Anjney Midha

We've got 40,000 square feet in Menlo Park where we've got AI models that are predicting new... The goal is to try to find a room temperature superconductor.

And so these models predict...

The summer of room temperature superconductors.

I forgot about that.

Yes.

This time we will verify.

If we ever put something out, you will know it's not real.

That's not going to be us.

But the AI system predicts new materials candidates.

Then we have robots that synthesize the new material in the lab and then use X-ray diffraction machines to test whether the material has the properties the AI said it would.

And that's verifiable feedback from reality, from physics.

And then we pipe that data back into the training loop over and over again.

That context feedback is very factually verifiable.

And that's where progress is the fastest today, because that feedback doesn't result in the kind of hallucinations that you often experience with these models on more subjective tasks.

It's also, by the way, why the models are terrible at subjective tasks, like creative writing.

And sometimes it can get quite toxic, to be honest, if you get them down the wrong loop.

I don't know if you've been using it as a therapy bot and so on.

That's great.

No, go ahead.

Yeah, that's a great point.