Richard Sutton is the father of reinforcement learning, winner of the 2024 Turing Award, and author of The Bitter Lesson. And he thinks LLMs are a dead end.

After interviewing him, my steel man of Richard’s position is this: LLMs aren’t capable of learning on-the-job, so no matter how much we scale, we’ll need some new architecture to enable continual learning.

And once we have it, we won’t need a special training phase — the agent will just learn on-the-fly, like all humans, and indeed, like all animals.

This new paradigm will render our current approach with LLMs obsolete.

In our interview, I did my best to represent the view that LLMs might function as the foundation on which experiential learning can happen… Some sparks flew.

A big thanks to the Alberta Machine Intelligence Institute for inviting me up to Edmonton and for letting me use their studio and equipment.

Enjoy!

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Sponsors

* Labelbox makes it possible to train AI agents in hyperrealistic RL environments. With an experienced team of applied researchers and a massive network of subject-matter experts, Labelbox ensures your training reflects important, real-world nuance. Turn your demo projects into working systems at labelbox.com/dwarkesh

* Gemini Deep Research is designed for thorough exploration of hard topics. For this episode, it helped me trace reinforcement learning from early policy gradients up to current-day methods, combining clear explanations with curated examples. Try it out yourself at gemini.google.com

* Hudson River Trading doesn’t silo their teams. Instead, HRT researchers openly trade ideas and share strategy code in a mono-repo. This means you’re able to learn at incredible speed and your contributions have impact across the entire firm. Find open roles at hudsonrivertrading.com/dwarkesh

Timestamps

(00:00:00) – Are LLMs a dead end?

(00:13:04) – Do humans do imitation learning?

(00:23:10) – The Era of Experience

(00:33:39) – Current architectures generalize poorly out of distribution

(00:41:29) – Surprises in the AI field

(00:46:41) – Will The Bitter Lesson still apply post AGI?

(00:53:48) – Succession to AIs

Sergey Levine, one of the world’s top robotics researchers and co-founder of Physical Intelligence, 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.

Watch on YouTube; listen on Apple Podcasts or Spotify.

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 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 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: ai.studio/banana

To sponsor a future episode, visit 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?

In this lecture, military historian Sarah Paine explains how Britain used sea control, peripheral campaigns, and alliances to defeat Nazi Germany during WWII. She then applies this framework to today, arguing that Russia and China are similarly constrained by their geography, making them vulnerable in any conflict with maritime powers (like the U.S. and its allies).

Watch on YouTube; listen on Apple Podcasts or Spotify.

Sponsors

* Labelbox partners with researchers to scope, generate, and deliver the exact data frontier models need, no matter the domain. Whether that’s multi-turn audio, SOTA robotics data, advanced STEM problem sets, or even novel RL environments, Labelbox delivers high-quality data, fast. Learn more at labelbox.com/dwarkesh

* Warp is the best interface I’ve found for coding with agents. It makes building custom tools easy: Warp’s UI helps you understand agent behavior and its in-line text editor is great for making tweaks. You can try Warp for free, or, for a limited time, use code DWARKESH to get Warp’s Pro Plan for only $5. Go to warp.dev/dwarkesh

To sponsor a future episode, visit dwarkesh.com/advertise.

Timestamps

00:00:00 – How WW1 shaped WW2

00:15:10 – Hitler and Churchill’s battle to command the Atlantic

00:30:10 – Peripheral theaters leading up to Normandy

00:37:13 – The Eastern front

00:48:04 – Russia’s & China’s geographic prisons

01:00:28 – Hitler’s blunders & America’s industrial might

01:15:03 – Bismarck’s limited wars vs Hitler’s total war