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

00:00 Intro
00:27 Open vs closed models, and who actually captures the value
05:12 China, harnesses, and where the real training leverage sits
08:40 Sovereign compute and the national security case for building models
11:18 Uncensored open weights and the bioweapon question
14:29 Anthropic vs OpenAI, ideology and politics
19:35 The Mythos ban and the Fable 5 delays
24:30 The AGI narrative, the talent drain, and antitrust
28:12 Why researchers join Anthropic, and the open Slack culture
34:04 Nathan's next 12 months: character training and big RL runs
37:55 Continual learning, RSI, and why Nathan is skeptical
43:19 Boom or bubble, tokens vs GPUs
45:12 Why all that token spend never produced 10x products
48:38 Job displacement and the small-business future
52:49 Robotics, world models, and why multimodal lags
57:44 What the open ecosystem should actually do
1:03:17 Why NVIDIA isn't building a frontier model
1:07:34 The RLHF book, and whether RLHF still matters
1:11:06 GRPO vs PPO and on-policy distillation

Music

"Kid Kodi" - Blue Dot Sessions - via Free Music Archive - CC BY-NC 4.0.