Inside the Model Factory — Eiso Kant, Poolside AI

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 ha