Kevin Weil

a day, two days, a week, two months at a time to answer even harder problems.

Because just like you and me, if you could give me problems that I couldn't solve in 20 minutes, but I could given two hours.

Same is true of the models.

The more time they think, the more impressive problems they can solve.

So you start with things like math and physics.

But then, you know, some of the biggest ways, the most important ways that accelerating science is going to feed back into all of our lives in a positive way is through stuff that we can feel in real life.

It's like, you know, one of our relatives survives cancer because we've made advances in medicine.

You have new devices and materials because we've been able to make advances in material science.

And those things require labs.

You can't do those just in silico.

Although I think the...

importance of simulation is going to go up pretty meaningfully because you will be able to apply huge amounts of compute to these problems.

But then you're still going to need experimental validation.

You're going to need to try things in the real world.

I think it's going to be a while before we have a model that can, you know, first principles go from like a quark all the way through a model of a cell all the way through human biology.

Like experiment matters.

So, you know, you start to think about how you do that at scale.

There's lots of opportunity to partner with existing labs, and we will, but I think there are really interesting, you know, the science of the future will definitely involve robotic labs and reinforcement learning loops that go through the real world where the model is thinking, maybe running a simulation, thinking some more, refining the experiment that it can run using the best possible parameters, and then sending that to a bunch of robotic labs, which, by the way, you can scale horizontally,

having the experiments run in real life, the results come back to the model, the model thinks, runs more simulations, thinks, and you have this, you have like multiple loops.

You have tight loops with the model thinking and the simulation.