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
Eiso Kant · CTO & co-founder
How do you build a foundation model that can write code at a human level? Eiso Kant (CTO & co-founder, Poolside) reveals the technical architecture, distributed team strategies, and reinforcement learning breakthroughs powering one of Europe’s most ambitious AI startups. Learn how Poolside operates 10,000+ H200s, runs the world’s largest code execution RL environment, and why CTOs must rethink engineering orgs for an agent-driven future.
Show notes.
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
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