David Hsu: preference

Transcript context

…It is 100% on time. And yes, you can actually already saw a lot of people actually are building AI workflows down retool, which is what we're gonna talk about in a second. But a hot take here is actually, I think a lot of the utility in AI today, I would probably argue 60 70% of the utility, like, you know, businesses have found an AI is mostly via chat GPT, and across the board. And the reason for that is, I mean, the chat GPT is sort of a UI, you could say, or interface and user experience is just really quite good, you know, you can sort of converse, you know, with an AI, basically. But that said, there are downsides to it. If you talk to like a giant company, like a J.P. Morgan Chase, you know, for example, they may be reticent to have people copy paste data into chat GPT, for example, even on chat GPT Enterprise, for example. Some problems are that I think chat is good for one off tasks. So if you're like, hey, I want a first version of representation or something like that, you know, and help me write this first version of a doc or something like that, chat is great for that. It's a great, you know, very portable, you know, if you will form factor, so you can do that. However, if you think about it, you think about some economic productivity, more generally, like chat, again, will help you like 10 or 20%. But it's unlikely that you're going to replace an employee with chat, you know, you're not gonna be like, oh, I'm a relationship manager at J.P. Morgan Chase, and I've replaced them with an AI chatbot. It's kind of hard to imagine, right, because, like, the employees are doing a lot of things besides, you know, just, you know, generating, you know, maybe another way of putting it is like, chat is like a reactive interface, like, it's like, when you have an issue, you will go reach out to chat and chatbot solve it. But like, chatbot is not going to solve 100% of your problems, it'll solve like, you know, 25% of your problems, like, pretty quickly, right. And so what we think the next like, big breakthrough in AI is, is actually like automation. It's not just like, oh, I have a problem, let me go to a chatbot and solve it. Because like, again, like, people don't spend 40 hours a week in a chatbot, they spend like two hours a week in a chatbot, for example. And so what we think can be really big, actually, is you're able to automate entire processes via AI. Because then you're really realizing the potential of AI, it's like, not, it's not just like, you know, a human copy pasting data into an AI chatbot, you know, pasting it back out or copying back out. Instead, it's like the whole process now was actually done in an automated fashion without the human. And that, I think, is what's going to really unlock sort of big 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. 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.…

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