Two Kates, One Future: Managing Humanity in the Age of AI

And I also wonder, you know, just in your own mind, as you ponder these things, like, what does it mean to you to improve performance without flattening people, right? Or the human experience? Like, what, how does that show up in the process?

Yeah.

Whenever we think about developing features or developing new features, we think about how can we dimensionalize a person? You know, how do we see the richness of Kate O'Neill with two L's? better and how do we help take that information to help other people understand her better and that's it right like that's the whole game if we can do that as humans create more understanding between two people what is you know what is better than that

what better could there be absolutely

Yeah, it

seems like what you're talking about, like, turning a person's potential up to 11 for that person, right? Like, within the context of how they can contribute in the environment that they're in.

And you can, you can only really do that, I think, with the kind of signal matching that you're that you're talking about.

Yeah, when we talk about like dimensionalizing people, we also think, I mean, this might be too technical, so cut it out if it is.

But all HR tech to, you know, the last year or so has been built on relational table databases, predefined structures.

If there are data that comes in that doesn't match that predefined structure, it's gone, it's struck, it's out, right? Think about the bias that was built into those systems for the last 30 years because we had to structure it.

There was no like expanding gases version of architecture that would help us to continually create understanding and new information to keep dimensionalizing humans.

That is, you know, knowledge graph.

And we're still, you know, kind of in the early stages of that.

But like, if you think about a system, not as a hard structure, but as a expanding gases that grow over time to understand more about a human.

It's like, it's three-dimensional, right? Or four-dimensional rather than this 1D dimension.

way we've been doing this in the past

yeah i i think that's not too technical for our audience i think they're going to be people who are very excited that you've brought that up because what then that makes me think about is um you know how not only the dimensionality of the human experience but the way that your you're learning as CEO of Opry.ai, right? Like you're getting a chance to learn about what new nodes get surfaced in that model, right? And you can take that back as instruction to your own product development, I would imagine.

Has that happened thus far?

It's happening.

We're still very early stages.

So I can't say like we're, you know, but the goal, right, is to get to this point where what are the signals across whole sets of people of entire industries jobs you know we've never had it's been survey data that's it that's all we've ever had from a work you think about gallup it's survey data which is you know it's still important to ask people how they feel and all these things but think about all of the information we could be learning you know yeah that's the that's what i'm excited about

That's very exciting.