AI Research & Frontier Labs · August 2026
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.
Transcript
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.
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 just been one of the greats in propelling the field forward. So thank you for taking the time to join us today. Rich is joined by Kuram Javed, his co-founder and former student from the University of Alberta. The two of you have set off to found Oak Lab. I'm very excited to talk to you about that today. So for today's session, we're going to start talking about the Bitter Lesson, the state of the world as we know it today, whether LLMs will get us there or not. And then we're going to transition to start talking about your research agenda and your plan for Oak. Rich, maybe take us back. I was going to start with the Bitter Lesson, but I actually want to start earlier than that. Decades ago, you decided to dedicate your career to reinforcement learning, to deep reinforcement learning in particular, and you established the University of Alberta as a bastion of that back when I think the field was very much in its infancy. What gave you the conviction to do that?
What else are you going to
do?
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