Eric Topol

But if you can do it, the question here is, you know, right today we talk about, oh, let's make an organoid.

so we can figure out how to treat this person's cancer or understand this person's rare disease or whatever.

And instead of having to wait weeks for this culture and all the expense and whatnot, you could just do it in a computer and silica.

And you have this virtual twin of a person's, you know, cells and their tissue and whatnot.

So the opportunity here is, I don't know if people, you know, get this, is just extraordinary and quick and cheap, you know, if you can get there.

I guess it's such a bold initiative idea.

Who will pay for this, do you think?

You know, I think it has the looks of like the Human Genome Project, which at the time, as you know, when it was originally launched, people thought, oh, this is impossible.

And look what happened.

It got done.

And now, you know, the sequence of genome is just a, you know, commodity, very relatively very inexpensive compared to what it used to be.

Yeah, I think another thing that, of course, is happening concurrently to add the, I think, likelihood that you'll be successful is we've never seen the foundation models coming out in life science as they have in recent weeks, months, never.

I mean, I have a paper in science this tomorrow coming out of summarizing the progress about not just RNA, DNA,

I mean, the whole idea, AlphaFold3, but now, Boltz and so many others, it's just amazing how fast the torrent of new foundation models.

So, Cheryl, what do you think accounts for this?

This is unprecedented in life science to see foundation models coming out of this clip on evolution, on, I mean, you name it, design of every different

molecule of life or, of course, in cells included in that.

What do you think is going on here?

Yeah, I mean, it's pretty, and we're not even talking about just sequence because we've also got imaging, which has gone to a revolution, be able to image subcellular without having to use any types of stains or things that would disrupt cells.

That's another part of kind of the deep learning era that came along.