Can you just explain a little bit about sort of your contribution to that conversation and what it is that the message you were trying to communicate there?

Well, and I actually like stole the concept.

Like, I mean, so like this is like a 10-year-old thing that Greenhouse did, right? So Maya Jobacavelli, who's the current head of revenue for Stripe, was previously like the head of like marketing and like strategy for Greenhouse.

created this.

And I thought like she created out of thin air.

Actually about 10 years before that in 07, there was an academic paper that mentions like employee lifetime value, but not the ELTV kind of like thing.

But for me, it was like when I first saw this, like I was just totally taken aback because when I show this to like executives that go, what the hell do like does hr do what does ta do what does all this do and you go hey here's a visual that kind of explains really what the roi is and so if you think of a human as a machine right we buy a machine it costs us all this money up front we put it in the factory and then little by little we start to make money on this machine and eventually we go positive on the roi really humans are exactly the same and so they think okay so from a ta perspective When we go and we go out and post jobs and screen and assess and blah, blah, blah, we finally hire somebody, we're actually negative ROI in terms of the value of that employee until they come up to speed, right? So now great onboarding, great manager, leader, motivation, training, great benefits and pay, all of that stuff potentially has an impact to this employee lifetime value.

And these are the metrics that I think really do lead us to start, if you really start picking through them, you start all of a sudden asking all design questions and talent density questions to Tim's point as well.

They have no context for what they're looking at.

No.

It, you know, and David, there's a part of me that thinks maybe it's really hasn't changed a whole lot.

'Cause 10 years ago we would say they had no idea what their infrastructure of, of their SAS program that they were built, like they were using, was built on either, and we, and did we really care that they knew that or not, right? It's like no, does it work? Does it not work? Um, for me it was always about, well, at the very least as an HR leader, and let's say you're using a big HCM program, you should know the, the, the capabilities of what it actually can do and can't do.

You don't have to know how it's built, what it's built on, blah, blah, blah, but are you, do you know what it's actually capable of? And are you using that capability to the, the most you can, right? Like that would be top 10% HR leader in technology in the last 10 years, if they actually just knew the capabilities.

And so then I go, well, maybe with AI it's just also the same.

Do you know the capabilities of AI? And then you and I talking offline are like, AI is moving so fast, we have a hard time keeping up with what the capabilities might be or could be.

Yeah.

I mean, at the end of the day, does it just mean treating your employees well or engagement or listening?

Yeah.

I mean, I think it is with all the data stuff too, is like talent management, workforce planning.

Like we still, like, we still react, like, like we're fighting fires all the time.

And I'm always just like, why don't other parts of the business don't react this way? Logistics, supply chain.

Like, I mean, again, we all have fires from here and there, but like on a normal kind of running machine.

We don't have that.

Okay, last question is, what's keeping you up at night these days?