#124 - The Path to AGI: Inside poolside’s AI Model Factory for Code with Eiso Kant

Building Human-Level AI for Code: Model Factories, RL at Scale, and Distributed Teams

Technical Deep Dives:

• Poolside’s model factory: end-to-end automation from raw data to production models
• Scaling RL from code execution: 800,000+ containerized repos, millions of agent tasks
• Immutable versioning with Apache Iceberg for full traceability
• Distributed team structure: 120+ engineers across US/EU, monthly in-person sprints
• Hardware orchestration: 10,000+ H200s, hot swap failover, dynamic allocation
• Leadership: dividing responsibilities, low-ego culture, and the MIT principle
• Future of software: managing agent workforces, context window strategies, continual learning

"Our model factory runs thousands of experiments before a single production model is trained. It’s an empirical science—every component, from data ingestion to evals, is versioned and traceable." – Eiso Kant

Chapters: [00:04:28] Poolside’s unique approach to foundation models [00:13:02] Scaling hardware: 10,000+ H200s and orchestration [00:17:42] RL, agents, and the future of developer tools [00:24:56] Immutable versioning and evaluation frameworks [00:36:04] Distributed team structure and monthly sprints [00:40:26] Leadership, decision-making, and low-ego culture [00:45:54] Lessons for CTOs: breaking process dogma, preparing for agent-driven orgs [00:50:54] The next 3 years: AGI, agent workforces, and the end of manual coding [00:53:44] Context window, continual learning, and model memory [00:56:20] Everything collapses into the model: product, research, and daily life [00:59:46] Advice to a younger self: scale compute, trust RL+LM, and the four-minute mile