AI researchers debate how close we are to recursive self-improvement
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What technical obstacles might prevent recursive self‑improvement from happening soon?
Today I'm chatting with three of my AI researcher friends from whom I learn a lot every time we talk, and who also happen to be at somewhat open-ish uh labs and companies, so you guys can actually um say things on the record. I'm joined by Baron Millish, who is the CT of Zyphra, which is developing open source models. John Schulman, who is the chief scientist at Thinking Machines, previously the co-founder of OpenAI, led the RLHF work that led to ChatGPT, and Charlie. Carly O'Neill, who is head of model training at Base 10. The first question I have, if we're in 2036, it's been 10 years, and we don't have like crazy uh billions of crazy superintelligences that are running around that have like radically transformed the world.
What is the most likely reason? that that doesn't end up being the case. Other than sort of exogenous political shocks, or like there's a war or they banned AI or something. But what is the most likely technical reason? That we don't like twenty thirty six isn't like a crazy alien superintelligence world.
I mean like my reason would just be like it's got to be that sort of Like there's been a classic thing almost like Marvek's paradox, right? Where like we see like, you know, we think of the AI being like, if it can do this, it's going to be amazing, right? Like if it can solve these hard mouth problems, if it can win a chest, blah blah. And then it solves these things, and then it's like not that impactful. Obviously it's somewhat impactful, but like not everything. It's like if somehow that continues and like there's never like the true like spark of generalization that occurs. I think that could lead to like the AI is just being like extremely good at kind of everything that people like put into a benchmark, put into an environment, but like there is still some persistent like sim to reel which is somehow blocking everything.
I think this is kind of unlikely. I think we do actually see this kind of generalization even from RL in practice already. But like if it is just like ridiculously hard to like generalize meta learning plus like we don't solve continual learning and it's just like super hard and impossible. Yeah. Yeah. Like this would be my like default scenario in that case.
Yeah, I agree with that. Humans uh have a lot of ad advantages over models now. And uh each time a new c model comes out, it'll sort of uh it'll catch up in some of these areas. Um but uh like you end up getting bottlenecked by the places where the model is weaker and where it has uh worse judgment or um the models can't check themselves well enough. Yeah. So so there's this Uh cycle that keeps repeating where people think uh where a new model comes out and people are blown away and they're like, this is it, this is the this is AGI, but then uh they use it a bit and and then it starts to feel dumb after a month or so. So that cycle just might keep going and it's hard to predict how many times it's it's gonna repeat.
And uh like right now you don't get explosive growth um in capabilities because uh you still get bottlenecked enough. when you're trying to do research and engineering, that even if the model can write way more code than a person, it doesn't make you like a hundred times more productive. But yeah, so so maybe maybe there are just more of these cycles than we would uh than we would expect.
For me it's like a question of how far off like this global optimum of a learner you could have on a chip is like the transformer plus like RL, basically like the current recipe. So like I think people imagine that even once like once you have a a an agent which is better than all humans at AI research, even if it's like point one percent better than all humans, then the fact that you can run like, you know, hundreds of thousands If not millions of these in parallel, you can run them much faster, like chip's gonna speed up. That's gonna outweigh every other bottleneck and like you're eventually just gonna like hit this very fast takeoff with recursive self-improvement.
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Chapters
8 chapters
1
What technical obstacles might prevent recursive self‑improvement from happening soon?
0:00–13:20
2
How are Chinese AI labs accelerating progress and what drives their breakthroughs?
13:20–25:56
3
What challenges do automated AI researchers face when learning from simulated environments?
25:56–39:55
4
How does the sim‑to‑real gap affect the speed of AI research and deployment?
39:55–52:17
5
How much of recent AI progress can be explained by better data versus compute or architecture?
52:17–1:04:04
6
What are the prospects for AI agents becoming fully autonomous remote workers?
1:04:04–1:16:12
7
When might we see AI systems that outperform top human experts across all computer‑based tasks?
1:16:12–1:28:30
8
What timelines do experts predict for achieving artificial superintelligence?
1:28:30–1:37:00
Speakers
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