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Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. The core thesis: the same scaling dynamics that transformed language models may also unlock robotic intelligence. But only if you resist every commercial pressure pushing you toward specialization. The central challenge isn’t mechanical design. It’s intelligence: how robots learn, generalize, and interact with a physical world that is far harder to simulate than it is to describe. Before launching Physical Intelligence, Karol worked at Google Brain and Stanford University, studying robot learning alongside researchers Sergey Levine and Chelsea Finn, who later became his co-founders.
In our conversation, we explore:
How growing up in a small town in Poland and watching Star Wars sparked Karol’s fascination with robots
The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning
Why robotics has historically lagged behind breakthroughs in language models
The case for building a general “AI brain” for the physical world rather than a single specialized robot
The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel
The return of reinforcement learning and the parallels between human learning and robot training
The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models
Thank you to the partners who make this possible
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Explore the episode
Timestamps
(00:00) Intro
(04:05) Karol’s early fascination with robots
(07:38) How Karol relates to Fei-Fei Li’s biography
(08:52) What inspired Karol to build better robots
(11:19) Philosophical influences
(15:33) Parallels between The Inner Game of Tennis and robotics
(18:21) Karol’s entry point to robotics and PhD program
(25:49) Combining robotics with LLMs: The Taylor Swift demo
(30:48) The 1970s SHRDLU AI experiment
(32:33) Founding Physical Intelligence
(35:13) How Lachy Groom got involved
(39:40) How research shapes what Physical Intelligence builds
(45:22) The importance of real-world data
(49:07) The return of reinforcement learning in robotics
(53:31) The risk of commercializing too early
(55:47) Finding the right partners for the business
(57:13) Open research questions
(1:00:00) NVIDIA’s simulation engines
(1:01:57) The surprising speed of progress
(1:04:16) Reliability in robotics
(1:07:31) Compensating for missing senses
(1:12:28) Book recommendation
Follow Karol Hausman
LinkedIn: https://www.linkedin.com/in/karolhausman
Resources and episode mentions
Books
Worlds I See: https://www.amazon.com/Worlds-I-See-Fei-Fei-Li/dp/1250389895
The Inner Game of Tennis: The Classic Guide to the Mental Side of Peak: https://www.amazon.com/Inner-Game-Tennis-Classic-Performance/dp/0679778314
On the Move: https://www.oliversacks.com/oliver-sacks-books/on-the-move
Why Greatness Cannot Be Planned: The Myth of the Objective: https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective-ebook/dp/B00X57B4JG
People
Fei-Fei Li on X: https://x.com/drfeifei
Baruch Spinoza: https://en.wikipedia.org/wiki/Baruch_Spinoza
Sergey Levine on X: https://x.com/svlevine
Brian Ichter on LinkedIn: https://www.linkedin.com/in/brian-ichter-26875978
Lachy Groom on LinkedIn: https://www.linkedin.com/in/lachy-groom-b218895
Chelsea Finn on LinkedIn: https://www.linkedin.com/in/cbfinn
Lee Sedol: https://en.wikipedia.org/wiki/Lee_Sedol
Other resources
Physical Intelligence: https://www.pi.website
Karol’s post on X about Worlds I See: https://x.com/hausman_k/status/1732087549034889688
Ontology: https://en.wikipedia.org/wiki/Ontology
The History of AI in 7 Experiments: https://www.generalist.com/p/the-history-of-ai
NVIDIA: https://www.nvidia.com
Proprioception: https://en.wikipedia.org/wiki/Proprioception
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