Short answer: Ben Lerner says Espresso started about six months after ChatGPT came out. Its models read incoming SQL and compare it to previous runs to predict whether adding a query will queue, add capacity or slow existing queries.
Watch this answer from Episode 27 (starts at 11:16) · Episode 27 notes and transcript
From Episode 27: We Let AI Control Our Data Warehouse — The Results Were SHOCKING, starting at 11:16.
Jordan Metzner [11:15]: Yeah. Totally makes sense to me. Maybe you can talk a little bit about how kind of AI, like the LLMs or whether that's impactful on your business or not, and kind of, like, obviously, I mean, just from a coding perspective, how amazing Opus is or whatnot, or just Claude Code, and kind of a little bit about how that's impacted kind of the way you guys build. Because I think that'll be most interesting to our audience, and then they can also talk a little bit about kind of how AI's had the impact on your business.
Ben Lerner [11:40]: Yeah. So I think we're a little bit so one answer is, like, AI massively impacts and informs our business. And we started this business because of the newer generation of models and the sorts of things that we thought they could do and that we have figured out that they, in fact, can do. It's little bit off the beaten path, so I think that there's a lot of interest right now in, like, vibe coding and taking vibe coding to the next level. So we don't benefit as much from that, and I'll touch on that in a second. But for the actual business, like, the really phenomenal thing about transformers and these transformer based models is that they can understand and work with code in a much deeper way than previous models. Right? And so this is the fundamental thing that's powering things like cursor and Claude Code and codex and so on. And you can leverage that understanding of code in different ways. So one is obviously with, like, autocomplete and, like, natural language prompts and so on.
Ben Lerner [12:37]: What we're doing is quite different. What we're doing is using those models to understand your workloads and how they scale, right, and how much like, what resources they actually need. So we actually have models that will look at your queries as they come in, like, read the SQL, and effectively compare them to previous runs and understand, okay. There's seven queries running in parallel on this machine now. What happens if I add an eighth one? Is it going to queue because the cluster's full? Is it gonna cause us to spin up more capacity? And if so, is that good or bad? Is it gonna slow down the existing queries because it's you know, there's enough capacity to run it, but it's slicing into what they're doing or not? All of these things are really, really difficult to unsolved problems. There's a bunch of literature on people who've tried it before. And, obviously, there's, like, some heuristics and some understanding of cost, but it's pretty coarse compared to the kinds of results that you can get out of transformers and out of modern, like, LM based models. So that is one of the things that makes our business possible. And, we basically started this, like, six months or so after ChatGPT came out. So this is very much focused on the modern generation models.
