Dhanji R. Prasanna: evaluation

Transcript context

…Maybe going one level deeper in terms of how people actually work at a day. So if you're looking at an engineering team, say the average engineering team and maybe also the top most optimal engineering team, how is the way they work today different from a couple of years ago? In the small, certain teams that are very, very AI natives or teams that are building AI first everywhere are working much differently than before because they're using vibe code tools and they're essentially building without writing lines of code by hand, and that just wasn't true through the three years ago. I don't think it was true anywhere in the world. So that's dramatically different in teams that are still working with very heavy legacy code bases. It's less true, but they're also encountering these background AI processes. So we have these tools that run 24/7 or run in the CI pipeline and they're analyzing vulnerabilities. They're looking at even bugs filed on tickets and trying to build patches while engineers are asleep. So they come in the next day and look at it. So I would say there are a number of ways in which they're different, but different teams have adapted in different ways depending on how close they are to the tools. Okay, so let me lean into that AI piece, which is I think where you guys are most ahead of a lot of other companies. You guys built your own agent I think is how you describe Goose. So there's a lot of talk about productivity gains through AI. There's this camp of people are like, you don't understand how much productivity there is to gain from AI. It's the future, this is the way it's all going to work. We're all accelerating 10X. There's also this camp where people are like, I'm so overhyped, nothing's working. People talk about it. All these pilots are failing. Nobody's actually adopting this at scale. I feel like you're probably in that first camp. What sort of gains have you seen practically from AI tools on your teams?…

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