Kevin Weil

And, you know, within research, there's kind of a, there's a spectrum, right?

There are, there are parts of our research team that are, that are just like deep research.

It's almost academic in nature because they're trying to just like, they're, they're looking for new breakthroughs.

They're trying to, to find things that nobody has ever, you know, figure out things that nobody's ever figured out before.

And those kinds of things, you don't want to be product-driven at all because you want to give a lot of room for exploration and fundamental breakthroughs.

And then there's kind of the other end of research,

It's more on the post-training side where you really are trying to teach the models to do specific things very well.

And those teams tend to be much more like partnered with product and engineering teams with a common goal.

And then it's kind of a spectrum in between.

I think the right way for us to be is not, we certainly don't want to be entirely product-led.

That's not the magic of this place.

It's not maybe entirely research-led either because it's good to know feedback about what problems you can solve for people and how we can make the biggest impact in the world.

It's really sort of a combination of both with research really at the core though.

And I've loved it.

It's the most fun in the world and moves super fast.

Computers can do things that they couldn't do two months ago and we're constantly in that state.

Yeah, sort of.

I think in some ways for understanding where the model is good and where it's not.

If you think of the model as sort of an intelligence of some sort, intelligence is so multifaceted.

People are smart in a million different ways, and one smart person is better than another in certain areas and worse than another.