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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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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OpenAI CFO Sarah Friar and Khosla Ventures founder Vinod Khosla argue the greatest challenges in AI right now are keeping up with demand and making sure more people get the benefit. They unpack what's driving big investments in compute and why this moment is different from other technology cycles — with meaningful advances in health, agents, and robotics still ahead.

Chapters

00:00:00 — What’s the AI story of 2026?

00:07:28 — AI in healthcare

00:12:01 — Scaling compute to match revenue

00:18:05 — Difference between now and dot-com bubble

00:27:41 — Ads in ChatGPT

00:30:05 — Will consumers have more than one AI subscription?

00:36:41 — Winning in enterprise

00:39:44 — How can startups succeed?

00:44:05 — Robotics and beyond

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What does it mean for an AI model to have "personality"? Researcher Christina Kim and product manager Laurentia Romaniuk talk about how OpenAI set out to build a model that delivers on both IQ and EQ, while giving people more flexibility in how ChatGPT responds. They break down what goes into model behavior and why it's an important, but still imperfect blend of art and science.

Chapters:

- 00:00:43 — GPT-5.1 goals and the shift to reasoning models

- 00:02:18 — Differences between GPT-5 and GPT-5.1

- 00:04:55 — Unpacking the model switcher

- 00:07:24 — Understanding user feedback

- 00:08:27 — Measuring progress on emotional intelligence

- 00:10:02 — What is model personality?

- 00:14:25 — Model steerability, bias, and uncertainty

- 00:21:59 — Advantages of memory in ChatGPT

- 00:25:27 — Looking ahead and advice for getting the most out of models

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AI is beginning to change how science gets done. Head of OpenAI for Science Kevin Weil and OpenAI research scientist Alex Lupsasca talk about the early signs of acceleration researchers are seeing with GPT-5—from surfacing literature across fields and languages, to speeding up complex calculations, to designing follow-up experiments. They unpack what’s possible today, what doesn’t work yet, and why the next few years could reshape the trajectory of scientific progress across physics, math, biology and beyond.

Chapters

- 00:00:40 — OpenAI for Science mission

- 00:06:00 — Literature search and intersections across fields

- 00:11:19 — A fusion physicist shows what GPT-5 can do

- 00:15:08 — GPT-5 Pro and black hole symmetries

- 00:19:02 — Getting the most out of the models

- 00:24:33 — OpenAI’s new research paper (https://openai.com/index/accelerating-science-gpt-5/)

- 00:29:59 — Looking ahead to the next 5 years

- 00:32:05 — Will predictions outpace experiments?

- 00:36:43 — The pace of model improvement

- 00:40:31 — What do scientific benchmarks look like?

- 00:44:16 — Fusion and the promise of abundant energy

- 00:48:07 — Closing: Science 2.0 moment

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How will the internet feel when your browser can actually help do things for you? OpenAI’s Ben Goodger and Darin Fisher, whose past work shaped some of the most popular modern browsers, dive into the making of ChatGPT Atlas. They explore how AI changes what a browser can be, from tabs you can talk to, to agents that take over tedious tasks. Learn more about the decisions they made along the way and what’s coming next.

- 00:00:45 What is Atlas?

- 00:03:34 The state of browsers and AI on the web

- 00:13:55 Under the hood: why browsers are hard (OWL, rendering)

- 00:22:00 Building with AI: Codex, cross-language, Swift on Windows

- 00:33:39 Search in Atlas: one box plus model response

- 00:41:28 Favorite features: scrolling tabs and tab search

- 00:45:23 Side Chat in action: summarize, shop, build forms

- 00:46:59 Real-world wins with Agent (cloud bill, medical results)

- 00:52:45 Why Chromium? Compatibility and extensions

- 01:07:57 Five-year vision: an agentic web and reduced toil

- 01:13:11 Power tips and closing remarks

Learn more about OWL

https://openai.com/index/building-chatgpt-atlas/

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Sam Altman and Greg Brockman from OpenAI sit down with Broadcom’s Hock Tan and Charlie Kawwas to discuss their new partnership—and what it means for the future of AI. From custom silicon to global-scale infrastructure, they share how compute innovation is shaping the road to AGI.

00:00 Announcing the partnership

03:06 The scale of AI infrastructure

06:03 Collaboration and innovation in chip design

08:49 Historical context and future vision

12:10 Role of compute in AI development

15:01 Optimizing for specific workloads

18:02 Journey towards AGI

21:00 Future of AI and compute capacity

23:50 Wrap-up and future projects

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The OpenAI Podcast is live for the first time. Host Andrew Mayne sits down with startups Cursor, Abridge, SchoolAI, and http://jam.dev/—each reimagining how AI can transform their industries. From healthcare and education to coding and collaboration, we explore how these builders are putting AI to work in the real world.

00:23 Caleb Hicks (SchoolAI)

14:14 Dani Grant (Jam.dev)

26:20 Zach Lipton (Abridge)

44:38 Lee Robinson (Cursor)

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What happens when AI becomes a true coding collaborator? OpenAI co-founder Greg Brockman and Codex engineering lead Thibault Sottiaux talk about the evolution of Codex—from the first glimpses of AI writing code, to today’s GPT-5 Codex agents that can work for hours on complex refactorings. They discuss building “harnesses,” the rise of agentic coding, code review breakthroughs, and how AI may transform software development in the years ahead.

1:15 – The first sparks of AI coding with GPT-3

2:20 – Why coding became OpenAI’s deepest focus area

4:00 – What a “harness” is and why it matters for agents

5:30 – Lessons from GitHub Copilot and latency tradeoffs

8:20 – From terminal prototypes to agentic software engineers

19:30 – agents.md and the future of collaborative coding

22:55 – Refactoring, code review, and breakthrough use cases

29:45 – Launching GPT-5 Codex and the road to multi-agent systems

35:00 – Security and the 2030 outlook

43:00 – Compute scarcity

46:30 – Should you still learn to code in the AI era?

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How close are we to automating scientific discovery? What do AI competition wins really tell us about progress toward AGI? OpenAI Chief Scientist Jakub Pachocki and researcher Szymon Sidor share inside stories—from gold medals at the International Math Olympiad to surprising leaps in reasoning—that reveal where AI is headed next.

1:20 – From high school in Poland to AI research leaders

4:50 – Explaining AGI: technical and everyday perspectives

6:30 – Automating scientific discovery with AI

7:50 – Breakthroughs in medicine, AI safety, and alignment

10:30 – Today is a decade in the making

14:30 – Benchmark saturation and its limits

16:50 – Why math competitions matter for AI

18:15 – How models reason without tools

21:45 – Recognizing when a model can’t solve a problem

23:30 – Storytime: AtCoder competition in Japan

26:50 – How reasoning breakthroughs really happen

28:55 – What’s next for scaling and long-horizon reasoning

30:30 – What AGI will look and feel like

36:25 – Balancing trust and personal value

34:00 – Advice to high school students in 2025

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AI is redefining how we learn — from personalized tutoring to entirely new teaching models. OpenAI’s Head of Education, Leah Belsky, joins host Andrew Mayne to discuss what this shift means for students, educators, and society. Special guests include college students Yabsera and Alaap, who share their perspectives on learning in the AI era.

00:22 – Leah’s path to OpenAI & the moonshot

01:40 – ChatGPT as a global learning platform—countries lean in

03:50 – Universities: equal access, trust, and adoption

05:12 – From AI detectors to better policy and practice

06:50 – Study Mode explained

09:51 – AI as a tutor that builds confidence

11:35 – Workforce skills graduates need

14:15 – The great brain rot debate

18:00 – A personal learning anecdote

19:30 – Meet the students

21:30 – First experiences with AI

25:25 – How professors are adapting

29:28 – Trying Study Mode

33:20 – ChatGPT vs. social media

41:43 – Cheating, challenges, and advice for students

49:24 – The future of learning with AI

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The future of work is arriving faster than expected. In this episode, OpenAI COO Brad Lightcap and Chief Economist Ronnie Chatterji join Andrew Mayne to discuss the impacts of AI on software, science, small business, education, and jobs.

00:00 Intro

01:00 Brad Lightcap on OpenAI’s deployment mission

02:00 Birth of ChatGPT: from playground to product

06:15 AI’s impact on work & productivity

08:55 Supercharging science with AI

09:55 Small teams with big leverage

13:10 What sectors are next?

17:05 Defining AI agents

20:30 Small business growth with AI agents

22:08 AI in emerging markets & agriculture

25:53 Return of the “Idea Guy”

28:20 Why EQ and soft skills matter

31:35 Education for the AI era

36:11 Partnering with Cal State & educators

39:14 From bans to buy-in in schools

42:00 Ronnie’s research: sectors, geography, communication

45:46 What should we tell our kids?

48:14 What history teaches us about disruption

52:04 Expanding participation in the economy

55:35 AI increases demand

59:19 Why OpenAI will grow after AGI

1:02:05 Favorite ChatGPT use cases

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