Q Yeah, yeah, that's true. Okay, so and then I wanted to actually build out the, the sort of dream engine, uh, vision. Um, where does this all lead?
A So one of the thing we, uh, realized maybe a couple years back is that Actually, every single database engine out there, especially on the analytics side, are kind of a decade old. Um, pretty much everything that had reasonable traction are about a decade old. And they all started targeting some very specific, narrow use cases, and then over time, it's become more and more successful. They've grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created. They were not for those use cases. And then, but you can kind of support them more or less. Okay. And before you know it, after 10 years of organic evolution that way, it becomes a gigantic pile of shit. Um, the, and, but that includes Databricks and very, very few company or very few systems, I think have the, uh, gut to say, let's go start from scratch. Let's go back to the drawing board, the design, knowing everything we know today after a decade, The workloads and probably billions in revenue. Let's attempt to rewrite it from scratch and actually make sure it will work. And then we can support all of these use cases. So we started doing that, but it's a very ambitious project. Uh, by the way, you can search on Wikipedia. There's a thing called second system syndrome.
AI assessment note: “Let's attempt to rewrite it from scratch and actually make sure it will work.”