
Nathan Lambert: Inside Post-Training and the Open Model Fight
Samstag, 8. August 2026 · 1 h 15
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Shownotes
Nathan Lambert spent three years as post-training lead at Ai2, where he built the OLMo models, and he writes Interconnects, one of the most-read technical newsletters in AI. He left Ai2 in June and is now working on a new project. He's also the author of the RLHF book. We talked a lot about open models, their capabilities, and why they are better than he expected. We get into what that means over the next two to five years, why he thinks recursive self-improvement is overblown, what the market for training environments actually looks like now, and why he expects Anthropic's famously open internal culture to break after its IPO.
Key Topics
• Open vs closed models and who actually captures the value
• Anthropic and OpenAI as opposite cultures, and the talent concentration problem
• Boom vs bubble, and why token spend hasn't produced 10x better products
• Continual learning, RSI skepticism, and what Nathan wants to work on next
• What the open ecosystem needs economically to survive
Timeline
Intro
Open vs closed models, and who actually captures the value
China, harnesses, and where the real training leverage sits
Sovereign compute and the national security case for building models
Uncensored open weights and the bioweapon question
Anthropic vs OpenAI, ideology and politics
The Mythos ban and the Fable 5 delays
The AGI narrative, the talent drain, and antitrust
Why researchers join Anthropic, and the open Slack culture
Nathan's next 12 months: character training and big RL runs
Continual learning, RSI, and why Nathan is skeptical
Boom or bubble, tokens vs GPUs
Why all that token spend never produced 10x products
Job displacement and the small-business future
Robotics, world models, and why multimodal lags
What the open ecosystem should actually do
Why NVIDIA isn't building a frontier model
The RLHF book, and whether RLHF still matters
GRPO vs PPO and on-policy distillation
Music
• "Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.