“Having something that is agent-writable and human-reviewable is actually not a super easy design constraint to satisfy. I have spoken to folks on data teams who have gone so far down the rabbit hole of having agents write all of their code that they've lost the ability to review it. Otherwise the conversation is, 'Are you sure this is correct?' And I cannot tell you on my own authority that it's correct. I'm going to turn around to my agent and ask it that question, and then I'm going to have to rely on its answer.”

…And it turned out that I was able to build an absolute garbage version of this in, I don't know, I think it was like two days or something, which was enough to make me think like, yeah, the ergonomics of this are really good, but there's really going to be work involved in making the thing useful.

…here, it's coming. And how do we make sure that people and AI can work well together? And having that right seam where like it's still this thing that you can audit and test and humans can still have an eye on it and monitor and govern that data, I think is going to be really, really important.

I think even in a future, I think about a future where like we're not even looking at the code, you know, it's still important to have those right level of abstractions, even for the AI. Like Markdown is inherently valuable to AI. It's like abbreviates like its stuff. It uses less tokens. It's a…

So as in everything else, a new technology paradigm changes the way that we think about constructing our tools. We talked about the transition to the cloud before and how Chartio and a bunch of other companies were, you know, responses to the rise of the cloud. And this is DBT charts is an answer…

You know, in hindsight, you know, in the early days of Chartio, like my big competitor was Looker. And, you know, I kind of disliked its whole philosophy. In hindsight, you know, a lot of the code first, they were really trying to do this code first. You could even do these, kind of do these…