Sholto Douglas

Yeah, maybe that's one thing I think that's changed since last year.

I remember you asked, do these models really reason?

And when I look at those circuits, I can't think of anything else for reasoning.

Oh, it's so freaking cool.

What are normal people's jobs?

When we discussed something related to this before, was like, yeah, in a normal job, you don't get feedback for an entire week.

It's only when you record your next podcast that you get feedback on your YouTube views.

Don't you ever work a job?

I think, like, the highest thought a bit is I don't think there's anything fundamentally different

about computer use than there is about software engineering than there is about... As long as you can represent everything in tokens and input space, which we can, we know the models can see.

They can draw bounding boxes around things in their images, right?

So that is a solved problem.

We know that they can reason over concepts and difficult concepts too.

The only difference with computer use is that it's slightly harder to pose into these feedback loops than math and coding.

And so...

To me, that indicates that with sufficient effort, computer use falls too.

And I also think that it's underappreciated just...

like, how far from a perfect machine these labs are.

Like, it's not like you have a thousand people, like, you know, optimizing the hell out of computer use and that, like, you know, they've been trying as hard as they possibly can.

Like, everything of these labs, every single part of the model generation pipeline is best effort pulled together under incredible time pressure, incredible constraints as these companies are rapidly growing, trying desperately to pull and, like, upskill enough people to do the things that they need to do.