# Rich Sutton
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Top podcast episodes featuring rich sutton.
Episode 41: The Verification Crisis: Why Trust Is the New Bottleneck in AI
Noah Smith, economist and author of Noahpinion, joins High Signal to look at what AI is already changing… and what it isn’t. The conversation moves beyond the usual productivity hype to ask harder questions: Is agentic coding actually increasing revenue per hour worked? Will software remain a high-…
#108 – Sergey Levine: Robotics and Machine Learning
Sergey Levine is a professor at Berkeley and a world-class researcher in deep learning, reinforcement learning, robotics, and computer vision, including the development of algorithms for end-to-end training of neural network policies that combine perception and control, scalable algorithms for inve…
"I've Never Seen a Model Say 'This File Is Getting Too Big'" - Ep 02
Recorded July 17, 2026. We settle the model-access bet. We talk about how the labs talk to us, plus extend an open invite for OpenAI, Anthropic, SpaceX, and Thinking Machines to come on the show and be specific about their best-case scenarios about how AI makes the world better. Thinking Machines s…
The Barriers to Mediocrity Have Collapsed
Dick and Paul dig into why general-purpose AI models are doing to robotics what they already did to language — and why that's a bigger deal than the humanoid-robot hype suggests. They trace the throughline from Chinese EVs to laundry-folding robots to Rich Sutton's "Bitter Lesson," then make the ca…
Rich Sutton and Khurram Javed: Why AI Models Stop Learning, and How to Start It Again
Rich Sutton, who helped pioneer reinforcement learning and wrote the seminal AI essay The Bitter Lesson, has now cofounded Oak Lab with his former student Khurram Javed. Their goal: to build agents that continuously learn from their own experience rather than from us. Rich doesn't think he holds a …
Fourteen out of fifteen, and nobody outside has checked it yet
These were not simulations. Anthropic says Claude designed the proteins, and then they were built and tested at a bench. How many worked? Fourteen of the fifteen targets they tried. Anthropic puts the hit rate around thirty five percent, which it says is about double what this kind of design usuall…
Do We Need to Rethink What Work Is?
The episode opened with Apple Vision Pro being used to map a house while running Ethernet cable, letting a worker see marked locations through floors and walls. That led to a wider discussion about digital twins, AI-native electricians and plumbers, and how augmented reality and small robots could …
Daron Acemoglu on pro-worker AI and vibes based capital
We’re honored to host Daron Acemoglu, Institute Professor at MIT and 2024 Nobel laureate. Daron has spent a decade arguing that AI should be built to make human expertise more valuable rather than to replace it, most recently in “Building Pro-Worker AI” with David Autor and Simon Johnson, and in a …
The Fall 2026 Workflow for Starting Projects with Coding Agents
GPT-6 Astra and Fable 5.1 are changing how we start new software projects:Less harness engineering, less process, less prescribing. More deliberate steering on the few decisions that compound through the whole project.We walk through the Codex conversation of an actual project that we built: where …
#108 – Sergey Levine: Robotics and Machine Learning
Sergey Levine is a professor at Berkeley and a world-class researcher in deep learning, reinforcement learning, robotics, and computer vision, including the development of algorithms for end-to-end training of neural network policies that combine perception and control, scalable algorithms for inve…
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I have no special talent. I am only passionately curious, and
I listen to podcasts.
‐ Albert "Llamacorn" Einstein
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