Sayash Kapoor

Or like the normal technology thesis sort of stops being accurate or helpful in a world where we have like humans in the cloud, let's say.

Yeah.

Yeah, I mean, we've worked on several evals that, for example, Anthropic has used and were saturated with the release of Opus 405.

We were the first ones to say that, look, this is like solved now.

And I think this progress will continue.

I think as long as we can specify things well enough, we'll continue to build AI systems that can solve those tasks.

Where I differ perhaps is whether the natural endpoint of this process is something like, you know, we solve data efficiency.

I'm skeptical about that for a couple of reasons.

First, you know, sample efficiency or data efficiency is not the only bottleneck to getting what we call humans in the cloud earlier.

And the past sort of, if you look at past progress in AI,

We've continued to develop these more general systems, but at any given level of generality, we've been really bad at predicting what the bottlenecks to the next level are.

We've been really bad at knowing when we solve those bottlenecks and what underlying transformative breakthroughs are needed to solve them.

And, you know, like as evidence of that, perhaps we can take the transformer moment.

And before that, we can take all of the skepticism about neural networks that pervaded the research community in AI.

And, you know, it took a matter of like a few years until the community pivoted and now everyone is all in on transformers.

But perhaps that's not the right architectural choice either.

Perhaps we're sort of yet to discover these new architectures that would allow us to make these data efficient AI systems.

And perhaps those will still not be enough to get us to the point where we have the sample efficiency of humans in the cloud.

So that's sort of the broad stroke of things.

I think the AI community in general has been really accurate about near-term predictions, about things that are within the event horizon, so to say, and has been really bad at predicting transformative shifts that sort of change the entire research paradigm.