The Exchanges

Q So, so tell me more about your users. I, you know, one of the hardest things that I thought about building products at Retool was that it's such a widely applicable tool when you're building developer tools. And how do you decide who your target user is and who to build for?

A Uh, so from, from the beginning, a part of the vision was like, okay, there's going to be a rapid explosion of new startups with, like, one person creating a lot with just AI, and we want to build for those founders. And as, like, we started building out the product and the AI wasn't, like, as good as it is today, of course, then we looked around and thought, like, similarly to Retool, actually, is this a tool for, Um, building internal applications, and, um, we thought that that might be the case because we spoke to a lot of users that liked it for this application, but then once we just launched and, um, really saw what people were using it for, uh, it became clear that We don't need to be very, very specific about what you should use it for. Uh, today it's used, um, a lot for, as you said, like creating this first version of your startup by, both by teenagers or kids that are like, I want to make money and create something. And serial entrepreneurs that raised like fifty million dollars, but now they were completely alone in Lovable. Uh, and now increasingly inside of companies, like you said, even you had a hackathon at Stripe, right? Where you could, uh, where it's a tool for designing or ideating exactly what type of things should, should you as a company add to your offering or, uh, you, uh, use as a tool to, uh, make yourself more productive. So, so it's, it's used all,…

AI assessment note: “we don't need to be very, very specific about what you should use it for”

Q Yeah. I mean, it's incredible. It's like, like you said, you're just democratizing, Democratizing access to, to development, which, you know, most of us just have, have never had. Um, how much education do you have to do with users on this just completely new paradigm, paradigm of building?

A Yeah, this is a great question. I, I think. The wow moment for like, oh, this would have taken months to build this beautiful websites in the past. That, that happens instantly almost. And that hooks people and they're like, they realize that in itself educates people that I can build something now. This wasn't possible before. Now I can build something. I have to become a bit better at how to prompt and so on. Um, but as you start building something more complex when it requires, um, like setting up payments to Stripe is quite simple, but like if you have many features, Then, uh, you do need a bit of education to understand what are the different components here. So there's some things running in my browser, and then there's some things that need to, um, for logging, logging in, for example, you connect to our default integration to Supabase. Uh, and that takes a bit of figuring out or watching a YouTube video, for example. And, uh, yeah, we're increasingly letting the AI do more and more of the education as it talks to the, to the user.

AI assessment note: “you do need a bit of education to understand what are the different components here”

Q size that you guys are at, it's in itself sort of pretty unprecedented. Um, I, I think at Stripe we, ah, we're, we, we found we're shipping 30% more code. Um, and so, and who knows if it's better code, but it's, we are shipping more. We're seeing some, some, some traction there. Um, what models are you doing? Have you, are you using? Have you built your own models?

A We haven't done that. We looked, we have looked at fine tuning our models and what we're seeing is that the foundation model labs, they, they come with out with new models and we, the hardest part to get right is this inter interaction between like the AI, the complex agentic systems of like how the models are used and the user experience. And we want to spend as much part on the last two as possible. Like how, how are the, how are the AI reasoning and so on used, and they use, how does this translate to a user experience for, um, what the human actually experiences. And if we're spending time on the AI, it's time we could have spent on making the other two parts better. Um, there's, there are other opportunities in making the response times faster by creating our own model. So we might look at it in the future, but it's not the lowest hanging fruit.

AI assessment note: “We haven't done that. We looked, we have looked at fine tuning our models”

Q talent density problem? You just need to get everyone together. Like everyone's, you know, everyone's kind of spread out. There isn't sort of a clear like Silicon Valley hub. Is it like a funding problem? Is it a, like, you know, if you were to sort of like solve Europe, solve Europe's, uh, yeah, having to build a startup in hard mode problem, what would you, what would you say?

A Yeah. Yeah. Yeah. So it's, um, it's like the, the talent is, I guess the same and it's more available in Europe. The, The seniority on different things is lower, ah, like the specific, um, skill sets and functions, um, but the, um, ambition level is the biggest problem, I think. Like, there's fewer people that are, like, super ambitious, like, okay, this is a unique opportunity in the history of mankind, and we're here to build the best way to build software applications with AI. Instilling that mindset is easier in the US. Uh, and if there was, if it was more of this high, super high ambition in Europe, then we would see much more successful companies from here.

AI assessment note: “ambition level is the biggest problem, I think.”

Q online, there's, there's a new growth number. It used to be that we had some like pretty clear benchmarks for what it, what it means to be like a great looking SaaS company, you know, like the three X, three X, two X, two X year over year growth over four years. Obviously that growth curve does not apply to many AI companies. How do you know you're doing well?

A I mean, we do look at those revenue numbers, but, but our North star is not on the revenue. It's about the number of users that are, uh, are sticking with us. And, um, now we have a 120,000 are paying and it's a simple, simplest way to measure that. Um, what we're increasingly doing is to look at, like, what's the success for our users? And that's that you've built something that gets Usage. Or for the, like, design phase, it's just getting eyeballs, getting some kind of eyeballs, and then it's getting, having them have high retention numbers on their users. And we have, um, we have many apps that are like hundreds of thousands of users on their applications as well, or like visitors. Um, and just the number of our users that get to that stage is what I, what I, we're like optimised, starting to optimise for.

AI assessment note: “our North star is not on the revenue. It's about the number of users”

Q growth company from the bottom up. Folks are, are just kind of adopting the product self-serve and building a bunch of cool tools in it. And then it seems like Because you're on this accelerated growth path, you're also starting to go up market a lot faster than most businesses would in this time in their life cycle. Uh, is that right? And how are you guys thinking about that?

A Um, yeah, so we launched the team plan, um, a month ago, and we've already have more than four, I think, 5000, uh, people who paid more to get on the, on the team's plan where you collaborate, and that's done by a company, right? Um, and from the team's plan, then just in general, there's, I mean, hundreds or thousands of larger companies and enterprises that have seen, oh wow, I can get move much faster. I'm not bottlenecked by engineering. In, uh, to the same extent I was before. Um, and what we're doing is just to ensure they have a great experience and get their, their, their questions answered. Um, for now, that's everything we do, but of course we want to figure out how can like engineer some lovable team, uh, as we scaled like the, the customer facing engineering function out, out help in getting as much value as possible with using AI to, um, Move faster as a business and move like you had thousands of excellent engineers at the company.

AI assessment note: “we launched the team plan, um, a month ago, and we've already have”