Dario Amodei

Yeah, so I think for a little background, before I worked in AI, before I worked in tech at all, I was a biologist.

I first worked on computational neuroscience, and then I worked at Stanford Medical School on finding protein biomarkers for cancer, on trying to improve diagnostics and curing cancer.

And one of the observations that I most had when I worked in that field was the incredible complexity of it.

You know, each protein has a level localized within each cell.

It's not enough to measure the level within the body, the level within each cell.

You have to measure the level in a particular part of the cell and the other proteins that it's interacting with or complexing with.

And I had the sense of, man, this is too complicated for humans.

We're making progress on all these problems of biology and medicine, but we're making progress relatively slowly.

And so what drew me to the field of AI was this idea that, you know, could we make progress more quickly?

We've been trying to apply AI and machine learning techniques to biology for a long time.

Typically, they've been for analyzing data.

But as AI gets really powerful, I think we should actually think about it differently.

We should think of AI as doing

you know, doing the job of the biologist, right?

Doing the whole thing from end to end.

And part of that involves proposing experiments, coming up with new techniques.

I have this section where I say, look, a lot of the progress in biology has been driven by this relatively small number of insights that lets us measure or get at or intervene in the stuff that's really small.

You look at a lot of these techniques, they're invented very much as a matter of serendipity, right?

CRISPR, which is one of these gene editing technologies, was invented because someone went to a lecture on the bacterial immune system and connected that to the work they were doing on gene therapy.

And that connection could have been made 30 years ago.