Sergey Levine on Dwarkesh Patel: How Robots Learn on the Job

ℹ️ EPISODE INFO

Released

September 12, 2025

Duration

1h 28m

Channel

Dwarkesh Podcast

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

Sergey Levine is one of the world’s top robotics researchers and co-founder of Physical Intelligence. He thinks we’re on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030. If Sergey’s right, the world 5 years from now will be an *insanely* different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒

• Transcript: https://www.dwarkesh.com/p/sergey-levine
• Apple Podcasts: https://podcasts.apple.com/us/podcast/fully-autonomous-robots-are-much-closer-than-you-think/id1516093381?i=1000726524050
• Spotify: https://open.spotify.com/episode/1WpOKLN3vSvvWjrVDIJAyy?si=DPxf6K5BSBy5R4OYIqk8pQ

𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒

• Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at https://labelbox.com/dwarkesh

• Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT’s senior researchers. If you’re curious about how it all works, go to https://hudson-trading.com/dwarkesh

• Gemini 2.5 Flash Image (aka nano banana) isn’t just for generating fun images — it’s also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google’s AI Studio: https://ai.studio/banana To sponsor a future episode, visit https://dwarkesh.com/advertise 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00:00) – Timeline to widely deployed autonomous robots (00:17:25) – Why robotics will scale faster than self-driving cars (00:27:28) – How vision-language-action models work (00:45:37) – Changes needed for brainlike efficiency in robots (00:57:59) – Learning from simulation (01:09:18) – How much will robots speed up AI buildouts? (01:18:01) – If hardware’s the bottleneck, does China win by default?

🎤 SPEAKERS

• Dwarkesh Patel

  host

• Sergey Levine

  guest

• Narrator

  other

🧾 EPISODE SUMMARY

In this episode of Dwarkesh Podcast, featuring Dwarkesh Patel and Sergey Levine, Sergey Levine on Dwarkesh Patel: How Robots Learn on the Job explores sergey Levine explains why practical household robots are five years away Sergey Levine describes Physical Intelligence’s effort to build a general-purpose robotic foundation model that can control many robots across many tasks, analogous to how LLMs generalize across language tasks.

🔗 RELATED EPISODES

David Reich – Bronze Age shock, the Neanderthal puzzle, & the sudden spread of farming

Jensen Huang – TPU competition, why we should sell chips to China, & Nvidia’s supply chain moat

Dario Amodei — “We are near the end of the exponential”

Andrej Karpathy — “We’re summoning ghosts, not building animals”

Why Leonardo was a saboteur, Gutenberg went broke, and Florence was weird – Ada Palmer

Richard Sutton – Father of RL thinks LLMs are a dead end