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Rich Sutton

Things Rich Says on Podcasts

Argues that agents unable to keep learning from experience after deployment are capped from the start, a view described as the big world hypothesis: the world is too complex for any simulation to fully capture, so continual learning matters more than better pretraining. Also known for the bitter lesson, the observation that general methods that lean on computation tend to outperform approaches built on hand-designed features. Professor of computing science at the University of Alberta and chief scientific advisor at the Alberta Machine Intelligence Institute. Co-author of the textbook Reinforcement Learning: An Introduction and co-recipient, with Andrew Barto, of the 2024 ACM A.M. Turing Award for foundational work in reinforcement learning. Appeared on the Training Data podcast.

Where to Find Them

Rich Sutton writes Eye On A.I. . They have also been a guest on Training Data .

Recently: “Rich Sutton and Khurram Javed: Why AI Models Stop Learning, and How to Start It Again” on Training Data (August 2026); “Rich Sutton Edit V5-Norm 01-01” on Eye On A.I. (March 2019).

What They Said

“The world is infinitely complex, and any simulation of it is like microscopic.” — Rich Sutton, Training Data

His answer to the synthetic-data strategy every frontier lab is betting on. Sutton's big world hypothesis says the world is massively more complex than any agent or any simulator — so an agent that cannot keep learning from its own experience is capped from the start.

Training Data · 2026-08-18 Permalink → Listen →
Training Data Around 14:28 into the episode
Sonya Huang

But you can have infinite synthetic worlds. The existing world is finite.

Khurram Javed

…So it could totally learn from its own experience, but it wouldn't be able to. It doesn't matter how much synthetic data you generate. It doesn't matter if you generate synthetic data that captures 50 different universes. It would not allow you to do that task without humans figuring it out first.

Rich Sutton

…minds matter to us. You know, like I talked to you guys about investing today, so I care what you're going on. Your minds, and how could I get synthetic data on such a thing? Really, you can't even get synthetic data on anything. You can't get synthetic data on how the drone is going to interact with its environment, in the physical world, and the friction and wear in the motors of this robot. The world is infinitely complex, and any simulation of it is like microscopic. The big world hypothesis, let's say what it is, is that the world is massively more complex than your mind, than any agents, any agent. And this is obvious because the world contains many other agents. So, because the world is massively complex, there's no way you can do anything like anything that might claim to be optimal or perfect. You're going to be imperfect, and you have to have…

Sonya Huang

Yeah. I'm going to push on this one more time. And sorry, I'm being argumentative for the sake of being argumentative, but I'm trying to understand. My understanding is that the newest cohort of self-driving car companies, many of them were primarily trained in sim, and then they, you know, have to…

Khurram Javed

Yeah. So, I think the important question to ask here is: how many engineers were involved in building that simulation? And are we ready to say that the only problem worth solving are those where we can hire a large team of engineers to first make a simulation? And I'm sure they had to do multiple…

Rich Sutton

it actually drives, again, something unexpected will happen.

Speaker names from our own diarization · position estimated from where the line sits in the episode
“I'm not weird. The field is weird. The field, they need to call it continual learning. It's just learning.” — Rich Sutton, Training Data

He opens the episode with it and returns to it word for word an hour later. People keep telling Sutton his position is radical; he thinks the rest of the field started thinking strangely about a decade ago — before the AI boom, nobody needed the phrase "continual learning" because no other kind existed.

Training Data · 2026-08-18 Permalink → Listen →
Training Data Around 00:00 into the episode
Rich Sutton

People think I have a radical point of view sometimes. They start questions saying how what I'm thinking is so different from everyone else. But I don't see it that way at all. I see it as like I'm thinking the ordinary way. It's just everyone else that's thinking a bit weird. And I mean that, like, you know, it's just the recent times people are thinking weird. Before there was all this AI craziness, you talk about, you wouldn't have to say continual learning because it wouldn't make any sense to talk about learning that wasn't continual. All learning is continual. We always act and we learn. That's just the normal way of thinking. I'm not weird. The field is weird. The field, they need to call it continual learning. It's just learning.

Sonya Huang

We are honored to have the great Rich Sutton with us here today. Rich, you invented reinforcement learning. You wrote the seminal textbook. You're the key students in the field, folks like Dave Silver. You wrote the essay, The Bitter Lesson, that I believe is the Bible of the field. And you have…

Rich Sutton

What else are you going to

Sonya Huang

do?

Speaker names from our own diarization · position estimated from where the line sits in the episode

Collections They Appear In

AI Research & Frontier Labs