Scott Alexander

And now, like, all the capital infrastructure was growing really fast compared to, like, the human population size.

I think I'm imagining something maybe similar happening with algorithm progress.

And it's not that, like, again, with population, population still matters a ton today.

Like...

In some sense, progress is bottlenecked on having larger populations and so forth.

But it's just that the population growth rate is just inherently kind of slow.

And the growth rate of capital is much faster.

And so it just comes to be a bigger part of the story.

I'm just thinking, like... So we are actually imagining online learning happening.

Oh, really?

Yeah, but, like, not so much real-world as in, like... Like, the thing is that, like, if you're trying to train your AIs to do really good AI R&D, then, like...

Well, the AI R&D is happening on your servers.

And so, like, you can just, like, kind of have this loop of, like, you have all these AI agents autonomously doing AI R&D, doing all these experiments, et cetera, and then they're, like, online learning to get better at doing AI R&D based on how those experiments go.

But then they autonomously build new benchmarks.

I think what you're saying is maybe this whole process just goes off the rails due to lack of contact with ground truth outside in the actual world, like outside the data centers.

Um, maybe again, part of my, part of my guess here is that like a lot of the ground truth that you want to be in contact with is stuff that's happening on the data centers.

Things like how fast are you improving on all these metrics?

And like, you know, you have these vague ideas for like new architectures, but you're struggling to get them working.

How fast can you get them working?

Like all that, but then, and then separately, insofar as there is like a bottleneck of like talking to people outside and stuff, um,