So what have you learned in terms of sort of what kind of decisions is AI really good at predicting and what others not that good?

Yeah, I think the first place that you look when you define what good means is understanding the relative for how people make those decisions today.

So let's take something that's very simple, product launches.

So you have someone who's going in front of a survey.

You're asking people questions.

It's easy to benchmark.

And that's a very interesting market.

And so on that note, so do you think you need to tune sort of your models by culture and by countries?

And so I think part of the reason we built this company is because we wanted to use this technology as a force for good.

So how does it work? Well, within what we can share, of course.

No, it's really interesting because you think about the architecture of our system, you think about the proliferation of language models and how much more accessible starting certain elements has been and all these things.

And certainly these systems would not be capable without language models, but they're not the only thing that makes them what they are, right? You think about what a language model is built for and its most basic principles.

It is not to capture the irrationality of human behavior, right? It's not.

It's in fact quite the opposite.

To produce an average result.

And so the real thing is when you're looking at real behavioral outcomes, we've shown time and time again that we can be more accurate than traditional public opinion research.