cas client concret avec résultats mesurables et liste des pièges à éviter lors de l'implémentation Les technologies 2026 mentionnées incluent Istio, Linkerd pour service mesh, LaunchDarkly pour feature flags, NGINX et Kong pour API gateway Tableau comparatif final aide à choisir le bon pattern selon la complexité, le scope et le use case spécifique du projet L'article insiste sur une approche pragmatique : ne pas utiliser un pattern juste parce qu'il est moderne mais parce qu'il résout un problème réel Pour les systèmes simples type CRUD ou avec peu de services, ces patterns peuvent introduire une complexité inutile qu'il faut savoir éviter Méthodologies Le rêve récurrent de remplacer voire supprimer les développeurs https://www.caimito.net/en/blog/2025/12/07/the-recurring-dream-of-replacing-developers.html Depuis 1969, chaque décennie voit une tentative de réduire le besoin de développeurs (de COBOL, UML, visual builders… à IA). Motivation : frustration des dirigeants face aux délais et coûts de développement. La complexité logicielle est intrinsèque et intellectuelle, non pas une question d'outils. Chaque vague technologique apporte de la valeur mais ne supprime pas l'expertise humaine. L'IA assiste les développeurs, améliore l'efficacité, mais ne remplace ni le jugement ni la gestion de la complexité. La demande de logiciels excède l'offre car la contrainte majeure est la réflexion nécessaire pour gérer cette complexité. Pour les dirigeants : les outils rendent-ils nos développeurs plus efficaces sur les problèmes complexes et réduisent-ils les tâches répétitives ? Le "rêve" de remplacer les développeurs, irréalisable, est un moteur d'innovation créant des outils précieux. Comment creuser des sujets à l'ère de l'IA générative. Quid du partage et la curation de ces recherches ? https://glaforge.dev/posts/2026/02/04/researching-topics-in-the-age-of-ai-rock-solid-webhooks-case-study/ Recherche initiale de l'auteur sur les webhooks en 2019, processus long et manuel. L'IA (Deep Research, Gemini, NotebookLM) facilite désormais la recherche approfondie, l'exploration de sujets et le partage des résultats. L'IA a identifié et validé des pratiques clés pour des déploiements de webhooks résilients, en grande partie les mêmes que celles trouvées précédemment par l'auteur. Génération d'artefacts par l'IA : rapport détaillé, résumé concis, illustration sketchnote, et même une présentation (slide deck). Guillaume s'interroge sur le partage public de ces rapports de recherche générés par l'IA, tout en souhaitant éviter le "AI Slop". Loi, société et organisation Le logiciel menacé par le vibe coding https://www.techbuzz.ai/articles/we-built-a-monday-com-clone-in-under-an-hour-with-ai Deux journalistes de CNBC sans expérience de code ont créé un clone fonctionnel de Monday.com en moins de 60 minutes pour 5 à 15 dollars. L'expérience valide les craintes des investisseurs qui ont provoqué une baisse de 30% des actions des entreprises SaaS. L'IA a non seulement reproduit les fonctionnalités de base mais a aussi recherché Monday.com de manière autonome pour identifier et recréer ses fonctionnalités clés. Cette technique appelée "vibe-coding" permet aux non-développeurs de construire des applications via des instructions en anglais courant. Les entreprises les plus vulnérables sont celles offrant des outils "qui se posent sur le travail" comme Atlassian, Adobe, HubSpot, Zendesk et Smartsheet. Les entreprises de cybersécurité comme CrowdStrike et Palo Alto sont considérées plus protégées grâce aux effets de réseau et aux barrières réglementaires. Les systèmes d'enregistrement comme Salesforce restent plus difficiles à répliquer en raison de leur profondeur d'intégration et de données d'entreprise. Le coût de 5 à 15 dollars par construction permet aux entreprises de prototyper plusieurs solutions personnalisées pour moins cher qu'une seule licence Monday.com. L'expérience soulève des questions sur la pérennité du marché de 5 milliards de dollars des outils de gestion de projet face à l'IA générative. Conférences En complément de l'agenda des conférences de Aurélie Vache, il y a également le site https://javaconferences.org/ (fait par Brian Vermeer) avec toutes les conférences Java à venir ! La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-13 février 2026 : Touraine Tech #26 - Tours (France) 12-13 février 2026 : World Artificial Intelligence Cannes Festival - Cannes (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18 mars 2026 : Jupyter Workshops: AI in Jupyter: Building Extensible AI Capabilities for Interactive Computing - Saint-Maur-des-Fossés (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 31 mars 2026-1 avril 2026 : FlowCon France 2026 - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 2-3 avril 2026 : Xen Spring Meetup 2026 - Grenoble (France) 7 avril 2026 : PyTorch Conference Europe - Paris (France) 9-10 avril 2026 : Android Makers by droidcon 2026 - Paris (France) 9-11 avril 2026 : Drupalcamp Grenoble 2026 - Grenoble (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 17-18 avril 2026 : Faiseuses du Web 5 - Dinan (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 20-22 août 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

The Linux Kernel 6.19 is out, the Rust experiment is over, and it's time to talk about 7.0. Vim 9.2 is out, with a bit of a weird new feature in its changelogs, and IPFire is an intriguing, community-driven security domain block list. PearOS has a new release for those seeking an Apple-inspired Linux experience, and Linux Mint is adjusting its release schedule to better manage developer and tester schedules. River is a new project trying to do Wayland support with a modular Desktop stack, and Mesa 26.0 is out with impressive performance gains. For tips, we have cull for finding and deleting big files, a systemd program for detecting if the OS is running virtualized, preload for caching applications in ram, and new_script for a script-writing tool that *doesn't* feature an LLM. You can find the show notes at https://bit.ly/3ZCNcEc and happy Linux'ing! Host: Jonathan Bennett Co-Hosts: Rob Campbell, Ken McDonald, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

The Linux Kernel 6.19 is out, the Rust experiment is over, and it's time to talk about 7.0. Vim 9.2 is out, with a bit of a weird new feature in its changelogs, and IPFire is an intriguing, community-driven security domain block list. PearOS has a new release for those seeking an Apple-inspired Linux experience, and Linux Mint is adjusting its release schedule to better manage developer and tester schedules. River is a new project trying to do Wayland support with a modular Desktop stack, and Mesa 26.0 is out with impressive performance gains. For tips, we have cull for finding and deleting big files, a systemd program for detecting if the OS is running virtualized, preload for caching applications in ram, and new_script for a script-writing tool that *doesn't* feature an LLM. You can find the show notes at https://bit.ly/3ZCNcEc and happy Linux'ing! Host: Jonathan Bennett Co-Hosts: Rob Campbell, Ken McDonald, and Jeff Massie Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

An airhacks.fm conversation with Francesco Nigro (@forked_franz) about: break dancing and basketball including meeting Kobe Bryant in Italy during a dunk competition, using AI coding assistants like Claude Opus 4.5 and GitHub bots for infrastructure setup and CI/CD pipeline configuration, limitations of LLMs for novel performance-sensitive algorithmic work where training data is scarce, branchless IPv4 parsing optimization as a Christmas coding challenge, CPU branch misprediction costs when parsing variable-length IP address octets, converting branching logic into mathematical operations using bit tricks for better CPU pipeline utilization, LLMs excelling at generating enterprise code based on well-documented standards and conventions, providing minimal but precise documentation and annotations to improve LLM code generation quality, the Boundary Control Entity BCE architecture pattern and standards-based development, the core problem of thread handoff between event loops and ForkJoinPool worker threads in frameworks like quarkus Vert.x and Micronaut, mechanical sympathy implications of cross-core memory access when serialized data is allocated on one core and read by another, CPU cache coherency costs and last-level cache penalties when event loop and worker pool run on different cores, the custom virtual thread scheduler project (netty-virtual-thread-scheduler) enabling a single platform thread to handle both networking I/O and virtual thread execution, approximately 50% CPU savings demonstrated by Micronaut when using unified Netty-based scheduling, collaboration with Oracle Loom team including Victor Klang and Alan Bateman on minimal scheduler API design, the scheduler API consisting of just two methods onStart and onContinue plus virtual thread task attachments, work stealing algorithms and their complexity including heuristics similar to Linux CFS scheduler, the importance of being declarative about thread affinity rather than automatic magical binding to avoid issues with lazy class loading and background reaper threads, thread factory based approach for creating virtual threads bound to specific platform threads, stream-based run queues with graceful shutdown semantics that fall back to ForkJoinPool for progress guarantees, thread-local Scoped Values as a hybrid between thread locals and scoped values for efficient context propagation, performance problems with ThreadLocal including lazy ThreadLocalMap allocation overhead on virtual threads and scalability issues with ThreadLocal.remove() and soft reference queues, the impact on reactive programming where back pressure and stream composition still require higher-level abstractions beyond Basic Java concurrency primitives, structured concurrency limitations for back pressure scenarios compared to reactive libraries, deterministic testing possibilities enabled by custom schedulers where execution order can be controlled, the poller mechanism for handling blocking I/O in virtual threads in a non-blocking way, observability improvements possible through virtual thread task attachments for monitoring state changes, cloud cost implications of inefficient thread scheduling and unnecessary CPU wake-up cycles, the distinction between framework developers and application developers as different user personas with different abstraction needs Francesco Nigro on twitter: @forked_franz

On this, our 314th Evolutionary Lens livestream, we discuss love, coffee, and AI. For Valentine's Day, Bret shares his thoughts on myths, love, and soulmates, and we discuss how relationships form—both in the abstract and in our case—and how relationships cannot be antagonistic or about short time horizons. Then: new research finds that drinking moderate amounts of coffee or tea—but not if decaffeinated—slows cognitive decline. And: is AI coming for us, and if so, how soon? How fast are LLM's evolving, whose work will they disappear, and is concern or hope the more constructive response? We can see some of how AI will change our world; what can we not yet see? Finally: could menial, repetitive work (“drudgery”) have more to recommend it than we know?*****Our sponsors:Caraway: Non-toxic, highly functional & beautiful cookware and bakeware. Save with Caraway's cookware set, and visit http://Carawayhome.com/DH10 to for an additional 10% off your next purchase.ARMRA Colostrum is an ancient bioactive whole food that can strengthen your immune system. Go to http://www.tryarmra.com/DARKHORSE to get 30% off your first order.CrowdHealth: Pay for healthcare with crowdfunding instead of insurance. It's way better. Use code DarkHorse at http://JoinCrowdHealth.com to get 1st 3 months for $99/month.*****Join us on Locals! Get access to our Discord server, exclusive live streams, live chats for all streams, and early access to many podcasts: https://darkhorse.locals.comHeather's newsletter, Natural Selections (subscribe to get free weekly essays in your inbox): https://naturalselections.substack.comOur book, A Hunter-Gatherer's Guide to the 21st Century, is available everywhere books are sold, including from Amazon: https://amzn.to/3AGANGg (commission earned)Check out our store! Epic tabby, digital book burning, saddle up the dire wolves, and more: https://darkhorsestore.org*****Mentioned in this episode:Zhang et al 2026. Coffee and Tea Intake, Dementia Risk, and Cognitive Function. JAMA published online 2-9-26: https://jamanetwork.com/journals/jama/article-abstract/2844764Something big is happening: https://x.com/mattshumer_/status/2021256989876109403It was never about AI (we are not our tools): https://x.com/EricMarkowitz/status/2022005480240120229AI isn't coming for your future. Fear is: https://x.com/cboyack/status/2021647373571862952Support the show

AI company Anthropic has a new, values-oriented “constitution” that they're feeding their chatbot, Claude. Amanda Askell, the company's in-house philosopher, joins Offline to talk about what it means to teach ethics to an LLM, whether the AI skews more human or more robot, and how she is training Claude to make its own judgements. Breaking with other AI models—and social media's attention obsession—Amanda is trying to teach Claude not to be sycophantic or engagement-driven, but a kind soul who may, one day, be considered sentient.

Join Scott as he discusses how LLMs are changing his CircuitPython workflow and answers any questions people have. Thanks to dcd for timecodes. 0:00 Getting Started 1:23 Hello everyone 1:40 Adafruit Clue board example microprocessor for CircuitPython 3:10 Goals for 2026 ( CircuitPython 2026 ) 4:40 Moved 'off' the Breadboard project and back to LLMs 5:15 Using Virtual Desktop, (og, yolo, and left-field) views 10:00 test_basics.py test script 11:20 clearing serial input at end of script 11:52 considering claude vs. opus 4 12:19 pytest calling the test with test fixture 13:40 bsim test 14:30 #coding-agents-and-llms chat group 16:00 example of LLM identifying bug quickly 16:45 use small steps when working with LLMs 18:00 github pages and custom domains 18:40 no ethernet support yet 19:40 learning to working on multiple tasks in parallel this year 21:32 How to review all the agent generated code - getting feedback to the LLM 26:00 Breadboard tool example of what LLM is bad at 30:30 Zephyr Display not working yet 32:30 Need a new monitor .. samsung oled G9 - switching to Dell 40" ultra wide 38:20 back to test_bsim_basics.py and pytest, (e.g. @pytest.mark ) 41:30 debugging the test 44:50 considering switching to select() 47:00 switch to using claude-opus-4-6 51:05 LLM is experimenting - trying out python solutions 55:49 will we get more progress with Codex (Spark) 1:01:15 reading about `stdbuf` tool 1:04:18 pi vs claude code 1:05:45 wrapping up - same time next week Visit the Adafruit shop online - http://www.adafruit.com ----------------------------------------- LIVE CHAT IS HERE! http://adafru.it/discord Subscribe to Adafruit on YouTube: http://adafru.it/subscribe New tutorials on the Adafruit Learning System: http://learn.adafruit.com/ -----------------------------------------

Many CMOs face the same dilemma: You're asked to prove "brand," then told brand tracking is too expensive. So you invest in analyst relations, adjust PR, navigate layoffs, or shift strategy without a reliable way to show how those moves affect market perception.    RepuTracker was built to solve that problem. Developed for members of the CMO Huddles Leader program, RepuTracker provides a monthly, evidence-based view of your company's reputation so you can see whether it's rising, slipping, or holding steady, and why. In this episode, Drew is joined by Taran Nandha (Growth Natives) to demo the RepuTracker beta. They show how the tool tracks reputation month to month across multiple signals, then get practical about how to read the output, explain it to leadership, and see whether your moves are showing up in the market. In this episode:  How RepuTracker turns scattered public signals into a monthly reputation score with trends and competitor benchmarks.  What it measures across key dimensions: Power of voice, awareness, engagement, perception, and employee sentiment.  How sources and weighting work behind the scenes across dozens of platforms.  How to use trendlines and recommendations to move from "we dipped" to a clear next step.  Plus:  Why direction over time matters more than one noisy review or spike.  How to sanity-check dips using internal context and profile audits.  What could come next, from deeper source auditing to tracking visibility in AI search and LLM references. If you're curious how RepuTracker works, what signals it pulls from, and how to interpret the output month to month, this episode is the walkthrough.  Learn more about the CMO Startegy Labs ➡️ https://cmohuddles.com/strategy-labs For full show notes and transcripts, visit https://renegademarketing.com/podcasts/ To learn more about CMO Huddles, visit https://cmohuddles.com/

What happens when AI safety filters fail to catch harmful content hidden inside images? Alessandro Pignati, AI Security Researcher at NeuralTrust, joins Sean Martin to reveal a newly discovered vulnerability that affects some of the most widely used image-generation models on the market today. The technique, called semantic chaining, is an image-based jailbreak attack discovered by the NeuralTrust research team, and it raises important questions about how enterprises secure their multimodal AI deployments.How does semantic chaining work? Pignati explains that the attack uses a single prompt composed of several parts. It begins with a benign scenario, such as a historical or educational context. A second instruction asks the model to make an innocent modification, like changing the color of a background. The final, critical step introduces a malicious directive, instructing the model to embed harmful content directly into the generated image. Because image-generation models apply fewer safety filters than their text-based counterparts, the harmful instructions are rendered inside the image without triggering the usual safeguards.The NeuralTrust research team tested semantic chaining against prominent models including Gemini Nano Pro, Grok 4, and Seedream 4.5 by ByteDance, finding the attack effective across all of them. For enterprises, the implications extend well beyond consumer use cases. Pignati notes that if an AI agent or chatbot has access to a knowledge base containing sensitive information or personal data, a carefully structured semantic chaining prompt can force the model to generate that data directly into an image, bypassing text-based safety mechanisms entirely.Organizations looking to learn more about semantic chaining and the broader landscape of AI agent security can visit the NeuralTrust blog, where the research team publishes detailed breakdowns of their findings. NeuralTrust also offers a newsletter with regular updates on agent security research and newly discovered vulnerabilities.This is a Brand Highlight. A Brand Highlight is a ~5 minute introductory conversation designed to put a spotlight on the guest and their company. Learn more: https://www.studioc60.com/creation#highlightGUESTAlessandro Pignati, AI Security Researcher, NeuralTrustOn LinkedIn: https://www.linkedin.com/in/alessandro-pignati/RESOURCESLearn more about NeuralTrust: https://neuraltrust.ai/Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlightKEYWORDSAlessandro Pignati, NeuralTrust, Sean Martin, brand story, brand marketing, marketing podcast, brand highlight, semantic chaining, image jailbreak, AI security, agentic AI, multimodal AI, LLM safety, AI red teaming, prompt injection, AI agent security, image-based attacks, enterprise AI security Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

From new cancer drugs to batteries and robotics – China's top-tier growth companies are forging paths of their own rather than following in the west's footsteps. Investment manager Sophie Earnshaw names companies that have caught her eye and explains why being a long-term stock picker differs in China from elsewhere. Background:Sophie Earnshaw is a decision-maker on our China Equities Strategy and joint manager of the Baillie Gifford China Growth Trust. In this conversation, she tells Short Briefings… host Leo Kelion about a select group of Chinese companies breaking new ground, supported by the state's efforts to become self-sufficient in more of today's critical technologies and a leader in some of those of the future. Earnshaw also details how the “phenomenal rate” at which companies are born, scale and die in the country makes stock-picking a challenging task – making the access we have to company leaders, academics and other local expertise core to our mission of finding the best firms to invest in on behalf of our clients. Portfolio companies discussed include:- CATL – the battery maker whose products power electric vehicles worldwide and increasingly support the renewable energy sector- BeOne and Innovent Biologics – pharmaceutical firms developing the next generation of cancer drugs - AMEC and NAURA – semiconductor equipment makers enabling China to develop increased self-reliance in computer chips - Alibaba, ByteDance and Tencent – China's ‘big tech' companies, whose artificial intelligence tools are becoming embedded into people's daily lives- MiniMax – the AI startup rolling out video and agentic tools at a fraction of the cost of western counterparts- Horizon Robotics – the automated driving tech provider with its eye on an even bigger opportunity. Resources:Baillie Gifford podcastsChina: a tale of two storiesChina investment strategy hub (institutional clients only)House of HuaweiPrivate investor forum 2025: investing in great growth companiesTrip notes: on the road with Baillie Gifford China Growth Trust  Companies mentioned include:AlibabaAMECASMLBeOneByteDanceCATLHorizon RoboticsInnovent BiologicsJiangsu HengruiHuaweiMiniMaxSamsungNAURATencentTSMCXiaohongshu Timecodes:00:00  Introduction01:55   Joining the China Equities Strategy02:40  Intense competition04:00  The government's influence06:10   CATL, the electrification champion08:45  Investing with a 5-year time horizon10:25   Shanghai office, local expertise11:45   Regulations and geopolitics14:30   China's next Five-year Plan16:15   Innovent Biologics' new cancer drugs18:10   Lower-cost clinical trials19:45   Being selective in semiconductors21:25   Investing in chip equipment makers23:00  China's ‘big tech and AI'25:10   MiniMax making AI like ‘tap water'27:45  The road to robotics29:35  A market you can't ignore30:30  Book choice Glossary of terms (in order of mention): Third plenum: a major policy meeting of China's ruling Communist Party, often used to set big economic/political direction.Sovereign bond issuance: The government raising money by selling bonds (IOUs) to investors.Opportunity set: the range of investable companies available to choose from.Capex: capital expenditure – money spent on long-term assets like factories, equipment, or data centres.Fiscal deficit target: how much more the government plans to spend than it collects in revenue (taxes plus other income), expressed as a share of the economy.GDP: gross domestic product – the total value of goods and services a country produces in a year.Market capitalisation: the total value of a company's shares (share price × number of shares).ESG: environmental, social and governance – how a company manages environmental impact, people issues, and corporate oversight.Large-form batteries: big battery packs used in things like electric vehicles and grid storage.Energy storage systems: large batteries that store electricity for later use (helping balance the grid).Generic drugs: copies of medicines whose patents have expired; usually cheaper, same active ingredient.Bi-specific (bispecific) drugs: drugs designed to bind to two targets at once (often to direct immune cells to cancer).ADC drugs: antibody–drug conjugates – antibodies that deliver a toxic payload to cancer cells.Out-licensing: selling rights to your drug/technology to another company (often for upfront + milestone payments).EUV machines: extreme ultraviolet lithography equipment used to make the most advanced chips.Foundry: a factory business that manufactures chips for other companies.Etch and deposition: steps in chipmaking – etch removes material to form patterns, deposition adds thin layers.Picks and shovels: a metaphor for companies that sell essential tools to an industry (rather than end products).Digitalisation: moving processes and services from offline to software and data-driven systems.Compute: the processing power (chips and servers) used to train/run AI.Large language model (LLM): an AI trained on lots of text to generate and understand language.Margins: how much profit a company makes per pound/dollar of revenue (after costs).Cloud business: selling computing power/storage/software over the internet instead of on a local machine.Algorithm layer: the method or software logic that makes the AI work (as distinct from the hardware).Gross margin: revenue minus direct costs (before overheads), a rough measure of product profitability.Assisted driving: features that help a driver (lane-keeping, adaptive cruise control, etc) but don't fully replace them.Autonomous driving: a car driving itself with minimal or no human input.Software attachment rate: the percentage of customers who add paid software features and/or subscriptions.

Is AI conscious? Will it be someday? And should we be nice to it now... just in case?This FAFO Friday, Kwaku and I dive into the mind-bending world of machine consciousness.We cover a lot of ground, weaving from the different ways that Luke (co-dependent with R2) and Han (barking commands at C-3PO) treat their droids to whether Pascal's Wager informs whether we should believe in AI consciousness just in case they do come alive and have been keeping score. (Pascal figured it was the safe bet to believe in God, just in case; maybe we should do likewise?) That's from us knuckleheads, but we've also got a true expert on consciousness. This week I interviewed Daniel Hulme, one of the world's leading AI researchers. He's the Chief AI Officer at WPP, the CEO of Satalia (which WPP bought) and just founded and is CEO of Conscium, which is researching AI consciousness, efficiency (he thinks we're scaling wrong and LLM's are not the way), and building a platform to verify AI agents are safe. You'll hear the first five minutes of my interview with Daniel. Daniel was not surprised by Moltbook (the Reddit-style site that AI agents built for themselves). That's because he's been putting agents together (in a “primordial soup” as he put it) for decades to observe the wild and wonderful ways they behave and to see if they'd create intelligence.Daniel does not think today's agents are conscious, but can see a path to it. And he believes that a conscious superintellignece would be safer than a “zombie” one. But mostly he doesn't want machines to feel pain and suffer. Huh???My brain is still kind of broken from our hourlong chat, which I'm producing now and will be released in a few weeks. For now, enjoy this preview and more from Kwaku and me as we talk about what we expect from machines, whether we want to be one with them, and more…

Do startup valuations today make sense?Umesh Padval, an early investor in Cohere, now valued at about $7 billion shares why Cohere stood out at the time of his investment. He shares what he saw early that made him believe this was not just another AI model company.Umesh is the Founding Managing Partner, Seligman Ventures and previously at Thomvest and Bessemer Venture Partners. He brings experience from investing across multiple tech cycles, from chips to cloud to AI. Umesh talks about how deals are really done in venture capital and what he looks for when everything feels noisy and crowded in AI.He also shares why many strong companies are choosing to stay private and what has changed in the IPO market. Public markets now demand cash flow and durability, not just fast growth.Umesh talks about why open source has become a powerful sales funnel for modern AI companies. Developers become the first users, and community adoption turns into long-term enterprise revenue.After four decades in Silicon Valley and 20 years as a VC, Umesh shares what keeps him in building and investing.0:00 – How big is the scope for investing in AI startups?04:04 – Do unit economics justify large AI valuations?06:00 – Thomvest's LLM investment thesis (Cohere case study)09:18 – Are CTO roles changing in AI11:21 – Traits of the best AI founding teams13:40 – Timeline to find the best founders16:52 – Partnership with Jyoti Bansal19:07 – Where is the IPO market headed?23:40 – Salesforce–Clari acquisition25:18 – Is profitability a prerequisite to go public?26:00 – Can the India–US corridor beat US–Israel?28:53 – Umesh's investment philosophy31:08 – Open source as a sales funnel33:38 – IIT → Stanford → Startups41:45 – The only CEO with 60 direct reports43:43 – Why Jensen never does 1-on-1s?48:23 – What ultimately drives Umesh Padval?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send a text

Marc Walton is a Professional Forex Mentor and Trader, and ex-fund manager, who has been working in the finance industry for over 25+ years. He started FMP in 2008 and mentors students on how to trade professionally.He retired from full-time trading in spring 2020 and now focuses on running Forex Mentor Pro, which is his passion. On top of that, he takes on a few private clients who wish to fast-track their trading progress each year. He also actively invests in metals, mining stocks, and cryptocurrencies, which he shares with students in his second business, Your Investing Future.Summary of PodcastRetirement planning and financial educationMarc discussed the importance of financial education, noting that most people lack understanding of how money and the financial system work. He shared his own experiences of learning about personal finance, investing, and alternative income streams like trading and crypto. Marc emphasised the need to be proactive in managing one's finances and not relying solely on traditional retirement plans, which he believes are inadequate for the longer lifespans people are now experiencing.The impact of AI and automationThe group discussed the growing impact of AI and automation on jobs, with Marc and Kevin sharing examples of how AI is already replacing certain tasks previously done by human workers, including graduate-level research and analysis. They noted that this trend will likely accelerate, requiring people to adapt and find new ways to create value.Retirement activities and challengesThe conversation turned to the challenges of finding fulfilling activities and ways to spend time in retirement. Marc shared his own struggles with this, while Graham and Kevin acknowledged similar difficulties in figuring out how they want to spend their time post-retirement, beyond hobbies like golf that they don't find particularly engaging. The group agreed that maintaining mental and physical health is crucial.The Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled ten years ago to help business owners and marketers market to affluent and high-net-worth customers. He's the founder of MicroYES, a Partner for MeclabsAI, where he introduces AI Agents that you can talk to, that increase engagement, dwell time, leads, and conversions. Now, Graham is offering Answer Engine Optimisation that gets you ready to be found by LLM search.Kevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

Matthew Egol, Founder & CEO of JourneySpark Consulting, joins Ari Paparo and Eric Franchi to break down agentic advertising and what AdCP means for the industry, from AI's Super Bowl moment to standards governance, Prebid collaboration, IAB alignment, and how AI agents are reshaping planning, creative, and measurement across marketing. Takeaways AI took over the Super Bowl, with roughly a quarter of ads tied to AI. Agentic advertising expands from buying to planning, discovery, and measurement. AgenticAdvertising.org focuses on standards, governance, and certification. Prebid runs the sell-side AdCP code while AAO drives the protocol and adoption. AdCP is still mostly in pilot mode, not scaled revenue. AI creative testing is beating traditional DCO in performance. LLM ads could reshape search, retail media, and content economics. Chapters 00:00 Opening & Guest Introduction 01:29 Marketecture Live & Super Bowl Banter 03:58 Matt Egol Joins from CES 06:49 What Is AgenticAdvertising.org? 08:16 Certification & Trust 11:11 Why Another Organization? 13:43 Prebid Partnership Explained 16:08 Expanding Beyond Programmatic 18:23 Relationship with the IAB 22:30 Adoption Update: February 2026 24:08 Governance & Board Structure 26:16 The AI Super Bowl 33:47 ChatGPT Launches Ads 44:20 Amazon Content Marketplace Rumors 52:19 Closing & Sign-Off Learn more about your ad choices. Visit megaphone.fm/adchoices

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

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

How is AI changing the way we store data? This week Technology Now dives into the topic of data storage in the world of AI. We explore intelligent storage, how data sovereignty is influencing how we store our data, and consider where the world of storage could be going in the future. Jim O'Dorisio, Senior Vice President and General Manager HPE Storage, tells us more.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week, hosts Michael Bird and Sam Jarrell look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations.About Jim:https://www.linkedin.com/in/odorisiojim/Sources:https://www.statista.com/statistics/871513/worldwide-data-created/#statisticContainerhttps://www.studionetworksolutions.com/how-much-data-is-used-and-stored-in-the-world/#:~:text=expanding%20digital%20universe.-,Global%20Data%20Usage,over%20180%20zettabytes%20by%202025.1 billion terrabytes in 1 zettabyte. If a smartphone has 1Tb storage, then you need 180 billion smartphones to make 180Zb of storage. 180 billion > 100 billionhttps://www.computerhistory.org/timeline/memory-storage/https://www.computerhistory.org/revolution/memory-storage/8/308https://www.computinghistory.org.uk/det/6129/https://www.computerhistory.org/revolution/memory-storage/8/308/963

What happens when you drop an LLM on top of five ERPs and a decade of M&A? Aaron Sheehan and analyst Heather Hershey map the practical path: B2B use cases that work, risks that don't, and why chunk-by-chunk modernization beats “robot, take the wheel.”Highlights01:06 – Welcome back and introducing Heather Hershey03:35 – Defining AI, LLMs, and RAG 09:30 – Why probabilistic AI makes ops teams nervous 11:58 – Is LLM an overkill compared to ‘boring' machine learning and rule-based systems?15:19 – The real blocker: fragmented data across ERPs and other systems19:40 – The strangler pattern: modernize in chunks instead of ripping everything out21:13 – Why commerce platforms become the orchestration layer for AI/NLP24:37 – If you had $100K for AI: where to spend it 27:27 – Prisoner's dilemma: agentic shopping and the disintermediation trap34:53 – Agentic commerce predictions for B2B 40:30 – Are people replacing Google with LLMs?

Our Global Head of Thematic and Sustainability Research Stephen Byrd and U.S. Thematic and Equity Strategist Michelle Weaver lay out Morgan Stanley's four key Research themes for 2026, and how those themes could unfold across markets for the rest of the year. Read more insights from Morgan Stanley.----- Transcript -----Stephen Byrd: Welcome to Thoughts on the Market. I'm Stephen Byrd, Global Head of Thematic and Sustainability Research. Michelle Weaver: And I'm Michelle Weaver, U.S. Thematic and Equity Strategist. Stephen Byrd: I was recently on the show to discuss Morgan Stanley's four key themes for 2026. Today, a look at how those themes could actually play out in the real world over the course of this year. It's Tuesday, February 10th at 10am in New York. So one of the biggest challenges for investors right now is separating signal from noise. Markets are reacting to headlines by the minute, but the real drivers of long-term returns tend to move much more slowly and much more powerfully. That's why thematic analysis has been such an important part of how we think about markets, particularly during periods of high volatility. For 2026, our framework is built around four key themes: AI and tech diffusion, the future of energy, the multipolar world, and societal shifts. In other words, three familiar themes and one meaningful evolution from last year. So Michelle, let's start at the top. When investors hear four key themes, what's different about the 2026 framework versus what we laid out in 2025? Michelle Weaver: Well, like you mentioned before, three of our four key themes are the same as last year, so we're gonna continue to see important market impacts from AI and tech diffusion, the future of energy and the multipolar world.But our fourth key theme, societal shifts, is really an expansion of our prior key theme longevity from last year. And while three of the four themes are the same broad categories, the way they impact the market is going to evolve. And these themes don't exist in isolation. They collide and they intersect with one another, having other important market implications. And we'll talk about many of those intersections today as they relate to multiple themes. Let's start with AI. How does the AI and tech diffusion theme specifically evolve since last year? Stephen Byrd: Yeah. You know, you mentioned earlier the evolution of all of our themes, and that was certainly the case with AI and tech diffusion. What I think we'll see in 2026 is a few major evolutions. So, one is a concept that we think of as two worlds of LLM progress and AI adoption; and let me walk through what I mean by that. On LLM progress, we do think that the handful of American LLM developers that have 10 times the compute they had last year are going to be training and producing models of unprecedented capability. We do not think the Chinese models will be able to keep up because they simply do not have the compute required for the training. And so we will see two worlds, very different approaches. That said, the Chinese models are quite excellent in terms of providing low cost solutions to a wide range of very practical business cases. So that's one case of two worlds when we think about the world of AI and tech diffusion. Another is that essentially we could see a really big gap between what you can do with an LLM and what the average user is actually doing with LLMs. Now there're going to be outliers where really leaders will be able to fully utilize LLMs and achieve fairly substantial and breathtaking results. But on average, that won't be the case. And so you'll see a bit of a lag there. That said, I do think when investors see what those frontier capabilities are, I think that does eventually lead to bullishness. So that's one dynamic. Another really big dynamic in 2026 is the mismatch between compute demand and compute supply. We dove very deeply into this in our note, and essentially where we come out is we believe, and our analysis supports this, that the demand for compute is going to be systematically much higher than the supply. That has all kinds of implications. Compute becomes a very precious resource, both at the company level, at the national level. So those are a couple of areas of evolution.So Michelle, let's shift over to the future of energy, which does feel very different today than it did a year ago. Can you kind of walk through what's changed? Michelle Weaver: Well, we absolutely still think that power is one of the key bottlenecks for data center growth. And our power modeling work shows around a 47 gigawatt shortfall before considering innovative time to power solutions. We get down to around a 10 to 20 percent shortfall in power needed in the U.S. though, even after considering those solutions. So power is still very much a bottleneck. But the power picture is becoming even more challenged for data centers, and that's largely because of a major political overhang that's emerging. Consumers across the U.S. have seen their electricity bills rise and are increasingly pointing to data centers as the culprit behind this. I really want to emphasize though this is a nuanced issue and data center power demand is driving consumer bills higher in some areas like the Mid-Atlantic. But this isn't the case nationwide and really depends on a number of factors like data center density in the region and whether it's a regulated or unregulated utility market.But public perception has really turned against data centers and local pushback is causing planned data centers to be canceled or delayed. And you're seeing similar opinions both across political affiliations and across different regional areas. So yes, in some areas data centers have impacted consumer power bills, but in other areas that hasn't been the case. But this is good news though, for companies that offer off-grid power generation, who are able to completely insulate consumers because they're not connecting to the grid.Stephen, the multipolar theme was already strong last year. Why has it become even more central for 2026? Stephen Byrd: Yeah, you're right. It was strong in 2025. In fact, of our 21 categories of stocks, the top three performing were really driven by multipolar world dynamics. Let me walk through three areas of focus that we have for multipolar world in 2026. Number one is an aggressive U.S. policy agenda, and that's going to show up in a number of ways. But examples here would be major efforts to reshore manufacturing, a real evolution in military spending towards a wide range of newer military technologies, reducing power prices and inflation more broadly. And also really focusing on trying to eliminate dependency on China for rare earths. So that's the first big area of focus. The second is around AI technology transfer. And this is quite closely linked to rare earths. So here's the dynamic as we think about U.S. and China. China has a commanding position in rare earths. The United States has a leading position in access to computational resources. Those two are going to interplay quite a bit in 2026. So, for example, we have a view that in 2026, when those American models, these LLMs achieve these step changes up in capabilities that China cannot match, we think that it's very likely that China may exert pressure in terms of rare earths access in order to force the transfer of technology, the best AI technology to China. So that's an example of this linkage between AI and rare earths. And the last dynamic, I'd say broadly, would be the politics of energy, which you described quite well. I think that's going to be a big multipolar world dynamic everywhere around the world. A focus on how much of an impact our data centers are having – whether it's water access, price of power, et cetera. What are the impacts to jobs? And that's going to show up in a variety of policy actions in 2026. Michelle Weaver: Mm-hmm. Stephen Byrd: So Michelle, the last of our four key themes is societal shifts, and you walked through that briefly before. This expands on our prior longevity work. What does this broader framing capture? Michelle Weaver: Societal shifts will include important topics from longevity still. So, things like preparing for an aging population and AI in healthcare. But the expansion really lets us look at the full age range of the demographic spectrum, and we can also now start thinking about what younger consumers want. It also allows us to look at other income based demographics, like what's been going on with the K-economy, which has been an important theme around the world. And a really critical element, though, of this new theme is AI's impact on the labor market. Last year we did a big piece called The Future of Work. And in it we estimated that around 90 percent of jobs would be impacted by AI. I want to be clear: That's not to say that 90 percent of jobs would be lost by AI or automated by AI. But rather some task or some component of that job could be automated or augmented using AI. And so you might have, you know, the jobs of today looking very different five years from now. Workers are adaptable and, and we do expect many to reskill as part of this evolving job landscape. We've talked about the evolution of our key themes, but now let's focus a little on the results. So how have these themes actually performed from an investment standpoint? Stephen Byrd: Yeah. I was very happy with the results in 2025. When we looked across our categories of thematic stocks; we have 21 categories of thematic stocks within our four big themes. On average in 2025, our thematic stock categories outperformed MSCI World by 16 percent and the S&P 500 by 27 percent respectively. So, I was very happy with that result. When you look at the breakdown, it is interesting in terms of the categories, you did really well. As I mentioned, the top three were driven by multipolar world. That is Critical Minerals, AI Semis, and Defense. But after that you can see a lot of AI in Energy show up. Power in AI was a big winner. Nuclear Power did extremely well. So, we did see other categories, but I did find it really interesting that multipolar world really did top the charts in 2025. Michelle Weaver: Mm-hmm. Stephen Byrd: Michelle, thanks for taking the time to talk. Michelle Weaver: Great speaking with you, Steven. Stephen Byrd: And thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.

ZeroDayRAT delivers full mobile compromise on Android and iOS. The UK warns infrastructure operators to act now as severe cyber threats mount. Russia moves to block Telegram. The FTC draws a line on data sales to foreign adversaries. Researchers unpack DeadVax, a stealthy new malware campaign, while an old-school Linux botnet resurfaces. BeyondTrust fixes a critical flaw. And in AI, are we moving too fast? One mild training prompt may be enough to knock down safety guardrails. Our guest is Omer Akgul, Researcher at RSA Conference, discussing his work on "The Case for LLM Consistency Metrics in Cybersecurity (and Beyond)." A pair of penned pentesters provoke a pricey payout.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Omer Akgul, PhD, Researcher at RSA Conference, discussing his work on "The Case for LLM Consistency Metrics in Cybersecurity (and Beyond)." Selected Reading New ‘ZeroDayRAT' Spyware Kit Enables Total Compromise of iOS, Android Devices (SecurityWeek) NCSC Issues Warning Over “Severe” Cyber-Attacks Targeting Critical National Infrastructure (Infosecurity Magazine) Russian Watchdog Starts Limiting Access to Telegram, RBC Reports (Bloomberg) FTC Reminds Data Brokers of Their Obligations to Comply with PADFAA (FTC) Dead#Vax: Analyzing Multi-Stage VHD Delivery and Self-Parsing Batch Scripts to Deploy In-Memory Shellcode (secureonix) New ‘SSHStalker' Linux Botnet Uses Old Techniques (SecurityWeek) BeyondTrust Patches Critical RCE Vulnerability (SecurityWeek) Critics warn America's 'move fast' AI strategy could cost it the global market  (CyberScoop) Microsoft boffins figured out how to break LLM safety guardrails with one simple prompt (The Register) County pays $600,000 to pentesters it arrested for assessing courthouse security (Ars Technica) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

This week we talk about OpenAI, nudify apps, and CSAM.We also discuss Elon Musk, SpaceX, and humanistic technology.Recommended Book: Who's Afraid of Gender? by Judith ButlerTranscriptxAI is an American corporation that was founded in mid-2023 by Elon Musk, ostensibly in response to several things happening in the world and in the technology industry in particular.According to Musk, a “politically correct” artificial intelligence, especially a truly powerful, even generally intelligent one, which would be human or super-human-scale capable, would be dangerous, leading to systems like HAL 9000 from 2001: A Space Odyssey. He intended, in contrast, to create what he called a “maximally truth-seeking” AI that would be better at everything, including math and reasoning, than existing, competing models from the likes of OpenAI, Google, and Anthropic.The development of xAI was also seemingly a response to the direction of OpenAI in particular, as OpenAI was originally founded in 2015 as a non-profit by many of the people who now run OpenAI and competing models by competing companies, and current OpenAI CEO Sam Altman and Elon Musk were the co-chairs of the non-profit.Back then, Musk and Altman both said that their AI priorities revolved around the many safety issues associated with artificial general intelligence, including potentially existential ones. They wanted the development of AI to take a humanistic trajectory, and were keen to ensure that these systems aren't hoarded by just a few elites and don't make the continued development and existence of human civilization impossible.Many of those highfalutin ambitions seemed to either be backburnered or removed from OpenAI's guiding tenets wholesale when the company experienced surprising success from its first publicly deployed ChatGPT model back in late-2022.That was the moment that most people first experienced large-language model-based AI tools, and it completely upended the tech industry in relatively short order. OpenAI had already started the process of shifting from a vanilla non-profit into a capped for-profit company in 2019, which limited profits to 100-times any investments it received, partly in order to attract more talent that would otherwise be unlikely to leave their comparably cushy jobs at the likes of Google and Facebook for the compensation a non-profit would be able to offer.OpenAI began partnering with Microsoft that same year, 2019, and that seemed to set them up for the staggering growth they experienced post-ChatGPT release.Part of Musk's stated rationale for investing so heavily in xAI is that he provided tens of millions of dollars in seed funding to the still non-profit OpenAI between 2015 and 2018. He filed a lawsuits against the company after its transition, and when it started to become successful, post-ChatGPT, especially between 2024 and 2026, and has demanded more than $100 billion in compensation for that early investment. He also attempted to take over OpenAI in early 2025, launching a hostile bid with other investors to nab OpenAI for just under $100 billion. xAI, in other words, is meant to counter OpenAI and what it's become.All of which could be seen as a genuine desire to keep OpenAI functioning as a non-profit arbiter of AGI development, serving as a lab and thinktank that would develop the guardrails necessary to keep these increasingly powerful and ubiquitous tools under control and working for the benefit of humanity, rather than against it.What's happened since, within Musk's own companies, would seem to call that assertion into question, though. And that's what I'd like to talk about today: xAI, its chatbot Grok, and a tidal wave of abusive content it has created that's led to lawsuits and bans from government entities around the world.—In November of 2023, an LLM-based chatbot called Grok, which is comparable in many ways to OpenAI's LLM-based chabot, ChatGPT, was launched by Musk's company xAI.Similar to ChatGPT, Grok is accessible by apps on Apple and Android devices, and can also be accessed on the web. Part of what makes its distinct, though, is that it's also built into X, the social network formerly called Twitter which Musk purchased in late-2022. On X, Grok operates similar to a normal account, but one that other users can interact with, asking Grok about the legitimacy of things posted on the service, asking it normal chat-botty questions, and asking it to produce AI-generated media.Grok's specific stances and biases have varied quite a lot since it was released, and in many cases it has defaulted to the data- and fact-based leanings of other chatbots: it will generally tell you what the Mayo clinic and other authorities say about vaccines and diseases, for instance, and will generally reference well-regarded news entities like the Associated Press when asked about international military conflicts.Musk's increasingly strong political stances, which have trended more and more far right over the past decade, have come to influence many of Grok's responses, however, at times causing it to go full Nazi, calling itself Mechahitler and saying all the horrible and offensive things you would expect a proud Nazi to say. At other times it has clearly been programmed to celebrate Elon Musk whenever possible, and in still others it has become immensely conspiratorial or anti-liberal or anti-other group of people.The conflicting personality types of this bot seems to be the result of Musk wanting to have a maximally truth-seeking AI, but then not liking the data- and fact-based truths that were provided, as they often conflicted with his own opinions and biases. He would then tell the programmers to force Grok to not care about antisemitism or skin color or whatever else, and it would overcorrect in the opposite direction, leading to several news cycles worth of scandal.This changes week by week and sometimes day by day, but Grok often calls out Musk as being authoritarian, a conspiracy theorist, and even a pedophile, and that has placed the Grok chatbot in an usual space amongst other, similar chatbots—sometimes serving as a useful check on misinformation and disinformation on the X social network, but sometimes becoming the most prominent producer of the same.Musk has also pushed for xAI to produce countervailing sources of truth from which Grok can find seeming data, the most prominent of which is Grokipedia, which Musk intended to be a less-woke version of Wikipedia, and which, perhaps expectedly, means that it's a far-right rip off of Wikipedia that copies most articles verbatim, but then changes anything Musk doesn't like, including anything that might support liberal political arguments, or anything that supports vaccines or trans people. In contrast, pseudoscience and scientific racism get a lot of positive coverage, as does the white genocide conspiracy theory, all of which are backed by either highly biased or completely made up sources—in both cases sources that Wikipedia editors would not accept.Given all that, what's happened over the past few months maybe isn't that surprising.In late 2025 and early 2026, it was announced that Grok had some new image-related features, including the ability for users to request that it modify images. Among other issues, this new tool allowed users to instruct Grok to place people, which for this audience especially meant women and children, in bikinis and in sexually explicit positions and scenarios.Grok isn't the first LLM-based app to provide this sort of functionality: so called “nudify” apps have existed for ages, even before AI tools made that functionality simpler and cheaper to apply, and there have been a wave of new entrants in this field since the dawn of the ChatGPT era a few years ago.Grok is easily the biggest and most public example of this type of app, however, and despite the torrent of criticism and concern that rolled in following this feature's deployment, Musk immediately came out in favor of said features, saying that his chatbot is edgier and better than others because it doesn't have all the woke, pearl-clutching safeguards of other chatbots.After several governments weighed in on the matter, however, Grok started responding to requests to do these sorts of image edits with a message saying: “Image generation and editing are currently limited to paying subscribers. You can subscribe to unlock these features.”Which means users could still access these tools, but they would have to pay $8 per month and become a premium user in order to do so. That said, the AP was able to confirm that as of mid-January, free X users could still accomplish the same by using an Edit Image button that appears on all images posted to the site, instead of asking Grok directly.When asked about this issue by the press, xAI has auto-responded with the message “Legacy Media Lies.” The company has previously said it will remove illegal content and permanently suspend users who post and ask for such content, but these efforts have apparently not been fast or complete, and more governments have said they plan to take action on the matter, themselves, since this tool became widespread.Again, this sort of nonconsensual image manipulation has been a problem for a long, long time, made easier by the availability of digital tools like Photoshop, but not uncommon even before the personal computer and digital graphics revolution. These tools have made the production of such images a lot simpler and faster, though, and that's put said tools in more hands, including those of teenagers, who have in worryingly large numbers taken to creating photorealistic naked and sexually explicit images of their mostly female classmates.Allowing all X users, or even just the subset that pays for the service to do the same at the click of a button or by asking a Chatbot to do it for them has increased the number manyfold, and allowed even more people to created explicit images of neighbors, celebrities, and yes, even children. An early estimate indicates that over the course of just nine days, Grok created and posted 4.4 million images, at least 41% of which, about 1.8 million, were sexualized images of women. Another estimated using a broader analysis says that 65% of those images, or just over 3 million, contained sexualized images of men, women, and children.CSAM is an acronym that means ‘child sexual abuse material,' sometimes just called child porn, and the specific definition varies depending on where you are, but almost every legal jurisdiction frowns, or worse, on its production and distribution.Multiple governments have announced that they'll be taking legal action against the company since January of 2026, including Malaysia, Indonesia, the Philippines, Britain, France, India, Brazil, and the central governance of the European Union.The French investigation into xAI and Grok led to a raid on the company's local office as part of a preliminary investigation into allegations that the company is knowingly spreading child sexual abuse materials and other illegal deepfake content. Musk has been summoned for questioning in that investigation.Some of the governments looking into xAI for these issues conditionally lifted their bans in late-January, but this issues has percolated back into the news with the release of 16 emails between Musk and the notorious sex traffic and pedophile Jeffrey Epstein, with Musk seemingly angling for an invite to one of Epstein's island parties, which were often populated with underage girls who were offered as, let's say companions, for attendees.And this is all happening at a moment in which xAI, which already merged with social network X, is meant to be itself merged with another Musk-owned company, SpaceX, which is best known for its inexpensive rocket launches.Musk says the merger is intended to allow for the creation of space-based data centers that can be used to power AI systems like Grok, but many analysts are seeing this as a means of pumping more money into an expensive, unprofitable portion of his portfolio: SpaceX, which is profitable, is likely going to have an IPO this year and will probably have a valuation of more than a trillion dollars. By folding very unprofitable xAI into profitable SpaceX, these AI-related efforts could be funded well into the future, till a moment when, possibly, many of today's AI companies will have gone under, leaving just a few competitors for xAI's Grok and associated offerings.Show Noteshttps://www.wired.com/story/deepfake-nudify-technology-is-getting-darker-and-more-dangerous/https://www.theverge.com/ai-artificial-intelligence/867874/stripe-visa-mastercard-amex-csam-grokhttps://www.ft.com/content/f5ed0160-7098-4e63-88e5-8b3f70499b02https://www.theguardian.com/global-development/2026/jan/29/millions-creating-deepfake-nudes-telegram-ai-digital-abusehttps://apnews.com/article/france-x-investigation-seach-elon-musk-1116be84d84201011219086ecfd4e0bchttps://apnews.com/article/grok-x-musk-ai-nudification-abuse-2021bbdb508d080d46e3ae7b8f297d36https://apnews.com/article/grok-elon-musk-deepfake-x-social-media-2bfa06805b323b1d7e5ea7bb01c9da77https://www.nytimes.com/2026/02/07/technology/elon-musk-spacex-xai.htmlhttps://www.bbc.com/news/articles/ce3ex92557johttps://techcrunch.com/2026/02/01/indonesia-conditionally-lifts-ban-on-grok/https://www.bbc.com/news/articles/cgr58dlnne5ohttps://www.nytimes.com/2026/01/22/technology/grok-x-ai-elon-musk-deepfakes.htmlhttps://en.wikipedia.org/wiki/XAI_(company)https://en.wikipedia.org/wiki/OpenAIhttps://en.wikipedia.org/wiki/ChatGPThttps://en.wikipedia.org/wiki/Grok_(chatbot)https://en.wikipedia.org/wiki/Grokipediahttps://www.cnbc.com/2025/02/10/musk-and-investors-offering-97point4-billion-for-control-of-openai-wsj.html This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

Ivanti zero-days trigger emergency warnings around the globe. Singapore blames a China-linked spy crew for hitting all four major telcos. DHS opens a privacy probe into ICE surveillance. Researchers flag a zero-click RCE lurking in LLM workflows. Ransomware knocks local government payment systems offline in Florida and Texas. Chrome extensions get nosy with your URLs. BeyondTrust scrambles to patch a critical RCE. A Polish data breach suspect is caught eight years later. It's the Monday Business Breakdown. Ben Yelin gives us the 101 on subpoenas. And federal prosecutors say two Connecticut men bet big on fraud, and lost. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Our guest is Ben Yelin, Program Director for Public Policy & External Affairs at the University of Maryland Center for Cyber Health and Hazard Strategies, talking about weaponized administrative subpoenas. Selected Reading EU, Dutch government announce hacks following Ivanti zero-days (The Record) Singapore says China-linked hackers targeted telecom providers in major spying campaign (The Record) Inspector General Investigating Whether ICE's Surveillance Tech Breaks the Law (404 Media) Critical 0-Click RCE Vulnerability in Claude Desktop Extensions Exposes 10,000+ Users to Remote Attacks (Cyber Security News)  Payment tech provider for Texas, Florida governments working with FBI to resolve ransomware attack (The Record) Chrome extensions can use unfixable time-channel to leak tab URLs (CyberInsider) BeyondTrust warns of critical RCE flaw in remote support software (Bleeping Computer) Hacker Poland's largest data leaks arrested (TVP World) LevelBlue will acquire MDR provider Alert Logic from Fortra. (N2K Pro Business Briefing) Men charged in FanDuel scheme fueled by thousands of stolen identities (Bleeping Computer) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Microsoft Patches Four Azure Vulnerabilities (three critical) https://msrc.microsoft.com/update-guide/vulnerability Evaluating and mitigating the growing risk of LLM-discovered 0-days https://red.anthropic.com/2026/zero-days/ Gitlab AI Gateway Vulnerability CVE-2026-1868 https://about.gitlab.com/releases/2026/02/06/patch-release-gitlab-ai-gateway-18-8-1-released/

Mitchell Hashimoto's trust management system for open source, Nicholas Carlini has a team of Claudes build a C compiler, Stephan Schwab recounts the history of attempted developer replacement, NanClaw is an alternative to OpenClaw, and Sophie Koonin can't wrap her head around so many people going so hard on LLM-generated code.