OpenAI Podcast

Racing is a sport of tiny margins and mountains of data. OpenAI researcher Joyce Ruffell and RaceTek Systems co-founder Chase Holden are each using AI to help teams make better use of information from the track, the garage, and the people behind the wheel. They discuss OpenAI’s research collaboration with Chip Ganassi Racing and how Chase used ChatGPT and Codex to go from hosting a NASCAR podcast to building a racing intelligence company. They also examine how AI could give smaller teams an edge without replacing the human expertise at the heart of racing, and why car culture runs so deep at OpenAI.

Chapters

00:43 Intro to OpenAI researcher Joyce Ruffell and Chip Ganassi Racing
02:43 Intro to Chase Holden and RaceTek Systems
07:45 Joyce’s path into racing
10:00 Helping racing teams work with AI
15:31 Combining human feedback with racing data
18:26 How AI helps smaller teams compete
21:43 Spreadsheets and the “data wars” in racing
27:03 Getting started with AI and Codex
30:08 How RaceTek won its first customer
33:01 Humans, robot racing, and the future of competition
39:26 Using AI beyond the racetrack

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The old tests are getting too easy. Tejal Patwardhan leads OpenAI’s frontier evals team, which is finding new ways to measure and forecast progress as models become more capable. She and host Andrew Mayne discuss why evals matter for research, how benchmarks can break or get gamed, and what models need to be judged on next.

Chapters

00:00:24 Growing up at OpenAI
00:03:10 Why reasoning changed everything
00:06:28 What made o1 surprising
00:11:20 Why old benchmarks stopped working
00:14:45 What makes a good benchmark
00:17:35 Why evals are getting harder
00:22:09 Measuring voice and vision models
00:24:48 Testing models on real science
00:33:23 How OpenAI tracks frontier progress
00:40:47 What AI means for work

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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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