Inside the Model Factory — Eiso Kant, Poolside AI

Building foundation models requires an industrialized "model factory" approach that prioritizes engineering rigor, data immutability, and rapid experimentation cycles. Poolside, led by Eiso Kant, emphasizes that model building is primarily an engineering challenge rather than purely theoretical research. By treating data as immutable and maintaining perfect reproducibility, the team accelerates the transition from experimental ideas to production-ready models. The release of the Laguna S model proves that smaller, highly persistent models can outperform significantly larger counterparts, suggesting that behavioral improvements—specifically in reasoning and tool interaction—are as critical as raw parameter scaling. This strategy supports a broader mission to democratize intelligence, ensuring that the future of AGI remains a competitive, open ecosystem rather than a consolidated oligopoly controlled by a handful of frontier labs.