The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI
Eiso Kant, CEO of Poolside AI, discusses his company's mission to foster an open and competitive AI landscape, moving away from an oligopoly of intelligence. He highlights Poolside's 'Model Factory' approach to rapidly build and iterate LLMs and shares insights on how 'behavioral intelligence' in smaller models can offer significant capabilities, challenging the exclusive focus on raw scale. Kant also offers strong opinions on the future of LLM training, tool calls, and the ethical implications of open versus closed AI development.
- Poolside AI's Laguna S, an 118B parameter model, solved complex coding challenges like Erdos 397, proving smaller models can achieve results previously expected only from much larger ones. Read →
- Poolside AI raised $500M, making a calculated bet on diversified AI at a time when many investors doubted the strategy, committing to an open competitive AI landscape. Read →
- For LLM builders, Reinforcement Learning (RL) time, not pre-training, is now the critical bottleneck. Poolside AI CEO Eiso Kant points to batch size constraints as the limit for scaling compute. Read →