Why AI Can Now Make Discoveries - my conversation with @danintheory, Lead of the Foundations of Reinforcement Learning team at @OpenAI

00:00 Intro: AI's wild week in mathematics

01:21 What OpenAI's Foundations of RL team does

03:08 Dan's journey: from black holes and quantum gravity to frontier AI

07:04 Are AI systems becoming useful for real science

08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic

08:52 Why the OpenAI result was an act of exploration

10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof

12:13 RL 101: learning by doing, not just watching

15:10 Why reinforcement learning works

15:58 How RL breaks: sparse feedback and long-horizon tasks

17:03 RLHF: how human feedback shaped early language models

18:48 Move 37, self-play, and the search for novel strategies

22:16 Explore vs. exploit in scientific discovery

24:49 Why RL may now be "the cake," not the cherry on top

25:46 Why RL started working with large language models

27:29 Is RL "sucking supervision through a straw"?

28:47 Why language may be the grounding layer for intelligence

31:46 A contrarian take on the Bitter Lesson

32:41 What test-time compute actually is

34:50 How RL gives models the ability to think

35:40 Verifiable rewards, math, coding, and the messy real world

38:00 What physics can teach us about AI

42:08 Is there a thermodynamics of AI?

43:08 From Erdős problems to Einstein-level AI

45:16 Is AI already doing original science?

45:51 How far are we from AI automating AI research

47:41 Why Dan is excited about the future of science