OpenAI Podcast

Last month AI found something mathematicians had missed for decades. Reasoning researchers Alexander Wei, Hongxun Wu, and Lijie Chen join the podcast to discuss how a general-purpose model helped disprove an 80-year-old conjecture from famed mathematician Paul Erdős. They walk through the moment the result started looking real, what it took to verify the proof, and what’s happened since sharing the discovery with the world. They also explore what this means for the future of math and for researchers learning to work with AI.

Chapters

0:44 AI and the International Math Olympiad and International Olympiad of Informatics
6:35 An OpenAI model disproves the Erdős unit distance conjecture
8:33 Running the model and checking the proof
11:04 Why general models matter for discovery
15:55 Creativity, tools, and how the proof worked
18:25 Why AI should feel empowering for mathematicians
22:31 Advice for researchers using AI
27:24 What comes next for math and AI research
37:30 Cryptography, quantum computing, and the future

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People are generating over 1.5 billion images a week in ChatGPT. In this episode, Product lead Adele Li and researcher Kenji Hata share some of the new use cases and trends since the launch of Images 2.0. Together with host Andrew Mayne, they trace the progress from the early DALL-E days and dive into the latest capabilities, including better text rendering, photorealism, multilingual support, world knowledge, aspect ratios, and character consistency. They also explore what comes next as image generation models evolve into more capable creative assistants.

Chapters
00:36 How Adele and Kenji came to work on Images
02:27 Images 2.0 launch reception
05:25 Productivity use cases and and 360 images
09:34: Viral trends, authenticity, and imperfection
10:51 Training breakthroughs and photorealism
14:06 Evals, prompting, and creative control
22:16 Creative agents and what comes next
22:27 Images + Codex
28:08 Prompt tips
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Training frontier models isn’t as simple as adding more GPUs—one small problem and the whole coordinated dance falls apart. OpenAI’s Mark Handley and Greg Steinbrecher discuss how a new supercomputer network design, used to train some of the company’s latest models, keeps the whole system moving in lockstep, even with record numbers of GPUs. They break down Multipath Reliable Connection, a new protocol OpenAI developed with AMD, Broadcom, Intel, Microsoft, and Nvidia, and why they’re making it available for the whole industry to use.

Chapters
00:00 Intro
00:39 Greg and Mark's paths to OpenAI
04:34 Why training AI stresses networks differently
10:05 Bottlenecks, failures, and the cost of waiting
15:19 How Multipath Reliable Connection works
18:59 A protocol to route around failures
25:05 Why OpenAI is making MRC an open standard
35:09 Could AI compute move to space?

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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

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What does it take to build AI systems that can actually help scientists? Research lead Joy Jiao and product lead Yunyun Wang discuss how OpenAI is developing models for life sciences and what responsible deployment means in a field with real biosecurity stakes. They explore how AI is already improving research workflows and where it could lead in drug discovery and more autonomous labs — including why a future with less pipetting sounds pretty good to most scientists.

Chapters

0:39 Introducing the Life Sciences model series
3:47 Joy’s path into life sciences
5:00 Autonomous lab with Ginkgo Bioworks
7:27 Yunyun’s path into life sciences
8:12 OpenAI’s life sciences work
9:48 Biorisk, access, and safeguards
15:43 What models can do in the lab
17:51 Building scientific infrastructure
20:14 Why compute matters for science
24:54 Where are we in 6-12 months?
29:51 Scientific adoption and skepticism
33:17 Advice for students and researchers
40:27 Where are we in 10 years?
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