Grant Harvey

Um, I guess like switching gears just a tiny bit here.

There's been buzz about the new version of Codex and, you know, a lot of people have been talking about how it can run for seven hours straight in some instances.

How do you think about like scaling, you know, from a five hour ICPC problem to these more longer horizon agent tasks, like in terms of what you're going to be doing next, you know?

No, we both clearly sparked something.

Yeah, on the outside, you know, I'm just like, that's cool, but I don't understand it.

I actually, my question is kind of related to what Corey's saying.

So like when it comes to tool use, I've always wondered like how exactly, and you know, you can just kind of like, you can talk about this at whatever level of abstraction surface level you want, but how is it that the AI is actually able to use tools?

Is it just like getting the responses of the feedback, like streamed back to it?

Like that part still kind of blows my mind how that works.

Overall, with some of these tool uses, yeah, it tries out the tool.

Right.

And so if it was doing that in a science on like a science model, like you mentioned, like, uh, like if it's doing like a fit, like physics simulation or something, it would still be getting the data back and then it would still be kind of working the same way.

So what I didn't hear there was whether or not you're over or under on a college degree.

And I say that jokingly, but I do actually want to know, based on where we're at now, do we need to rethink education to a certain degree for human programmers now that AI can excel at these types of contests or all the things where AI is continually improving?

Is it still worth it to go to school to learn programming?

I mean, this could be your personal opinion.

Is there a, like, a competitive science competition?

And I was thinking, well, the Nobel Prize.

Like, next goal unlocked is AI wins a Nobel Prize, right?

I could see it.