Jacob Kimmel

So it seems to be an incredibly consistent feature of trying to make new medicines.

Okay, I'm going to slightly dodge your question first to maybe analyze something really interesting that you highlighted, which is you have these two phenomena, again, ML scaling and then scaling in terms of the cost for new drug discovery.

Why is it that the patterns of investment have been so different?

I think there are probably two key features that might explain this difference.

One is that the returns to the scaled output in the case of ML actually are expected to increase super exponentially.

If you actually reach AGI, it's going to be a much larger value than just even a few logs back on the performance curve that people are following.

Whereas in the life sciences thus far, each of those products we're generating further and further out on the e-room slot curve as time moves forward haven't necessarily scaled in their potential revenue and their potential returns quite so much.

And so you're seeing these increased costs not counterbalanced by increased ROI.

The other piece of it that you highlighted is that unlike building a general model where potentially by making larger investments, you can be able to solve a broader addressable market, moving from solving very narrow tasks to eventually replacing large fractions of white-collar intelligence.

Yeah.

In biotech, when you're traditionally able to develop a medicine in a given indication, I was able to treat disease X, it doesn't necessarily engender you to be able to then treat disease Y more readily.

Typically, where these firms, biotech firms in general, have been able to develop unique expertise is on making molecules to target particular genes.

So I'm really good at making a molecule that intervenes on gene X or gene Y. And it turns out that the ability to make those molecules more rapidly is

isn't actually reducing the largest risk in the process.

And so this means that the ability to go from one or two outputs one year to then going to four the next is much more limited.

And so this brings us then to the question of what would the general model be in biology?

And I think it kind of reduces down to how do you actually imbue those two properties that create the ML scaling law curve of hope and bring those over to biology so that you can take the Eroom's law curve and potentially give it the same sort of potential beneficial spin.

So I think there are a few different versions of this you could imagine, but I'll address the first point.

How do you get to a place where you're actually able to generate more revenue per medicine so that potentially the outputs you're generating are more valuable, even if each output might cost a bit more?

Traditionally, when we've developed medicines, we go after fairly narrow indications, meaning diseases that fairly small numbers of people get.