#245 – Rohin Shah on what it's really like to run AGI safety at Google DeepMind (and where I disagree with 'doomers')
episodePreviously titled “What it's really like to run AGI safety at Google DeepMind (and where I disagree with 'doomers') | Rohin Shah” — renamed by the publisher on Aug 4, 2026
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Who is Rohin Shah and what is his role at Google DeepMind?
MARK MANDELMANN- Today, I'm speaking with Rohin Shah, who is head of AGI alignment and safety at Google DeepMind. And so I suppose, Rohin, you've ended up, for better or worse, hopefully for better, being one of the more influential, dare I even say powerful people, to come out of the AGI alignment and safety ecosystem and school of thought. And I guess you were generous enough to be super opinionated with me when you came on the show two years ago. And I think judging by the notes that you've sent over this week, you're ready to be opinionated again. Thanks so much for coming back on the show, Rohin.
Thanks a lot, Rob. And that's a very generous intro. And yeah, in the interest of being very opinionated, I do want to emphasize that these opinions are mine alone. They're not meant to represent the opinions of Google or Google DeepMind.
That's how we like it. If you were representing Google DeepMind, it might sound more like a press release. So you were really very early in the scheme of things to whole misalignment, AI, AGI security issues. I suppose you got involved in 2017. So the first few percent, I suppose, are people who, I guess, started working on this professionally. But despite that, you think that probably we're not going to get catastrophic misalignment, that our chances are really pretty good, and that probably prosaic, like ordinary alignment techniques, the kinds of things that Google DeepMind and other AI companies are doing, will probably succeed at preventing at least catastrophic misalignment. Why do you think our chances are so good?
There's a few different disjunctive reasons. I don't feel like there's one particular thing. Probably the highest level bit is that I don't feel like there is any particularly compelling argument that this is the thing that happens by default. I think there's a lot of arguments that are suggestive that maybe it could happen, such that you should find it plausible. I think that's sufficient to justify a significant amount of effort into averting it, which is why I work in the area that I do work in. But none of them really rise to the level of like, oh, yeah, now I'm expecting this to happen by default. I think they're like every argument that I've seen, they're like pretty significant holes one could poke if you try to take them as arguments for this is what happens likely as opposed to this is a plausible thing that could happen.
Yeah. I mean, people have tried to put forward arguments for why this is likely or inevitable. There's obviously the Yudkowsky-style argument, which I guess is focused on misgeneralization and adversarial examples. I guess, yeah, there's the Ajay Khotra and Joe Carlsmith take, which I think I guess Carlsmith describes best in Is Power Seeking AI an Existential Risk?, which is more focused on, I guess, accidentally teaching AIs to deceive us by having, I guess, an unfortunately inaccurate feedback, I suppose. then I guess empirically people point to the fact that models lie and scheme a bunch now. They do a whole bunch of reward hacking as a result of reinforcement learning, and they expect that to perhaps just get worse over time because we don't have sufficient mitigations.
Do you basically just find none of those or any other similar arguments that people have put forward to be sufficiently persuasive to think that it's likely? Yeah, I think that's right.
So if you take the... Kotra and Carl Smith arguments of like you know we'll well they have like a variety of arguments but I think in fact one of the common ones which you pointed to is like you know we might accidentally train them to be deceptive totally true I agree that is pretty likely something that at least could happen pretty easily and like maybe it's even likely but you know, we're not going to do reinforcement learning over the course of one year trajectories. We're going to do, maybe we're going to do reinforcement learning over like a week or a month at most. So like, it seems very plausible that like what the, like, I think the default prediction you should have for that is like what you train, what the AI system learns to do is like,
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Chapters
8 chapters
1
Who is Rohin Shah and what is his role at Google DeepMind?
0:00–7:26
2
Why does Rohin believe catastrophic misalignment is unlikely?
7:26–20:14
3
What are the arguments against AI systems being deceptive?
20:14–1:06:33
4
How does Rohin view the effectiveness of current alignment techniques?
1:06:33–1:24:53
5
What are the challenges in grading AI safety assessments?
1:24:53–1:26:16
6
How does transparency impact AI safety practices?
1:26:16–1:27:33
7
What is the significance of model cards in AI governance?
1:27:33–1:28:29
8
What roles are currently in demand at Google DeepMind?
1:28:29–2:48:23
Speakers
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