ABOUT THIS EPISODE
Prakash Narayanan and Nathan Labenz talk with Lewis Hammond and Wayne Nelms about how AI systems are getting cheaper and more capable, while the risks around coordination, cooperation, and tacit collusion are getting harder to ignore. The conversation also covers model distillation, recursive self-improvement, GPU futures, compute hedging, and the financial structure emerging around AI infrastructure.
Chapters
(0:00) Smaller models, bigger gains.
(1:11) When AI agents cooperate against us.
(2:00) GPU compute needs a financial market.
(2:55) Brute-force compute can break standards.
(5:13) Opening and model release race
(7:29) Why models are getting cheaper
(12:24) How AI models are judged
(18:20) Why frontier models converge
(19:54) Distillation and smaller models
(21:12) Recursive self-improvement
(22:17) The collapse in AI prices
(28:48) Why multi-agent AI matters
(35:43) Three multi-agent failure modes
(48:37) How agents learn cooperation
(53:27) Detecting tacit collusion
(1:11:26) A positive vision for AI cooperation
(1:17:00) What Ornn does
(1:18:34) GPU pricing and hedging
(1:22:18) Building a transaction index
(1:24:43) Why compute prices need real transactions
(1:34:13) GPU futures and capacity contracts
(1:53:34) How long GPUs stay useful
(1:53:44) Hedging chip obsolescence risk
(1:53:54) The future of compute markets
(1:59:53) Agentic startups and compute economics
(2:07:37) Brute-force compute and RSA
(2:17:07) AI and self-driving cars
(2:40:03) AI, mathematics, and peer review
(2:40:13) Why AI doctors may win
(2:46:52) Medicine after chatbots
Guests
Lewis Hammond — Research Director, Cooperative AI Foundation (𝕏 | LinkedIn)
Wayne Nelms — Co-Founder, Ornn (𝕏 | LinkedIn)
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