Inside the Model Factory — Eiso Kant, Poolside AI

In recent months, the open vs closed, and US vs China https://www.latent.space/p/ainews-kimi-k3-28t-a50b-the-largest discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI https://x.com/poolsideai/status/2079613777343848465 are finally emerging with new models, like Laguna S 2.1 https://x.com/poolsideai/status/2079613777343848465, that are beating Thinking Machines’ recent release nearly 10 times their size https://www.latent.space/p/ainews-thinkys-inkling-975b-a41b. Poolside’s recent tech report https://x.com/robert_mchardy/status/2059297942301782062 got a lot of praise due to their level of detail, and Vibhu first covered Laguna’s recent technical report on our paper club: From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five. We go deep on Poolside’s Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S https://poolside.ai/blog/introducing-laguna-s-2-1, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web. We also discuss model-harness co-design, Poolside’s path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside’s $500 million raise, open-source AI, regulation, NVIDIA and TSMC’s influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside. We discuss: * How Andrej Karpathy’s RNN work inspired Eiso to start building language models for code in 2015 * Why Eiso spent four years and $12 million pursuing an idea before the market cared * Why ChatGPT felt like vindication and brought Poolside back to open source * Why Eiso would prefer 100 foundation model companies over an oligopoly of five * The difference between releasing open weights and publishing genuinely open research * Why Poolside deliberately built a global research organization outside the Bay Area talent war * Why model building is ultimately 90% engineering * The Model Factory: Poolside’s end-to-end system for rapidly training and improving models * How fewer than 70 researchers run roughly 10,000–20,000 experiments each month * How Poolside moved from six-month model cycles to five- and eight-week launches * Why streaming data directly into training unlocked faster experimentation * How immutable data, versioned code, and reproducibility enable rigorous model research * Why Eiso wants capable researchers to leave their labs and become Poolside’s competitors * Why 95% of model building can be reduced to better data or compute efficiency * Laguna S and why persistence, verification, and backtracking can outperform raw intelligence * Why smaller models may handle far more knowledge work than previously expected * Why reinforcement learning will move earlier into pre-training * Why next-token prediction is still failing to extract enough knowledge from the web * Why distillation and environments have become the AI industry’s favorite “drugs” * Why mid-training is really an early form of curriculum design * Low-precision training, networking bottlenecks, and the next gains in compute efficiency * Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch * Why model builders can often evaluate a new checkpoint within its first 30 minutes * Model versus harness: where agent capabilities actually come from * Why Poolside sees coding and long-horizon software tasks as a path to AGI * Why Eiso thinks MCP and traditional tool calls are “stupid” * Why future agents will write scripts instead of choosing from dozens of predefined tools * The case for minimal harnesses, containers, and model freedom * Why Poolside is prioritizing vision but does not expect to work on audio soon * Why language may be the most compute-efficient modality for encoding knowledge and reasoning * The real cost of model development and why the final training run is anticlimactic * The story behind the Poolside name and why it represents refusing to lower ambitions * How Poolside raised $500 million while investors still questioned whether AGI was real * Why intelligence could become the world’s most demanded and commoditized resource * When open models may become too capable to release without restrictions * Why unilateral AI safety does not work in a globally competitive environment * How regulation could accidentally lock in an oligopoly of two or three AI companies * NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress * Why reinforcement-learning wall-clock time is one of Poolside’s biggest bottlenecks * Why Poolside trains models from scratch instead of simply distilling larger models * How AI changes the way companies should measure engineering productivity * Why agency may become the most important quality for employees in the AI era * How leaders align high-agency people through shared goals and clear constraints * Hiring across research, post-training, pre-training, architecture, evals, and engineering at Poolside Eiso Kant LinkedIn: https://www.linkedin.com/in/eisokant X: https://x.com/eisokant Poolside: https://poolside.ai Timestamps 00:00:00 Introduction 00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers 00:02:26 The $12M Failure and ChatGPT Vindication 00:03:39 Open Source and the Case for 100 Foundation Model Companies 00:09:22 Open Weights, Open Research, and Poolside’s Global Team 00:16:04 The Model Factory: Why Model Building Is 90% Engineering 00:20:19 Agents, Automated Experiments, and Early Signs of RSI 00:24:04 Streaming Data, Reproducibility, and Scientific Rigor 00:30:35 Creating More Foundation Model Companies 00:36:07 Laguna S: Persistence vs. Raw Intelligence 00:43:01 Reinventing Pre-Training, RL, and Curriculum Design 00:52:33 Low-Precision Training and Squeezing More From Smaller Models 00:58:37 Model Harnesses, Coding Agents, and the Path to AGI 01:09:26 Why MCP and Traditional Tool Calls Are “Stupid” 01:13:04 Vision, Multimodality, and Why Language Still Matters 01:18:15 Scaling Models and the Real Economics of Training 01:20:40 Why Poolside Is Called Poolside and Raising $500M 01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly 01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck 01:41:52 Smaller Models, Distillation, Engineering Productivity, and Hiring Transcript Introduction: Eiso Kant, Poolside, and Open Models Swyx [00:00:00]: All right, we’re here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome. Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here. Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I’m on my way to SF.” I was like, “You’re on a plane right now, right?” Like, hey. Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let’s do it. Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don’t have to prepare that much because if you’re truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don’t live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we’re gonna talk about. But I guess, like, what got you into democratization of AI? Like, it’s not obvious from your LinkedIn or something. From Karpathy’s RNN Post to Sourced Eiso Kant [00:00:57]: No, it’s not at all. I don’t think it’s obvious how I got in this space. I owe getting into this space to Andrej Karpathy. Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.” Swyx [00:01:10]: Neural Nets, yep. Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There’s a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn’t. and there’s a little-- There’s an example of code a little bit further down. Yeah, so Shakespeare. Swyx [00:01:47]: Shakespeare. Swyx [00:01:49]: Cool Eiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared. Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn’t obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn’t obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors’ money, which was a lot back then. Swyx [00:03:18]: Yep. Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn’t really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It’s like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we, ChatGPT, Vindication, and Returning to Open Source Eiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you’re building more capable intelligence, it should be open and open source. Eiso Kant [00:04:04]: When we started Poolside, that wasn’t the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not. Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn’t roll out of an existing lab. So we picked up the papers and started writing code and figuring things out. Eiso Kant [00:04:59]: And it wasn’t until the beginning of this year that me and my founder, Jason, picked up the open source conversation again. Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there. Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn’t, this didn’t happen overnight. It was, like, a little bit we were seeing this and we’re like, “Okay, The world’s going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we’re working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone’s trying to figure things out. You’d get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward. Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I’m a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier. Eiso Kant [00:06:41]: If we were at the frontier, I don’t think we could have changed our mind. and I don’t mean this like it’s when the moment there’s too much capital involved, too much expectations, you’ve built up things, right? We’re a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there’s big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I’ll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more. Neo-Labs, Model Choice, and the Token Economy Swyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labs Eiso Kant [00:08:10]: Yeah Swyx [00:08:10]: That people are now calling that. And, we’re, we’re doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don’t have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20. Eiso Kant [00:08:36]: I really hope so, right? I think we I’m, I’m excited about their release, and I’m excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases. Swyx [00:09:03]: Yeah. Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don’t think there’s any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.” Swyx [00:09:25]: Yeah. Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there’s the DeepSeek of the West. Is it today? Okay, maybe it’s thinking machines reflection, but there aren’t many, right? So, one of the things you guys started in France, Europe, but very much now you’re taking that American standpoint and more than just that, the point is the Chinese models that we see, they’re not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here’s a breakdown blog, paper, technical report of here’s everything for state of the art to build, frontier intelligence and you’re filling that gap too, right? So not just only open weight, not just Western, but also pretty open research. Open Weights vs. Open Research Eiso Kant [00:10:20]: No, I appreciate it. Look, I think it’s, I think it’s the most meaningful contribution, right? Weights are a binary. Let’s call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you’re doing, right? And so now there’s challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it’s been haunting us for quite a few years. We from day zero were an American company. Swyx [00:10:55]: Yeah. They moved Poolside’s Global Team and American Company Story Swyx [00:10:56]: To France. Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We’re not gonna hire any researchers in the Bay Area. We’re gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn’t fully obvious yet. I think today it very much is. And we also realized that, like, some of the world’s most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we’re an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company’s grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it’s why you’re seeing like the progress, I think, on our models and the cadence at which we release, is because we didn’t roll out of an existing lab. Right? we didn’t, we didn’t have a lot of the information that’s freely flowing around here at the time. We just took this point of view as like, “Okay, well, let’s just work the problem. Let’s just go and, like, read the few papers that are out there, and let’s just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the years Eiso Kant [00:12:35]: Like especially in the first 12 months. there’s a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn’t a fork of any open source. It was just like, “Okay, let’s build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn’t get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we’re just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there’s this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that’s starting to show up in results. Swyx [00:13:52]: Just ‘cause we probably won’t revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won’t tell the solution, but we’ An Optimizer Bug and the Value of Building From Scratch Eiso Kant [00:14:01]: So the - This - You’re gonna test my memory here, Swyx [00:14:04]: Oh, okay Eiso Kant [00:14:04]: So but I think Swyx [00:14:05]: Directly Eiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilon Swyx [00:14:12]: Yeah Eiso Kant [00:14:13]: Which is, right, like in the denominator Swyx [00:14:14]: Momentum and weights. Yeah Eiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don’t know if it was E minus four or whatever, like a high value for epsilon. Eiso Kant [00:14:31]: And if you think about this during training, it’s like a bit weird and counterintuitive that we’re adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don’t recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we’re like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you’re just trying to avoid division by zero, why can’t the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard. Eiso Kant [00:15:33]: Right? Like - It’s such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don’t even. Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys saw Eiso Kant [00:15:58]: Yeah Swyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point. Model Building as Engineering Eiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering. Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where’s every researcher spending their time, they’re spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You’re going from raw data, right? Like training raw material, the web, et cetera. you’re doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that’s, far more complex than it was three years ago. then you’re training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It’s become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it’s your big data pipelines, if it’s your crawling ingestion of the web, if it’s your, large-scale distributed training, and then you’ve got your reliability. And we said, “Well, why don’t we take some of the world’s smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it’s thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance. The Model Factory and Experiment Velocity Eiso Kant [00:18:18]: Right? Because we don’t have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training. Eiso Kant [00:18:42]: And in the. And because it’s such an experimental science, ultimately, in the beginning when it wasn’t that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we’re a small team, right? We’re less than 70 researchers, another 35 engineers. and we are running, I haven’t checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it’s ultimately it’s, it’s you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we’re gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we’re launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we’re now training. And so the model should be an artifact of someone’s process. It shouldn’t be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they’re rolling off, and no one is really thinking about the next launch anymore. So it’s just another launch, it’s another launch, another rocket comes off. And that’s what we’re trying to do with model building. Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It’s perfect for agents. Agents Inside the Model Factory Eiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory. Vibhu [00:20:43]: Yeah. Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we’re, we come together, in our monthly, we do monthly onsites, and I walk behind people’s screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They’re launching the jobs. They’re evaluating the results that are coming back from the model runs. They are, making the changes. And we’re still in the driver’s seat. We’re still coming up with the ideas. We’re still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it’s starting to become more on the architecture side as well. You’re starting to see these twinklings of what RSI is gonna look like. Eiso Kant [00:21:27]: And that’s. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn’t matter if it’s a training like big run or if it’s now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run. Eiso Kant [00:21:57]: So there’s not like a cutoff 90 days before. Like no, it’s like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven’t had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that’s usually there’s a little bit of intervention, but that’s always within like call periods, right? Not on call. And I think that’s starting to now compound. So the model we’re releasing now, I love it. It’s amazing, but we’re already onto the next one. and I think that’s the way it should be. Laguna, Five-Week Builds, and Zero On-Call Events Vibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model’s dropped. Haven’t heard about it.” We were Eiso Kant [00:23:02]: Yeah, we’re very used to doing this every few months. Vibhu [00:23:03]: We’re, we’re very much like, “ okay, look, it’s like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it’s a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I’m like, oh, this paper is not about here’s a tech report of benchmarks and here’s how many tokens it was trained on. Like for people that wanna dive more from what we’re not gonna discuss on the podcast, it’s all laid out here, right? From Eiso Kant [00:23:38]: Yeah Vibhu [00:23:39]: Custom software that agents can use to interface with training code, training data. Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah. Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but, Technical Report Principles and Streaming Training Data Eiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I’ll just call out that Dagster just got bought by a Prefect. Vibhu [00:24:01]: Yeah. Eiso Kant [00:24:01]: Isn’t it fun? But yes, I’m very familiar with Dagster. just anything where like they trigger some story. Vibhu [00:24:07]: So, well, I would say, well, experiments code’s obvious, but I think one of my favorite things is, I don’t know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts. Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no sense Eiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you’ve got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren’t we streaming data into training? Right? Something that’s very common and like just basic Vibhu [00:25:00]: Like just in time Eiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn’t matter if it’s a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it’s not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we’ve got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things. Vibhu [00:25:36]: But when you say AWS, it’s not actual AWS, it’s your internal AWS. Eiso Kant [00:25:39]: It’s our internal-- No, it’s our internal like just running like our infrastructure Vibhu [00:25:42]: Site web services Eiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right? Vibhu [00:25:47]: Yeah. Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don’t have to wait for the whole data set to materialize. Immutable Data, Experiments as Code, and Scientific Rigor Eiso Kant [00:26:00]: You now all of a sudden when you’re running data experiments about mixing data, it’s a config. Because you’ve got these data sources that are coming in, and you just - we have this service called Blender that’s in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code. Vibhu [00:26:47]: Yeah. Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn’t understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs. Vibhu [00:27:08]: Yeah. Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who’d been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there’s just fun stuff like, and Vibhu [00:28:16]: Yeah, a lot of it’s fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There’s like a random small paragraph in here where it’s just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don’t even need to look at it. I’m like, “Wow, a lot of engineering rigor there.” And there’s just, there’s just a lot in here. Publishing Research and Giving Back Eiso Kant [00:28:40]: Yeah, and it’- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven’t earned the right for yet for a long time. So you earn the right to spend time, publishing research once you’re at the frontier, because until then, you’re catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn’t allow us to catch up. But in this case, we said, “Okay, we’re gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it’s easy to like put it out. And so there’s so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you’ll see us like be way more proactive, and just trying to keep dropping some of those like things that we’ve learned along the way that can help others like speed up. Vibhu [00:29:40]: Which is the other cool side of this, right? It’s, it’s not like, back to your point, it’s not just here’s the benchmarks of our training. If you want to replicate, here’s experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here’s your system for how to do it? So it’s, it’s really like promoting Eiso Kant [00:29:59]: No, thank you Vibhu [00:29:59]: Other people can do the same. Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we’ve taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it’s important that there’s, from every country and every culture and background, including like Western companies like us, there’s different models that come out that people can choose to trust. But I think we do have to give credit where credit’s due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you’re on the receiving end of something coming to you, I think it’s, you also have an obligation to give back. Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek. Chinese Labs, Zhipu, and Persistence Eiso Kant [00:30:44]: That’s a good question. Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah. Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone’s been talking about Zhipu lately, with 5.2. I think what most people don’t realize is when they started. Swyx [00:30:59]: Yeah. Eiso Kant [00:30:59]: Right? They started years before ChatGPT. Swyx [00:31:02]: They just rebranded. Yeah Eiso Kant [00:31:03]: And so, I’ve like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn’t the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we’d call, machine learning on code with some of these models. we would-- people would just laugh at us, like they’d be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they’re probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we’re, that we’re now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It’s no longer counted in months or years. But this stuff’s hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don’t do so, we’re, we’ve got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my current Eiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor. Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we’re not gonna be in the world where, I don’t want to just be the fifth or the sixth company that wins. I wanna look at a world where there’s lots of choice. Starting a Foundation Model Company Vibhu [00:32:57]: What else do people not see in starting a foundation model? it’s, there’s a lot of compute, there’s a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there’s a lot there, right? That’s, Eiso Kant [00:33:10]: Well, look, it’s, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people’s minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you’re just doing two things. You’re improving data or you’re improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we’re generating new data, we’re improving data. and the only way to do that is to look at the data, right? That’s a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They’re bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you’d be at AGI probably already tomorrow. Eiso Kant [00:34:12]: Right? Like it’s not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that’s, those are the main things. And to just realize that this is engineering. I think it’s become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you’re doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don’t get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we’re doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that’s, it feels far for people to do so. But I’ve seen in our own company, we’ve seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would’ve not been what I think most people assumed was possible, a couple of years ago. Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you’re not only using your own models. There’s no way. But like, what’s that percentage over time? Laguna S, Persistence, and Behavioral Gains Eiso Kant [00:36:10]: This is the first model that we’re releasing that is starting to meaningfully contribute to our own work. It’s not a it’s not state-art model yet. Fable and other, they’re, they’re very capable models, but Laguna S Is really interesting. I’m gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it’s been burned in my brain ever since because the Laguna S model, as you’ll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it’s just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It’s able to do complex programming tasks. It’s able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it’s, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I’ve probably spent eight to ten hours a day with this model for the last ten days. Eiso Kant [00:38:05]: I’m not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model. Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there’s intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you’re wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we’re using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work. Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science. Small Models, Knowledge Work, and Commoditization Eiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it’s something we clearly see that as models get larger and more capable, they’re able to pull more ideas and threads together that a smaller model wouldn’t be able to. Eiso Kant [00:39:36]: And we’re starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I’m a software developer at heart first and foremost probably, although I probably can’t say it that much anymore as I don’t write production code in years, is that what makes us good is our persistence. It’s our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it’s because of the behaviors. And so now the question I have, and I don’t have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI. Eiso Kant [00:40:52]: So I know that at the very limit, I’m not gonna use the world’s largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I’m starting to size down for certain tasks. Eiso Kant [00:41:07]: So it means that there is an optimal. It means there’s some curve that goes as we go up to model size for knowledge work, at some point we’re at the peak, and after that, the return on investment of using a bigger model, just doesn’t make sense. Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away. Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I’m no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It’s an argument that open source can win and, like, succeed in this world. And now it’s of course a self-serving argument and it’s a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I’m against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we’re gonna be king of open source small models. I think that’s, It’s a out. It’s trying to be king of your own kingdom, but not realizing what the rest of the world’s doing. All of us rather use a smarter, faster, more model. It’s a sign of hope. And so I don’t wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters. Pre-Training, Mid-Training, and RL Moving Earlier Vibhu [00:43:03]: Is that mostly post-training? Like Eiso Kant [00:43:05]: Yes Vibhu [00:43:05]: Right. Eiso Kant [00:43:06]: It’s entirely post-training. Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done? Eiso Kant [00:43:12]: No. Vibhu [00:43:12]: Okay. Eiso Kant [00:43:13]: So Vibhu [00:43:13]: I just wanted to cover training, and then we go post-training Eiso Kant [00:43:15]: Training is not done. I mean, look, there’s a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That’- but those are ultimately, engineering challenges. Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training. Vibhu [00:43:42]: Yeah, training. Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we’ve been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that’s the web. and the web, I think we could arguably say probably has The totality of humanity’s knowledge somewhere encoded in different places. It’s a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot. Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it’s a place where maybe not as open as we’re on other things, but we will become more over time. we’ve been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there’s a huge amount of gold to be found there. I think we are right now in, we’ve got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they’re great, and they make us feel good, and they make the models better, and like we’re all addicted to them, and we’ll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web. Eiso Kant [00:45:33]: I think just next token prediction during training is not enough. Eiso Kant [00:45:36]: And Vibhu [00:45:38]: Yeah Eiso Kant [00:45:38]: I think we’ll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we’re, we’re scaling it up. Everyone is. But I wonder if we need to go as far as we’re going today with environments. I’m not sure yet Vibhu [00:46:01]: You mean we’re going too far? Eiso Kant [00:46:02]: I’m, I’m not sure if the path to AGI is just Vibhu [00:46:06]: Is more environment Eiso Kant [00:46:07]: More environments. Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?” Eiso Kant [00:46:19]: I think there is, I think there’s an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we’re able to get so much more out of it. It just changes a little bit the way you think about intelligence. Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don’t learn as much new knowledge. You shift Eiso Kant [00:46:46]: Yeah. Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still just Eiso Kant [00:47:00]: It’s just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it’s effectively just like, Vibhu [00:47:06]: Second phase Eiso Kant [00:47:07]: It’s the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you’d want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I’m pretty sure that you’ll start to see people talking soon about some other term, and there’s two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we’re doing is we’re trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It’- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there’s a training team now, right? There’s people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don’t, you have infinite stages. Now, we’re not there. Compute’s not there. Organization design is not there for it yet. but I think we’ll get there. we’ll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn’t really do a good job at like Curriculum, Auto Research, and New Objectives Vibhu [00:48:48]: The building that curriculum will get you that in the industry. Eiso Kant [00:48:51]: And I’ll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don’t have the answers, but you have some ideas. Vibhu [00:49:02]: We have some ideas. We’re not ready to talk about it yet. Eiso Kant [00:49:05]: Yeah. Vibhu [00:49:05]: We’ve been working on them for years, and I think that’s the one thing that’s also like you asked earlier about, like what’s not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe works Eiso Kant [00:49:19]: Yeah. Vibhu [00:49:19]: Versus like your, my crazy Eiso Kant [00:49:22]: Pure research Vibhu [00:49:22]: Breakthrough. Eiso Kant [00:49:22]: Yeah. Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important. Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some point Vibhu [00:49:35]: Yes Eiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever. Vibhu [00:49:47]: Man, it’s also Eiso Kant [00:49:48]: Like what you’re looking for. You’re looking for loss curves like that, like Vibhu [00:49:51]: It’s also a thing people take bets on, right? When you say more Neo labs, you’re doing a version of we’ll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you’re right. It’s all, compute efficiency, and that’s the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They’re not just Eiso Kant [00:50:19]: Yeah Vibhu [00:50:19]: They’re like, 99% not balancing, here’s the vanilla and scale up. They’re 99% on, here’s novel research that’ll change everything. Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right? Pure Research vs. Table Stakes Vibhu [00:50:30]: Yeah. Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that’s no longer novel by far, but we were like, - that’s where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that’s different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn’t Vibhu [00:50:53]: It’s cool. this was like your original 2023 blog Eiso Kant [00:50:57]: Yeah Vibhu [00:50:57]: Of purpose. Eiso Kant [00:50:58]: Yeah. Vibhu [00:50:59]: And like you do lay it all out here. Eiso Kant [00:51:01]: We laid Vibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on. Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning. Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B. Eiso Kant [00:51:35]: When we Vibhu [00:51:35]: Yeah Eiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was just Vibhu [00:51:42]: Yeah Eiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org. Vibhu [00:51:45]: Mistral. Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right? Vibhu [00:51:49]: Yeah. Eiso Kant [00:51:49]: So this wasn’t even, there was only, I think, Llama out at the time Vibhu [00:51:52]: Snell Eiso Kant [00:51:52]: And that’s it, right? And so, but I agree. I think we want, We want as many diversity of ideas, and I do think if you’re starting today, you want something that gives you an edge, right? and what I do think we sometimes over. Eiso Kant [00:52:13]: I think every archit- like at the limit, every architecture works. An RNN works, it’s just not compute efficient, right? Like, say if you had infinite compute, you could probably just, take a basic RNN from back in the day, and you could get pretty far. Eiso Kant [00:52:27]: Now there have been, meaningful breakthroughs, attention, other things that are there. but I think we’re still, we’re still very early in figuring these out. The things I’m most excited about, I’m most excited about people doing extremely low precision training, right? So like the ternary stuff that we’re seeing, and it Vibhu [00:52:47]: Oh my God Eiso Kant [00:52:47]: Very cool. The Bonsai stuff yesterday was super cool to see. I think that if you can find tweets from me going back to 2023, which is like the notion of like, well, it’s an obvious trade-off. Bigger model, lower precision equals, smaller model with higher precision, by definition, right? It’s just what is, like how does that play out, right? What’s the actual size limit? So you now have companies that are trying to figure that out, but those are the things that can change our industry if they’re done right. Low-Precision Training and Compute Efficiency Vibhu [00:53:14]: Yeah. Eiso Kant [00:53:14]: Because ultimately, like our bottleneck on compute is a MatMul bottleneck, and a networking bottleneck, and the moment you start doing those things. So I’m excited about that. We’re not doing - I mean, we’re doing the usual, like, Laguna S was trained in FP8. only thing that in this run I have to admit that wasn’t FP8 was the all to all in the new run we just started yesterday. The FP8 was all to all. That was just like cut off date, like, oh, we’re not perfectly comfortable wanting to do it. you’ve got amazing work by Nemotron and NVFP4 training. Like, I think it’s underrated what they’ve done there. I’m excited to get to NVFP4 training. doesn’t make sense yet ‘cause we’re still training on Hoppers, right? We’re like relatively small. We’re 10K H200 cluster company right now. We’ll be scaling to a lot more soon, but, and really a lot more if someone is thinking about applying for a job. but like the. Yes, I think it’s, there’s so much more juice to squeeze out of this, and hopefully Laguna S shows people that a model at this size can get a lot more and we did this thing in eight weeks. We think there’s a lot more juice to squeeze out at any model size. we’re now scaling up because it’s the most optimal thing to do for us as a company. But if I had infinite time, I would love to push more the capabilities at other model sizes. Vibhu [00:54:34]: I don’t think we’ve properly announced what your new size is. So we have XS, which was 30B-ish. Laguna S Model Size and Naming Eiso Kant [00:54:41]: Yep. Vibhu [00:54:41]: Old medium was 200B, which is gonna be deprecated Eiso Kant [00:54:45]: Yeah Vibhu [00:54:45]: It seems. So new Laguna Small Eiso Kant [00:54:48]: So Laguna S, Laguna Small, 118 billion total parameters, 8B active, so very sparse. It’s a scale-up of the XS architecture. It’s the classic, or call it classic these days, like three-one ratio of sliding window attention to global attention. It’s just, it’s a nice size, for a couple of reasons. One, it’s just very cost efficient. For us, it was a good way to - We wanted to get our progress out quickly. One of the things that we’ve seen is that it’s a balance inside a foundation model company between focus on releasing and shipping And, like, your new novel research. But with the model factory, we are able to, like, treat the release of a model as less of a time investment from the team because it’s just, oh, at this moment in time, do the training run, done, apply the latest post-training. And so this is, I think, a nice weight class. It’s one that also will fit on a DGX Spark, which, I have a small, like, soft spot for. I love having that little thing, like, run a good model. Swyx [00:55:52]: Yeah, we covered it on this pod, GTC last year. Eiso Kant [00:55:54]: Nice. Swyx [00:55:55]: I think a OSS 120B was the first because it’s a large single GPU, which was the H100, right? Eiso Kant [00:56:02]: Exactly. Swyx [00:56:02]: Rent one H100, now you’ve got 128 gig Macs, Mac Minis, Sparks. It’s, it’s the home sweet spot. Eiso Kant [00:56:10]: But I think what I’m most excited about is that this model hopefully shows people what is possible in this size because, when you’ll look at the benchmarks and start using it, you’ll realize that we are outperforming models two or three times their size. Swyx [00:56:24]: Yeah, and they think-- So for example, today’s Thinky model is like a trillion params. Eiso Kant [00:56:28]: So yeah, exactly. And look, and by the way, I’m excited about-- I have-- It just came out, so for those of you who are listening to this at like, I saw it on my phone Swyx [00:56:34]: If you’re, if you’re listening Eiso Kant [00:56:34]: If you’re humming Swyx [00:56:35]: Yeah. Eiso Kant [00:56:35]: Like two seconds, so I haven’t even had a chance to read the post. Swyx [00:56:39]: But somehow you are, not only you’re, you’re better than Thinky, which is like one of those benchmarks, but also, like, on certain benchmarks, like the τ-bench one, like you’re state-art. Eiso Kant [00:56:51]: We’- Look, we’re doing, I’m not sure if we’re state-art on I mean, 3 banking, I haven’t checked where we sit on the leaderboard. but I think we are, within our weight class, I feel very comfortable to say, and even in some weight classes twice larger, that we are probably state-art. I also want to caveat this, like, best model still in the world right now is definitely, give me a Fable, give me a 5.6. To your point earlier, we also use other models. Swyx [00:57:15]: Yeah. Swyx [00:57:15]: I think the, so the interesting thing you mentioned earlier is you’re starting to shift a lot of your actual usage to it, right? Benchmarks are like Eiso Kant [00:57:21]: Yeah Swyx [00:57:22]: They’re good to compare, but they’re not super realistic. It’ Eiso Kant [00:57:24]: They have to, right? This is how they’re gonna dog food benchmarking. Eiso Kant [00:57:27]: No, you have to. Like, you have to use your own models, and you have to have your own internal evals and benchmarks. And what the funny thing is, like within first 30 minutes of a new checkpoint coming out that’s, the first post-train after a train, you yourself can feel in the first 30 minutes of where this model’s gonna be. Like, you don’t know exactly, but like when this one came out, we were like, “Oh,” like, “this is different.” Like, and I think that’s, I think that’s the best example. but it’s a little bit like your kids. I don’t have kids, but parents, like, they see their kid and it’s perfect and they love it, and then like, they don’t see all the rough edges. You always get that when you build your own model. It’s the most fun part is that you, like, you love a little bit every model that you do. We try to say this thing constantly, it’s like, “It’s the worst model we’ll ever train.” And so I know the team now is like already onto Swyx [00:58:18]: Yeah Eiso Kant [00:58:19]: The next one, as it should be, because this is a race. and this model is a moment in time that hopefully shows people that we are serious about this race, that we wanna work really hard at it, that we want feedback, right? Where is it good? Where is it not? Like, one of the nice things about having your models out in open weight and out in the world is that you get a lot of feedback. Swyx [00:58:40]: How do you think about building it with like, working with a harness, right? So OpenCode, Codex, you have your own pool CLI tool. getting people to use it, the design of model harness, training it in. Eiso Kant [00:58:54]: So you need to do some multi-harness training. Like if you, especially at these smaller sizes, like you wanna do a little bit of multi-harness training for these models to just get the right. And it’s very little. Like, you don’t need a lot, but it’s just like to get the right behaviors that you see in your harness transferring to the harness that, like, you, other people might use it in. we internally have been just calling this polishing, which is like you’ve got your model and you do a little bit of polishing so that, like, it’s able to work well in other harnesses as it is in your own. Eiso Kant [00:59:24]: No doubt it’s going to be better in your own harness, and it’s just because of like where are you putting your reinforcement learning compute, right? You’re putting your RL and your synthetic data, you’re putting it to your own harness because it’s the one that you understand the best and you’re able to push the most. because that end control is what allows you to make it better. then transferring those capabilities is more about just making sure the model, induces the right amount of reasoning and like, understands some of the maybe more complex weird tool call formats that might exist somewhere else. and so we do some multi-harness polishing, as we call it. it’s not really what drives capabilities, but it does create a better experience. I think everyone probably does these days, but it is totally fair to see why your own harness is going to still be better than others. And I think we see this with all the foundation model companies. and it’s just that when you are pushing capabilities, you don’t really wanna trade it off by putting 10 harnesses in your RL runs because it’s just complexity. It’s complexity of engineering because these-- When you’re trying to do good science, right, when you’re trying to really understand what made my model improve, you wanna make one variable change to something you understand. And a harness from someone else, you don’t know or understand in the same way as you understand your own, right? They might have different agents or different prompts Why Poolside Is Called Poolside Swyx [01:00:48]: Yeah, Eiso Kant [01:00:48]: In different places Swyx [01:00:49]: If it’s open source, you can look at the source. Eiso Kant [01:00:50]: Yeah, but it’s time, right? Like I really cannot stress, like I know I’m like a weird person on this because like I have friends like, “Can we meet up?” Or, “Can we do this?” Or, “Can we go out?” I’m like, “No.” Because ultimately, this is a race, and time is the only thing that matters. And if I look at our team and say, “Okay What is complexity worth introducing on our general trajectory to building more capable models? Which generalized to other harnesses quickly. And by the way, our model works well on other harnesses. I really encourage people to do it. It works well. Like I’we’ve been testing it in OpenCode and Kilo Code and others and like, and in Claude Code. Swyx [01:01:22]: Which just got bought today. Eiso Kant [01:01:24]: I saw it. Swyx [01:01:25]: I mean Honda. Yeah. Eiso Kant [01:01:25]: Exactly. Swyx [01:01:26]: Everything’s getting bought. Eiso Kant [01:01:27]: Exactly. and I think part of that is like, and there’s some amazing. I’m, I’m excited, like I think Hermes is a ridiculously cool harness like, and Swyx [01:01:37]: And, part of the question was just like how much of it is model versus model plus harness, right? So new benchmarks like Agents Last Exam, it’s not wanting to just measure the model. same with models getting more and more agentic. They need a harness to operate in, right? Eiso Kant [01:01:55]: I think for when you’re asking that question to a model company, I think you can separate it in two parts, which is like The harness, like we have a very slimmed down harness. When you look at it’s like six tools. It’s like shell and like shell kill, shell wait, write, fetch web, and like, I don’t know, bash. Like I think I’m missing one, but like that’s effectively all the tools. And it’s very simple. It’s very lightweight. So it is not a harness that is designed to try to do well on a benchmark or try to do well on a certain subset of things, right? It’s not a deep research harness. So I think we see incredible ability for complex harnesses that build lots of prompts around and extra data sources and other tools to really push capabilities of models forward. Eiso Kant [01:02:41]: But our model is still better than some other harnesses who do that in coding-like tasks because it was RL’d with it. Eiso Kant [01:02:48]: Now, I do encourage people, I think our model, by the way, is perfectly fine and good on ours. The differences are probably maybe too small for anyone to notice, but we see it ultimately still on benchmarks, by a little bit. So I think it’s both are true. Foundation model companies with their harnesses will really push them because it’s just operationally, the best way to have scientific rigor in improving your models. But also someone who takes our model and really does a lot of work on improving a harness is going to compete us, as they should. and that’s just because the harness is the stopgap between what the model is capable of And what it needs as additional instructions, and what it needs is access to data and tools, right? And that’s ultimately, I think, what a harness is. It’s like, is it able. As you build more capable models, you’re improving the instruction following the models. And so additional harness is just saying, “Hey, if you encounter X, Y, or Z, behave this way.” And so even if you would say that two models with two different harnesses can equally reach the same capability that you care about, a harness that is really tailored towards a capability will do it more efficiently. Eiso Kant [01:03:58]: It’s like a person who’s getting a manual of how to do the task in the most efficient way with the right tools and the right data sources versus a really smart person like, “Go figure it out.” They’ll both solve the task, but one will do it a lot more efficient. So I’m a big fan of all the harness development that’s happening in the world, and we want to work with more harness like creators to also make sure that like if it needs some additional training, like publishing, that we will do it. Swyx [01:04:22]: I mean, I think when you say it’s a race, there’s a question of what are you racing to? are you racing to be the best coding model company or the best coding model plus harness company? I think that’s a, those are different things. Swyx [01:04:36]: Or neither. Eiso Kant [01:04:37]: Or neither. Swyx [01:04:37]: Yeah. Eiso Kant [01:04:38]: So we. I race to AGI. Coding for us since day zero of our website has been, and we’ve said this over and over again, we think focusing on coding and long horizon like software tasks is a path towards AGI because it forces us to solve the hard problems. It’s, it forces us to solve the ability to do extremely long horizon complex work that requires lots of reasoning, external tools, data, et cetera. And one of the things I can show you, so we’ll, we’ll have a web chat on with this model, and I’ve loved this model for deep research, just using it in my coding harness. It was never trained for it. It was never like looked at it, but it’s great at it, in my opinion. because ultimately, the skills transfer, they generalize. Now, where we are not focused on today is to make sure that the world’s greatest medical knowledge is encoded in this model or the world’s greatest legal knowledge. But it did. We won’t be publishing this benchmark ‘cause we didn’t have time to really do proper, but it did really well on LegalBench. and at least on our first runs, and we are very rigorous. When we publish evals, we have Checked them for every little thing. We have run them many times. We’ve passed, like we’ve gone and we’ll, like we try to be extremely honest with this, so if we haven’t spent enough time on a benchmark that we use internally that is public, we just say that we won’t publish it. and Swyx [01:06:01]: I mean, the other way is just to give it to artificial analysis and let them run it. Swyx [01:06:04]: Like third party standards. Eiso Kant [01:06:05]: Oh, 100%, and we are gonna be doing this as well. And still it takes time and effort, right? Because you’re working with people to understand like, the infra failures and like the tools they’re using and like, are they set up well. But I agree. You absolutely want to. I’m a big fan of companies like Vals and Artificial Analysis and like others that are doing this stuff. Swyx [01:06:21]: I found it very nice. You’re the first to bring it up. Eiso Kant [01:06:22]: Yeah. I think they’re great. They’ve got like. I loved like a lot of the work they’ve done and put out. and so, and there’s, I think, many more, and please create more eval companies. Like create more evals. I think it’s so valuable for the industry. Swyx [01:06:34]: It’s an actual monopoly I feel like. Oh, and duopoly maybe. Eiso Kant [01:06:37]: I think it can be broken. Swyx [01:06:39]: Yeah. Eiso Kant [01:06:40]: Because I think it can be broken really easily because creating an eval for many people isn’t sexy work, but whoever does it, everyone is happy to get a good eval. You’ve like if an eval is well constructed, everyone’s celebrating it, and everyone’s willing to pay for it, and everyone’s willing, like the foundation model Swyx [01:06:55]: Oh, yeah. I think creating eval, yes. But like in terms of being like we are the industry standard ones that will Eiso Kant [01:07:01]: Yeah Swyx [01:07:01]: Τ-bench and make sure that you didn’t, you didn’t cheat Eiso Kant [01:07:03]: Yeah, that’s true Swyx [01:07:03]: And I’ll run it the same way that you run it versus your competitor run it. Eiso Kant [01:07:05]: Yeah. That is very true, and we need that. And it’s nice that’s like a few standard places that we all have to like, adhere to. It keeps us all honest. I think that’s super important to do so, And, but yeah, no, I think our goal is to build the world’s most capable models. and right now we are focused on the coding agent capabilities, long horizon work. But what you see with that is that you get a lot for free. I’ve always said it’s a lot easier for us as we get to SOTA and frontier on coding to then say, “Okay, now we’re going to obsess in using the model factory to add more data for places that, we’re not as strong on,” like could be medical or legal or any other areas. and similarly, I think what we see, and we see this with reasoning models a lot, if you give models access to the right knowledge sources and they have capable ways of reasoning, they’re able to go very well into domains that are less known to them or even seen less in their training data. So, but yeah. Are we a agent like model plus harness comp-? No, we’re a model company. but I think models today cannot be trained without harnesses. It’s not possible. So it is just like where before it was just the weights in the container, well, now there’s an agent harness that’s attached to it. and but I think there’s a big difference in being an agent harness as a model company than someone who’s truly building an agent company. I think they can do far more than we can. Swyx [01:08:27]: Yeah. understood. Yeah. I think that is my minor pushback. If you are truly identified as a model company, then make the best model for OpenCode, right? Instead of for pool or whatever. I think that’s not as, that’s, that’s minor compared to if the goal is AGI, make the best model for Hermes. Swyx [01:08:45]: Right? Like just ‘cause that is the next stage after coding. Eiso Kant [01:08:48]: I’look, and we’re working like very closely with them Swyx [01:08:52]: Yeah Eiso Kant [01:08:52]: Because I do think like it’s, and, you have to care, you have to invest in it. It’s why we do the polishing and we spend time on it. and I think over time, yeah, you’re, you’re right that you wanna balance that out. but ultimately you just want general capabilities that everything works equally in every harness. Swyx [01:09:10]: Just on the topic, do you guys do much with like Hermes, OpenAI Codex, NanoCodex, whatever? Pi? Swyx [01:09:16]: Pi. Eiso Kant [01:09:17]: Pi. Swyx [01:09:17]: No, Pi is different. Eiso Kant [01:09:18]: It’s more coding. Swyx [01:09:19]: Yeah. Eiso Kant [01:09:19]: I’m a big fan of Pi, though, I have to say. I think it’s a really sexy Swyx [01:09:22]: I forgot to mention Pi. Eiso Kant [01:09:23]: Yeah. Swyx [01:09:23]: Pi, you sound closest to Pi in terms-- pool and Pi in terms of like the minimal surface Eiso Kant [01:09:28]: In the minimal yeah. Swyx [01:09:29]: Yeah. Eiso Kant [01:09:29]: It’s because I don’- I have a. Allow me for one more strong opinion. Swyx [01:09:33]: Yeah. Eiso Kant [01:09:34]: I’ve been saying this now for two years. Eiso Kant [01:09:37]: I think MCP and tools are stupid. Swyx [01:09:41]: Ooh. Let’s go. Swyx [01:09:42]: You support MCP. Eiso Kant [01:09:43]: I support MCP and we support tools and everything. They make absolutely no sense to me. Eiso Kant [01:09:48]: And like, and I’ll explain a little bit why and I think I can probably get people to come along on this one. Eiso Kant [01:09:56]: If you are looking for complex tasks, increasingly longer horizon, increasingly complex tasks, doesn’t matter if it’s coding or something else, You are gonna be interacting with data sources, right? And you’re gonna be interacting with things that are installed on some form of a virtual machine. Eiso Kant [01:10:15]: And what we are doing is that we’re putting a layer in between those things. We’re putting like MCP in between, we’re putting tool calls in between, and this is even more about tool calls than MCP, where the model can just write the code and interact with the system. And we’re starting to see that. Like Laguna S does this a lot. You’ll see this as well in like frontier models. They’re increasingly no longer, “Here we’re gonna stuff 50 tools in the like system prompt,” to “No, here’s a virtual machine with these binaries installed, this code base you can operate in. Here, a folder where you can write, your memory if you want to.” And the model is using code to do complex asks. And when it uses code, it is not one or two tool calls or three things that are chained together. It starts, using if statements and for loops and making things conditional. And so I think we’re moving from, we already are moving from tool calls, to effectively models writing code, little scripts, and you see this a lot when you get the Python, Swyx [01:11:15]: Code interpreter. Eiso Kant [01:11:16]: Exactly. Like in just the arrow in, written code in the file. I don’t know what you call Swyx [01:11:21]: EOF? Yeah. Eiso Kant [01:11:22]: Yeah, exactly. Like, you already see this happening more in models because when you start training them in RL, the models wanna be free. They wanna be able to do the thing they wanna do in the most efficient possible way, and it is not calling one of the 50 tools in their like system prompt. And so I’m a very big fan of Give the model a minimal harness, as minimal as possible, give it a container in which it has its own code base, right? The, got a models code base that has access to the API keys and data sources and little libraries and documentation that it needs, and just let it run free at the task. and I think that is the way we’re going. I think we will, in 12 months, not see a single system prompt that is stuffed with 20 or 30 or 40 tools anymore. Swyx [01:12:07]: No comment. no pushback there. I think there will be, it’ll be supported for a long time just because that’s, a lot of people are trained on that now, but maybe you guys don’t have to support it in your models, going forward. So, but yeah, I mean, if you can. I do think that’s, writing code is more generalist and it’s a, it’s a means to an end Eiso Kant [01:12:26]: And we do support tools. Swyx [01:12:27]: Yeah. Eiso Kant [01:12:27]: We support. And this is the first model we’re doing parallel tool calling in which we needed to catch up on. So like that’s there and like Swyx [01:12:32]: Yeah Eiso Kant [01:12:32]: So it’s, it’s there, but I, Swyx [01:12:35]: Yeah Eiso Kant [01:12:35]: It’s a personal, nitpick. I like, I want the models to have as many degrees of freedom and just like, be free and do capable things. Swyx [01:12:43]: Yeah. So and then, so that was on the path towards like, okay, how do you use Poolsides models and Laguna models for my Hermes or my OpenAI Codex Eiso Kant [01:12:52]: Yeah Swyx [01:12:52]: On all those things. And so typically what I look for is, Computer use or vision. That’s a, that’s a very big one. You guys have a blog post on that. but then also the persistence I think is very strong value, as well as long context, which you guys have a million token context. Anything else? Eiso Kant [01:13:08]: So for us, look, so for us, vision understanding is the next thing, right? Swyx [01:13:11]: Yeah. Eiso Kant [01:13:11]: Like we don’t have vision understanding. Swyx [01:13:12]: Which I was gonna say is Eiso Kant [01:13:14]: We don’t have vision understanding in these models yet. Swyx [01:13:16]: Yeah. Swyx [01:13:17]: To Eiso Kant [01:13:17]: And so this is something that we’ve, we’ve started efforts on. Like we think it’s, it’s super important to have visual understanding. Swyx [01:13:23]: That’s company vision. Eiso Kant [01:13:24]: And so no, we’ve got work to do there. and this is one of the things I loved about the Thinky model, like from the Two minutes I scrolled the blog post Swyx [01:13:33]: Yep Eiso Kant [01:13:33]: Multi, the multi Swyx [01:13:34]: They’re, they’re very committed to multimodal, including audio. Yeah. Vibhu [01:13:36]: They’re state-art audio, as much as it’s a trillion parameter state-art audio, but also all trained from scratch, right? Swyx [01:13:43]: Yeah. Vibhu [01:13:43]: No encoder in the sense Swyx [01:13:45]: To me, that’s, that’s, that’s one of the strongest reasons why you need to train from scratch, is you just have a different tokenizer, you’d have different Eiso Kant [01:13:51]: I’m fully aligned, like zero disagreement from me here. Like, just add the modality and don’t put. keep it simple. we’I don’t think we’ll touch audio for a very long time. Vibhu [01:14:05]: It’s in the name too, InkLink Inc. Eiso Kant [01:14:08]: True. Swyx [01:14:08]: Yeah. Swyx [01:14:09]: I mean, what’s so hard, what’s so hard about audio? Eiso Kant [01:14:11]: It’s not about what’s Again, it all comes down to focus. Swyx [01:14:14]: I see. Eiso Kant [01:14:15]: Right? Like saying no to things means that there’s a research or an compute that can go to making general progress, and our view is like general progress, is going to come from the ability to push these models to far more capable reasoning, far more longer horizon tasks. I don’t think audio Adds to that. I don’t think it pushes us close to AGI. I think it is a necessary modality as you get closer to AGI. I think visual understanding sits in the middle of those things. I think visual understanding can absolutely, do so, but it also unlocks capabilities that are just valuable today. so but this is the point, right? You want more diversity, you want more different foundation model companies who focus on different things. I think we are just like a horse with blinders on, just like Swyx [01:14:58]: Yeah, you have your path Eiso Kant [01:14:59]: We have our path, we wanna catch up to the frontier, and, we don’t wanna distract ourselves with anything else. Swyx [01:15:05]: Yeah. Swyx [01:15:06]: I will call out that one of the branches of research is DeepSeek OCR, which is can you just throw away the text tokenizer and just have only vision? Eiso Kant [01:15:13]: I find this-- I look, geek, the geek in me is like looks at this stuff and it’s like, okay, look at this, like look at the number of bits encode Swyx [01:15:20]: But they’re right. Eiso Kant [01:15:21]: I think it’s super cool, right? But I think this is what we’re gonna come back down to. Like probably works, it’s just is it compute efficient enough? Is it Like I think so many of these things ultimately will work. It’s just like, what’s the nice thing about text? And I referenced earlier, Peng Ming and Nikolai are my two heads of applied research who are just incredible, like we wouldn’t have gotten here without them and the entire team. Eiso Kant [01:15:45]: And Nikolai have-- and I have been debating, for years about like, should reasoning be in latent space? Should reasoning be in tokens? But one thing that I think him and I really agree on, and all three of us, and is that like Language is incredible because it’s such an incredibly dense way to encode knowledge and information and intelligence, right? If you think about like what went into a physics paper that then is, 20 or 30 pages, like the amount of intelligence and thought and whatnot to then generate that, like in that 20-page document, like those little amount of bits, there’s so much encoded. And other modalities like video and images are amazing, but they don’t have the same density of like knowledge or reasoning or however, like the things that we’re trying to push for that are encoded in that modality. They’re there. In many cases, you can watch an incredible lecture for, 50 minutes on YouTube, but the amount-- and but if you treat that as video in data versus text data, right, the bits to like signal-noise ratio, the compute efficiency of the modality is a lot less. And so we have this view as like with language you can go really far, but also when you have limited compute, limited, people, and they’re very much linked to two, I think we can push language. It’s the more, it’s the better investment. But I want all the modalities. I find it super cool and I love what DeepSeek and others are trying. Like I can retweet them all the time, but internally we’re just like, “Let’s stay focused.” Vibhu [01:17:17]: Which I’ll say, you can see somewhat works looking at Anthropic. OpenAI has a lot of vision, multimodality. Anthropic just didn’t, right? Fable’s a big step up in image processing, but like they’re not known as the multimodal company, right? They’re the language model coding company that has multimodal capabilities that’s never super flex and, goes pretty far. Eiso Kant [01:17:42]: I look, I in this I think Anthropic, I mean, they’ve done many things right, but I think this maniacal focus on just pushing capabilities, scaling up models is. I couldn’t agree more. I think it’s, it’- that’s the first hurdle, and once we get that, then we can improve a whole bunch of other things. and but at the same time, on the other end of the spectrum, it’s really exciting to see people, building these spatial models, right? That are, and the world models that are being built, like for very different, use cases. but I think ultimately it all comes together at some point. Vibhu [01:18:19]: Okay. So scaling models, this is Laguna S for small. Eiso Kant [01:18:23]: Yes. Vibhu [01:18:23]: You have good naming, extra small, medium, large. Eiso Kant [01:18:26]: Yeah. Vibhu [01:18:26]: Still scaling? Eiso Kant [01:18:28]: So the new medium started training, and it’s much bigger than the last medium, started training yesterday. so it’s a, 39-day training run. and, Vibhu [01:18:39]: How do the days and events? Just the compute model Eiso Kant [01:18:41]: Models factory. Vibhu [01:18:42]: Okay. Eiso Kant [01:18:42]: Right? And like at this point, like with the model factory, like it’ Vibhu [01:18:46]: I thought it was interesting. So in the Laguna medium and extra small, you even quoted number of GPU hours for how many days and whatever for different size. And I’m like, “Oh, you can also work backwards to how much that costs, right? What GPUs, how many hours “ Eiso Kant [01:18:59]: And you realize it’s not a lot. Vibhu [01:19:00]: No, it’s not. Eiso Kant [01:19:01]: It’s not a lot of money. and, you started with DeepSeek of the West and, I think that’s, The DeepSeek moment, right, was a moment when people realized that you can train incredibly capable models for not a lot of money on the training run. But I think that’s the falsehood, right? Like the training run is not the expensive part. The training run is a very anticlimactic event, right? Like we just had a Slack message come up yesterday saying, “The new model is training and here are the links, so you can follow along the evals,” and like that’s it. all the work that goes into that moment, it’s like how people talk I know nothing about sports, but how, like, athletes talk about, like, it’s all the preparation, it’s all the going to the gym, and then the game is just a game. I think that’s a little bit like with model training. Swyx [01:19:42]: Yeah. People had over-indexed on DeepSeek was trained for $5 million or whatever it was, right? It’s like there’s the amount of R&D before that, the infrastructure is built up. Yeah. Eiso Kant [01:19:51]: Exactly, all the things, the data. But no, so Laguna M is training, and yes, there will be an L and there will be an XL, and what you’ll Swyx [01:19:57]: Ooh. Eiso Kant [01:19:57]: What you’ll see with M, right, M is much larger than the last M, right? So these monikers are a little bit our version of the different Swyx [01:20:04]: Yeah, he was making fun of people for saying small is 24B or something. Swyx [01:20:08]: No, so, no. Small for Mistral now is over 100B. Eiso Kant [01:20:12]: What? Swyx [01:20:12]: Yeah, I can pull it up. Eiso Kant [01:20:13]: I mean, our small, right, is 118, so I don’t wanna say anything else. Like, it’ Swyx [01:20:17]: I mean, I think it’s also. Okay, yeah, your small is Eiso Kant [01:20:20]: We all know that the single hardest thing for any foundation model company is naming. Eiso Kant [01:20:25]: I don’t want to say that we’re good at it either. I mean, it’this is Laguna S 2.1. It’s, it’ Swyx [01:20:32]: But at least people understand, medium is bigger than small. Until you mess that up, like Eiso Kant [01:20:37]: Exactly Swyx [01:20:38]: You have a pass. Eiso Kant [01:20:38]: We try hard. Swyx [01:20:40]: While we’re on the topic of naming, this is gonna be at the end, but might as well Eiso Kant [01:20:43]: Sure Swyx [01:20:43]: Why Poolside? Why Laguna? Eiso Kant [01:20:46]: So When we started the company, it was gonna be called Snowball Apps. it was after the snowball effect because we expected this company to become a snowball effect, and it definitely has been a snowball effect for us. turns out it’s an Amazon trademark. Eiso Kant [01:20:59]: I kid you not that my founder’s next suggestion of a name was, “Let’s call it Bedrock.” And so at this point it was like, “Okay, no, you are amazing at naming things if you worked Amazon.” and so, early on in the company, before we were incorporated, we were at an annual conference of a very big Major tech company, and we had been discussing with them. And you have to realize the company at this point is me, my founder, our CEO, Margarita. We know the first person who’s gonna join us. We haven’t, like, incorporated yet. and we were discussing an OpenAI Microsoft-style deal with this big tech company. Like, they were going to provide us with a lot of compute. We would give them, perpetual access, a whole bunch of things. Eiso Kant [01:21:49]: And, we found out the name was trademarked, Snowball Labs, while we were at that conference and having this discussion that we had no right to have, right? We were a couple of guys who had nothing yet, but this big company was willing to entertain the fact that we might partner with them. And, we were discussing this, and it was in their annual conference in a public setting, and the chief scientist of that company said, “People can hear us here. Like, we should move somewhere else. Let’s go to the restaurant Poolside.” And for some reason, me and Jason looked at each other in that moment and said, “Oh.” and then later that night, - the name stuck with us. The word stuck with us, and we said, “Let’s call the company Poolside.” And ever since, we never ended up doing that deal, and we used it as a reminder to never turn down our, round down our ambitions, because that would’ve been the easy path. and the hard part was what we did, which is start and try to raise exorbitant amounts of money when you’re just a couple of guys who are not even building it in Silicon Valley, who don’t come from any, of the known knobs and things like this. And so everyone assumes Poolside because AGI, everyone sits Poolside, and it was a playful name, and we liked it, and it was a little bit different. But the name is, like, a reminder for us to never round down our ambitions, and whenever you’re faced with those decisions to just pick the harder path. Swyx [01:23:09]: Yeah. I mean, that’s a great story. I know you’ve told it before Eiso Kant [01:23:13]: Yeah Swyx [01:23:13]: But I just wanted Eiso Kant [01:23:14]: Right Swyx [01:23:14]: On the record. but that’s, that’s what I did the first time I met you. You told me, you sat me down. You were, you, we were in the hotel somewhere. Eiso Kant [01:23:21]: Yeah. Swyx [01:23:21]: And you were like, “We’re raising a $500 million.” I’m like. And then you gave me the whole vision, and then you did it. And I was like, well, it’s, I don’t have that much opportunities to ask, like, just how do you do that raise to that to those kinds of VCs? What are they looking for? like, yes, vaguely AGI, but, like, what do they want when Raising Huge Rounds and the AGI Investment Thesis Eiso Kant [01:23:42]: Look, it’s, the world’s definitely changed, right? When we were raising that $500 million round, the majority of investor conversations were still trying to explain that these models were not just stochastic parrots and that they were gonna keep going. I’ve seen the world go from OpenAI is gonna win it all and there’s no one else who can build company, right? I mean, Anthropic struggled, to raise their $500 million round. That’s like, well reported. They pulled it off, gladly. and so I think when we raised that, it was about a year and a half ago at this point, the world was very different than it is today. I think the world today, There’s been, there’s been this function where the number of people who believe AGI is real, Is probably a, an, a super linear or definitely some form of an exponential function itself. Eiso Kant [01:24:31]: And I think this is important because if you hold the belief that we had three years ago and a year and a half ago, and we looked for people who shared that belief, which is like, this technology is gonna fundamentally underpin everything that’s economically interest- or economically valuable and scientifically interesting for, like, the future, then the value function afterwards is easy to understand, which is like, hey, if you get there, you are one of the commodity, one of the players who can build this commodity. and over the years, building that commodity has become not just about building models, but also about building infrastructure and other things. Eiso Kant [01:25:03]: And so I think today, because the number of people is bigger and the outcomes have been proven, right? I think the incredible, like, financial success that Anthropic is having right now and, like, the growth that OpenAI’s had and others and Google no longer make this a question of is there product market fit, which really a couple of years ago was, like, part of the question. Like, how big can these things be? You tell people that, like, you’d be at these amount of revenue numbers in our industry right now, people were still, like, would laugh you out the room. Eiso Kant [01:25:33]: Now I think it’s a function of who in the world believes that it’s gonna be an oligopoly of intelligence And who believes that oligopoly can be broken by other companies. And I think that’s what divides investors more than anything else. For the ones who believe in AGI, and then you’ve got a whole layer that, is self-selecting out, foundation model companies because they’re like, “Look, I can’t make - The money I put there, compared to what I can put in an application company is very different.” I think there’s incredible application companies, and there should be many should be built. But I do think we are still in a world right now where this is the early innings - this can still be the early innings of who is going to, be part of the set of people who win. This - Intelligence is the most, in my view, gonna be the world’s most demanded commodity. It will more commoditize in margin and price. and the world wants choice and wants options. And so I think treating the world as like, “Oh, there’s only gonna be two players,” I think is very shortsighted from investors. Eiso Kant [01:26:41]: I think that group who thought that was a lot bigger at the beginning of the year than now. Eiso Kant [01:26:46]: I think the last couple of months have woken up a lot of people and going, “Holy s**t,” like, the world both can use a lot more intelligence, but also, like, the world is far more complex. We should have multiple choices, more options, things that can be turned off, that can’t be, that. The restrictions that people put on models now, I think, is another area of this, right? Eiso Kant [01:27:08]: Like, the fact that We are entering into a world where model companies are saying, “You’re not allowed to use me for foundation model company development.” They should be allowed to do this. It’s capitalism. It’s their business. It’s their work product. Eiso Kant [01:27:23]: But it is insane. Eiso Kant [01:27:25]: It is wild that we are, like, okay with that. Open Models, Democracy, and Regulation Swyx [01:27:30]: Do you have more problem with Anthropic saying it or the White House saying it? that-- that you’re picking Two different Eiso Kant [01:27:37]: Things Swyx [01:27:37]: Limitations and restrictions there. Eiso Kant [01:27:39]: Look, I think I, - I’ll put it this way. I think we wanna, as this technology gets more capable, for the better and worse, we do wanna yield to democracy to figure this out more and more. I think any single company making unilateral decisions, is, Is dangerous. It’s a concentration of power in a small number of people with very limited checks and balances. and that has never worked out well in history, in any way, shape, or form. and this is not a criticism on the existing foundation model companies. This is just more commentary on, like, how I’d like the world to be. I think in a world where the technology gets more capable, government needs to play an active role in determining, where is there real risks of misuse, right? And I do think we need to separate safety between misuse, and, doomsday scenarios that, I think No one knows if gonna, are gonna happen or not. And I think just, like, very practically, I think, I’m glad to see there’s a lot of conversation now starting to happen again at the government level of trying to figure this out. and now what the final decisions are, maybe I’m happy about them, maybe I don’t, maybe I agree, maybe not. But ultimately, like, that’s democracy always, right? Like, at any given moment, I might not be perfectly happy with one or the other, but people chose to vote in someone to make those decisions. And so I think over the long run, over a 20-year time span, the world directionally goes correct and democracy does work. At least, what’s the famous quote of like it’s the worst of - It’s the best of all the worst systems or something like that. Swyx [01:29:26]: It’s the worst form of, organization except for all the others that we’ve tried. Eiso Kant [01:29:30]: Exactly. That’s the one. Swyx [01:29:31]: You can always count on me for a Churchill quote ‘cause I’ve, studied Churchill a lot. Eiso Kant [01:29:35]: I love that. and so that’s what I hope for. Now, I do think we are in a critical moment of time, and so speaking up for anyone is important. I think, researchers who are thinking about starting their own foundation model companies start. people who wanna share their opinion and be vocal, if that’s with their representatives or just out on X, like, do so. Eiso Kant [01:29:57]: And but concretely to your point, I think we are not at a level of capability right now that we should start restricting, open models in any way, shape, or form. I think it will hurt innovation if we do so. Swyx [01:30:14]: Is there a point at which you will change your opinion there? Eiso Kant [01:30:17]: Yes. I mean, look, - And there has to be. Swyx [01:30:19]: Yeah. Eiso Kant [01:30:20]: Right? Like, you cannot. If you sit with a straight face and say, “This can be open forever in every way, shape, or form,” it is just as, I think, egregious as saying, the opposite of it all needs to be closed down right now. Like, I think at any ends of extremes of spectrums is where we go wrong. Eiso Kant [01:30:41]: Right? In society in any way, shape, or form. And so the answer is always more nuanced, and the answer is never black and white. And so I think as we encounter, like, real world scenarios where we have to say, “Hey, we have to be more careful,” we need to reevaluate. If that means training a model differently and opening it up, having different versions, some things that, That are restrict-- I think that’s totally okay because I don’t think anyone should be irresponsible. What I do wanna call out is that people have been calling for the fear of misuse of these models since 2, Right? And I still remember, like, “We cannot release 2 because the whole world will get “ Swyx [01:31:20]: I mean, that was Dario. Eiso Kant [01:31:21]: And so, like, this is not a commentary on Dario, it’s a commentary just in general in the space. And so We have not been very good at this so far, and we need to get better at it. And I do think that the work that’s happening with, like, safety institutes and better evals and things like that is probably the right direction. Swyx [01:31:38]: Yeah. I mean, I wanna say something in defense of this. It’s better to err on the side of safety and then roll it back rather than the other way because the other way, it’s a one, way decision. I think that’s, I think that’s true. Vibhu [01:31:53]: The caveat there is also the competition, right? You don’t have global error on the side of safety, right? You’re talking Swyx [01:32:01]: Yeah, exactly. Vibhu [01:32:02]: So Oh, yeah Swyx [01:32:02]: You don’t get to do unilateral safety because someone else will just be more unsafe than you. Vibhu [01:32:06]: Yeah, exactly. Swyx [01:32:07]: Yeah. Vibhu [01:32:07]: You can pause innovation here. It doesn’t mean it’s, it’s pausing elsewhere. Swyx [01:32:11]: They’ll just take over the world. It’s so easy. Eiso Kant [01:32:13]: They’re, they’re complex parts. Swyx [01:32:14]: Yeah. Eiso Kant [01:32:15]: Right? And I think we are much better off talking about certain capabilities that we can, commonly agree on and internationally agree on that we want to, limit or not have available, than we should talk about it in black and white of models available, yes or no. Like, the moment you start getting these big blanket statements, it’that’s when you start getting at the risk of, like. I always think back about when we banned advertising on cigarettes. Good thing. I’m not saying I’m against that. But it effectively established an oligopoly of cigarette companies because no one else could ever compete. and it was the, probably the best moment to the tobacco industry that ever happened, And we don’t wanna do that right now. If we pull up, walls behind innovation, and this is a self-serving comment because I’m not at the frontier yet, but it’s not just related to me. I think it’s related to everyone in the space. you are deciding right now in 2026, based on the current capabilities of models, that this is something that only two or three companies can build, and that to me reads like chapter 14 of the most dystopian fi novel that I could read because from there I think you can play out all the scenarios that happen in the world, and none of those are the ones that make me, excited about the future. and I think that’s the thing we should all think about. Like, what’s the future we wanna be excited about? What do we wanna have? And I think that’s a future where intelligence is a commodity. Everyone can access it. It becomes cheaper and cheaper, right? I think that’s important. It can, like, impact more of the world, and it’s not one where, a single company puts their thumb on their scale of both what it outputs, to or turns it on or off. Nvidia, Hardware, and the Compute Stack Swyx [01:33:56]: I think the one entity that has more power than the US government here is Nvidia. Swyx [01:34:02]: Because, like, whoever gets the allocations gets the compute. Vibhu [01:34:06]: You can take it down to TSMC or, Swyx [01:34:09]: And TSMC below that. But I just wanna test provocative statements to see if you have any response. Eiso Kant [01:34:18]: I need to think on that one. Vibhu [01:34:20]: Which I think they are regulated, right? Like, you can see the government Swyx [01:34:23]: Nvidia’s not regulated. Vibhu [01:34:24]: Can they ship to China? Swyx [01:34:26]: Okay, but they’re not China. Eiso Kant [01:34:30]: Look, I think this industry Has existed because of what Nvidia’s done. Swyx [01:34:35]: Yeah. Eiso Kant [01:34:35]: Right? I know they-- - People like it’s easy to give them flack, but I also wanna say, like, I remember when we started Source, right? In 2015 post that capacity article. It was able for this progress to happen because we were able to put consumer GPUs in servers, and they allowed us to do so, and then, like, and you kept going further. And so this is something, like, foundation models are so closely linked to their hardware and their systems. Swyx [01:34:58]: Yeah. Eiso Kant [01:34:59]: Why do we see these stepwise progress happening? We see them happening because of the next generation of networking and systems that come out, right? The difference of a model you could train on Hoppers versus GB300s is the difference between a trillion-parameter model and a five or six trillion-parameter model. And so these things really coexist, I think, very closely to each other, and I think the more interesting question, I think, for the future is going to become of, like, how do - what can we unlock in terms of model capabilities, like, as we start designing these things even more? And we’re seeing that with, like, the next generation of systems. And I think the world, abhors. Eiso Kant [01:35:42]: Like, capitalism does a really good job at trying to, like, push towards things that - that allow for more competition, right? And Nvidia allows for competition. It’s not. But if a government says no one else can build foundation models effectively through the regulation, that is very different. Now, is it hard to go build an Nvidia? Absolutely. Is it hard to build a foundation model? I think it’s very hard to build a foundation model. But we should, like, make the playing field one that where, if someone wakes up tomorrow and wants to do so, they are, like, allowed to do so, and they’re allowed to use the tools to do so. And I think there’s still a big difference between what we’re seeing in the discussions around model companies versus what we’re seeing with chip companies. Vibhu [01:36:25]: The gap also seems to be the expertise in who regulates it, right? Who at the government decides what’s too safe, too smart, too dangerous? but while we’re throwing spicy questions out there, do you have anything that comes to top of mind that could be changed? So, should OpenAI, Anthropic, open source models? Is it open weights? Is it what we do in RL that determines, your safety barriers? Is there anything that should be done there or just spitballing? RL Bottlenecks, Mixed Hardware, and Low-Precision RL Eiso Kant [01:36:53]: That’s a good question. yes. one of the things that I’m excited about that I think we’re more and more talking about, I don’t think anyone is doing yet, is, mix and match of hardware during RL training, right? Like, - You think about, like, the notion, and we’re seeing this in inference, right? The prefill and decode Vibhu [01:37:15]: Yeah Eiso Kant [01:37:16]: Just work better with, a general purpose, GPU and a more specialized, like, chip, right? Like, if the Groq chip at Nvidia, the LPU and the GPU combined, and there’s different versions of that in the industry. And RL is batch size constrained, Right? So, like, you are ultimately-- and then you’re batch size constrained because you don’t have infinite tasks, right? When you’ve got the entire web, you can be much more flexible in scaling up your batch size because you’ve got the entire web. But for RL, you have, X millions of tasks that you are gonna be training on, and so you cannot blow up your batch size massively, which means that you can’t scale compute to a certain extent with RL the same way you could scale compute with, like, training. and so I’m very excited about anything that improves that. And I think one of the best ways to start improving that is the things that we’re already starting to see in inference, which is the separation of the prefill and decode to different chips to come to reinforcement learning, right? and I think we’ll be there soon. and I think more people should be working on this, because then all of a sudden we’re able to just be way more efficient in how we train RL from a wall clock time. Again, coming back down to the fact that it’s a race, right? The race is measured not in how many GPUs, but the race is measured on calendar time, and that’s probably one of the biggest impacts we can have right now to speed up our industry. and so that’s one, like, technically I love geeking out about and talking to people. Yeah. Swyx [01:38:45]: Yeah, I would talk to Etched. I had a tour of their data center and, physically you can see how PD disaggregation is mapped out in the data center, and you have to own your own hardware to do that. Eiso Kant [01:38:57]: Yeah. No, look, I think it’- I think more innovation in the space is just, like, is the coolest thing. Swyx [01:39:02]: Yeah. Eiso Kant [01:39:03]: And so I’m, I’m excited because that’s like, all of us are. Eiso Kant [01:39:09]: Like, why don’t we finish, post-training this model, whatever, two weeks before release? Or no, sorry, between release, between training, then, training SFT, and then the time it takes for release. My biggest wall clock bottleneck right now is RL time. Eiso Kant [01:39:25]: Right? And it’s just because I can’t scale it up further because I can’t add more GPUs to it because of that batch size constraint. There’s a really cool, blog post that just came out that was showing, RL done in even lower precision than any of us are doing. I thought this was really cool. So just what date is it today? We’re on July 15, so this came out five days ago. and I thought this was very cool. I think, lower precision RL, while keeping it stable, we’re, we’re still doing this in FP8, and so, I was excited to see them sharing this work and bringing it out. it’s definitely something that I’m excited to be doing once we move to Blackwell GPUs. Swyx [01:40:05]: But yeah, cool. Part of open research, you take and you give. Eiso Kant [01:40:08]: Exactly. Yeah. Swyx [01:40:10]: I’ll just quickly mention, there was a paper that did a ablation on, levels of quantization, and they roughly concluded that four bit was the sweet spot. But I don’t remember Eiso Kant [01:40:20]: This was just a couple of years ago, right? I think I remember this. Swyx [01:40:22]: I think one year. Eiso Kant [01:40:23]: One year, okay. Swyx [01:40:24]: But like, I’m like, okay, maybe NVFP4 is it. You can’t really-- Like, the lowest you can go is ternary. Eiso Kant [01:40:30]: Yeah. Swyx [01:40:30]: That’s it. Like, there’s not that many. Eiso Kant [01:40:32]: Well, I mean, there’s, there’s, there’s still quite a difference between NVFP4 and four bit, right, in terms of what’s, what’s possible. But I think NVFP4 is, underrated in terms of what it is. I’m, I’m quite excited that - when it came out, it’s, just getting that extra, like, that trade-off between range, Swyx [01:40:51]: Yeah Eiso Kant [01:40:51]: Is very cool. Swyx [01:40:52]: A couple quick closing questions. Vibhu [01:40:54]: I have a quick one. XS, S, Distillation, and Model Cadence Swyx [01:40:55]: Yeah. Vibhu [01:40:55]: Okay, quick question back to technical side. So any big takeaways from XS 2.1 medium to training the new small, just general in terms of training models? You mentioned a lot in the earlier discussion about, okay, in training, there’s a lot you can squeeze out, right? You can learn a lot more from the web. at the same time, you took 30B and scaled it up to 120B, right? is there any gating on how small is too small? So I’m, I’m just gonna ramble for a bit. I’ll come to a question at the end. But, part of Carpathy’s thesis was cognitive core, right? We’ve seen Vipe Thinker, Nanbase, 3B, 4Bs that reason a lot, and then, the idea is you offload to a different model for the work. This, these are small reasoning models. So have you found anything interesting in model sizes, like 20, 30Bs on device, 100Bs on single GPU? can you squeeze out more there? Eiso Kant [01:41:56]: There’s a lot more to squeeze out. like, I think, not to make too many forward promises, but I think we can squeeze a lot more out of the XS size as well. and I think we learned a lot during S training that will allow us to improve XS, like, size even further. And I think already since then we have learned things that could have made S even better. I think there is a lot more still for, like, our space to squeeze out of models much smaller. I don’t think that’s an argument against scaling. It’s just an, And one, by the way, and I think this is a nice thing that, it’s really-- it’s not very helpful to have, a post-training recipe for a smaller model and try to apply it to a bigger model. Vibhu [01:42:38]: Yeah. Eiso Kant [01:42:38]: It just, in all cases, you’re gonna have to rethink most of the recipe. But, recipe for post-training for a bigger model applied to a smaller model is almost always just a really good, like, improvement and baseline. You can still tweak it more, but I don’t think that’s necessarily, like, obvious. and so - once you make your bigger models better, you often have a quick lever to quickly improve your smaller models again. but will we be able to squeeze a lot more out of smaller models? Laguna S gave me a lot of confidence that I think we can. and I think it’s around that discussion we had earlier about that it’s about the behaviors, not necessarily the raw intelligence, that you’re trying to improve the models for. Vibhu [01:43:23]: And that’s on all axes of, There’s like an axis of how long a model will reason, so how long can it stay agentic, then there’s also efficiency, right? You wanna ideally push on both. And the thing to clarify you guys aren’t doing right now, which we do see at Frontier Labs, is the distillation, right? You have a big model that you don’t really ship to users, and what you put out for inference is typically distilled from that, which gets you quite a bit of gains, right? Eiso Kant [01:43:50]: Look, I think it’s, it’s something we don’t do right now because of, like, why we’re also, like, building these models, right? These models are for us part of our research path. So we’ve, Laguna Medium was much larger than the last two models that, this one and last one that we’ve released and we’ve trained even bigger models in the past. So there is the engineering component of, like, a bigger model and every order of magnitude size, you’ll learn new things in training about stability. But at smaller model sizes, you are able to just iterate a lot quicker, like internally, right, on your research. And so, for us, distilling down to a smaller model doesn’t serve the purpose. These models are. It’s not the right term, but to us they’re dual purpose models. They are progress for us to weigh to see did we improve in the model factory and something to put out into the world. and so that’s why we don’t do it. We’ve done distillation experiments, and there’s, like, really cool things you can do, and I think if you have lots of user data, then, you can go even further, right, in that. But I think there’s something to be said in having a quick cadence of models trained end from scratch so that you as a research organization can learn the lessons and not wait. That was one of the big lessons we learned over the years when we used to have a much Eiso Kant [01:45:09]: Longer cadence between model trainings, like six months, and we would train just, like, a big model, wait six months, train another bigger model. you would be compounding so many changes of improvements That at the by the time you’re training your next model, it’s a bit of a soup, and you don’t really know what ingredients led to the outcomes. So when you are training far more frequently models, and this holds true for both post-training, and from training from scratch, you are much more able to get an understanding of what led to the improvements. and I think that’s important. Like, ultimately, we are all still. There is no true science yet of, deep learning for large language models. but we are all, I think, trying to gain insights from our experiments because it’s those insights that lead to scaling laws, that lead to the improvements that allow us to be, again, more compute efficient and get more capabilities. Swyx [01:46:02]: Yeah. amazing. I was gonna end off with a little bit more history. you spent some time looking at, metrics for engineering team productivity. How do you think about engineering team productivity today? Engineering Productivity in the Agent Era Eiso Kant [01:46:14]: I mean, it’s wild, right? I mean, it’s the, it’s like the golden age. Like, it’s the fact that you can just take an idea and build something by waiting overnight for an agent to do the work. Eiso Kant [01:46:26]: I don’t know. To Swyx [01:46:27]: Like, how do you measure when. Swyx [01:46:28]: ‘cause you literally in a theory Eiso Kant [01:46:30]: Yeah. Swyx [01:46:30]: You’re doing this, right? Eiso Kant [01:46:32]: Look, I think It’s a good question. It’s one I haven’t thought about in a long time. Swyx [01:46:36]: But, you’re qual- you’re pretty qualified to do it. Eiso Kant [01:46:38]: No, I’m gonna. - No, it’s a fair point. Let me take a second to think about it. Look, ultimately, what is code, what is software, what is engineering is to go from something that is valuable for an end user or sets of end users, like an idea, an extra, a bug fix, a feature, to, like, delivering that value. And I think what we’re doing with these models becoming more capable is that we are massively like, both cutting out middlemen and compressing the time that it takes to deliver that value. And ultimately, that iteration cycle for any startup or any company is what allows you to win, right? If you’re able to solve a bug in two hours versus it staying in the back log for three weeks, if you’re able to, like, be on a customer call and learn, hey, if this feature existed, it would, like, they’d be willing to pay more, and it’s more valuable to them, and you ship it in a week instead of in a month. And so I think ultimately, maybe the same things that we looked at years ago LLM still apply, and it’s just the notion of cycle time. But in this case, it’s lead time from the moment you have a valuable thing that you are looking to do for someone to the moment that it’s shipped to them. Every other metric is ultimately a leading indicator for that lagging indicator, right? It doesn’t matter if you’re looking at amounts of code, PR, reviews, all of these things. And so I think in this case, we are starting to move so quickly in some of these things that we can just sit back and look at what was traditionally the lagging indicator. We just named it the lead time from traditionally ticket to, like, an end result. what I would look at in this new world, that maybe we didn’t think about before is how much can a single person do with that, Eiso Kant [01:48:22]: Right? One of the most, like, if you look at AI native companies, they’re not designed like the engineering orgs of, LLM age. They’re designed with often just the builder, right? and as close to the customer to the ability to ship. there isn’t necessarily a huge team in between that sits there. And I think that is, I think, is exciting, like organizations where a single IC can just, get much closer to that. So I would look at From where the value sits that’s identified to the moment it’s shipped and how many people are involved in that. And you want the amount of people involved in that to be less, and you want the time end to be shorter. Swyx [01:49:05]: Okay. is there a way to eval that when you’re, interviewing somebody? Eiso Kant [01:49:12]: Oof. Swyx [01:49:13]: ‘Cause that is, Eiso Kant [01:49:14]: Look, Swyx [01:49:14]: The most compressed version. Agency, Constraints, and High-Impact Teams Eiso Kant [01:49:17]: I think the common answer to this is agency. Swyx [01:49:20]: Yeah. Eiso Kant [01:49:20]: How much agency does a person have? I think in the age of AI getting more capable, agency becomes probably one of the most important qualities for anyone. and I think agency is something you can look for in, what people have done in the past because agency is something that if you have it, you are demonstrating it, right? No one has just agency and is sitting back and not, like, exercising it. The whole definition of it is that it’s exercised. And so understanding, like, what were things that people did in their lives, in their professional and their personal projects that showed agency and, your personal backstory shows a ridiculous amount of agency. Swyx [01:49:56]: Oh, dear. Eiso Kant [01:49:58]: Like, I think that is ultimately it. It’s the Silicon Valley, quota the, of the last, year and a half or so is like you can just do things, right? Swyx [01:50:06]: Yeah. Eiso Kant [01:50:07]: That- that’s I think what you’re looking for. Swyx [01:50:08]: I think then aligning high agency people is very hard because they all wanna go their own way. That’s the whole point, right? Eiso Kant [01:50:15]: They-- Yeah, but I think the notion - Like, I think the notion of a good leader, right, in an organization is to be able to bring people together around, like, a common outcome. And I think what you wanna do with anyone who’s high agency-- I feel very lucky I’ve got an organization with incredibly high agency people. Like, I mean, I’m not the one who built the model, right? I cannot stress this enough. Like, it’s the team that, like, achieved this, and it’s a team that is incredibly high agency. And so if you look at, like, what does it take to bring that together, it’s, it’s ultimately a common goal and a common set of boundaries. Because if you allow to just go, “You can do everything,” you become an exploration algorithm. And this is what we see in big tech, right? In research, in big tech, everything is an exploration algorithm. Everyone can do anything as long as - And then it becomes political about gathering the resources. So when you say, “This is our common goal, and these are the boundaries that we’ve set,” right? “We’re not multimodal. We focus on RL.” Like, we do these things, and you’re upfront with people before they join the company, you get a lot of agency. You can run where you want, but these are the places where we Swyx [01:51:24]: Yeah, lanes Eiso Kant [01:51:24]: This doesn’t make-- This is the lanes Swyx [01:51:25]: Yeah Eiso Kant [01:51:25]: That makes sense. I think it gets the best out of people because, like, innovation comes from constraints. Eiso Kant [01:51:34]: We did this with relatively little compute and relatively little money compared to some of, like, the others that are out there. and I’ve thought back on that quite a bit recently and thought, it was a good thing Because those constraints forced us to become much better in certain other axes that might-- others might have not, right? We purchased relatively little external data. Swyx [01:52:01]: I was gonna ask about that. Yeah. Eiso Kant [01:52:02]: Exactly, right. That was a constraint. but it’s a constraint that pushed us to move on other areas to improve. And like, and there’s lots of versions of that. So I think high agency people, you wanna empower, you wanna get them really excited about what they’re doing, but you also wanna say, “Hey, if you join this mission, this is the outcome I need you to achieve. But these are the places that we don’t go, and maybe if you care about those places, go somewhere else.” Swyx [01:52:26]: Yeah. Great. last call to action, who are you hiring? Hiring, Impact, and Closing Eiso Kant [01:52:31]: We are hiring on every possible role in applied research and engineering in the company. so from Swyx [01:52:36]: Yeah Eiso Kant [01:52:36]: Training all the way to evals to post-training architecture. Like, we are still in a world where, individuals can have massive impact. And I think our pitch to join us, it’- We spoke a lot about the mission, how we think about things, but I think we are one of the places where it’s the highest ratio to individual to impact, Right? Less than 70 people built this model. Less than 115 between engineering and researchers, like, together did this effort, and that’s a very broad definition ‘cause I put myself in the 115 list. Eiso Kant [01:53:08]: And so being able to do this work on a mission that you’re aligned with, and you can have that - every individual still has huge impact. And I think Swyx [01:53:18]: And being able to publish, being able to open Eiso Kant [01:53:20]: It’ Swyx [01:53:20]: Open source the model. Eiso Kant [01:53:21]: Yeah, look, all of those things are part of that. But I think ultimately, when you can today pick between joining a very large foundation model company But you are one of many. Eiso Kant [01:53:35]: And not by any fault of them, but just by definition, the denominator has become really big. And our denominator is quite small, and so the level of impact you get to have is really high. And I think ultimately all of us, the most incredible high agency people I know, what are they optimizing for? They’re optimizing for impact. they’re optimizing for impact, and am I aligned with the mission? And if today you heard about the mission and aligned and you’re optimizing for impact, I think we’re a really good place to join. Swyx [01:54:05]: Okay. Eiso Kant [01:54:05]: Awesome. Swyx [01:54:05]: I think we end it there. That’s a fantastic statement. You did amazing on four hours of sleep. Eiso Kant [01:54:11]: Thank you, guys. Swyx [01:54:12]: So, podcast eval, definitely approved. Eiso Kant [01:54:14]: Appreciate it. I literally wrote it down. My eyes are, like, starting to go like this. I’m like, “Phew.” Swyx [01:54:17]: We’ll let you go. We’ll let you go back. Eiso Kant [01:54:19]: It was good to see you guys. Swyx [01:54:19]: Thank you for setting this up. We wanted to get this in because we think it’s a great model. Eiso Kant [01:54:23]: Appreciate it. Swyx [01:54:23]: I think a great story to tell. Thank you. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe https://www.latent.space/subscribe?utm_medium=podcast&utm_campaign=CTA_2

• 0:03Speaker 00

  All right, we're here in the studio with Isaac Khan from Poolside, together with Phoebe. Welcome. Thanks, thanks for having me, guys. Good to be. Yeah, fresh on the plane. You texted me, you're like, hey, I'm on my way to SF. I was like, you're on the plane right now, right?

• 0:16Speaker 01

  Like, hey, you know. After I texted you, I realized that probably coming in with major jet lag. I was gonna offer some fun experiences today, but let's do it.

• 0:23Speaker 00

  I mean, I think the thing I would tell guests is that they don't actually have to prepare that much. Because if you're truly working on this every single day, then even like what you hastily remember is going to be new for a lot of the audience that don't live in your world every day, right? So 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. And now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something?

• 0:57Speaker 01

  No, it's not at all. Actually, I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy. In 2015, he wrote an article called "The Unreasonable Effectiveness of Recurrent Nets." And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs and later LSTMs and transformer models to be able to write code. If you go to this article and you scroll down, you can kind of start seeing, like, this was the precursor to what ended up becoming language models. So at least when he was character-level language models, that were starting to actually predict letters. He has an example out here. There's a little Paul Graham generator. And you can kind of read it, and the text kind of makes sense, but it doesn't.

• 1:41Speaker 01

  And there's a little, there's an example of code a little bit further down. It's a Shakespeare. And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is pre-transformer paper. And I had built a completely unreasonable belief that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, you know, to a lot of things that are intelligence and the ability to write code. And so I started building Sourced, which was a fully open-source company, trying to build what we used to call machine learning on code, language models on code. And we spent about four or five years on this till the end of 2019.

• 2:25Speaker 01

  And that sounds really cool today, but back then, no one cared. Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to like the structure of code. We were, you know, when Attention came out, we were applying it to LSTMs, and then the transformer paper came out, and it was, it wasn't obvious. And what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who kind of took that confidence and kept going.

• 3:10Speaker 01

  We failed ultimately at the time, and it kind of was like biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.

• 3:18Speaker 00

  Yep.

• 3:18Speaker 01

  You spent, still a lot, but, and you spent years with like a group of 40 people just obsessing over this problem. And life took a different turn, and family kind of became the focus. And I kind of kept my head down. I'm running, frankly, didn't really look at language models for the following two years. Kind of big mistake, considering following years are gonna be really interesting. And then ChatGPT came out. And it was kind of like a vindication. It's like, people started texting me. I found like my old, you know, work decks and these old talks. And throughout that whole journey, you know, we, we kind of really had a strong point of view at the time that like, as you're building more capable intelligence, it should be open and open source.

• 4:04Speaker 01

  When we started Poolside, that actually wasn't the case at all. And I want to be very open about it. When we started Poolside, we were like, there was a premise of two things. One is, this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago, when we started, most people were still arguing if these were stochastic parrots or not. And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people kind of looked down on us a little bit, or like, you know, this is really gonna work. And so we just started working the problem. And we never really thought about open source again.

• 4:48Speaker 01

  We just kept our heads down, and we built our like knowledge understanding from scratch, right? We didn't roll out of an existing lab, so we picked up the papers and started writing code and figuring things out. And it wasn't until the beginning of this year that me and my co-founder Jason picked up the open source conversation again. And if you go back to some of the early things on our website, it was very straightforward. It was, we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.

• 5:19Speaker 01

  But we started talking at the beginning of this year because it became kind of obvious that the world was going in a direction that was starting to kind of like, kind of like pick at us a little bit. Like, it didn't, this didn't happen overnight. It was like a little bit, we were seeing this, and we're like, okay. The world's going down a path, and throughout this journey, there was something that I kind of used as a, as an analogy or things. I said, kind of, well, if I go back to back in those days, kind of 2015 or 2016, we're working on this, and I picked up a sci-fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, you know, decades. And it would have that first chapter where everyone's trying to figure things out.

• 6:04Speaker 01

  You'd get the chapter of ChatGPT coming out. And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four, a handful of companies, were going to create all of intelligence moving forward. And when I thought about that story, it felt like a dystopian sci-fi book, not a utopian sci-fi book. And the reality is, I'm a utopian sci-fi guy. Like, and so we kind of took a step back and said, hey, can we play a role here?

• 6:37Speaker 01

  Now, it was easy for us to do so because we were not at the frontier. But we were at the frontier, I don't think we could have changed our mind. And I don't mean this, like, it's, on the moment, there's too much capital involved, too much expectations. You've built up things, right? We're small team, you know, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And there were a lot of soul-searching, a lot of conversations, and said, no, this makes sense, even if there's big unanswered questions. Like, how the hell do you build a business model with foundation models about open source? Big, open-ended question that we do not fully have the answer to yet, right?

• 7:19Speaker 01

  At what point do you no longer wanna release open-source models? Because misuse of models has, you know, real potential risks associated with it. How is the government gonna respond to open source? But I think it all just came down to one thing, and I'll stop the monologue, is the fact that I'd rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up like our weights right now, and kind of figure out along the way how we can like do more.

• 8:01Speaker 00

  Yeah, I think if anything, over the past three years, that has become a bit more true. You are one of a cohort of Neo Labs, and that people are now calling that. And, you know, we're doing this on the day that Thinki launched their new model, and you are performing dev on their, on some benchmarks that they released, right? Like, they just don't have it yet. So it goes to show that I think, like, actually, this is one of those things where like, there actually is room for multiple players, and you are seeing a little bit more of the future, maybe more like 20, not 100, but like, you are one of the 20.

• 8:35Speaker 01

  I really hope so, right? I think we are, I'm excited about their release, or I'm excited about everyone kind of releasing, because like ultimately, like, choice competition is both gonna drive progress in the right direction, but the fact that like, you know, we create models, and while we all, you know, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases. And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore,

• 9:15Speaker 01

  then we want to be able to live in a world where companies, countries, people can choose and say, hey, I am most aligned and I trust most this provider for these kind of things.

• 9:26Speaker 02

  I think more than just one of the 20 Neo Labs. Up until recently, you know, most of open source innovation was coming from the Chinese labs, right? So there's the deep seek of the West. Is it today? Okay, maybe it's thinking machines, reflection, but there aren't many, right? So one of the things you guys started sort of in France, Europe, but very much now you're kind of taking that American standpoint. And more than just that, the, the point is the Chinese models that we see, they're not super open research. The, the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a sort of breakdown blog, paper, technical

• 10:09Speaker 02

  report of here's everything for state of whenever to build, you know, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.

• 10:20Speaker 01

  No, I appreciate it. Look, I think it's, I think it's actually the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them. We can change them, but like giving someone the weights does not allow them ultimately to recreate what you're doing, right? And, and so now there's challenges around releasing data sets, challenges around like these certain things, but being able to share your research, like, right? How do we do it? What are the lessons we learned that we spent, you know, tens of thousands of experiments of compute on? I think very much so. One correction though, Veebo, and I say this because it's kind of been haunting us for quite a few years. We actually from day zero, we're an American company.

• 10:55Speaker 02

  Yeah, they moved to.

• 10:56Speaker 01

  We, so the, the story once and for all is very, we started as an American company. We are always been an American company and early on we made a very conscious decision. We said, we're not going to hire any researchers in the Bay Area. We're going to actually look for talent everywhere else in the world. And that is everything from Middle America's Seattle to, you know, Serbia and to Taiwan and Singapore and other places. And it was because we kind of took a view that this was going to become a talent war for this. And I think it has over the years. Now, three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that like some of the world's most capable people, like the most interesting innovative ideas, were not just going to be here.

• 11:40Speaker 01

  And so it led us to create like a fully remote company. And we ended up opening an office in Paris and London and different places. And, and we have a lot of the team in the US and a lot of team outside, but we kind of always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others. But I think one of the things that it slowed us down in the beginning, but it has sped us up now. And it's why you're seeing like the progress, I think, on our models and the cadence that which we release is because we didn't roll out of an existing lab, right? We didn't, we didn't actually have a lot of the information that's freely flowing around here at the time.

• 12:22Speaker 01

  We just kind of took this point of view, it's like, okay, well, let's just work the problem. Let's just go and like read the few papers that are out there and let's just figure this stuff out. And we made some hilarious mistakes in model training because of that over the years, like especially in the first 12 months, there's a few that I think still haunt me and scare me. We can talk about them later. But it kind of created a, like a resiliency and persistency in the team, right? With extremely few people have left us over the years that like told us, okay, we can do this. When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was like, okay, let's build it from scratch.

• 13:03Speaker 01

  I remember we had this one moment where we spent three weeks working out an optimizer bug. Like it was like, training just couldn't get stable. We like obsessed over it and we thought like, maybe we were wrong. Maybe we should have just forked this repo or should have done. But then when we solved it, I still remember at the time we were like five people in the company. When we solved it, we're like, oh, we can do things. Like if we're just willing to work hard. And I think that culture with a very strong engineering bias has kind of helped us like get to where we were. And so there's this kind of notion of open source and talent and these things. I think we, we just took different decisions from a different starting point.

• 13:45Speaker 01

  And I think we are lucky, I don't want to definitely call it lucky, but also a lot of hard work in the team that now like that's starting to kind of show up in results.

• 13:52Speaker 02

  Just because we probably won't revisit this again, but and this is a fun recruiting challenge. If someone knows the answer, what was the bug? And then we, we won't tell the solution.

• 14:00Speaker 01

  So the bug, this is, you're going to test my memory here. So, so, but I think I can recall, so if you, so if you look at, so if you take like Adam as an optimizer, you have epsilon, which is right, like in the denominator, is exactly in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon like quite a bit, like they were like adding, I don't know if it was E minus four, whatever, like a high value for epsilon. And if you think about this during training, it's kind of like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively like a random number in the denominator, right? Like behind the decimal point.

• 14:45Speaker 01

  And, and I don't recall the exact bug, but it had, what I remember is once we solved it, we no longer had to juice epsilon as much as like was happening in the Llama paper and other places. And, and it was like one of those fundamental moments where we had trusted this paper that was out there. And we're like, oh, no, it has to be this way. It has to have this high value of epsilon. But it made no sense to us intuitively, like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? And, and, and that was kind of like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition, because the one thing you learn very quickly with model

• 15:26Speaker 01

  building is that your intuitions that you start with are going to get beaten up so hard, right? Like the, it's such an experimental science that the things that seem obvious, you very quickly get to learn like you were wrong and hopefully you figure out why. And sometimes you don't even.

• 15:45Speaker 02

  Yeah. Yeah. So, you know, one of the reasons that you, you know, when you released your new models, Veebo got really excited. I mean, everyone got really excited, but Veebo led our paper club on it and you guys saw it and obviously. Maybe talk through some lessons learned in that, whatever you can sort of disclose. We can focus on model factory stuff, whatever you think is a good starting point.

• 16:08Speaker 01

  So I would say that our view from very early on in the company was that model building is ultimately 90% engineering. And I think we all know it in the industry, because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and, and, and writing code. And so we kind of said, okay, the state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code basis for training and like data pipelines that were kind of patched together. And we kind of looked at this and said, well, ultimately model building is a process. You're going from raw data, right? Like pre-training, raw material, the web, et cetera.

• 16:51Speaker 01

  You're doing a whole bunch of filtering, cleaning up transformations, analyzing these days. That's, you know, far more complex than it was three years ago. Then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still, it's become a lot more reliable, was extremely flaky back then. And now with every new generation, we get our new sets of challenges. And, and then you go into the next stages, right? There was no mid-training back then, but like you go, you know, your post-training and then your reinforcement learning. And so we kind of looked at this and we says, well, this looks like an industrialized process. This looks like an end to end process that every single part of it kind of has its machinery, right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, you know,

• 17:35Speaker 01

  large scale distributed training, and then you've got your reliability. And we said, well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero? Not retrofitting it later on, but like really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today it's thousands of components. And I kind of tried to equate it to, if you think about like someone who was at the very early days of Foxconn, if they had been there for the following, you know, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance, right?

• 18:18Speaker 01

  Because we don't have the lineage and history of decisions that led to that. And so we kind of built early on from the beginning with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training. And in the, and because it's such an experimental science, ultimately, in the beginning, when it wasn't that complex, you could kind of patch your way around it, right? But now at any foundation model company, you are running, I mean, we're a small team, right? We're less than 70 researchers and another 35 engineers.

• 19:01Speaker 01

  And we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month. And so if you look at that skill of every model run that is like, it's ultimately it's, you need to be able to trust it. It's an infra problem. And so what we have now done over the years has gotten really good at that. And just by working it and improving it and obsessing over those kind of end to end decisions. So now what that means is that you looked up Laguna XS two that we launched. It was five weeks from the beginning of pre-training to launch. The model that we're going to talk about today was eight weeks from start of pre-training to launch.

• 19:41Speaker 01

  We start at the next model literally yesterday because we now finished the post-training required for the model we're launching, you know, next week or by the time this comes out today.

• 19:51Speaker 01

  And we moved that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we kind of treat this like the way you would look at like a SpaceX factory where yes, the first rocket really hard to build. But the much harder challenge was building the factory. And now they're rolling off and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.

• 20:23Speaker 01

  And what has been, which was not planned from day zero. It was kind of in the back of our mind, like this will happen one day. Is that when you build a really good end to end model factory, really good APIs and really good engineering systems. Well, what is it perfect for? It's perfect for agents. Because agents are now starting to take over more and more work in our model factory. Yeah. So I look at the screens when I walk, like when we come together in our monthly, we do monthly on sites. And I kind of walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs, they're evaluating the results that are coming back from the model runs. They are, you know, making the changes.

• 21:05Speaker 01

  And we're still in the driver's seat, we're still coming up with the ideas, we're still helping with the debugging. But more and more, and this is right now very profound on the data side of our, of our pipelines and both pre and post and the synthetic data pipelines. It's starting to become more on the architecture side as well. You're starting to kind of see these twinklings of what our SI is going to look like.

• 21:28Speaker 01

  And that's frankly, so when we talk about like your question about our models, I retalk about the model factory. And my coolest example of these things is always that when we kick off a new run. It's a matter of the pre-training, like big run, or if it's now a post, like one of 10 post-training versions we do for like pre-release or many experiments. Is that at any given moment, the changes that somebody made that they had experimental results on from the day before. Make it into that run. So there's not like a cutoff 90 days before, like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in reliability. So one of my favorite metrics about like Laguna S is that there was no on-call events.

• 22:13Speaker 01

  Right? Like completely zero. And actually, we haven't had a meaningful on-call event, like someone to wake up for, as far as I recall this entire year.

• 22:23Speaker 01

  Now, there is one asterisk to that in usually the first six hours of launching a new model run. Something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually, there's a little bit of intervention. But that's always within like in-call periods, right? Not, not on-call. And, and I think that's starting to now compound. So the model we're releasing now, I love it, it's amazing, but we're already on to the next one. And I think that's the way it should be. Hey, I also just want to point out, so for context, this was like a month ago. We found it actually in the tech report. So we just came in with, okay, new models dropped, haven't heard about it. We're very used to doing.

• 23:02Speaker 01

  We're very much like, ah, okay, look, it's like, you know, on par with Kimi, deep sea, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building. And then we hit this page, right? Like literally the page two of tech report is, uh, this process allowed us to build the small model from scat, from scratch to delivery within five weeks, applying the lessons. And then I'm like, oh, this paper is not about, here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that want to dive more from what we're not going to discuss on the podcast. It's all laid out here, right? From custom software that agents can use to interface with training code, pre-training data. Yeah, we'll link the paper.

• 23:46Speaker 01

  Yeah, yeah, all that stuff, you know, read the paper here, but. But I, I would like, I love principles. I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out the Dexter, just got bought by a prefect. Yeah. It's kind of fun, but yes, I'm actually very familiar with Dexter. Just anything where like they trigger some kind of story. Well, I would say, well, experiments code is obvious, but I think one of my favorite things is, uh, I don't know where it is in here, but early on, uh, and I still think this is the case, actually a lot of foundation model companies. People prepare their training data sets, they get packaged up, then they get copied over to a training cluster, distributed across all of the nodes, and then training starts.

• 24:31Speaker 01

  And we looked at this like three years ago and we were like, that makes no sense. You lose so much time because the moment you have to re-materialize the data, so you have to make a change, you have to fix something, et cetera. You've got all this time of like repackaging it, right? Tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basic. Like just in time. Just in time, like good computer science, like principle. And that was one of the first things that I think unlocked our, the model factory.

• 25:09Speaker 01

  Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small, like, you know, post-training experiment. Consumes a certain number of tokens per, per second, right? And it's actually not a lot, right? From like a data, moving data perspective. So we said, well, we have our training cluster. And then we've got like our AWS kind of setup where we can build these amazing big data pipelines. We can set things up, we use Spark underneath the hood, like all of these things. But when you say AWS, it's not actual AWS, it's your internal. It's our internal, like just running like, like our. Services. Exactly, our stuff running, running on like an AWS account or like any hardware, right? Yeah. And so, once we made that shift into I can stream data into training.

• 25:54Speaker 01

  All of a sudden, you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize. Now, all of a sudden, when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in and you just, you have the search, it's called Blender, that's in the report. Where we didn't say, okay, for this run, I want 20% of this source, 10% of this source, I want this much, so many epochs of repetition. I want this to be, you know, shuffled in a certain way. And your training job can start while the rest of the data is even still materializing. Also, what it does is because all of this underneath, so for us, we treated the data layer underneath as like an immutable data layer. And that was really important.

• 26:37Speaker 01

  Like experiments as code, immutable data later means that you can always go back and understand literally down to the single token at which cursor it went in, on which version of the code. Yeah. And it took us a, I have to admit, like the first year of poolside, we understood that engineering had to get great. But we didn't understand yet, uh, that this is ultimately in support of like a good rigorous scientific progress. We were quite a few, we were a very small number of people, so a lot of it was Yolo ideas and Yolo runs. Yeah. And we built great infra for the Yolo runs. But once we realized that we treated data as immutable and code as always version, and you could always track and trace every experiment end to end perfectly. You could repeat everything perfectly, right? You had perfect reproducibility.

• 27:22Speaker 01

  I can still reproduce runs from two years ago if I wanted to, right? It actually enables the scientific progress, like the scientific process. And I think that took us probably about a year, year and a half into the company to figure out. We also had some great hires, like our co-head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the, the early 2020s. I think brought that into the company of like, hey, we want to have even more rigor. And then once we kind of had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability. We were able to start actually, okay, every experiment is truly an ablation, we truly need to understand it.

• 28:05Speaker 01

  And, and I think we became much more scientifically rigorous in the last couple of years and the infra underneath enabled it. And then there's just fun stuff like.

• 28:16Speaker 01

  Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small, small paragraph in here where it's just like, oh yeah, pre-training data, we have some, um, we have an auto mixer, you know, it trains eight small models, scales them up, picks the pre-training data set, we don't even need to look at it. I'm like, wow, a lot of engineering rigor there. And there's just, you know, there's just a lot. Yeah, and it's, and look, and we want to put out more, like actually, we. We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, you know, publishing research once you're at the frontier, because until then, you're catching up. And every minute and hour in this industry matters.

• 28:59Speaker 01

  Like, I obsess over it, not just the wall clock time from idea to result, but just general, like, time every day that we, you know, waste is is one that doesn't allow us to catch up. But in this case, we said, okay, we're going to give ourselves, I think we gave the team like three or four days while still doing their work, like, give everything in there. And to your point earlier, if you know your stuff, it's easy to like, put it out. And so there's so many more things that we want to talk about over time, and we will definitely start doing. And as we earn more the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.

• 29:40Speaker 02

  Which is the other cool side of this, right? It's it's not like back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. You lay out a lot of it here alongside here's your system for how to do it, you know? So it's it's really like promoting other people can do the same.

• 30:00Speaker 01

  And by the way, I also want to make clear, right? We have been incredible, like, we take a lot of advantage of the fact of all the open research that others have published. Right? And and and you mentioned, you know, the Chinese labs and we, I think it's important that there's, you know, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The the incredible Chinese lab has done an amazing job at sharing their research. And we have definitely take like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's you also have kind of an obligation to give back.

• 30:39Speaker 00

  Do you have a favorite or underrated Chinese lab that you want to shout out? Everyone shout out Zhiyi.

• 30:44Speaker 01

  That's a good question.

• 30:45Speaker 00

  Move on, obviously.

• 30:48Speaker 01

  Yeah, look, I think.

• 30:51Speaker 01

  I think obviously everyone's been talking about Zifu lately with with GLM five point two. I think what most people don't realize is when they started.

• 30:59Speaker 00

  Yeah.

• 30:59Speaker 01

  Right? They started years before ChatGPT.

• 31:01Speaker 00

  This is rebranded.

• 31:02Speaker 01

  And and and so I have like, I remember how hard it was to work on these things before the rest of the world got excited about it. And so I have an immense amount of respect for people who were working on improving models when it wasn't the sexy thing to do, when believing in L L M's, you know, was was going to get you ridiculed. I remember like back in twenty sixteen, when we were doing what we'd call a machine learning on code with some of these models. You know, we would, people would just frankly laugh at us, like, be like, this makes no sense. Like, why are you wasting all these like millions of dollars on trying to figure this out? And and so I would say they're probably the one that I think deserves the shout out,

• 31:45Speaker 01

  not just because their latest model was very good, but because they they fought to get here. And I think. I think every foundation model company, you. It takes time to get here, right? It took us three years to get to the model that we're that we're now going to be releasing. And now the the time in between the models is coming is counted in weeks. It's no longer counted in months or years. But this stuff's hard. And if we can make it a little bit easier for the next person, like, we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible.

• 32:26Speaker 01

  And we should try to, in that window, encourage as many Neo Labs or whatever we want to call them, like, to start. And so one of my kind of current. Mission qualm is like, I want to encourage whoever is a researcher right now who thinks they can actually tackle this to go and leave and become like a competitor, like, start another foundation model company. Because I think we need it. I think otherwise, we're not going to be in the world where.

• 32:52Speaker 01

  I don't want to just be the fifth or the sixth company that wins. I want to look at a world where there's a lot of choice.

• 32:57Speaker 02

  What else do people not see in starting a foundation model? You know, it's a, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's a.

• 33:09Speaker 01

  Well, look, it's, I, I in turn, this is an oversimplification. And I, and I always asterisk with that because it can land a little bit the wrong way in in people's minds. But I actually think you can sum down. And I saw ninety five percent of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, gifted and skilled work people do. But if you really look at it, like, what are we doing? We are looking at data, we're generating new data, we're improving data.

• 33:43Speaker 01

  And the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that that allow for more model capabilities. But at the limit, if you could train a large enough model, right? Like, you had infinite compute, we probably, if you had infinite compute, you'd be at AGI probably already tomorrow. Right? Like, it's not, and so, and let me say that infinite compute with infinite ability of much faster networking, because networking and so being more of the bottleneck than than compute. But so I do think that that's, those are the main things. And to just realize that this is engineering.

• 34:29Speaker 01

  I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like, you're doing credible magic or rocket science, or only like, you know, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved. But a lot of the work that all of us are doing on a day to day is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers.

• 35:12Speaker 01

  And I think that's, it feels, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. And that happened in the span of like six months. That would have not been what I think most people assumed was possible, you know, a couple of years ago.

• 35:46Speaker 00

  Yeah. I think one of the interesting moments is when you can sort of self-host, like, you know, if a programming language, like, if you can compile the language in the in the language, the equivalent is, can you use your own tools, right? You have the pool CLI, you have your own models. Presumably, you're not only using your own models, there's no way. But like, you know, what's that percentage over time?

• 36:10Speaker 01

  This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a, it's not state of the art model yet, Fable and other, they're very capable models. But Laguna S is really interesting. I'm going to actually pull up the quote, Pengming, one of our co-heads of applied research, said something last week as the as the model came out about ten days ago, much better than frankly, we had hoped for, expected. And he said,

• 36:40Speaker 01

  I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on fifth of July on a Sunday. And it's kind of been burned in my brain ever since. Because the Laguna S model, as you'll see it, and and why it does so well in benchmarks and why it does so well in in using it on a day to day basis is that it's just incredibly persistent. But reasons a lot, I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes.

• 37:24Speaker 01

  But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion A B active model, which is not that large. It fits on a DGX spark and still runs at, you know, thirty, forty tokens a second on a spark, is able to solve Erdos three ninety seven independently. It's able to do complex programming tasks. It's able to, I asked it this morning to make me a Wi-Fi scanner without using any external libraries on my Mac. And it's like figuring out like the core W LAN API by really persistently trying to understand it without access to the internet. And more and more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days. I'm not exaggerating. I was on my eleven hour flight yesterday.

• 38:07Speaker 01

  I spent ten hours reading trajectories and traces and like of the model. And what I take away from it is exactly what Pengming said. We are going to be able to squeeze so much more out of smaller models than I think we had imagined in the industry. Because, yes, there's intelligence and larger models are more intelligent, like, no doubt about it. We should continue to scale up.

• 38:32Speaker 01

  But the behaviors of being really persistent, of being able to backtrack when you're wrong, of like, understanding how to interact with your environment, show us that we can get a lot more out of it. And this, for me, has created a bit of a question in my mind the last couple of days. If you think about where we're using models today, right? We are using models for knowledge work, represents twenty five percent of the global economy, you know, twenty five trillion dollars of work. As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science. And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places.

• 39:15Speaker 01

  Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that. And it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to. And we're starting to see examples of that in medicine and like in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software, I'm a software developer at heart, first and foremost, probably, although I probably can't say it that much anymore, because I don't write production code in years,

• 39:54Speaker 01

  is that what makes us good is actually our persistence. It's our ability to encounter a problem and backtrack and say, I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to like try different, five different ways to see like if I can solve it. But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted, or any previous like benchmarks that's shown for any model remotely this size, or even larger, at least on coding tasks, that it's because of the behaviors.

• 40:36Speaker 01

  And so now the question I have, and I don't have it answered, is I know at the limit, so infinite model size, or extremely large model, and the cost of that model is going to be very expensive to run. We know this, right? So larger model ROI.

• 40:52Speaker 01

  So I know that at the very limit, I'm not going to use the world's largest model one day, quadrillion parameter, whatever crazy like skill we scale up to do a basic coding task. Already today, I'm starting to size down for certain tasks. So it means that there is an optimal, it means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak. And after that, the return on investment of using a bigger model just doesn't make sense. Now, I think the question is, before I would have thought that peak was very far away.

• 41:30Speaker 01

  This model for me is the first sign that maybe that peak is at trillion, five trillion, 10 trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, you know, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, it is an argument for the commoditization of models. It's an argument that open source can win and like succeed in this world. And now it's of course a self-serving argument, and it's a hopeful argument. But theoretically, at the limit, it works.

• 42:14Speaker 01

  We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like, I think we should not put our head in the sand and say we're going to be king of open source, small models. I think that's frankly, it's a cop out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, you know, more model. But it's kind of a sign of hope. And so I don't want to overly state this is a good model. We have a long way to go to get to the state of the art. But what hopefully people take away when they use this model is that the behaviors inside of it

• 42:58Speaker 01

  are what push it to be far more capable, less than necessarily the number of parameters.

• 43:03Speaker 02

  Is that mostly post-training? Like, right?

• 43:06Speaker 01

  It's entirely post-training.

• 43:08Speaker 02

  Are we done improving anything on pre-training? Is like, pre-training done?

• 43:12Speaker 01

  No.

• 43:12Speaker 02

  Okay. So I I just wanted to cover pre-training and then we go post-training.

• 43:15Speaker 01

  Pre-training is not done. I mean, look, there's a part of pre-training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you've got to solve for. That's, those are ultimately, you know, engineering challenges.

• 43:31Speaker 01

  I have a, I would say a not commonly held opinion that reinforcement learning will move earlier and earlier into pre-training.

• 43:42Speaker 02

  Yeah, it's mid-training.

• 43:44Speaker 01

  Not even mid-training, like mid-training today, right, is like, if you look at, so we've been working on this for years already. And I think the best, I think the first time we saw it out in public was the DeepSeek Zero paper. This is a year and a half ago, I think, if I recall correctly,

• 44:00Speaker 01

  where, you know, you can, very early on in a model, as it starts capable of being able to use language, et cetera, induce reasoning. And so the question that I kind of have is like, we have this, with the data set, that's the web. And the web, I think we could arguably say probably has the totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality from garbage data. And like, once you look at pre-training data, you really get humbled of like what the web is to like, you know, the most greatest scientific papers and the best blog posts and like, you know, best transcripts and whatnot. And so now, what we are trying to figure out, and I've been doing a lot of work on, and it's

• 44:45Speaker 01

  a place where maybe not as open as we're on other things, but we will become more over time. We've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. And, and I think there's a huge amount of gold to be found there. I think we are right now in, you know, we've got some drugs in the industry. One of the drugs is distillation, another drug is, you know, more environments, like, and they're great, and they make us feel good, and they make the models better, and like, we're all addicted to them, and we'll use them, right, in various different ways.

• 45:25Speaker 01

  And, but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web. I think just next token prediction during pre-training is not enough. And so I think we'll see some very interesting things still happen.

• 45:43Speaker 01

  And that RL in post-training to induce behaviors, to improve things, like, I think we, the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yet if

• 46:01Speaker 02

  You mean you're going too far?

• 46:02Speaker 01

  I'm, I'm not sure if the path to AGI is just

• 46:06Speaker 02

  Is more environments.

• 46:07Speaker 01

  More environments.

• 46:08Speaker 02

  It seems like a never-ending, okay, I want instruction manual for this table, right? Am I going to environment out, building furniture? Are we just going to tail end? Like, we need some general solution.

• 46:19Speaker 01

  I think, I think there is, I think there's an ability to generalize more from the web. But also, I'm very encouraged, like, when I look at Laguna S, and I would just, post-training is, is the big impact there.

• 46:31Speaker 01

  And I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.

• 46:40Speaker 02

  The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You reshift, yeah, yeah. So, you know, you reshift distribution and you can have it reason towards what you want. On your point about mid-training, a lot of mid-training is still just continue pre-training in a domain, say medicine, then you do RL. So still just

• 47:00Speaker 01

  It's just better data, right? Like, I mean, mid-training, I like how we invented this word. Like, it's effectively just like, you know,

• 47:06Speaker 02

  A second phase.

• 47:07Speaker 01

  It's a second phase of pre-training with like, a really dumb way to do a curriculum. But like, ultimately, what you'd want is a curriculum from token zero to token 30, whatever, or 40 train tokens, that really truly is the optimal curriculum for the model to learn. But mid-training is essentially a two-stage curriculum on the web. Because we do not have to compute, and effectively, to try to ablate the perfect curriculum. Right? And so I'm pretty sure that you'll start to see people talking soon about some other terminus to, because now, now we do this, right? We talk stage two and stage three and stage four mid-training and like, but ultimately, all we're doing is we're trying to assign a curriculum to the web data

• 47:49Speaker 01

  that we have to, to allow the model to learn better. I think at some point, as things get compute, as, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have mid-training and like stage two and stage three is organizational, right? It's, this is, I think, a thing where, that we really try to avoid with the model factory is like, mid-training exists because there's a mid-training team now, right? There's people, or the people who decide to focus on like a mid-training effort. But what you really want is engineering and, and skill of experiments that allows for a much more continuous spectrum, that you don't, you have infinite stages.

• 48:33Speaker 01

  Now, we're not there, compute's not there, organization design is not there for it yet. But I think we'll get there. We'll look back on a couple of years and think, oh my God, it was so cute that we did our pre-training data like this in such a naive way, like, we barely ordered it, we didn't really do a good job at like, the kind of building that curriculum. We'll get good at it in the industry.

• 48:51Speaker 02

  And I'll confirm that, you know, when I talk to some researchers, that this is a lot of the focus now is like, how does pre-training change and what is the next objective other than next token prediction? I assume you don't have the answers, but you have some ideas?

• 49:02Speaker 01

  We have some ideas. We're not ready to talk about it yet. We've been working on them for years, and I think that's the one thing that's also like, you asked earlier about like, what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe you know works

• 49:18Speaker 02

  Yeah.

• 49:19Speaker 01

  versus like your, I might

• 49:21Speaker 02

  Purely research.

• 49:22Speaker 01

  Pure research and, and finding that balance and, and adjusting the percentage to it based on where you are in the race is really important.

• 49:31Speaker 02

  I mean, so like,

• 49:32Speaker 01

  This is a nice way. I was gonna bring out auto research at some point. Yes, another Andrej invention. Coinage, uh, which is like, I honestly, like, how many objective functions can there be, right? Like, just try a thousand of them, set it running, whatever. Like, you know what you're looking for. You're looking for loss curves like that, like.

• 49:50Speaker 02

  It's also a thing people take bets on, right? When you say more Neolabs, you're doing a version of, we'll do foundation models, scale them up next token predictors. A lot of other Neolabs that we see wanna take a completely different approach, right? At some level, you're right. It's all, uh, compute efficiency and that's the net objective. But you know, some are, okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not just, they're like, you know, 99% not balancing. Here's the vanilla and scale up there. 99% on, here's novel research that'll change everything.

• 50:27Speaker 01

  And I think, look, I think you, it depends when you started as well, right? When we started, like, the novel thing we did was reinforcement learning on code. No longer, that's no longer novel by far, but we were like, that's where we obsessed over when no one believed in RL. So you have to kind of, when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, you know, knowledge, but in the beginning it wasn't.

• 50:53Speaker 02

  It's cool, you know, this was like your original 2023 blog of purpose.

• 50:58Speaker 01

  Yeah.

• 50:59Speaker 02

  And like, you do lay it all out here. The blog is pretty underrated, right? The whole RL on code was very, very early on.

• 51:06Speaker 01

  Very, very early. And even, we had to argue with people, like, we, we say here things like, to push beyond current capabilities and to train your own foundation model. We had to argue with people that it mattered that you had your own, like, you know, base model. You can't fine tune your way to success, right? Major capabilities emerge from training a base model, accurate and useful during fine tuning.

• 51:23Speaker 02

  Which, like, for perspective at the time, you know, we knew closed models, OpenAI Anthropic were huge. The open models we had were like, Mistral 7B, a 30B, a 70B, not.

• 51:35Speaker 01

  When we actually, the date on this thing is wrong. When we published this, it was April, uh, April 2023. I think this is just happened on the migration, right, on archive.org.

• 51:45Speaker 02

  The stuff.

• 51:46Speaker 01

  Mistral had started, we started on the same month, right?

• 51:49Speaker 02

  Yeah.

• 51:49Speaker 01

  So this wasn't even, there was only, I think Llama out at the time, and that's it, right? And so, uh, but I, I agree. I think we want, we want as many diversity of ideas. And I do think if you're starting today, you, you want something that gives you an edge, right? Uh, and what, I do think we sometimes over,

• 52:13Speaker 01

  I think every architect, at the limit, every architecture works. And RNN works, it's just not compute efficient, right? Like, if you had infinite compute, you could probably just, you know, take a basic RNN from back in the day and you could get pretty far.

• 52:27Speaker 01

  Now, there have been, you know, meaningful breakthroughs, attention to other things that are there. But you,

• 52:36Speaker 01

  I think we are, we're still, we're still very early in figuring these out. The things I'm most excited about, I'm most excited about people doing extremely low precision training, right? So like the ternary stuff that we're seeing, and it was very cool. The bonsai stuff yesterday was super cool to see. I think there's, you can find tweets from me going back to 2023, which is like, kind of the notion of like, well, it's an obvious trade off. Bigger model, lower precision equals, you know, smaller model with higher precision. By definition, right? It's just, what is, like, how does that actually play out, right? What's the actual size limit? So you now have companies that are trying to figure that out. Like, those are the kind of things that can change our industry if they're done right. Because ultimately, like, our bottleneck on compute is, is, is a map mall bottleneck, uh, and, and a networking bottleneck.

• 53:21Speaker 01

  And the moment you start doing those things. So I'm excited about that. We're not doing any, I mean, we're doing the usual, like, Laguna S was trained in FP8. Only thing that in this run, I have to admit, that wasn't FP8, was the all to all. In the new run we just started yesterday, the FP8 was all to all. That was just like, cut off date, like, ah, we're not perfectly comfortable wanting to do it. You've got amazing work by Nematron in NVFP4 training, like, I think it's underrated what they've done there. Uh, I'm excited to get to NVFP4 training. Doesn't make sense yet, cause we're still training on hoppers, right? We're like, relatively small, we're 10K H200 cluster company right now. We'll be scaling to a lot more soon, but, uh, and really a lot more, if someone is thinking about applying for a job.

• 54:06Speaker 01

  But like, the, yes, I think it's, there's so much more juice to squeeze out of this. And hopefully Laguna S shows people that a model at this size can get a lot more. And, and we did this thing in eight weeks. We think there's a lot more juice to squeeze out at any model size. We're now scaling up because it's the most optimal thing to do for us as a company. But if I had infinite time, I would love to push more the capabilities at other model sizes.

• 54:34Speaker 02

  I don't think we've properly announced what your new size is. So we have XS, which was 30B-ish.

• 54:41Speaker 01

  Yeah.

• 54:41Speaker 02

  Uh, old medium was 200B, which is gonna be deprecated, it seems. So new.

• 54:47Speaker 01

  So Laguna S, Laguna Small, 118, 118 billion total parameters, 8B active, so very sparse. It's a scale up of the XS architecture. It's the kind of classic, or quite classic these days, like, three to one ratio of sliding window attention, a global attention. It's just, it's a nice size, uh, for a couple of reasons. One is just very cost efficient. For us, it was a good way to do, we wanted to get our progress out quickly. One of the things that we've seen is that it's a balance inside a foundation model company between focus on releasing and shipping and like your new novel research.

• 55:27Speaker 01

  But with the model factory, we are able to, like, treat the release of a model as less of a time investment from the team. Because it's just, oh, at this moment in time, you know, do the pre-training run, done, apply to latest post-training. And, uh, and so this is, I think, a nice weight class. It's one that also will fit on the DGX Spark, which, uh, I have a small, like, soft spot for. I love having that little thing, like, you know, run a good model.

• 55:52Speaker 02

  Yeah, we covered it on this pod, uh, GTC last year.

• 55:54Speaker 01

  Nice.

• 55:55Speaker 02

  I think GPT-OSS 120B was the first, because it's a large single GPU, which was the H100, right? Rent one H100, now you've got 128 gig max, Mac minis, sparks. It's, it's the home, the home sweet spot.

• 56:10Speaker 01

  But I think what I'm most excited about is that this model hopefully shows people what is possible in this size. Because, uh, when you look at the benchmarks and, and start using it, you realize that we are outperforming models two or three times their size.

• 56:24Speaker 00

  And I think, so for example, today's thinking model is like a trillion params.

• 56:28Speaker 01

  So, yeah, exactly. And look, and by the way, I'm excited about, I have, it just came out. So for those of you listening to this, like, I saw it on my phone.

• 56:34Speaker 02

  If you're humming in.

• 56:35Speaker 01

  Yeah. Two seconds, I haven't even had a chance to read the post.

• 56:39Speaker 00

  But somehow you are, not only you're, you're better than Thinky, which is like one of those benchmarks, but also like, on certain benchmarks, like the Talbench one, like, you're actually state of the art.

• 56:51Speaker 01

  We're, look, we're doing, uh, I'm not sure if we're state of the art on, I mean, Tau 3 banking. I haven't checked where we sit on the leaderboard, uh, but I think we are, within our weight class, I feel very comfortable to say. And even in some weight classes twice larger, that we are probably state of the art. I also wanna caveat this, like, you know, best model still in the world right now is definitely, you know, give me a Fable, give me a 5.6. To your point earlier, we also use other models.

• 57:15Speaker 02

  Yeah, I think, so the, the interesting thing you mentioned earlier is you're starting to shift a lot of your actual usage to it, right? Benchmarks are like, they're good to compare, but they're not super realistic.

• 57:24Speaker 00

  They have to, right? This is how they're gonna dog food at benchmark.

• 57:27Speaker 01

  No, you have to, like, you have to use your own models and, and you have to have your own internal evals and benchmarks. And, and what the funny thing is, like, within first 30 minutes of a new checkpoint coming out, that's, you know, the, the kind of first post-train after a pre-train, you yourself can feel in the first 30 minutes of where this model is gonna be. Like, you don't know exactly, but like, when this one came out, we were like, oh, like, this is different. Like, uh, and, and I think that's, I think that's the best example.

• 57:59Speaker 01

  But it's a little bit like, you know, like your kids. I don't have kids by, you know, parents, like, they see their kid and it's perfect and they love it. And then like, you know, they don't see all the rough edges. You always get that when you build your models. The most fun part is that you, like, you love a little bit every model that you do. We try to kind of say this thing constantly. It's like, it's the worst model we'll ever train. And so I know the team now is like already on to the next one, uh, as it should be, because this is a race. Uh, and this model is a moment in time that hopefully shows people that we are serious about this race, that we wanna work really hard at it, that we want feedback, right? Where is it good? Where is it not? Like, one of the nice things about having your models out in open weight now in the world is that you get a lot of feedback.

• 58:40Speaker 02

  How do you think about building it with like, um, working with a harness, right? So open code, codecs, you have your own pool CLI tool, um, basically getting people to use it. The code design of model harness, training it in.

• 58:54Speaker 01

  So you need to do some multi-harness training. Like if you, especially at these smaller sizes, like you want to do a little bit of multi-harness training for these models to just get the right, and it's very little, like you don't need a lot, but it's just like to get the right behaviors that you've seen your harness transferring to the harness that like you, other people might use it in.

• 59:15Speaker 01

  We internally have been kind of just calling this polishing, which is like you've got your model and you do a little bit of polishing so that like it's able to work well in other harnesses as it is in your own. No doubt it's going to be better in your own harness. And, and it's just because of like where are you putting your reinforcement learning compute, right? You're putting your RL and your synthetic data, you're putting it to your own harness because it's the one that you understand the best and you're able to kind of push the most. Because that end-to-end control is what allows you to make it better. Then transferring those capabilities is, is more about just making sure the model, you know, induces the right amount of reasoning and like, you know, understands some of the maybe more complex, weird tool call formats that might exist somewhere else.

• 59:58Speaker 01

  And so we do do some multi-harness polishing, as we call it. It's not really what drives capabilities, but it does create a better experience.

• 1:00:08Speaker 01

  I think frankly, I think everyone probably does these days, but it is totally fair to see why your own harness is going to still be better than others. And I think we see this with all the foundation model companies. And it's just that when you are pushing capabilities, you don't really want to trade it off by putting 10 harnesses in your RL runs because it's just complexity. It's complexity of engineering because these, when you're trying to do good science, when you're trying to really understand what make my model improve, you want to make one variable change to something you understand. And a harness from someone else you don't know or understand in the same way as you understand your own, right? They might have different sub-agents or different prompts.

• 1:00:48Speaker 00

  If it's open source, you can look at the source.

• 1:00:50Speaker 01

  Yeah, but it's time, right? Like, like I really cannot stress it, like I know I'm like a weird person on this because like I have friends like, can we meet up or can we do this or can we go as like, no. Because ultimately this is a race and time is the only thing that matters. And if I look at our team and say, okay,

• 1:01:07Speaker 01

  what is complexity worth introducing on our general trajectory to building more capable models, which generalize to other harnesses quickly? And by the way, our model works well, other harnesses are really critical to do it, it works well, like I've, we've been testing in open code and kilo code and others and like and in clock.

• 1:01:22Speaker 00

  Which just got bought today.

• 1:01:24Speaker 01

  I saw Honda.

• 1:01:25Speaker 00

  Exactly. Everything's getting bought.

• 1:01:27Speaker 01

  Exactly. And I, and I think part of that is like, you know, and there's some amazing, I'm excited. Like I think Hermes is actually ridiculously cool harness, like, and so.

• 1:01:37Speaker 00

  And you know, part of the question was actually just like, how much effort is model versus model plus harness, right? So new benchmarks like agents last exam, it's not wanting to just measure the model. Same with models getting more and more agentic, they need a harness to operate in, right? So.

• 1:01:55Speaker 01

  I think, I think for, when you're asking that question to a model company, I think you can separate it in two parts, which is like, the harness, like we have a very slimmed down harness. When you look at it, it's like six tools. It's like shell and like shell kill, shell wait, write, fetch web and like, I don't know, bash, like I think I'm missing one, but like that's effectively all the tools. And it's very simple. It's very lightweight. So it is not a harness that is designed to try to do well on a benchmark or try to do well on a certain subset of things, right? It's not a deep research harness. So I think we see incredible ability for complex harnesses that built lots of prompts around and extra data sources and other tools to really push capabilities of models forward.

• 1:02:40Speaker 01

  But our model is still better than some other harnesses who do that in coding-like tasks because it was RL'd with it. Now I do encourage people, I think our model by the way is perfectly fine and good in ours, that the differences are probably maybe too small for anyone to notice, but we see it ultimately still on benchmarks by a little bit. So I think it's, both are true. Foundation model companies with their harnesses will really push them because it's just operationally, frankly, the best way to have scientific rigor in improving your models, but also someone who takes our model and really does a lot of work on improving a harness is going to outcompete us as they should. And that's just because the harness is the stopgap between what the model is capable

• 1:03:23Speaker 01

  of and what it needs as additional instructions and what it needs is access to data and tools. Right? And that's ultimately, I think what a harness is, it's like, is it able, as you build more capable models, you know, you're improving the instruction following the models. And so additional harness is just saying, hey, if you encounter X, Y, or Z, behave this way. And so even if you would say that two models with two different harnesses can equally reach the same capability that you care about, a harness that is really tailored towards a capability will do it more efficiently. It's kind of like a person who's getting a, a manual of how to do the task in the most efficient way with the right tools and the right data sources versus a really smart person that go figure it out.

• 1:04:07Speaker 01

  They'll both solve the task, but one will do it a lot more efficient. So I'm a big fan of all the harness development that's happening in the world. And we want to work with more harness like creators to also make sure that like, if it needs some additional training, like polishing, that we will do it.

• 1:04:22Speaker 00

  I mean, I think when you say it's a race, there's a question of what are you racing to? Are you racing to be the best coding model company or the best coding model plus harness company? I think that's a, those are different things. Or tell it in the black.

• 1:04:37Speaker 01

  Or neither.

• 1:04:39Speaker 01

  So we, I, I raced AGI. Coding for us since day zero of our website has been, and we've said this over and over again, we think focusing on coding and long horizon like software tasks is a path towards AGI because it forces us to solve the hard problems. It's, it forces us to solve the ability to do extremely long horizon, complex work that requires lots of reasoning, external tools, data, et cetera. And one of the things I can show you, so we'll, we'll have a web chat on with this model. And I've loved this model for deep research, just using it in my coding harness. It was never trained for it. It was never like looked at it, but it's great at it, in my opinion.

• 1:05:22Speaker 01

  Because ultimately the skills transfer they generalize. Now where we are not focused on today is to make sure that the world's greatest medical knowledge is encoded in this model or the world's greatest legal knowledge. But it actually did, we won't be publishing this benchmark because we didn't have time to really do proper, but it did really well on legal bench. And at least on our first runs, and we are very rigorous when we publish evals, we have checked them for every little thing. We have run them many times, we've passed, like we've gone and will, like we try to be extremely honest with this. So if we haven't spent enough time on a benchmark that we use internally that is public, we just say that we won't publish it.

• 1:06:00Speaker 01

  And so.

• 1:06:01Speaker 00

  I mean, the, the other way is just to give it to artificial analysis and let them run it, like third parties.

• 1:06:05Speaker 01

  Oh, a hundred percent. And we are going to be doing this as well. And, and still it takes time and effort, right? Because you're working with people to understand like, you know, the infra failures and, and like the, the tools they're using and like, are they set up well? But I agree, you absolutely want to, I'm a big fan of companies like Vowels and AA and like others that are doing this stuff.

• 1:06:20Speaker 00

  I found it very nice. You're the first to bring it up.

• 1:06:22Speaker 01

  Yeah, I think they're great. And they've got like, I, I love like a lot of the work they've done and put out. And so, and there's, I think many more. And please create more eval companies, like create more evals. I think it's so valuable for the industry.

• 1:06:34Speaker 00

  Actual monopoly kind of, I feel like, oh, and duopoly maybe, you know?

• 1:06:38Speaker 01

  I think it can be broken.

• 1:06:39Speaker 00

  Yeah.

• 1:06:39Speaker 01

  Because I think it can actually be broken really easily because creating an eval for many people isn't sexy work, but whoever does it, everyone is happy to get a good eval. You've never, like if an eval is well constructed, everyone's celebrating it and everyone's willing to pay for it and everyone's willing to make the foundation mark.

• 1:06:55Speaker 00

  Yeah, yeah. I, I think creating eval, yes, but like in terms of being like, we are the industry standard ones that will run to bench and make sure that you didn't, you didn't cheat and I'll run it the same way that you run it versus your competitor run it.

• 1:07:05Speaker 01

  Yeah, that is very true and we need that. And it's actually nice that that's like a few standard places that we all have to like, you know, adhere to it, keeps us all honest. I think it's super important to do so. And, but yeah, no, I think our goal is to build the world's most capable models. And right now we are focused on the coding agent capabilities, long horizon work, but what you see with that is that you get a lot for free. I've always said it's a lot easier for us as we get to, you know, Sota and Frontier on coding to then say, okay, now we're going to obsess in using the model factory to add more data for places that, you know, we're not as strong on, like could be medical or legal or any other areas.

• 1:07:46Speaker 01

  And similarly, I think what we see, and we see this with reasoning models, a lot, if you give models access to the right knowledge sources and they have capable ways of reasoning, they're able to go very well into domains that are less known to them or even seen less in their training data. So, but yeah, are we agent like model plus harness company? No, we're model company. But I think models today cannot be trained without harnesses. It's not possible. So it's kind of, it's just like where before it was just the weights in the container. Well, now there's an agent harness that's attached to it. And, but I think there's a big difference in being an agent harness as a model company than someone who's truly building an agent company. I think they can do far more than we can.

• 1:08:27Speaker 00

  Yeah, understood. Yeah, I, I think that, that is my minor pushback. If you are truly identify as a model company, then make the best model for open code, right? Instead of for pool or whatever. I think that that's not as, you know, that's, that's minor compared to actually if the goal is AGI, actually make the best model for Hermes. Right? Like this is, because that is the next stage after coding.

• 1:08:48Speaker 01

  I'm, look, and we are, we're working actually like very closely with them because I do think like it's, and you have to care, you have to invest in it. It's why we do the polishing and we spend time on it.

• 1:09:00Speaker 01

  And I think over time, yeah, you're, you're right that you want to kind of balance that out. But ultimately you just want general capabilities that everything works equally in every, in every harness.

• 1:09:11Speaker 00

  Just on the topic, do you, do you guys do much with like Hermes, OpenClaw, NanoClaw, whatever? Pi, so. Pi. Pi. No, Pi is a different.

• 1:09:18Speaker 01

  It's more coding.

• 1:09:19Speaker 00

  Yeah.

• 1:09:19Speaker 01

  I'm a big fan of Pi though, I have to say, I think it's a really.

• 1:09:22Speaker 00

  I forgot to mention Pi. Pi, you sound closest to Pi in terms, pool and Pi in terms of like the minimal surface.

• 1:09:28Speaker 01

  In the minimal. It's because I don't, I have a, allow me for one more strong opinion. I've been saying this now for two years.

• 1:09:37Speaker 01

  I think MCP and tools are stupid.

• 1:09:41Speaker 00

  Oh, let's go. Now you support MCP.

• 1:09:43Speaker 01

  I support MCP and we support tools and everything. They make absolutely no sense to me. And like, and then I'll explain a little bit why, and then I think I can probably get people to come along on this one.

• 1:09:56Speaker 01

  If you are looking for complex tasks, increasingly longer horizon, increasingly complex tasks, it doesn't matter if it's coding or something else.

• 1:10:05Speaker 01

  You are going to be interacting with data sources, right? And you're going to be interacting with things that are installed on some form of a virtual machine. And what we are doing is that we're putting a layer in between those things. We're putting like MCP in between, we're putting tool calls in between, and this is even more about tool calls than MCP. Where the model can just write the code and interact with the system. And we're starting to see that, like Laguna S does this a lot, you'll see this as well in like frontier models. They're increasingly no longer, here we're going to stuff 50 tools in the like system prompt to no, here's a virtual machine with these binaries installed, this code base you can operate in here, a folder where you can write, you know, your memory if you want to.

• 1:10:50Speaker 01

  And the model is using code to do complex asks. And when it uses code, it is not one or two tool calls or three things that are chained together. It actually starts, you know, using if statements and for loops and, and making things conditional. And so I actually think we're moving from, we already are moving from tool calls, you know, to effectively models writing code, little scripts, and you see this a lot when you get the Python, you know.

• 1:11:15Speaker 02

  Code interpreter.

• 1:11:16Speaker 01

  Exactly, like in just the arrow, arrow in, you know, written code and the file. I don't know what you call the.

• 1:11:21Speaker 02

  EOS?

• 1:11:22Speaker 01

  Yeah, exactly, like you already see this happening more in models, because when you start training them in RL, the models want to be free. They want to be able to do the thing they want to do in the most efficient possible way, and it is not calling one of the 50 tools in their like system prompt. And so I'm a very big fan of give the model a minimal harness, as minimal as possible. Give it a container in which it has its own code base, right? The model's code base that has access to the API keys and data sources and little libraries and documentation that it needs, and just let it run free at the task. And I think that is the way we're going. I think we will in 12 months, not see a single system prompt that is stuffed with 20 or 30 or 40 tools anymore.

• 1:12:07Speaker 02

  No comment, no, no, no pushback there, I think, I think there will be, it'll be supported for a long time, just because that's, a lot of people are trained on that now. But maybe you guys don't have to support it in your models, you know, going forward, so. But yeah, I mean, if you can, I do think that's, writing code is more generalist and it's a, it's a means to an end.

• 1:12:26Speaker 01

  And we do support tools, we support, and this is actually the first model we're doing parallel tool calling in, which we needed to catch up on, so like that's there and like, so it's, it's there, but I

• 1:12:34Speaker 02

  Yeah.

• 1:12:35Speaker 01

  It's a personal nitpick, I like, I want the models to have as many degrees of freedom and just like, you know, be free and, and do capable things.

• 1:12:43Speaker 02

  Yeah, so and then, so that was on the path towards like, okay, how do you use pool sides, models and Laguna models? For my Hermes or my Open Cloud on all those things. And so typically what I look for is

• 1:12:57Speaker 02

  computer use or vision, that's a, that's a very big one, you guys have a blog post on that. But then also the persistence, I think is very strong value, as well as long context, which you guys have a million token context, anything else?

• 1:13:08Speaker 01

  So for us, look, so for us, vision understanding is, is the next thing, right? Like we don't have vision understanding.

• 1:13:12Speaker 02

  I was gonna say is the.

• 1:13:13Speaker 01

  We don't have vision understanding in these models yet.

• 1:13:16Speaker 02

  Yeah.

• 1:13:16Speaker 01

  And so this is something that we've, we've started efforts on, like, we think it's, it's super important to have visual understanding.

• 1:13:23Speaker 02

  That's company vision.

• 1:13:25Speaker 01

  And so, no, we've got work to do there. And this is actually one of the things I loved about the Thinky model, like from the two minutes I scrolled the blog post.

• 1:13:33Speaker 02

  Multi, they're very committed to multimodal, including audio.

• 1:13:36Speaker 01

  Yeah.

• 1:13:36Speaker 02

  Their state of the art audio, as much as it's a trillion parameter, state of the art audio, but also all trained from scratch, right?

• 1:13:43Speaker 01

  Yeah.

• 1:13:43Speaker 02

  No, no encoder in the.

• 1:13:45Speaker 01

  To me, that's, that's, that's one of the strongest reasons why you need to trace from scratch is you just have a different tokenizer, you have different. I'm fully aligned, like zero, zero disagreement from me here, like just add the modality and, and, and don't put, you know, keep it, keep it simple. We're, I don't think we'll touch audio for a very long time.

• 1:14:05Speaker 02

  It's kind of in the name too, you know, inkling, ink.

• 1:14:07Speaker 01

  True, yeah, yeah.

• 1:14:09Speaker 02

  Maybe what, what's so hard, what's so hard about audio?

• 1:14:11Speaker 01

  It's not about what's, again, it all comes down to focus.

• 1:14:14Speaker 02

  I see, I see.

• 1:14:15Speaker 01

  Right, like saying no to things means that there's a researcher and compute that can go to making general progress. And our view is like, general progress is going to come from the ability to push these models to far more capable reasoning, far more longer horizon tasks. I don't think audio adds to that. I don't think it pushes us close to AGI, I think it is a necessary modality as you get close to AGI. I think visual understanding sits in the middle of those things. I think visual understanding can absolutely do so, but it also unlocks capabilities that are just valuable today. So, but this is the point, right? You want more diversity, you want more different foundation model companies to focus on different things. I think we are just kind of like a horse with blinders on, just like.

• 1:14:58Speaker 02

  Yeah, you have your path.

• 1:14:59Speaker 01

  We have our path, we want to catch up to the frontier and we don't want to distract ourselves with anything else.

• 1:15:05Speaker 02

  Yeah, I, I will call out that one of the branches of research is DeepSeek OCR, which is, can you just throw away the text tokenizer and just only vision?

• 1:15:13Speaker 01

  I, I find this, I look, geek, the geek in me is like, looks at this stuff and it's like, okay, look at this, like, look at the number of bits in code.

• 1:15:19Speaker 02

  Right.

• 1:15:21Speaker 01

  I, I think it's super cool, right? But I think, I think this is what we're going to come back down to, like, probably works, it's just, is it compute efficient enough? Is it go, like, I think so many of these things ultimately will work. It's just like, you know, what's the nice thing about text and, and I, I referenced earlier Pengming and Nikolai, my two co-heads of applied research, were just incredible, like we wouldn't have gotten here without them and the entire team. And, and Nikolai and I have been debating, you know, for years about like, should reasoning be in latent space, should reasoning be in tokens? But one thing that I think him and I really agree on, and all three of us, and is that like language is incredible because it's such an incredibly dense way

• 1:16:04Speaker 01

  to encode knowledge and information and intelligence, right? If you think about like what went into a physics paper, that then is in a 20 or 30 pages, like the amount of intelligence and thought and whatnot to then generate that. Like in that 20 page document, like those little amount of bits, there's so much encoded. And utter modalities like video and images are amazing, but they don't have the same density of like knowledge or, or reasoning or whatever, like the things that we're trying to push for that are encoded in that modality, they're there. In many cases, you can watch an incredible lecture for, you know, 50 minutes on YouTube. But the, and, but if you treat that as video in data versus text data, right? The, the bits to like signal to noise ratio.

• 1:16:48Speaker 01

  The compute efficiency of the modality is a lot, you know, a lot less. And so we kind of have this view, it's like with language, you can go really far. But also when you have limited compute, limited, you know, people, and they're very much linked to.

• 1:17:04Speaker 01

  I think we can push language, it's the more, it's a better investment. But I want all the modalities, I find it super cool, and I love what DeepSeek and others are trying, like I can retweet them all the time, but internally we're just like, let's stay focused.

• 1:17:17Speaker 02

  Which I'll say, you know, you can see somewhat works, looking at Anthropic. OpenAI has a lot of vision, multimodality. Anthropic kind of just didn't, right? Fable's a big step up in image processing, but like, they're not known as the multimodal company, right? They're the language model coding company that has multimodal capabilities, that's never super flex and, you know, goes pretty far.

• 1:17:42Speaker 01

  I, look, I, in this, I think, I think Anthropic, I mean, they've done many things, right? But I think this maniacal focus on just pushing capabilities, scaling up models is, I couldn't agree more. I think it's, it's, that's the first hurdle, and once we get that, then we can improve a whole bunch of other things. And, but at the same time, on the other end of the spectrum, it's really exciting to see people, you know, building these spatial models, right? That are, in the world, models that are being built, like for very different, you know, use cases. But I think ultimately it all comes together at some point.

• 1:18:19Speaker 02

  Okay, so scaling models, this is Laguna S for small.

• 1:18:23Speaker 01

  Yes.

• 1:18:23Speaker 02

  You have good naming, extra small, small, medium, large.

• 1:18:26Speaker 01

  Yeah.

• 1:18:27Speaker 02

  Still scaling?

• 1:18:28Speaker 01

  So the new medium started training and it's much bigger than the last medium started training yesterday. So it's a 39 day pre-training run. And.

• 1:18:38Speaker 02

  How do you know the days in advance? Just the computer.

• 1:18:41Speaker 01

  Model factory. Right, and, and like at this point, like with the model factory, like it's.

• 1:18:46Speaker 02

  I thought it was interesting, so in, in the Laguna, medium and extra small, you even quoted number of GPU hours for how many days and whatever for different sizes, and I'm like, oh, you can also work backwards to how much that costs, right? What GPUs, how many hours?

• 1:18:59Speaker 01

  And you realize it's not a lot.

• 1:19:00Speaker 02

  No, it's not.

• 1:19:01Speaker 01

  It's not a lot of money. And, you know, you started with DeepSeek of the West and, and, and I think that's

• 1:19:09Speaker 01

  the DeepSeek moment, right, was a moment when people realized that you can train incredibly capable models for not a lot of money on the training run. But I think that's the falsehood, right? Like the training run is not the expensive part, the training run is a very anticlimactic event, right? Like, we just had a Slack message come up yesterday, say, hey, the new model is training and here are the links, you know, so you can, can follow along the evals and like, that's it. All the work that goes into that moment, it's kind of like how people talk. I know nothing about sports, but how like athletes talk about like, you know, it's all the preparation, it's all the going to the gym, and then the game is just a game. I think that's a little bit like with models.

• 1:19:42Speaker 02

  Yeah, people had over indexed on DeepSeek was trained for $5 million or whatever it was, right? It's like, there's the amount of R and D before that, the infrastructure is built up.

• 1:19:51Speaker 01

  All the things, the data, but no, so Laguna M is training and yes, there will be an L and there will be an XL and, and what you'll see what you'll see with M's, right? M is much larger than the last M, right? So these monikers are a little bit our version of the.

• 1:20:04Speaker 02

  Yeah, he, he does making fun of people for saying small is 24B or something. No, so no, no, and small, small for Mistral now is over 100B.

• 1:20:12Speaker 01

  What?

• 1:20:12Speaker 02

  Yeah, I can pull it up.

• 1:20:13Speaker 01

  I mean, our, our small, right, is 118, so I don't want to say anything else, like it's.

• 1:20:17Speaker 02

  I mean, I think it's also, okay, you're small is not.

• 1:20:20Speaker 01

  We all know that the single hardest thing for any foundation model company is naming. I don't want to say that we're good at it either, I mean, it's, this is Laguna, you know, Laguna S 2.1, it's, it's.

• 1:20:32Speaker 02

  But at least people understand, you know, medium is bigger than small, until you mess that up, like, you have a passion.

• 1:20:38Speaker 01

  We, we try, we try hard.

• 1:20:40Speaker 02

  While we're on the topic of naming, this is going to be at the end, but might as well, why poolside, why Laguna?

• 1:20:46Speaker 01

  So when we started the company, it was going to be called Snowball Apps. It was after the snowball effect, because we expected this company to become a snowball effect, and it definitely has been a snowball effect for us. Turns out it's an Amazon trademark. I kid you not that my co-founder's next suggestion of a name was, let's call it Bedrock.

• 1:21:06Speaker 01

  And so at this point, it was like, okay, no, you are amazing in naming things if you were at Amazon. And so, uh, early on in the company, uh, before we were incorporated, uh, we were at an annual conference of a very big major tech company. And we had been discussing with them, you have to realize the company at this point is me, my co-founder, COO Margarita. We know the first person is going to join us. We haven't like incorporated yet. Uh, and we were discussing an open AI Microsoft style deal with this big tech company. Like they were going to provide us with a lot of compute. We would give them, you know, perpetual access, a whole bunch of kind of things.

• 1:21:49Speaker 01

  And, uh, we found out the name was trademarked Snowball Labs while we were at that conference. And having this discussion that we had no right to have, right? We were a couple of guys who had nothing yet. But this big company was willing to entertain the fact that we might partner with them. And, uh, we were discussing this and it was in the their annual conference in a public setting. And the chief scientist of that company said, people can hear us here, like, we should move somewhere else. Let's go to the restaurant poolside. And for some reason, me and Jason looked at each other in that moment and said, oh. Uh, and then later that night, uh, we, the name stuck with us, the word stuck with us. And we said, let's call the company poolside. And ever since, we never ended up doing that deal.

• 1:22:34Speaker 01

  And we used it as a reminder to never turn down our, round down our ambitions. Because that would have been the easy path. Uh, and the hard path was what we did, which is start and try to raise exorbitant amounts of money. When you're just a couple of guys who are not even building it in Silicon Valley, who don't come from any, you know, the, the known NAVs and things like this. And so everyone assumes poolside because AGI, everyone sits poolside. And it was a playful name and we liked it and it was a little bit different. But actually the name is like a reminder for us to never round down our ambitions. Uh, and whenever you're faced with those decisions to just pick the harder path.

• 1:23:09Speaker 00

  Yeah, uh, I mean, that's a great story. Uh, I, I know you've told it before, but I just wanted to, on the record. That's, that's what I did the first time I met you. You told me, you sat me down, you were, we were in the hotel somewhere.

• 1:23:21Speaker 01

  Yeah.

• 1:23:21Speaker 00

  And you were like, we're raising a 500 million. I'm like, and then you gave me the whole vision and then you actually did it. And I was like, uh, well, it's, uh, you know, I don't have that much opportunities to, to, to ask like, just how do you do that kind of raise to that kind of, to those kinds of VCs? What are they looking for? You know, like, yes, vaguely AGI, but like, what do they want when you?

• 1:23:41Speaker 01

  Look, it's, the world's definitely changed, right? When we were raising that $500 million round, the majority of investor conversations, we're still trying to explain that these models were not just stochastic parrots and that they were going to keep going. I've seen the world go from OpenAI is going to win it all and there's no one else who can build company, right? I mean, Anthropic struggled, you know, to raise their $500 million round. It's kind of like well-reported. They pulled it off gladly.

• 1:24:07Speaker 01

  And so I think when we raised that, it was about a year and a half ago at this point, the world was very different than it is today. I think the world today,

• 1:24:17Speaker 01

  there's been, there's been this function where the number of people who believe AGI is real,

• 1:24:25Speaker 01

  is probably a, an, a super linear or definitely some form of an exponential function itself. And I think this is important because if you hold the belief that we had three years ago and a year and a half ago, and we will look for people who share that belief, which is like, this technology is going to fundamentally underpin everything that's economically interesting or economically valuable and scientifically interesting for like the future. Then the value function afterwards is easy to understand, which is like, hey, if you get there, you are one of the commodity, one of the players who can build this commodity. Uh, and over the years, building that commodity has become not just about building models, but also about building infrastructure and other things. And so I think today, because the number of people is bigger and the outcomes have been proven.

• 1:25:10Speaker 01

  I had an incredible like financial success that Anthropic is having right now and like the growth that OpenAI has had and others and Google, no longer make this a question of is there product market fit? Which really, a couple of years ago was like part of the question, like, how big can these things? When you tell people that like, you know, you'd be at these amount of revenue numbers in our industry right now, people were still like, would laugh you out the room.

• 1:25:34Speaker 01

  Now I think it's a function of who in the world believes that it's going to be an oligopoly of intelligence? And who believes that oligopoly can be broken by other companies? And I think that's what divides investors more than anything else. For the ones who believe in AGI. Uh, and then you've got a whole layer that, you know, is kind of self-selecting out, uh, foundation model companies because they're like, look, I, I can't make a, the money I put there, you know, compared to what I can put in an application company is very different.

• 1:26:08Speaker 01

  I think there's incredible application companies and there should be many should be built. But I do think we are still in a world right now where this is the early innings of, this can still be the early innings of who is going to, you know, be part of the set of people who win. This is, intelligence is the most, in my view, going to be the world's most demanded commodity. It will more commoditize in margin and price.

• 1:26:31Speaker 01

  And the world wants choice and wants options. And so I think treating the world as like, oh, there's only going to be two players, I think is very short-sighted from investors. I think that group who thought that was a lot bigger at the beginning of the year than now. I think the last couple of months have woken up a lot of people and going, holy shit. Like, the, the world both can use a lot more intelligence, but also like the world is far more complex. We should have multiple choices, more options, things that can be turned off, that can't be that. The restrictions that people put on models now, I think is, is another area of this, right? Like the, the fact that

• 1:27:10Speaker 01

  we are entering into a world where model companies are saying, you're not allowed to use me for foundation model company development. They should be allowed to do this. It's capitalism, it's their business, it's their work product. But it is insane. It is, it is wild that we are like, okay with that.

• 1:27:30Speaker 00

  Do, do you have more problem with Anthropic saying it or the White House saying it? You know, that you're picking two, the two different, uh, you know, limitations and restrictions there.

• 1:27:39Speaker 01

  Look, I think, I, I, I'll, I'll put it this way. I think we, we wanna, as this technology gets more capable, for the better and worse, we do want to yield to democracy to figure this out more and more. I think any single company making unilateral decisions, uh, is

• 1:27:59Speaker 01

  is, is dangerous. It's a concentration of power in a small number of people with very limited checks and balances. Uh, and that has never worked out well in history, uh, in any way, shape or form. And this is not a criticism on the existing foundation model companies. This is just more commentary on like how I like the world to be.

• 1:28:18Speaker 01

  I think in a world where the technology gets more capable, government needs to play an active role in determining, you know, where, where is the real risks of misuse, right? And I do think we need to separate safety between misuse and, you know, doomsday scenarios that, you know, I think are no one knows if are going to happen or not. And I think just like very practically, I think, uh, I'm glad to see there's a lot of conversation now starting to happen again at the government level of trying to figure this out.

• 1:28:54Speaker 01

  And now what the final decisions are, maybe I'm happy about them, maybe I don't, maybe I agree, maybe not. But ultimately like, that's kind of democracy always, right? Like at any given moment, I might not be perfectly happy with one or the other, but people chose to vote in someone to make those decisions. And so I think over the long run, over, you know, over a 20-year time span, the world kind of directionally goes correct, democracy does work, at least. What's the famous quote of like, it's the, you know, the worst of all? It's the best of all the worst systems or something like?

• 1:29:26Speaker 00

  It's the worst form of, uh, organization except for all the others that we've tried.

• 1:29:30Speaker 01

  Exactly.

• 1:29:31Speaker 00

  That's the one. You can always count on me for a Churchill quote because I've studied Churchill a lot.

• 1:29:35Speaker 01

  I love that. And so that's where I hope for it. Now, I do think we are in a critical moment of time. And so speaking up for anyone is important. I think, you know, researchers who are thinking about starting their own foundation model companies start. You know, people who want to share their opinion and be vocal, if that's with their representatives or just out on X, like do so.

• 1:29:57Speaker 01

  And, but completely to your point, uh, I think we are not at a level of capability right now that we should start restricting, you know, open models in any way, shape or form. I think it will hurt innovation if we do so.

• 1:30:14Speaker 00

  Is there a point at which you will change your opinion there?

• 1:30:17Speaker 01

  Yes. I mean, look, and there has to be.

• 1:30:19Speaker 00

  Yeah.

• 1:30:20Speaker 01

  Right? Like you cannot, if you sit with a straight face and say, this can be open forever in every way, shape or form. It is just as, I think, egregious as saying, you know, the opposite of it all needs to be closed down right now. Like, I think at any ends of extremes of spectrums is where we go wrong. Right? And, and in, in society, in any way, shape or form. And so the, the answer is always more nuanced and the answer is never black and white. And so I think as we encounter like real world scenarios where we have to say, hey, we have to be more careful. We need to reevaluate if that means training a model differently and opening it up, having different versions, some things that, you know, that are restricted, I think that's totally okay.

• 1:31:05Speaker 01

  Because I don't think anyone should be irresponsible. What I do want to call out is that people have been calling for the fear of misuse of these models since GPT-2.

• 1:31:16Speaker 01

  Right? And I still remember like we cannot release GPT two because the whole world will get

• 1:31:19Speaker 00

  I mean, that was Dario.

• 1:31:21Speaker 01

  And so like this is not a commentary on Dario, it's a commentary in general in the space. And so we have, we have not been very good at this so far and we need to get better at it. And I do think that the work that's happening with like safety institutes and better evals and things like that is is probably the right direction.

• 1:31:38Speaker 00

  Yeah, I mean, I would honestly say something in defense of this. It's better to err on the side of safety and then roll it back rather than the other way, because the other way, it's a one way decision.

• 1:31:49Speaker 01

  I think, I think that's, I think that's true.

• 1:31:53Speaker 02

  The caveat there is also the competition, right? You, you don't have global error on the side of safety, right? You're talking.

• 1:32:01Speaker 00

  Yeah, exactly. So you don't get to do unilateral safety because someone else will just be more unsafe than you.

• 1:32:06Speaker 02

  Yeah, exactly. You can pause innovation here. It doesn't mean it's, it's pausing.

• 1:32:11Speaker 00

  Just take over the world. So easy.

• 1:32:13Speaker 01

  They're, they're complex sport, yes, right? And, and I think we are much better off talking about certain capabilities that we can, you know, commonly agree on and internationally agree on that we want to, you know, limit or not have available. Then we should talk about it in black and white of models available, yes or no. Like the moment you start getting these big blanket statements, it's that's when you start getting at the risk of like, I always think back about when we banned advertising on cigarettes. Good thing. I'm not saying I'm against that, but it effectively established an oligopoly of cigarette companies because no one else could ever compete. And it was the, probably the best moment to the tobacco industry that that

• 1:32:54Speaker 01

  ever happened. And we don't wanna do that right now. If we, if we pull up, you know, walls behind innovation, and this is a self-serving comment because I'm not at the frontier yet, but it's not just related to me. I think it's related to everyone in the space.

• 1:33:09Speaker 01

  You are deciding right now in 2026, based on the current capabilities of models, that this is something that only two or three companies can build. And that to me reads like chapter 14 of the most dystopian sci-fi novel that I could read, because from there, I think you can play out all the scenarios that happen in the world. And none of those are the ones that make me, you know, excited about the future. And I think that's the thing we should all think about it. What's the future we want to be excited about? What do we want to have? And I think that's a future where intelligence is a commodity. Everyone can access it. It becomes cheaper and cheaper. Right? I think that's important. It can like impact more of the world. And it's not one where,

• 1:33:49Speaker 01

  you know, a single company puts their thumb on their skill, but what it outputs to or turns it on or off.

• 1:33:56Speaker 00

  I think the one entity that has more power than the US government here is Nvidia.

• 1:34:02Speaker 00

  Because like basically whoever gets the allocations gets the compute.

• 1:34:06Speaker 02

  You can take it down to TSMC or.

• 1:34:09Speaker 00

  And TSMC below that. But I, I just wanna test provocative statements to see if you, you have any response.

• 1:34:18Speaker 01

  I need to think on that one.

• 1:34:20Speaker 02

  Which actually I think they are regulated, right? Like you can see that government.

• 1:34:23Speaker 00

  They're regulated.

• 1:34:24Speaker 02

  Can they ship to China?

• 1:34:26Speaker 00

  Okay, but you know, they're not China.

• 1:34:30Speaker 01

  Look, I think this industry has existed because of what Nvidia has done.

• 1:34:35Speaker 00

  Yeah.

• 1:34:35Speaker 01

  I know that people like, it's easy to give them flack, but I also wanna say, like, I remember when we started sourced, right? And, and, and in 2015 post that Kapati article, it was able for this progress to happen because we were able to put consumer GPUs and servers and they allowed us to do so. And then like, and you kept going further. And so this is something like foundation models are so closely linked to their hardware and their systems. Why do we see these stepwise progress happening? We see them happening because of the next generation of networking and, and systems that come out. Right? The, the difference of a model you could train on hoppers versus GB 300s is the difference in a trillion parameter model and a five or six trillion parameter model. And so these things really coexist, I think, very closely to each other. And I think the,

• 1:35:21Speaker 01

  the more interesting question, I think for, for the future is going to become of like, how do we, what can we unlock in terms of model capabilities? Like as we start co-designing these things even more. And we're seeing that with like the next generation of systems. And I think the world, the world, you know, abhors, like capitalism does a really good job at trying to like, you know, push towards things that are, that allow for more competition. Right? And Nvidia allows for competition. It's not, you know, but if a government says no one else can build foundation models effectively through the regulation, that is very different. Is it hard to go build an Nvidia?

• 1:36:03Speaker 01

  Absolutely. Is it hard to build a foundation model? I think it's very hard to build a foundation model. But we should like make the playing field one that where, you know, if someone wakes up tomorrow and wants to do so, they're like allowed to do so, and they're allowed to use the tools to do so. And I think there's still a big difference between what we're seeing in the discussions around model companies versus what we're seeing with chip companies.

• 1:36:25Speaker 02

  The gap also seems to be the expertise in who regulates it, right? Who at the government decides what's too safe, too smart, too dangerous. But while we're throwing spicy questions out there, do you have anything that comes to the top of mind that could be changed? So, you know, should OpenAI anthropic open source models? Is it open weights? Is it what we do in RL that determines, you know, your safety barriers? Is there anything that should be done there? Just spitballing?

• 1:36:55Speaker 01

  Yes. One of the things that I'm excited about, that I think we're more and more talking about, I don't think anyone is doing yet, is mix and match of hardware during RL training. Right? Like the, you think about like the notion, and we're seeing this in inference, right? The, the pre-fill and decode.

• 1:37:15Speaker 02

  Yeah.

• 1:37:15Speaker 01

  Just work better with, you know, a general purpose, a GPU and a more specialized like chip, right? Like the Grok chip at Nvidia, the LPU and the GPU combined. And there's different versions of that in the industry. And, and RL is batch size constraint. Right? So like you are ultimately, and you're batch size constrained because you don't have infinite tasks. When you've got the entire web, you can be much more flexible in scaling up your batch size because you've got the entire web. But for RL, you have, you know, X millions of tasks that you are going to be training on. And so you cannot blow up your batch size massively, which means that you actually can't scale compute to a certain extent with RL, the same way you

• 1:37:56Speaker 01

  could scale compute with like pre-training. And so I'm very excited about anything that improves that. And I think one of the best ways to start improving that is the things that we're already starting to see in inference, which is the separation of the, the pre-fill and decode to different chips to come to reinforcement learning. Right? And I think we'll be there soon. And I think more people should be working on this. Because then all of a sudden, we're able to just be way more efficient in how we train RL from a wall clock time. Again, coming back down to the fact that it's a race, right? The, the race is measured not in how many GPUs, but the race is measured on, on calendar time. And that's probably one of the biggest impacts we can have right now

• 1:38:38Speaker 01

  to speed up our industry. And so that's one like technically I love geeking out about and talking to people.

• 1:38:45Speaker 00

  Yeah, I would talk to Edge. I had a tour of their data center and physically you can see how PD disaggregation is mapped out in the, in the data center. And you have to own your own hardware to do that.

• 1:38:57Speaker 01

  Yeah. No, look, I think it's, I think more innovation in the space is just like, is the coolest thing. And, and so I'm, I'm excited because that's frankly, like all of us are, but why, why did we finish, you know, post-training this model, whatever, two weeks before release or not, sorry, between release, between pre-training, then, you know, mid-training SFT, and then the time it takes for release, my biggest wall clock bottleneck right now is RL time. Right? And it's just because I can't scale it up further because I can't add more GPUs to it because of that batch size constraint. There's a really cool blog post that just came out that was showing RL done in even lower precision than any of us are doing. I thought this was really cool. So this, what date is it

• 1:39:41Speaker 01

  today? We're on July 15th. So this came out five days ago. And I thought this was very cool. I think, you know, lower precision RL while keeping it stable. We're, we're still doing this in FP8. And so I was excited to see them sharing this work and, and bringing it out. It's definitely something that I'm excited to be doing once we move to, to Blackwell GPUs.

• 1:40:05Speaker 02

  But yeah, cool. Part of open research, you know, you, you take and you give.

• 1:40:08Speaker 01

  Exactly. Yeah.

• 1:40:10Speaker 00

  I'll just quickly mention, there was a paper that did a variation on levels of quantization, and they roughly concluded that four bit was the sweet spot.

• 1:40:19Speaker 01

  But I don't remember, this is just a couple of years ago, right? I think I remember this.

• 1:40:22Speaker 00

  I think one year.

• 1:40:23Speaker 01

  One year came out.

• 1:40:24Speaker 00

  But like, I'm like, okay, maybe NVFP4 is it. You can't really, like the, the lowest you can go is ternary. That's it. Like there's not that many.

• 1:40:32Speaker 01

  Well, I mean, there's, there's still quite a difference between NVFP4 and four bit, right? In terms of what's, what's possible. But I think NVFP4 is, you know, underrated in terms of, of what it is. I'm, I'm quite excited that when it came out, it's, you know, just getting that extra, like that trade off between, between range. Yeah, it's very cool.

• 1:40:52Speaker 00

  A couple of questions.

• 1:40:54Speaker 02

  I have a quick one. Okay. Quick question back to technical side. So any big takeaways from XS 2.1 medium to training the new small, just general in terms of training models? You mentioned a lot in the earlier discussion about, okay, in pre-training, there's a lot you can squeeze out, right? You can learn a lot more from the web. At the same time, you took 30B and scaled it up to 120B, right? Is there any gating on how small is too small? So I'm, I'm just gonna ramble for a bit. I'll come to a question at the end. But you know, part of Karpati's thesis was cognitive core, right? We've seen vibe thinker, NAN page, 3B, 4Bs, that reason a lot. And then, you know, the, the

• 1:41:39Speaker 02

  idea is you offload to a different model for the work. This, these are small reasoning models. So have you found anything interesting in model sizes? Like 20, 30Bs on device, 100Bs on single GPU? Can you squeeze out more there?

• 1:41:56Speaker 01

  There's a lot more to squeeze out. Like, I think, not to make too many forward promises, but I think we can squeeze a lot more out of the excess size as well. And I think we learned a lot during S training that will allow us to improve excess like size even further. And I think already since then, we have learned things that could have made S even better. I think there is a lot more still for like our, our space to squeeze out of models much smaller. I don't think that's an argument against scaling. It's just an, and one, by the way, I think this is a nice thing that, you know, it's really, it's not very helpful to have a post-training recipe for a smaller model and try to apply it to a bigger model.

• 1:42:38Speaker 00

  Yeah.

• 1:42:38Speaker 01

  It just, in all cases, you're gonna have to rethink most of the recipe. But a recipe for post-training for a bigger model applied to a smaller model is almost always just a really good like improvement and, and baseline. You can still tweak it more, but I don't think that's necessarily like obvious. And, and so you, once you make your bigger models better, you often have a, a quick lever to quickly improve your smaller models again. But will we be able to squeeze a lot more out of smaller models? Laguna S gave me a lot of confidence that I think we can. And I think it's around that discussion we had earlier about that it's about the behaviors, not necessarily the raw intelligence that you're trying to improve the models for.

• 1:43:23Speaker 00

  And that's on all axes of, there's like an axis of how long a model will reason. So how long can it stay agentic? And there's also efficiency, right? You wanna ideally push on both. And the, the thing to clarify you guys aren't doing right now, which we do see at Frontier Labs is the distillation, right? You have a big, big model that you don't really ship to users. And what you put out for inference is typically distilled from that, which gets you quite a bit, right?

• 1:43:50Speaker 01

  Look, I think it's, it's something we don't do right now because of like, why we're also like building these models, right? These models are for us part of our research path. So we've, you know, Laguna Medium was much larger than the last two models that, this one and last one that we've released. And we've trained even bigger models in the past. So there is the, the engineering component of like a bigger model and, and every kind of order of magnitude size, you'll learn new things in pre-training about stability. But at smaller model sizes, you are able to just iterate a lot quicker, like internally, right? On your research. And so for us, distilling down to a smaller model doesn't actually serve the purpose. These models are kind of, it's not the right term, but to us they're dual-purpose models.

• 1:44:34Speaker 01

  They are progress for us to wait to see, did we improve in the model factory and something to put out into the world? And so that's why we don't do it. We've done distillation experiments and there's like really cool things you can do. And I think if you have lots of user data, then you can go even further, right? In that. But I think there's something to be said in having a, a quick cadence of models trained end to end from scratch. So that you as a research organization can learn the lessons and not wait. That was actually one of the big lessons we learned over the years when we used to have a much longer cadence between model trainings, like six months. And we would train just like a big, big model, wait six months, train another bigger model.

• 1:45:18Speaker 01

  You would be compounding so many changes of improvements that at the, by the time you're training your next model, it's a bit of a soup and you don't really know what ingredients led to the outcomes. So when you are training far more frequently models, and this holds true for post-training and from pre-training from scratch, you are much more able to get an understanding of what led to the improvements. And I think that's important. Like ultimately, we are all still, there is no true science yet of, you know, deep learning for large language models. But we are all, I think, you know, trying to gain insights from our experiments because it's those insights that lead to scaling laws that lead to the kind of improvements that allow us to be, again, more compute efficient and get more capabilities.

• 1:46:03Speaker 00

  Amazing. I was gonna end off with a little bit more history. You spent some time looking at metrics for engineering team productivity. How do you think about engineering team productivity today?

• 1:46:14Speaker 01

  I mean, it's wild, right? I mean, it's the, it's like the golden age, like it's the fact that you can just take an idea and build something by waiting overnight for an agent to do the work. I don't know, to me.

• 1:46:27Speaker 00

  Like, how do you measure on, you know, because you're literally in a.

• 1:46:30Speaker 01

  Yeah, you're doing this. Look, I think, it's a good question. It's one I haven't thought about in a long time.

• 1:46:36Speaker 00

  But you know, you're, you're pretty qualified to do it.

• 1:46:38Speaker 01

  I'm gonna, no, it's a fair point. Let me take a second to think about it. Look, ultimately, what is code, what is software, what is engineering is to go from something that is valuable for an end user or sets of end users.

• 1:46:53Speaker 01

  Like an idea, an extra bug fix, a feature to like delivering that value. And I think what we're doing with these models becoming more capable is that we are massively like both cutting out middlemen and compressing the time that it takes to deliver that value. And ultimately, that iteration cycle for any startup or any company is what allows you to win. Right? If you're able to solve a bug in two hours versus in staying in the back walk for three weeks. If you're able to like be on a customer call and learn, hey, if this feature existed, it would like, you know, they'll be willing to pay more and it's more valuable to them and you ship it in a week instead of in a month. And so I think ultimately, maybe the same things that we looked at years ago, pre-LM still apply. And it's just a notion of cycle time.

• 1:47:37Speaker 01

  In this case, it's lead time from the moment you have a valuable thing that you're looking to do for someone to the moment that is actually shipped to them. Every other metric is ultimately a leading indicator for that lagging indicator. Right? And it doesn't matter if you're looking at amounts of code, PR, reviews, all of these kind of things. And so I think in this case, we are starting to move so quickly in some of these things that we can just sit back and look at what was traditionally the lagging indicator, which is the lead time from traditionally ticket to like, you know, like an end result.

• 1:48:11Speaker 01

  What I would look at in this new world that maybe we didn't think about before is how much can a single person do with that? Right? One of the most, if you look at AI native companies, they're not designed like the engineering orgs of, you know, pre-LM age. They're actually designed with often just the builder, right? And as close to the kind of customer to the ability to ship.

• 1:48:41Speaker 01

  There isn't necessarily a huge team in between that sits there. And I think that is, I think is exciting, like organizations where a single IC can just, you know, get much closer to that. So I would look at from where the value sits that's identified to the moment it's shipped and how many people are involved in that. And you want the amount of people involved in that to be less and you want the time end to end to be shorter.

• 1:49:05Speaker 00

  Okay. Is there a way to eval that when you're interviewing somebody?

• 1:49:13Speaker 00

  Cause that is, you know, the most compressed version.

• 1:49:17Speaker 01

  I think the, the common answer to this is agency. And how much agency does a person have?

• 1:49:24Speaker 01

  I think in the age of AI getting more capable, agency becomes probably one of the most important qualities for anyone. And I think agency is something you can look for in, you know, what people have done in the past. Because agency is something that if you have it, you are demonstrating it. Right? No one has just agency and is, is sitting back and not like exercising it. The whole definition of it is that it's exercised. And so understanding like what were things that people did in their lives and their professional and their personal project showed agency? And your personal backstory shows a ridiculous amount of agency, right?

• 1:49:58Speaker 01

  Like, I think that is ultimately it. It's the, you know, the Silicon Valley, you know, quote of the, of the last, you know, year and a half or so. It's like, you can just do things, right? That's, I think, what you're looking for.

• 1:50:08Speaker 00

  I think then aligning high-agency people is very hard because they all wanna go their own way. That's the whole point, right?

• 1:50:15Speaker 01

  Yeah, but I think the, the notion of, like, I think the notion of a good leader, right, in an organization is to be able to bring people together around like a common outcome. And I think what you wanna do with anyone who's high agency, I feel very lucky. I've got an organization with incredibly high-agency people, like, I mean, I'm not the one who built the model, right? I cannot stress this enough, like it's the team that like achieved this and it's a team that is incredibly high-agency. And so if you look at like, what does it take to bring that together? It's, it's ultimately a common goal and a common set of boundaries. Because if you allow to just go, you can do everything, you become an exploration algorithm. And this is what we see in big tech, right?

• 1:50:58Speaker 01

  In research and big tech, everything is an exploration algorithm. Everyone can do anything as long as, and then it becomes political about gathering the resources. So when you say, hey, this is our common goal and these are the boundaries that we've set, right? We're not multimodal, we focus on RL and we do these things. And you're upfront with people before they join the company. You know, you get a lot of agency, you can run where you want. But these are, these are the places where we say, this is the lanes that make sense. I think it actually gets the best out of people because like innovation comes from constraints. We did this with relatively little computing, relatively little money compared to some of like, you know, the others that are out there.

• 1:51:42Speaker 01

  And I've thought back on that quite a bit recently and thought, actually, it was a good thing. Because those constraints kind of forced us to become much better in certain other axes that my others might have not. Right? We, we purchase relatively little external data.

• 1:52:00Speaker 00

  I was gonna ask about that.

• 1:52:02Speaker 01

  Exactly, right? That was a constraint. But it's a constraint that pushed us to, to move on other areas to improve. And like, and there's lots of versions of that. So I think high-agency people, you wanna empower, you wanna get them really excited what they're doing, but you also wanna say, hey, if you join this mission, this is the outcome I need you to achieve, but these are the places that we don't go. And maybe if you care about those places, go somewhere else.

• 1:52:26Speaker 00

  Yeah.

• 1:52:28Speaker 00

  Boss call to action, who are you hiring?

• 1:52:30Speaker 01

  We are hiring on every possible role in applied research and engineering in the company. So from pre-training all the way to evals to post-training, architecture, like we are still in a world where, you know, individuals can have massive impact. And I think our pitch to join us, it's, we spoke a lot about the mission, how we think about things. But I think we are one of the places where it's the highest ratio to individual to impact. Right? Less than 70 people built this model, less than 115 to an engineering and a researcher is like together did this effort. And that's a very broad definition because I put myself in the 115 list. And so being able to do this kind of work on a mission that you're aligned with.

• 1:53:15Speaker 01

  And you can have that in, every individual still has huge impact.

• 1:53:18Speaker 00

  And being able to publish, being able to open source the model.

• 1:53:21Speaker 01

  Yeah, look, all of those things are, are part of that. But I think ultimately, when you, you can today pick between joining a very large foundation model company. And, but you are one of many. And not by any fault of them, but just by definition, the denominator has become really big. And our denominator is quite small. And so the level of impact you get to have is really high. And I think ultimately, all of us, frankly, the most incredible high-agency people I know, what are they optimizing for? They're optimizing for impact. They're optimizing for impact and am I aligned with the mission? And, and if today you heard about the mission and lines and you were optimizing for impact, I think we're a really good place to try.

• 1:54:05Speaker 00

  Okay, I think we ended there. That's a fantastic statement. You did amazing on Four Hours of Sleep.

• 1:54:11Speaker 01

  You guys.

• 1:54:11Speaker 00

  So, you know, podcast eval, definitely appreciated.

• 1:54:15Speaker 01

  I literally, my eyes are like starting to go like, I'm like, well, let you go, let you go. It was good to see you guys.

• 1:54:19Speaker 00

  Thank you for setting this up. We wanted to get this in because we think it's a great model and I think it's a great story to tell.

• 1:54:24Speaker 01

  Thank you.