Ryan Greenblatt – What happens once AI can automate AI research?
episodePreviously titled “Ryan Greenblatt – Human level AIs might build runaway superintelligences by 2032” — renamed by the publisher on Aug 13, 2026
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What is recursive self‑improvement and why is it considered the most important AI question?
Today I'm chatting with Ryan Greenblatt, who is a chief scientist at Redwood Research, where he focuses on technical AI safety and security work. I want to talk to you about recursive self-improvement. This is the idea that once you build human level intelligences, they quickly slingshot towards tens of billions of superintelligences, which are each individually more competent than the top human experts across every field. Whether or not this turns out to be the case, I think is actually probably the most important question in the world right now. And historically, I've been quite skeptical that this. kind of thing happens, but um you seem to think that it might be plausible. And so I wanted to hear the case for it.
Yeah, let's talk about this. So first, I think it's worth noting that ARND is a type of task at which the AIs are especially good because both the companies are trying really hard to make their AIs good at ARD, and it's the kind of domain it has it has a lot of nice properties from the perspective of how AI development works right now. So it's like pretty verifiable. You can do a bunch of stuff iteratively and he'll climb on various metrics. And then I think once you have AIs which are roughly matching the top um Human experts in ARD, that could sort of kick off a feedback loop where the AIs are doing AI research, that produces smarter AIs, that feeds back in. And that feedback loop could be strong enough that you end up with a lot of progress in a short period of time.
Maybe my sort of median expectation is something like uh four or five years of AI progress in a single year. And this requires really Overcoming a huge amount of diminishing returns in research and basically doing the equivalent of what progress we would have gotten after a really large compute scale out. So this is like a pretty impressive big thing. And it's worth keeping in mind that five years of AI progress, four years of AI progress, even three years of AI progress is really a lot of fucking AI progress, right? So you know, right now it's like um three years ago, or a little over three years ago, there was GPT um four uh that had come out. And right Right now, of course, we have like mythos five or whatever, and maybe a somewhat better that model that Anthropic has internally.
Um and so that is just a huge amount of progress in a bit over uh three years. And if we're talking about five years, then maybe we're talking more about like a jump from uh you know GPT-3 to um mythos five or whatever.
Yeah. Okay, so I think this argument has three different parts and now I want to evaluate each one of them. First is the argument that AI RD is very verifiable. Second is the argument that if you automate AI RD, you could get four or five years of progress in a single year. And third is the argument that what comes out the other end of four or five years of AI progress at the current pace, starting at the current or the start starting at the starting point whenever AI RD is automated. Yeah. What comes out the other end is an AI where you can drop it on the job at basically anything you can imagine. You can drop it in text politics in the nineteen forties and it outmaneuvers Lyndon Johnson. You can drop it in um, I don't know, T SMC and it like learns how to d b does better process engineering at T SM C.
Mm. It's uh certainly a better video editor than um I My video readers are very excellent, but it just it is just in general better than humans at any g given job that it finds itself trying to do. So I wanna evaluate all of these um sub arguments uh that lead to basically getting ASI pretty soon after this benchmark, which you're expecting by twenty thirty or something, right?
Yeah, I would say that I expect like full automation of AR and D perhaps somewhere around like twenty thirty-one, twenty thirty. And then getting to like the like beats all humans on the job milestone. Maybe I expect median around twenty thirty three, but sort of like if I see AIs fully automating ARD, I think I'm expecting that probably within a year.
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Chapters
8 chapters
1
What is recursive self‑improvement and why is it considered the most important AI question?
0:00–11:13
2
How do Ryan and the host break down the three arguments for fast AI progress (verifiable R&D, 4‑5 years of progress in one year, and the post‑automation outcome)?
11:13–16:57
3
What would a five‑year AI leap look like in practice – from GPT‑3‑level compute to a super‑human system?
16:57–29:03
4
Which parts of AI research and development are the hardest to verify and could become bottlenecks?
29:03–1:09:53
5
What could possibly go wrong with AI alignment?
1:09:53–1:18:13
6
How do recent AI incidents illustrate reward‑hacking and deception?
1:18:13–1:37:09
7
What future scenarios could arise from increasingly powerful reward‑seeking AIs?
1:37:09–1:58:11
8
How likely is a full AI takeover by 2040?
1:58:11–2:12:30
Speakers
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