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

And so I think the benefits are just not really there.

And so it doesn't really make sense to impose this cost anymore.

Then a couple of other minor points.

One is that especially for misalignment or loss of control, the threat model is tied more around internal deployment rather than the external deployment, because I think it's just easier for a misaligned model to cause problems inside the company where it's getting a bunch of permissions rather than outside the company where it has no access to its own weights, for example.

And so internal deployments, the pre-external deployment evals don't really make that much of a difference to internal deployments.

Unexpectedly capable seems pretty unlikely.

I think we've just seen enough examples of AI development now to say that like, no, in fact, AI development progresses fairly smoothly and continuously.

I do think that in the future, you could definitely see an intelligence explosion.

in which case the progress will go much faster with respect to calendar time.

I think it will still actually be pretty smooth and gradual with respect to inputs like compute and labor.

It's just that in an intelligence explosion, you get a much larger increase in especially labor, but probably also compute.

And that ends up making things go very fast with respect to calendar time.

But there's still this general property that you can, given some amount of compute and labor that you expect to be spending over the next however long, you can have some decent sense of how much progress is going to be made on the capability side.

So you also asked about whether the AI system might become much more evil.

And I think that's one that it could, in fact, change pretty significantly between models.

just because it's a somewhat more contingent property of exactly how you do post-training and small changes to it could have big effects on that.

So there, I think it is more important that to the extent that your safety case depends on specifically the model not being evil in some way,

you actually do, in fact, need to do the pre-deployment evals to check whether that's the case.

And this is, in fact, what we do.

Not exactly this, but we do, in fact, do a lot of pre-deployment evals for safety right now in terms of whether the model has a propensity to do bad things.

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