Shawn Wang: prediction

Transcript context

…g canonical productivity, or that's what I'm really excited about. And I think part of the problem right now is, I'm sure you all thought a lot about agents is that the agents are actually quite hard. Because like, you know, the AI is wrong, like, you know, 2% of the time, but then you like, you know, a score, if you let's say, you know, raise to the power seven, for example, that's actually wrong, you know, quite often, for example. And so what we've actually done with workflows is we prefer, we've learned, actually, is that we don't want to generate the whole workflow for you by AI. Instead, what we want you to do, actually, is we want you to actually sort of drag and drop the workflow yourself. Maybe you can get a vSphere or something by AI, but it's coded, basically, you should actually be able to modify the steps yourself. But every step can use AI. And so what that means is like, it's not the whole workflow is created by AI, every step is AI automated. And so if you go back to, for example, like the users are talking about, you know, with a clothing manufacturer, that's actually a workflow, actually. So basically, what they say is, hey, every day, we see all the data, you know, from our sales systems into our database. And then we do some data analysis, and, you know, it's just raw SQL, basically, it's nothing too surprising. And then they use AI to generate new ideas. And then the analysts will look at the new ideas and approve or reject them, basically. And that is like a, you know, that's true automation. You know, it's not just like, you know, a designer, copy pasting things as a chat, you can be like, hey, you know, give me a design. It's actually designs are being generated and generated 10,000 designs every day. And then you have to go and approve or reject these designs, which I think is a lot, you know, that's a lot more economically productive than just copy pasting something. So we think sort of the AI workflow space is a really exciting space. And I think that is the next step in sort of delivering a lot of business value by AI. I personally don't think it's, you know, AI chat or AI agents quite yet, so. That's a pretty reasonable take. It's disconcerting, because like, I know a lot of people trying to build what you already have in workflows. So you have that sort of, you're the incumbent sort of in their minds, I'm sure it doesn't feel that way to you. But like, I'm sure, you know, you're the incumbent in their minds, and they're like, okay, like how do I, you know, compete with retool or, you know, differentiate from retool. As you mentioned, you know, all these connections, it does remind me that you're running up against Zapier, you're running up against maybe Notion in the distant future. And yeah, I think that there'll be a lot of different takes at this space and like whoever is best positioned to serve their customer in the way that they need to shape is going to win. Do you have a philosophy against around like what you won't build, like what do you prefer to partner and not build in-house? Because I feel like you build a lot in-house. Yes, there's probably two philosophical things. So one is that we're developer first. And I think that's actually one big differentiator between us and Zapier and Notion, and we're so very rare we'll see them actually, and the reason is we're developer first. Because developers, like, if you're like building a sales ops tool, you're probably not considering Notion if you're a developer, you're probably like, I want to build this via React, basically, or use retool. And so are you we build for developers, it's pretty interesting, actually, I think one huge advantage of some of the developers is that developers don't want to be given an end solution. They want to be given the building blocks liquid to themselves to build the end solution. And so for us, like, interesting point that equilibrium we don't get to, it's basically to say, hey, retool is a consulting company, and we basically build apps for everybody, for example. And what's interesting is, we've actually never gotten to that equilibrium. And the reason for that is for some of the developers, developers don't want, you know, like a consultant coming in and building all the apps for them. Developers like, hey, I want to do it myself, just give me the building blocks, give me the best table library, give me, you know, good state management, give me an easy way to query the rest of the API. So I'll do it myself, basically. So that is pretty, so we generally end up basically always building building blocks that are reusable by multiple customers. We have, I think, basically never built anything specific for one customer. So that's one thing that's interesting. The second thing is when it comes to sort of, you know, let's say like, in the AI space, we're going to build and we're not going to build, we basically think about whether it's all core competency or whether there are unique advantages to us building it or not. And so we think about the workflows product, we think workflows actually is a pretty core competency for us. And I think the idea that we can build a developer first workflows automation engine, I mean, I think after we released, you know, workflows, virtual workflows, there have been a sort of few copycats that are, I think, quite, quite far behind, actually, they sort of are missing a lot of more critical features. But like, if you look at the space, it's like, Zapier on one side, and then maybe like, Airflow on the other. And so virtual workflows actually is fairly differentiated. And so we're like, okay, we should go build that. This is the one I was going to build, so I'm just going to build it. Whereas if you look at like vectors, for example, you look at vectors like, wow, there's a pretty thriving space already, if you know vector databases. Does it make sense for us to go build our own? Like, what's the benefit? Like, not much, we should go partner with or go find technology off the shelf. Narcissus is pretty effective.…

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