Tristan Harris

So maybe just to sort of summarize, COVID had unique advantages because there was one, easy recruitment from the general population because it was a shutdown the whole world.

People would actually want to volunteer for this.

Two, clear, rapid outcomes.

You could test whether something worked in weeks, not years.

Compared to cancer, which requires years of follow-up, harder recruitment, and the disease also is heterogeneous.

You have so many different variations of the disease, whereas COVID is much more similar.

So what makes AlphaFold different?

So AlphaFold, people will remember, is what I think Demis Hassabis got the Nobel Prize for because it accelerated what it's like decades of research that would have taken a single PhD, their whole PhD to get one protein.

And now we got like hundreds of millions of them or something like that.

What distinguishes AI that's accelerating that and protein folding versus the broader cures to cancer?

And before we move on, I think we should explain what protein folding is and what it has to do with medical interventions in general.

Can you just explain protein folding?

So we had the right data sets that we could actually find the patterns.

Whereas with cancer, you have someone whose disease is progressing over a decade and we don't have all the data of what's happening at each interim step for every patient available in some database to look at everything we were doing and changing their health habits, what they were eating differently, what drugs they were taking.

So we don't have that basis, that library in the same way that we did for protein folding.

Amelia, you said something in other interviews.

You talk about how there's a difference between curing cancer readily in mice versus in humans.

What is that?

So something like a cure for cancer, I think you've just shown, is not constrained by intelligence as the core bottleneck.

But it does seem definitely constrained by systems.