OpenAI Podcast

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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