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Co-CEO of Waymo and a founding member of Google's self-driving car project, which he joined in 2009.

From one piece Waymo Co-CEO Dmitri Dolgov: The Demo Is Only 1% Of The Work 2 beliefs, in the piece's order there

• Our readingkorrents.com

  Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.

  Their words

  And the problem of building a good realistic simulator is just as hard as building the agent itself.

  youtube.com

• Our readingkorrents.com

  A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.

  Their words

  So, a deployment of your agent uh in the real world generates data. That data then grounds the simulator and makes it more realistic. The simulator generates harder edge cases for the critic to score and for the agent to learn from.

  youtube.com

Co-founder of NVIDIA and, as of 2026, its chief executive; the company designs the GPUs most large AI models are trained on.

• Our readingkorrents.com

  Nvidia does not run rival chip architectures in parallel because it simulates them and they come out provably worse.

  Their words

  Yeah, we we could do all of those things. Um it's just not better. And we simulate it all. they're in our simulator provably worse and so we wouldn't do it.

  Jensen Huang – Will Nvidia’s moat persist?youtube.com

Founded PSPDFKit in 2011 and ran it for a decade. Came back from a break to work on AI agents — the OpenClaw project, and OpenAI, joined in February 2026. Writes at steipete.me.

• Likedrcmnd.app

  Swift

  Their words

  Swift’s build infra is good enough for most things these days. codex knows how to run iOS apps and how to deal with the Simulator. No special stuff or MCPs needed.

  steipete.me

Professor at Berkeley working on robot learning; co-founded Physical Intelligence, and argues that robots will learn from data rather than be programmed.

From one piece Fully autonomous robots are much closer than you think – Sergey Levine 2 beliefs, in the piece's order there

• Our readingkorrents.com

  Simulation works for a trainee pilot because the pilot knows what the simulator is for; a model trained across domains has no such goal.

  Their words

  but something to remember is that when a pilot is using a simulator to learn to fly an airplane, they're extremely goal- directed. So, their goal in life is not to learn to use a simulator. Their goal in life is to learn to fly the airplane.

  youtube.com

• Our readingkorrents.com

  The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does.

  Their words

  It tells us that the key is not necessarily to do really good simulation. The key is to figure out how to answer counterfactuals.

  youtube.com

Neuroscientist at UC Davis; studies memory and wrote Why We Remember.

• Our readingkorrents.com

  We have not replicated human intelligence until a machine makes the same characteristic mistakes people do — making different mistakes is not the same as being intelligent.

  OpenAI

  Their words

  I don’t think we’ve replicated human intelligence, unless I know that the simulator is making exactly the same kinds of mistakes that people do, because people make characteristic mistakes. They have characteristic biases, they have characteristic heuristics that we use, and those have yet to see evidence that ChatGPT will do that.

  youtube.com