Mm

...

there is so much research and care that needs to go into building, uh, sophisticated AI models, uh, and, and the application of those in the enterprise, and it's a challenging thing.

I think what's been going on recently though, just in the, the deja vu we're going through, this particular flavor of AI, generative AI and large language models, I think have captivated a lot of people, and more than just the nerds, right? Like, we have captivated everybody, pro- probably yourself, people who are, people who are in journalism or, or people who are, uh, doing media production.

It's the first time that AI has really impacted, uh, sort of everybody globally.

So I think there's a lot of anticipation and a lot of hope for what AI can do based on what people see, you know, using it in their day-to-day jobs.

But the difference between a tool for an end user and something that's making a critical, sophisticated decision at the core of a business is a really wide gulf, and so that's the challenge.

Mm-hmm.

Or are they optimizers of the present to position for the future?

First of all, let's talk about the work of a data scientist in particular, because that's being very much disrupted by AI.

And there are... parts that are going away, there are parts that are becoming more important, and there are new jobs to be done, right? There's a real big shift here.

And I will say data scientists sort of parallels a little bit of software engineering.

You know, we talk a lot about how the coding assistance is a killer use case for generative AI, and it's sort of changing how software engineering works.

I mean, a similar thing is playing out with data science.

And Really, it's changing the role of a data scientist.

Your future cast for us, where does this go?