Sayash Kapoor

Hey, Daniel.

That's exactly right.

I think the main reason for that is basically this disagreement boils down to whether the bottlenecks to this intelligence explosion, the bottlenecks to automating R&D are all computational.

or whether they rely on real-world bottlenecks, that'll be really hard to automate away.

So I guess this is one place where we disagree.

I think that in a lot of domains, making these advances won't be as easy as it had been in coding.

And to really get to sort of artificial superintelligence, you need to cover all of these different domains.

You need sample efficiency across the board.

which is much easier to do in a field like programming where you have these simulators, these virtual environments, but much harder to do in the real world.

And some evidence bears this out.

Adoption of AI systems has been indeed far slower in other domains as opposed to coding.

I mean, look, I mean, to be honest, I think these are just two independent, self-consistent worldviews about the future of AI.

And the reason that Daniel and I have had such productive conversations is that we are basically trying to figure out where these worldviews differ.

Now, speaking of, I think like Daniel's actions and Daniel's predictions are entirely self-consistent with the worldview that we'll get to AI systems at this point.

Unfortunately, in order to get evidence we're going one way or the other,

we need to actually carry out lots of evaluations.

We need evaluations to be of a much higher standard than we have today.

So to give you one example of a bottleneck, the other day I was talking to a lawyer friend of mine, and he uses these tools, he's very bullish about them,

But what has turned out to be the case is as he started using these tools for bigger and bigger tasks, the rate of hallucinations, the rate of unreliable outputs has sort of remained the same, right?

It's not because the AI systems haven't gotten better.