Shawn Wang: belief

Transcript context

…Yeah, it's a good example of like, that's an application that an engineer probably does not want to build, like building an app on top of Salesforce or something that is not exciting. And so it sucks. It's very limited. It's like not a fun experience at all. But piecing together a Retool is quite a bit easier. So yeah, let me know if you have any feedback, but also, yeah, no, of course, like more recently, I think about three, four months ago, you launched Retool AI, obviously, AI has been sort of in the air. I'd love for you to tell the journey of AI products ideation within Retool. Given that you have a degree in this thing, I'm sure you're not new to this, but like, when would you consider sort of this the start of the AI product thinking in Retool? So we actually had a joke internally at Retool. We are part of roadmap for every year, it was like 2019 or something. We had this joke, which was like, what are we going to build this year? We're going to build AI programming, is what we always said as a joke. And so, but it was funny, because we were like, that's never gonna happen. But like, let's add it because it's like a buzzword thing that enterprises love. So let's look at it. And so it was almost like a funny thing, basically. But it turns out, you know, we're actually building that now. So this is pretty cool. So I would say maybe AI thinking on Retool probably first started maybe like, I don't know, a year and a half ago, something like that. And when we first started thinking about it, sort of in a philosophical way, if you will, it's like, well, what is the purpose of AI? And how can it help, you know, what Retool does? And there were two main prongs, if you will, value. One was helping people build apps faster. And so you've probably seen Copilot, you've seen sort of so many other coding assistants, P0 to them, you know, stuff like that. So that's interesting, because, you know, engineers, as we talked about, do some grunt work. And grunt work, you know, maybe could be automated by AI was sort of the idea. And it's interesting. So we actually, I would say, kind of proved or disproved the hypothesis a little bit. If you talk to most engineers today, like a lot of engineers do use Copilot. But if you ask them, like, how much time does Copilot save you? It's not like coding is 10x faster than before, you know, coding is maybe like 10% faster, maybe 20% faster, or something like that, basically. And so it's not like a huge step change, actually. And the reason for that, as we think, is because the sort of fundamental frameworks and languages have not changed. And so if you're building, let's say, you know, like the sales ops tool we're talking about before, for example, let's say you've got AI to generate you a first version of that, for example, the problem is that it probably generated it for you in like JavaScript, because you're writing for the web browser, for example, right. And then for you to actually go proofread that JavaScript, for you to go read the JavaScript to make sure it's working, you know, to fix the subtle bugs that AI might have caused, hallucinations, stuff like that, actually takes a long time and a lot of work. And so for us, the problem is actually not like the process of coding itself, it is more sort of the language or the framework, I think it's like way too low level. It's kind of like anything like punched cards, like, let's say, back in the day, who designed punched cards, and AI could help you generate punched cards, okay, you know, I guess that helps me punch the cards a little bit faster, because I have a machine punching them for me. But like, when there's a bug, I still have to go read all the punched cards and figure out what's wrong, right?…

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