About this episode

Matt Turck interviews Dan Roberts from OpenAI about the advancements in AI and its potential to contribute to scientific discoveries.

Are we witnessing the first real signs of AI becoming a scientist? In this episode of The MAD Podcast, Matt Turck sits down with Dan Roberts, lead of the Foundations of Reinforcement Learning team at OpenAI, to explore one of the biggest shifts happening in AI: the rise of reasoning models, test-time compute, and reinforcement learning as engines of scientific discovery. Dan brings a rare perspective - from theoretical physics, black holes, quantum information, and deep learning theory - to explain how models are learning to “think,” why language may be such a powerful foundation for intelligence, what recent AI math breakthroughs really mean, and whether we are beginning to see AI systems that can contribute to science itself. (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…

More episodes of The MAD Podcast with Matt Turck

• State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, Rise of Headless, and AI-Proofing Your Job · May 28, 2026 · 1h 13m
• OpenAI's Yann Dubois: Why AI Progress Suddenly Feels Real · May 21, 2026 · 1h 14m
• The Agent Harness: Building Secure Sandboxes for Autonomous AI Workloads · May 14, 2026 · 1h 5m

Explore listener stats, chart rankings, contacts and more on the The MAD Podcast with Matt Turck podcast page.