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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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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The more AI can do, the more we need to ask what it should and shouldn’t do. In this episode, OpenAI researcher Jason Wolfe joins host Andrew Mayne to talk about the Model Spec, the public framework that defines intended model behavior. They discuss how the Model Spec works in practice, including how the chain of command handles  conflicts between instructions, and how OpenAI evolves it based on feedback, real-world use, and new model capabilities.

Chapters

00:00 Introduction

01:10 What is the Model Spec?

03:55 How does the Model Spec work in practice?

06:26 Transparency: Where to read the Model Spec & give feedback

07:51 How did the Model Spec originate?

10:02 How does the spec translate into model behavior?

11:26 What is the hierarchy / chain of command?

13:35 Handling edge cases like Santa Claus

17:41 How does the Model Spec evolve over time?

19:59 What happens when models disagree with the spec?

22:05 How do smaller models follow the spec?

23:16 Is chain-of-thought useful for alignment?

24:16 Model Spec vs Anthropic’s Constitution

26:28 What surprised you most?

26:56 How do you define the scope of the spec?

27:44 What is the future of the Model Spec?

31:16 How should developers think about the spec?

34:44 Asimov’s laws vs Model Spec

37:16 Could AI write a Human Spec?

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Healthcare systems around the world are under strain, and both patients and clinicians are feeling the impact. OpenAI's Head of Health Dr. Nate Gross and Karan Singhal, who leads Health AI Research, discuss how AI can help address the biggest challenges. They cover how OpenAI is training models to handle sensitive health questions in collaboration with physicians, and how that foundation is unlocking a new generation of tools for patients, clinicians, and healthcare systems.

Chapters

00:00:38 – Origins of Nate and Karan’s interest in AI and healthcare

00:05:01 – Strategy for building AI tools for clinicians

00:06:57 – How AI models are trained for health use cases

00:10:15 – How OpenAI is able to score well on health evals

00:14:21 – Key challenges deploying AI in healthcare

00:21:05 – Collaboration with hospitals and healthcare systems

00:23:05 – Practical everyday uses of AI health assistants

00:26:43 – Biggest “wow” moment during development

00:28:46 – Feedback from clinicians and early users

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How should advertising work in an AI product? Asad Awan, one of the ad leads at OpenAI, walks through how the company is approaching this decision and why it’s testing ads in ChatGPT at all. He explains how ads are built to stay separate from the model response, keep conversations with ChatGPT private from advertisers, and give people control over their experience.

Chapters

00:00:29 — Mission and principles

00:04:01 — Separation between ads and answers

00:07:31 — Who will see ads

00:08:52 — Internal input and decision-making process

00:11:06 — Controls and how ads will work

00:15:53 — Guardrails for sensitive conversations

00:17:33 — Skepticism about ads

00:20:26 — Helping small businesses

00:24:13 — Future of ads

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