Podcast
OpenAI Podcast
Hosted by OpenAI · EN
Hosted by Andrew Mayne, The OpenAI Podcast features conversations with the people building with and working at OpenAI. Topics range from how new features are developed to what users are doing with the technology. It’s a practical look at how AI is made and where it’s going, told by the people closest to the work.
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20episodes
Episodes
Newest firstAll episodes
Episode 19 - Inside image generation’s Renaissance moment
3w ago00:29:22Summary readyPeople 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.Chapters00:36 How Adele and Kenji came to work on Images02:27 Images 2.0 launch reception05:25 Productivity use cases and and 360 images09:34: Viral trends, authenticity, and imperfection10:51 Training breakthroughs and photorealism14:06 Evals, prompting, and creative control22:16 Creative agents and what comes next22:27 Images + Codex28:08 Prompt tips Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 18 - Why AI needs a new kind of supercomputer network
4w ago00:37:38Summary readyTraining 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.Chapters00:00 Intro00:39 Greg and Mark's paths to OpenAI04:34 Why training AI stresses networks differently10:05 Bottlenecks, failures, and the cost of waiting15:19 How Multipath Reliable Connection works18:59 A protocol to route around failures25:05 Why OpenAI is making MRC an open standard35:09 Could AI compute move to space? Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 17 - What happens now that AI is good at math?
Apr 2800:43:28Summary readyMath 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. Chapters01:27 The surprising progress of AI’s math capabilities 03:01 Solving an open problem with ChatGPT06:57 How models went from basic math to research level11:32 Why math matters for AGI14:26 AI and the Erdős problems21:26 Building an automated researcher28:19 The role of humans as models improve33:52 Verifying proofs with AI36:00 The risk of shallow understanding41:19 Advice for learning math with ChatGPT Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 16 - Building AI for Life Sciences
Apr 1600:44:25Summary readyWhat 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.Chapters0:39 Introducing the Life Sciences model series3:47 Joy’s path into life sciences5:00 Autonomous lab with Ginkgo Bioworks7:27 Yunyun’s path into life sciences8:12 OpenAI’s life sciences work9:48 Biorisk, access, and safeguards15:43 What models can do in the lab17:51 Building scientific infrastructure20:14 Why compute matters for science24:54 Where are we in 6-12 months?29:51 Scientific adoption and skepticism33:17 Advice for students and researchers40:27 Where are we in 10 years? Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 15 - Inside the Model Spec
Mar 2500:37:26Summary readyThe 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.Chapters00:00 Introduction01:10 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 feedback07:51 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 Claus17:41 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 Constitution26:28 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 Spec37:16 Could AI write a Human Spec? Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 14 - Building AI for better healthcare
Mar 1600:30:54Summary readyHealthcare 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.Chapters00:00:38 – Origins of Nate and Karan’s interest in AI and healthcare00:05:01 – Strategy for building AI tools for clinicians00:06:57 – How AI models are trained for health use cases00:10:15 – How OpenAI is able to score well on health evals00:14:21 – Key challenges deploying AI in healthcare00:21:05 – Collaboration with hospitals and healthcare systems00:23:05 – Practical everyday uses of AI health assistants00:26:43 – Biggest “wow” moment during development00:28:46 – Feedback from clinicians and early users Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 13 - The Thinking Behind Ads in ChatGPT
Feb 900:25:34Summary readyHow 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.Chapters00:00:29 — Mission and principles00:04:01 — Separation between ads and answers00:07:31 — Who will see ads00:08:52 — Internal input and decision-making process00:11:06 — Controls and how ads will work00:15:53 — Guardrails for sensitive conversations00:17:33 — Skepticism about ads00:20:26 — Helping small businesses00:24:13 — Future of ads Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 12 - State of the AI Industry
Jan 1900:49:41Summary readyOpenAI 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. Chapters00:00:00 — What’s the AI story of 2026?00:07:28 — AI in healthcare00:12:01 — Scaling compute to match revenue00:18:05 — Difference between now and dot-com bubble00:27:41 — Ads in ChatGPT00:30:05 — Will consumers have more than one AI subscription?00:36:41 — Winning in enterprise00:39:44 — How can startups succeed?00:44:05 — Robotics and beyond Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 11- Shaping Model Behavior in GPT-5.1
Dec 200:28:40Summary readyWhat 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 Hosted on Acast. See acast.com/privacy for more information.
SummaryEpisode 10 - How AI Is Accelerating Scientific Discovery Today and What's Ahead
Nov 2000:48:12Summary readyAI 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 Hosted on Acast. See acast.com/privacy for more information.
Summary