Sam Fazeli

You know, the chemical space is very large.

The combinatorial world is very large.

How much new chemistry do you feel you're identifying?

Just quantify that for us, please.

So Alex, in the broader AI software world, there's been this recent question arising as to how, which of the software models out there, the legacy type software models,

software as a service, or even some of the more advanced point of sale software companies, a whole variety, which one can survive the onslaught of open source models and even just a whole lot of these very large LLM companies that we all know, OpenAI, Anthropic

that they could come and just eventually displace you, partly by providing, replicating what you do, or partly by people using their capabilities and developing their own.

What is the risk to, I don't want to say in silico specifically, but companies that use AI for developing and discovering drugs?

When do you expect it to become a commodity in five years, 10 years, or whatever time frame?

Right.

So the proof of value in Insilico and other companies potentially, like you said, I don't know if there are others, but I'm sure you'll tell me, is that the value proposition is, number one, we go after, or Insilico goes after novel targets themselves that folks haven't targeted before, at least publicly announced.

And we have the ability to test the chemical space very broadly and cleverly and get it.

So in the end, that's what biotechs do.

A lot of them start with some science and technology or chemistry or drug design that they had worked on or discovered within a lab, and then they brought it out.

And here you're recreating that lab.

So have I got that right?

Have I summarized that right?

Correct.

Correct.

Yeah, we all have to live with the reality of what's out there, but at the same time, try and innovate to a point where, you know, with your bigger picture view, right?