SPEAKER_04

And so here's the difference is that there's data, there's evidence, there's conclusions and proof.

And that's an uphill climb.

But proof, the next one up is meaning.

My lab has been largely responsible, at least partly responsible, for the data deluge that's out there in the world, both in how to do tissue biopsy analysis, how to do single cell analysis, etc.

And, you know, data felt good for a while.

It was like this, you know...

This feedback loop of, oh, wow, I can get all this data.

And then suddenly you look at it and you go, well, what the fuck does it mean?

And so humanity has this habit of backing itself into a corner and then suddenly finding this eureka moment that gets it out.

And so our eureka moment about two years ago was artificial intelligence.

Where suddenly I had the ability – so normally I would collect all this data and go, okay, well, it seems myelid suppressor cells are important here and T regulatory cells are important here.

Okay, I get on the phone or I send an email to whoever the local expert is either on Stanford campus or around the world and try to get some information from them.

But then now you're dealing with hundreds of cell types, each individually of which have thousands of variations themselves.

And each subtle variation means something.

And there's no expert for any of that.

But AI can be, at least in part, that expert.

So suddenly I have 22 million papers published today.

In all the fields of science, several tens of millions just in immunology alone, an AI can be the sleuth for me, can be both the angel and the devil on my shoulder that can make sense of things in ways that I never would have been able to before.

Especially with agentic AI.

So we, for instance, in my lab have developed an agentic AI that is basically an immunologist scientist in a box.