Rohin Shah

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1,071 appearances 1 recordings 1 series first heard Jun 2026 last heard 2 Jun

Rohin Shah’s voice in public audio — every appearance, attributed to the second.

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80,000 Hours Podcast · #245 – Rohin Shah on what it's really like to run AGI safety at Google DeepMind (and where I disagree with 'doomers') · 2 Jun 2026 podcast

particularly high reward there.

But then like this part where you wrote the code, eh, you should have been like, you know, using this particular library you didn't, will give you like somewhat lower reward on that.

And that can like allow for a more effective and sample efficient learning than you would otherwise get.

I don't think our paper does not really get into questions like this.

This is more me speculating about what would happen in practice.

Yeah.

I think probably the biggest category in here comes more from our background assumptions, actually, and how that informs our planning rather than the actual technical approaches.

So like one background belief, which I think we've talked about a little bit already, is we call it the approximate continuity assumption.

This is basically saying that

AI progress is going to be relatively smooth and gradual with respect to inputs like compute and labor, not necessarily with respect to time due to the possibility of an intelligence explosion.

And as a result, our sort of like meta strategy involves essentially

roughly forecasting not maybe not formally forecasting but roughly in our heads you know having some sense of like what things are going to be potential problems over the next some time period call it three months um maybe longer maybe shorter who knows um and

identifying what sorts of considerations might become quite important during that time given the capabilities that we expect to have and making sure that we're prepared for those um and if you know if we're not prepared for that then possibly uh then slowing down or uh pausing development or talking to you know talking to governments trying to do advocacy um

But importantly, we're not really trying to forecast arbitrarily far into the future of all the problems that are going to arise with AI development.

The goal isn't know how to align ASI, else do nothing.

usually I like I think of us as looking at a time horizon of it depends on which particular thing we're doing but often somewhere between three months and five years but like the reason for this is just that like it's

not actually possible to know everything that's going to happen with future AI development.

And it'd be like sheer hubris to think that we had figured out every problem that possibly will arise with superintelligence ahead of time and say we've solved it.

Or even say we have a plan for solving it.

Like we haven't even identified all the problems, I'm sure.

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