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