interested.

Not insiders.

Maybe insider rejecters, people who don't define themselves as insiders, but people who are open to learning more about technology.

And our models are able to pull out that description and actually say, okay, based off of who, even something that simple is, like what is their urban-rural breakdown? What is their home-cooking meal frequency? Where do they spend their time on social media? How many hours a day? Do they go to school? What is their education? What is their income level? 300 plus different variables that we're storing.

Then once we chart all of that out, we're able to then go say, now, what questions do you want to ask? That's what Ned said, we do a lot of work as well in identifying the right questions to ask using our models.

Then once we have that question list, we're able to take that audience, we start with the distributions, we generate tens of thousands of agents who are logically consistent.

In the 21 to 29-year-old group, there's not going to be any seven-year-olds, there aren't going to be any 18-year-olds, there won't be any 31-year-olds.

Why are they wrong?

Okay.

And what you see is GLP-1 users are breaking away from the general population in some very consistent ways.

Think things like, uh, caring more about protein content, of course, they're caring about adding healthier options.

But what you're also seeing is that the shift between that current generation, the first and second wave of GLP-1 users, right? I mean, first we had people who had medically necessary GLP-1s.

Maybe we should be more open-minded.

I think this is one of the most interesting topics we've simulated in the last few months.

We were taking a look at, uh, general portfolio managers, right? So think investor and the core investor audience, and then also simulating procurement individuals inside of enterprises.

Think your enterprise buyer.

So I, I, I hear what you're saying, but I'm trying to square the circle of what's actually happening here.

Yeah, I mean, what we see, and this is something that, like, we're really good at predicting, right? Simulation and our technology's incredibly accurate at, is the long tail of predictions, right? So not just what's happening in the next two, three, four days or next four months, but rather what is gonna be the long-term impact of GLP-1s over the next two, three, four years.

And what we're seeing actually is you look at people who are on GLP-1s for a long time.

At the very beginning, alcohol usage does crater.

And as GLP-1s are continuing to be adopted, you're seeing that cratering occur at, like, a massive rate.

But as people stay on GLP-1s for longer and longer, we have been able to uncover, and this has started to be evidenced in market data now, I mean, this prediction was almost six months ago for us, we've started to uncover that people first level out in their alcohol consumption and then second are getting more social.

I mean, there are ways you see this, the first thing is that people-

What's the, what's the plan? Where, where do you hope to be two years, three years, five years from now?

Yeah.

We can understand how the differences in the price of eggs in someone's zip code is gonna change their likelihood to vote for one candidate or another, or to care about some different marketing campaign or another, right? And then as far as it comes to an individual simulation, one simulation is can, you know, composed of tens of thousands of agents for any audience on the globe, right? So remember, 'cause we're not constricted by what you can reach in a traditional survey, and then going and trying to train a model on top of survey responses, we can generate any audience we want, right? So we can generate maybe an audience of social media influencers.

We can even generate an audience of podcasters.

We have a client in the podcasting business-

Hmm

...

it is not going to be able to predict things at the margin.

And that's a big issue, because it's the predictions at the margin that are the most valuable, right? Those are the predictions of, like, what are Fortune 500 CEOs gonna do that we can nail that an LLM can't.