Are We Misreading the AI Exponential? Julian Schrittwieser on Move 37 & Scaling RL (Anthropic)
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Why do many people misinterpret the AI exponential growth curve?
The talk about AI bubbles seemed very divorced from what was happening in Frontier Labs and what we were seeing. We are not seeing any slowdown of progress. We are seeing this very consistent improvement over many, many years where every, say, three, four months is able to do a task that is twice as long as before, completely on its own. It's very hard for us to intuitively understand these exponential trends. If we manage to make everybody in society 10 times more productive, what kind of abundance can we achieve? What will we be able to unlock in the next five years? I think we can go extremely far.
Welcome to the Met Podcast. I'm Matt Turk from FirstMark. Today, my guest is Julian Schreitwieser, one of the world's most impressive AI researchers. Julian was a core contributor to DeepMind's legendary AlphaGo Zero and MuZero projects, and he is now a key researcher at Anthropic. We covered the exponential trajectory of AI and his predictions for 2026 and 2027, the frontier in reinforcement learning and AI agents, and the science behind AI creativity and the famous Move 37 from AlphaGo. Please enjoy this fantastic conversation with Julian. Thank you. Hey, Julian. Welcome. Hey, Matt. Thanks for having me. A couple of weeks ago, you wrote an incredible blog post that broke the internet entitled Failing to Understand the Exponential Again.
What is it that so many people are missing about the current trajectory of AI?
Yeah, it's funny that you bring up that blog post. I really didn't expect it to blow up that much. I actually had the idea when I was on holiday in Kyrgyzstan a few weeks ago on a very long car ride. And then I started thinking about this and like, all the talk about AI bubbles I've seen on X and this discussion. And it seemed very divorced from what was happening in Frontier Labs and what we were seeing. And that made me start to wonder a bit like, is it that things are moving so fast that people maybe struggle a bit to extrapolate and understand intuitively Oh, you know, maybe it's far away now, but, you know, it's doubling every so many months, which means that once it gets close to us, it's going to move past and become really good very quickly.
And that reminded me a lot in like a different way. But that will happen during early COVID where we had a similar situation where, you know, at the beginning, it's a very few cases. It's like, well, you know, it's never going to happen. It's only a few hundred people who cares. But if you understand the math and if you look at it, it's like, oh, it's going to double every week, two weeks. Clearly, it's going to be a massive scale. But it's very hard for us to intuitively understand these exponential trends because it's just not what we're used to in our normal environment. And so that's what got me thinking, oh, is something similar happening here with AI, right? We are clearly, if you're looking at many benchmarks we have, many evaluations we have,
We are seeing this very consistent improvement over many, many years. every, say, three, four months is able to do a task that is twice as long as before, completely on its own. And so we can extrapolate this, right? And we see that in a year from now, maybe two years from now, the top models are going to be able to work completely on their own for a whole day or more. Combined with this, combined with the fact that there's a huge number of knowledge-based jobs in the economy, knowledge-based tasks, and combined that in the frontier labs, we are not seeing any slowdown of progress. just extrapolating those things together over a very short time, like half a year, one year, already that is enough to know that there is going to be massive economic impact.
That means if you look at current, if you look at OpenAI, if you look at Anthropic, if you look at Google, Those evaluations, those revenue numbers are actually fairly conservative. I think some more thoughts, some things I have seen more recently is that it's maybe actually even more interesting and more complex that while those frontier labs and frontier models are clearly very capable and on an extreme trajectory, there are a lot of other companies that are trying to follow into the same AI sphere
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Chapters
8 chapters
1
Why do many people misinterpret the AI exponential growth curve?
0:00–9:50
2
What are Julian’s predictions for AI agents that can work a full day by 2026‑2027?
9:50–23:23
3
How did the famous Move 37 happen and why does it matter for modern AI?
23:23–30:14
4
Can pre‑training plus reinforcement learning replace the need for RL‑from‑scratch?
30:14–37:57
5
What is the current frontier of reward modeling (human prefs, rubrics, RLVR, process rewards)?
37:57–47:22
6
How are mechanistic interpretability techniques like “Golden Gate Claude” advancing model safety?
47:22–56:53
7
How does Anthropic integrate safety and alignment into its model launch process?
56:53–1:06:27
8
What economic and societal changes could 10× knowledge‑work productivity unlock?
1:06:27–1:09:56
Speakers
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