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What happens now that AI is good at math? — the OpenAI Podcast Ep. 17

Math is one of the clearest ways to see how far AI has come in a short span. OpenAI researchers Sébastien Bubeck and Ernest Ryu join host Andrew Mayne to explain what changed and what it could mean for the future of research.

Andrew MaynehostSébastien BubeckguestErnest Ryuguest

Apr 28, 202643mWatch on YouTube ↗

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EPISODE INFO

Released: April 28, 2026
Duration: 43m
Channel: OpenAI
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Math is one of the clearest ways to see how far AI has come in a short span. OpenAI researchers Sébastien Bubeck and Ernest Ryu join host Andrew Mayne to explain what changed and what it could mean for the future of research. They reflect on how Ernest used ChatGPT to help solve a 42-year-old open problem, the difference between deep literature search and original mathematical discovery, and what changes when AI can work over longer timelines. Chapters 01:27 The surprising progress of AI’s math capabilities 03:01 Solving an open problem with ChatGPT 06:57 How models went from basic math to research level 11:32 Why math matters for AGI 14:26 AI and the Erdős problems 21:26 Building an automated researcher 28:19 The role of humans as models improve 33:52 Verifying proofs with AI 36:00 The risk of shallow understanding 41:19 Advice for learning math with ChatGPT

SPEAKERS

Andrew Mayne
host
Host of the OpenAI Podcast.
Sébastien Bubeck
guest
OpenAI researcher and mathematician/ML theory expert; formerly a Princeton professor and previously at Microsoft.
Ernest Ryu
guest
OpenAI researcher with an applied mathematics background; formerly a mathematics professor at UCLA focused on optimization and machine learning theory.

EPISODE SUMMARY

In this episode of OpenAI, featuring Andrew Mayne and Sébastien Bubeck, What happens now that AI is good at math? — the OpenAI Podcast Ep. 17 explores AI’s math leap enables research, verification, and automated discovery workflows Researchers describe math as a uniquely clean benchmark for AI progress because problems are unambiguous and solutions are often verifiable, making capability jumps easy to measure.

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