No Priors Ep. 113 | With OpenAI's Eric Mitchell and Brandon McKinzie

ℹ️ EPISODE INFO

Released

May 1, 2025

Duration

38m

Channel

No Priors

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

This week on No Priors, Elad and Sarah sit down with Eric Mitchell and Brandon McKinzie, two of the minds behind OpenAI’s O3 model. They discuss what makes O3 unique, including its focus on reasoning, the role of reinforcement learning, and how tool use enables more powerful interactions. The conversation explores the unification of model capabilities, what the next generation of human-AI interfaces could look like, and how models will continue to advance in the years ahead. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mckbrando | @ericmitchellai Show Notes: 0:00 What is o3? 3:21 Reinforcement learning in o3 4:44 Unification of models 8:56 Why tool use helps test time scaling 11:10 Deep research 16:00 Future ways to interact with models 22:03 General purpose vs specialized models 25:30 Simulating AI interacting with the world 29:36 How will models advance?

🎤 SPEAKERS

• Sarah Guo

  host

• Brandon McKinzie

  guest

• Eric Mitchell

  guest

• Elad Gil

  host

🧾 EPISODE SUMMARY

In this episode of No Priors, featuring Sarah Guo and Brandon McKinzie, No Priors Ep. 113 | With OpenAI's Eric Mitchell and Brandon McKinzie explores openAI’s O3: Tool-Using Reasoning Model Redefines Deep, Steerable AI The episode explores OpenAI’s O3 reasoning model with researchers Eric Mitchell and Brandon McKinzie, focusing on how it ‘thinks before responding’ and uses tools to handle complex, multi-step tasks. They explain that O3 is trained heavily with reinforcement learning to solve hard problems, allocate compute at test time, and orchestrate tools like browsing and code execution. The conversation covers product tradeoffs between speed and depth, steerability for end users vs. developers, and why tool use dramatically improves test-time scaling, especially in vision and coding. They also discuss future directions such as computer use, robotics, multi-agent collaboration, better evals, and how AI can accelerate AI research itself.

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