Eric Topol

One thing I thought was fascinating in the paper in Cell, you wrote, for instance, the short read archive of biological sequence data holds over 14 petabytes of information, which is 1,000 times larger than the data set used to train chat GPT.

I mean, that's a lot of tokens.

That's a lot of stuff.

Compute resources.

It's almost like you're going to need a deep-seek algorithm

type of way to get this, I mean, not that deep-seek is as it's claimed to be so much more economical, but there's a data challenge here in terms of working with that massive amount that is different than the language of human language, that is, our language, wouldn't you say?

Yeah.

Well, there's two, I think, phenomenal figures in your cell paper.

The first one that takes across the capabilities of the virtual cell and the second that compares the virtual cell to the real or the physical cell.

And we'll link that with this in the transcript.

And the other thing we'll link is that there's a nice Atlantic article.

A virtual cell is a

is a holy grail of science.

It's getting closer.

That may not be quite close as like next week or year, but it's getting close.

And that's good for people who are not well grounded in this because it's much more taken out of the technical realm.

This is really exciting, what you're onto here.

What's interesting, Steve, since I've known you for so many years, earlier in your career, you really worked on omics, that is DNA and RNA.

In recent times, you've made this switch to cells.

Is that just because you're trying to anticipate the field?