How do you even know what good looks like in that case?

Yeah.

Yeah.

Um, e-even just taking the example of 1Password, right? Um, the key thing is it's not just an agent that does dev workflows.

It's an agent that does dev workflows, workflows within 1Password, and for that agent to do a good job, it needs to know what 1Password considers is good for a specific role or a set of developer tasks, right? And that's like the, the, the magic thing here, right? Um, even for simple workflows, just the general intelligence of the model is not sufficient, right? Let's just say reviewing a resume.

You could take the smartest model and then have it review a resume, but if it's not calibrated with your recruiting team and what that team's looking for in the talent profile of a resume, it's just gonna fall flat on its face.

So the question of how do we understand what good looks like requires extracting the knowledge from an enterprise.

Mm-hmm.

Do you think that's fair, and is that a niche market or a wedge into a much larger market in your mind?

Actually, our insight about the market is that human data and talent assessment have actually become the same thing, where, you know, I can take you back five years where when we think of this data labeling or human data stuff, it's essentially a crowdsourcing problem.

Let's say Waymo wants a, a bunch of their images labeled.

You get a bunch of people across the world to draw boxes around stop signs to make the model better at classifying stop signs.

Fast-forward to today, and the nature of human data work has changed a lot.

Now it's GPT-4.0 or whatever model is not good in a particular domain, so we actually need an expert to make the model better in that domain, and figuring out who that expert should be is 100% a talent assessment problem and is a, is a perfect application of the platform.

With a lot of the, the labs that we work with, we're able to figure out who are the exceptional people in very, very specific domains and, and have those people work with the, with the labs.

You're teaching me.