Reducing headcounts is not really the way to go with AI, is it?

Yes, that's a really interesting point.

I think about that in two ways.

One, just from a pure business perspective, you know, I don't know the exact adage, but it's something to the effect of no business has grown through cuts.

Right? I mean, sure, you want to be smart with your margins.

You want to be efficient.

Nothing wrong with that.

And I want to understand, you know, how does evaluating AI to a probability or risk lens change the way a CEO should budget for it?

So to that purpose, my question to you is that what does that model get right that traditional approaches consistently get wrong? Like if you can take a step back and kinda like walk us through the thought process about that talent model and then, you know, just share a little bit of details for my listeners.

Yeah, absolutely.

There's a lot to unpack there.

A little bit of context on Paradox, uh, because that's gonna inform the answer.

We, uh...

Paradox is like a Palantir for mid-market SMB companies, so we have data engineers and heads of data as your outsourced data team, and we have a data platform.

So those two combined is very unique in the market, targeted at, at mid-market companies, mid-sized companies.

How do you maintain that balance at scale, and what breaks first when data companies try to combine strategic rigor with deep implementation? Think about f- from both technical and strategic perspective, since you serve both the roles really well.