“AI has been very good at solving increasingly hard problems. But it's not currently, as far as I can tell, and I'm not a mathematician, so you can take it for what it's worth, I don't see it as defining what is interesting, what are the big questions. Those problems were all defined by human beings… I think the best way to think of it is it could be an incredibly powerful tool for humans to use to solve complicated problems.”

…the FDA will say, okay, you can perform it, you can register a clinical trial, but you still have to fund the trial. Somebody has to fund the trial. Somebody has to find the patients. You know, so it's a big process. And, you know, big pharmaceuticals companies are used to doing that sort of thing.

In other fields like math and computer science that are more closed, we see that these models are drastically changing the frontier and the state of the art. Do you expect a similar down the road in molecular biology?

I know in those fields, because the ability to propose a paper, the cost has gone down because of AI. I don't know if you or your peers have noticed in biology or similar fields, is there an influx of papers, maybe AI-assisted in either just the pros or maybe the experimentation?

I don't know because if it happens, it would happen a lot in computational biology and areas like that. Certainly, it's made coding a lot easier. So, you know, that's accelerating science. And we talked about protein prediction. There's now a new program released by DeepMind, which tries to predict…

You got a PhD in physics, and then at some point you did a hard pivot into biology. And I think a lot of people, whether because of sunk cost or some other reason, they might not have the confidence to pivot so hard. What gave you the confidence to switch?