How is your platform helping to accelerate clinical trials and the timeline and the amount of time that it takes to get to the, the last level, the, the Phase III trials?

Yeah.

Well, I mean, so every step from, you know, discovery and development, there are sort of standard timelines and in- investment required.

And what w- what we're seeing just in general in AI that, uh, is, you know, there's a massive shrinking in time and investment for drug target discovery, validation, and getting to the point that you have a drug that matches that target.

That's been clearly demonstrated.

But then when you move into the clinic, if you are able to really pinpoint the right patient, the exact right genetic and non-genetic profile of a patient that's gonna best respond to a drug, then you're going after a s- much smaller population, right? A subpopulation of maybe what you, uh, intentionally, originally, uh, intended to go after.

So then you're able to reduce the time required to recruit these patients.

So what are the biggest obstacles to broader AI adoption in this drug development space?

Yeah.

And, um, I had relationships with Johns Hopkins University.

I did my MBA there, and I was a mentor in residence as, as well.

And, um, you know, I was hunting around, and it turned out that they had a subsidiary called the Applied Physics Lab, or APL.

And, um, you know, uh, one thing led to another and, you know, discovered that, hey, this is a multi-billion dollar subsidiary, uh, that has incredible technology and does very sensitive defense work.

Um, and, like, you can literally can't even get into any of the buildings there.

I mean, it's a pretty impressive place.

Yeah, well, talk about, talk about that sort of start to finish.