Dr. Emilia Javorsky

Even if that were to be true.

The second piece of this that's really important to think about is we don't live in a world of infinite capital.

If we'd lived in a world of infinite resources and one bucket wasn't coming out of another, then there's a different argument to be made.

But we're seeing that biotech is at a 10-year low in terms of venture funding of new ideas.

And venture funding is really where you see the new breakthrough, exciting, high-risk types of projects that really can move the needle for patients.

We're living in a time where we're reducing our investments in sort of basic science, in science infrastructure, in data collection.

And so the essence here is if we're going to take money away from doing the things we know will unblock progress, then we better be really confident that that is actually the fastest way to save lives.

If you look at the amount of money going into building ASI and the infrastructure associated with that, that's an unprecedented amount of money in terms of investment in a technology.

In 2026 alone, they're looking at $540 billion plus, right?

And if we want to compare and contrast that to, let's say, the National Cancer Institute, which was a pretty good barometer of what are we investing in the public in the basic science and understanding and moving the needle in oncology, that's only $7.2 billion.

So it is a fraction of the amount on an annual spend that we're spending on actually solving the problem of curing cancer as opposed to an ASI spend.

So I think the AI for science promise gets all kind of bundled into one and cancer gets put into that along with physics and along with manufacturing and along with chemistry.

But it's really important to break those out because physics and biology are very different phenomenon.

And physics is a domain where, and math is similarly, where we're seeing this correlation between capabilities and progress in those sciences, where we have basic rules.

Like we know the laws of physics, we know the rules of physics, we know the rules of math.

But for biology, there are no first principles to work with.

There are no actual rules of the road to feed to an AI to learn and to model from and to analyze.

And people say, well, you have physics.

Everything's physics at the end of the day, right?

You have physics, you have everything.