Kevin Weil

And then over time, as those mature, they all kind of get merged back into single models.

Yeah, it's a good question.

It's one of the most interesting things about working here is you kind of have a sense of what's coming.

And, you know, I'm not in the research team, so I'm getting this, you know, as I collaborate and work with the researchers, I would say like,

you know, on the product side, we have a decent sense of what's coming in the next, say, three months, maybe a hazy sense over the next six months.

And beyond that, it's harder to say.

You know, you have a certain set of capabilities, you know, where like,

You know a bit, but you see things coming through the haze a little bit.

And sometimes capabilities are, you know, it's research, right?

So it's not like you just, you have the formula and you just turn the crank.

We're uncovering new things and it's unpredictable.

So sometimes things take longer than expected.

Other times you see these capabilities that you didn't expect at all that are kind of emergent.

And all of a sudden something just works.

Can you give an example of one that just worked that you weren't expecting?

Well, I mean, deep research is an interesting example where for a while there were a handful of researchers thinking about the, it was like, okay, we could probably make the model able to do this like iterative kind of research where, you know, with deep research, you give the model a arbitrarily complex query to go research something that would probably take you a week.

And it will go off and it'll go off and do like a hundred searches and

but not all at once.

It'll do three or four or five, and then it'll reason about the results that it gets back, try and understand how they pertain to what you asked and what gaps are still there.

And then it'll go off and do some more searches and maybe think again.