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- 来源: blogs_podcasts
- 原始来源: https://www.latent.space/p/poolside
来源摘要/节选
In recent months, the open vs closed, and US vs China 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 are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines’ recent release nearly 10 times their size.
Poolside’s recent tech report 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, 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
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
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