Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder)

Anton Osika: … Lovable is your personal AI software engineer. You describe an idea and then you get a fully working product. The reason is to enable those who have had such a hard time finding people who are good at creating software that’s been their absolute bottleneck and let them take their ideas and their dreams into reality.

Future Skill Trends

Lenny Rachitsky: You guys hit 4 million ARR in the first four weeks. You hit 10 million ARR in the first two months with just 15 people. You’re the fastest growing startup in all of Europe. How did you decide on Lovable is the name. It’s so sweet.

Anton Osika: The best word for a great product is that it’s lovable. A lot of jargon that I like to use to emphasize what we should be striving for is building a minimum lovable product and then building a lovable product and then building an absolutely lovable product. So I took that jargon with me in the company name.

Introducing the Guest

Lenny Rachitsky: People would wonder just what jobs will be more important, what skills will be less important?

Anton Osika: Doing a bit of everything. Being a generalist, I think much more important than it used to be. If I’m putting together a product team today, I would really obsess about getting as many skill sets as possible for each person I hire.

What is Lovable

Lenny Rachitsky: What have you done that has allowed you to grow this fast with so few people?

Anton Osika: People love the product. That’s the driver of the growth.

Companies Built on Lovable

Lenny Rachitsky: Today, my guest is Anton O-C-K. Anton is co-founder and CEO of Lovable, which is essentially an AI engineer that takes an English prompt and codes a product for you in minutes. You can then talk to it, iterate on the product, and then launch it to the world. It’s one of the fastest growing products in history. The fastest growing startup in Europe ever, and as Anton describes, their goal for Lovable is for it to be the last piece of software that anybody has to write because it’ll be able to create all future products for us. They launched just a few months ago in the first four weeks, hit 4 million ARR in the first two months across 10 million ARR, all with just 15 people. Absurd. In our conversation, we covered a lot of ground, including a live demo of Lovable, how their team operates, how they hire, what has most enabled their team to scale this quickly with so few people, pro tips for using Lovable, how it all started, how he recommends you build product teams going forward with tools like this existing, what skills will matter more and less going forward?

Plus how to think about Lovable versus competitors and so much more. If you’re trying to wrap your head around how product building will change with the rise of AI tools, this episode is a must watch. If you enjoy this podcast, don’t forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become a yearly subscriber of my newsletter, you now get a year free of Perplexity and Notion and Superhuman and Linear and Granola. Check it out at Lenny’snewsletter.com. With that, I bring you Anton, O-C-K.

Anton Osika: It’s a pleasure to talk to you, Lenny, great to be here.

Live Demo: Building an Airbnb Clone

Lenny Rachitsky: I don’t know how you have time to do this podcast. Your life must be insane these days with the pace at which you guys are scaling, just how much is changing in AI every day. So I just extra appreciate you making time for this. I think you said it’s 10:30, your time, is when we’re doing this.

Anton Osika: I’m a bit tired, yes. Mostly from the crazy pace of everything, but yes.

Backend Deployment and Architecture

Lenny Rachitsky: This is going to be an invigorating conversation and you’re not going to be able to sleep.

Future of Tools and Advice

Anton Osika: Yes. I’m sure. I’m sure.

On Naming Lovable

Lenny Rachitsky: Okay, so for folks that are maybe a little bit familiar with Lovable or not at all familiar, what is Lovable? What’s the simplest way to understand it?

Lovable’s Origin Story

Anton Osika: I’d say Lovable is your personal AI software engineer. You describe an idea and then you get a fully working product from the AI. And what this means is that entrepreneurs actually today, they turn their ideas into real businesses. We have a lot of designers and product managers that create the first version of their product ideas to show to the teams, and some of them become founders because of the empowerment from this, but also developers themselves, they actually writing code or creating products much faster. The reason, it’s pretty obvious for me, but I’ll spell it out, the reason why we’re doing Lovable is that I don’t know about your mom, but my mom doesn’t write code and-

From Open Source to Product: Lovable’s Birth

Lenny Rachitsky: Same.

Tech Breakthrough: Solving AI Stalls

Anton Osika: … almost all my friends throughout my life reached out for help. Like, “Anton, I need to build something. How do I find a great software engineer?” And we are building for this 99% of the population who don’t write code. Currently, if you’re technically inclined, you get much further, but over time, naturally the way to build software is by just talking to an AI. And that’s how we see it.

Lenny Rachitsky: I love the way that you guys describe it and you didn’t mention it, but I think it’s building the last piece of software ever. How do you phrase that?

Fast Growth for Small Teams

Anton Osika: Yeah, we say we’re building the last piece of software.

Lenny Rachitsky: The last piece of software. Okay, we’re going to do a live demo, but first of all, can you just share some stats on the scale of this business at this point because it’s quite absurd.

Team and Product Quality

Anton Osika: Yeah. So we launched Lovable less than three months ago, and now we have 300,000 monthly active users and 30,000 of those are actually paying and it is growing at the same rates, just almost only through organic word of mouth.

AI Tool Usage in Teams

Lenny Rachitsky: Okay. And I’ll share a couple stats in terms of revenue, just so folks know this, and we’ll have this in the intro too. I think you guys hit 4 million ARR in the first four weeks. You hit 10 million ARR in the first two months with just 15 people. You’re the fastest growing startup in all of Europe, and you guys had to rewrite your entire code base recently and you couldn’t ship any new features for a while. Is that right?

Anton Osika: That’s right, yeah. People were saying like, “Oh, you’re shipping so fast.” And we were all quite frustrated because we wrote our service in this kind of scripting language and then as we started scaling, we were just, no, we have to throw everything away and rewrite it in a more performant way.

Differentiating from Competitors

Lenny Rachitsky: Okay, before we get to the demo, last question, you shared there’s some companies that have started based on Lovable. I didn’t even know that. So what are some examples of companies slash businesses that have launched off of Lovable and now are actually companies?

Anton Osika: I mentioned designers using Lovable and one of our early users, Harry, he started shipping real web apps to his clients instead of just shipping designs. And then he went on to say, okay, wait, I’m going to start an AI startup. And his company, he launched on Product Hunt and everything and making money is just like, let’s anyone upload their photo library and then the AI parses and categorizes it. And if you go to launched.lovable.app, this is an app built with Lovable is again a product Hunt version where you can see a lot of businesses or small SaaS featured there.

The Vision for Lovable

Lenny Rachitsky: Okay, cool. So we’re going to come back to some of this stuff, but let’s get into demo. I rarely do demos on this podcast, but I’m finding that I think it’s really important for people to see these products in action because in a large part, this is the future of product building and a lot of people hear about, “Oh yeah, AI’s coming,” and I don’t think a lot of people actually see what the latest tools are capable of. And so I love showing these sorts of things on this podcast.

Anton Osika: So Lenny, I was thinking, did you ever consider making a copy and build your own Airbnb?

Future Skills That Matter Most

Lenny Rachitsky: I haven’t, but go on.

Evolving Founder and Engineer Skills

Anton Osika: How about you do that?

Team Size and Hiring Philosophy

Lenny Rachitsky: Let’s do it. Let’s do it. Okay, so we’re going to make our own Airbnb.

Anton Osika: Okay.

Building in Sweden and Europe

Lenny Rachitsky: Okay, cool.

Engineer-Led Priority Decisions

Anton Osika: So I just put in the first prompt for an Airbnb clone.

Lenny Rachitsky: Okay. And what is the prompt, just for folks that aren’t watching?

Development Pace and Roadmap

Anton Osika: Two words, Airbnb clones. That’s the prompt.

Lenny Rachitsky: Okay.

Defining AI Agents

Anton Osika: Just start simple and then what you get is that the AI says, okay, I can go through what does a beautiful Airbnb clone look like and it goes through a bit of design decisions and then I’ll zoom out to see more of it. We have this just UI that is… I mean it has all the nice things you would expect from Airbnb clone where you see different categories and you can see two listings from Airbnb with login buttons and everything. So far it doesn’t have the functionality of Airbnb, it just has the UI. I would now ask for an improvement on some of the functionality. Like if I’m switching category, I want to see different listings, let’s say. But if you have any thoughts on what we should build next, let me know.

Secret to Rapid Iteration

Lenny Rachitsky: Okay, and so you had this preloaded, so you didn’t see how long it would take, but how long would this normally take for it to just write all this code and have it for you?

Anton Osika: The first prompt takes 30 seconds.

Building Future Product Teams

Lenny Rachitsky: 30 seconds? Okay. And it’s like a very good copy of Airbnb. I love that you didn’t have to show it a design, you just tell it Airbnb and it knows. Okay, so your question is would I want to add to my own version of Airbnb? I’ve always wanted to explore buying the place that I look at just like, Is this for sale? So what if we see what that would feel like if you’re just a way to buy a listing.

Anton Osika: Okay. Okay. So how about we add, I mean prompting is important here, so let’s be specific, but we would ask, add a button on the listing which has purchased this Airbnb home. Is that it?

Core Hiring Philosophy

Lenny Rachitsky: Perfect.

Giving Everyone Superpowers

Anton Osika: Okay, so, add, and [inaudible 00:12:19]. I’ll be even more specific. It will pop up a model to purchase the listing.

Next Steps for Lovable

Lenny Rachitsky: Perfect. And I love… So I think something as you’re typing, I’m just going to share thoughts as you’re doing this. So the site that you asked this AI engineer to build, it’s actually a functioning website that you can browse around. It’s not just a design, obviously there’s no actual listings here, there’s no actual houses here. Say you were trying to actually build Airbnb and you wanted to start adding actual homes that plug into this, how does that sort of step work?

Existing Codebase Support

Anton Osika: So as you say, this is just kind of the mockup UI, but it’s also interactive. If I want to add login and add listing management, then we will connect something called the backend. So where data is stored, where user’s log information is stored, and I can show you how to do that. First let’s just try out where we got with this short prompt.

Areas of Failure

Lenny Rachitsky: Let’s do it.

Embracing AI Tools

Anton Osika: Adding the purchase listing and it didn’t do exactly what I wanted. I said, add a button… Or I didn’t say what a button should say, but it says book now, and if I click book now I get a booking confirmation. So the AI was like, okay, it didn’t… It was probably surprised by you wanting to buy the listing since it’s Airbnb. So it still says book the listing, but it shows a pretty model where I can click confirm and pay. And then it’s says booking confirmed.

Where to Find Us

Lenny Rachitsky: I’ll just say real quick, I love that this is actually a really good example of why being a good product manager is important. A lot of wasted time happens when you’re not clear about the problem you’re trying to solve and why you’re trying to solve it and all that kind of stuff. So it’s really cool that this is a use case where you have to be really good at explaining what it is you want. And it’s interesting, you don’t have to tell this AI-why. Humans want to understand, “Why is this important.” Mostly you need to be very clear about what it is you’re doing and I love that’s a really strong PM skill. Your PM’s really good at that. So we have to…

Anton Osika: Hey. Explaining exactly what you expect and what you’re not getting is even more important with AI than with the humans. So I’m going into hooking up more of the actual functionality, but first I’ll actually show you something. What’s the fastest way to change what went wrong, it’s created buttons that say book now and I want them to say, “Buy now.” And what I could do is select this item and say change it to buy now. But what we just realized is that you can actually edit this, this is a fully functioning product, but you can edit it visually like you do in Squarespace and Wix and so on. So I’ll just change the text to buy now and then it instantly changes. It actually changes deep down in the code base, but it’s very fast to do that.

Lenny Rachitsky: So I think people listening to this and seeing this, if you’re not aware this is the cutting edge of tools like this, no other tool out there lets you generate code from an AI engineer and then actually just change a small element of it of every other tool that I’m aware of. You have to ask the agent, do this for me, and then you hope that it does the right thing. So this is a huge deal which you just showed. Right?

Anton Osika: Yeah. Now it says buy now.

Lenny Rachitsky: Okay. Like that’s amazing. Okay, and that’s something you just launched?

Anton Osika: Yeah. Great. We just launched this a few days ago, but I won’t go into for building the full functionality, but what it looks like is that you connect an open source backend as a service and that’s called SuperBase. And I have this instance to connect to that’s completely empty, just like one click to set that up and now it’s connected to the backend. It’s just automatically generating some code and explaining what I can do next. And what I would do now is, say, let’s add login, let’s say let’s add login.

Lenny Rachitsky: And where is it actually hosted on the backend and everything in general?

Anton Osika: So everything can be one click deployed and then it’s running. It’s hosted by a cloud vendor, which is hosting, I think a huge chunk of the internet, it’s called Cloudflare, and the backend is hosted by also good cloud writer, which is called SuperBase.

Lenny Rachitsky: Amazing. Okay, let’s wrap up the demo, that was… Unless there’s anything else, was there anything else really important that you wanted to show?

Anton Osika: No I’ll just explain what I would do next. I would say, okay, let’s add login. Let’s make the listings editable by the users so users can upload listings and then this is going to take a bit more time, but with patience and good prompting skills, you’re going to get to a full working Airbnb.

Lenny Rachitsky: That was a really good piece to add. So basically this is getting to a place where it actually is not so different from actual Airbnb. People can log in, they can add their home, you can add internal tools to add listings for your, say, sales team, ops team. Basically it just will allow you to build a marketplace that looks a lot like Airbnb. Amazing. Okay, thank you for the demo. I think for a lot of people they’re like, “Yeah, yeah, I’ve seen this kind of stuff,” for most people, like, “Holy shit.” It’s unreal what… It’s almost like we’re taking for granted now. You can ask an app to build you a whole website and that costs probably like a few pennies. It took like five minutes versus it would’ve been tens of thousands and weeks and weeks and months to even build just a prototype.

Anton Osika: I mean, these tools as we see here, they’re already very good, it looks really good as well, but mainly I would say they’re getting better very, very fast. And I’d say one of the bigger bottlenecks is now they’re not integrated into the current way that you have your existing products and so on. But since they’re getting better so fast, I think the best thing for people who are interested in this or interested in just being a part of the future economies, get your hands very dirty with these tools because being in the top 10% in using them is going to absolutely set you apart in the coming months and years.

Lenny Rachitsky: So let me follow that thread. So say you are magically able to sit next to everybody that is using Lovable for the first time and you could just whisper a tip in their ear to be successful with Lovable, what would that tip be?

Anton Osika: It takes a lot to master using tools like Lovable and being very curious and patient and we have something called chat mode where you can just ask to understand like, “How does this work? I’m not getting what I want here, am I missing something? What should I do?” Is the best way to be productive is also one of the best ways to just learn about how software engineering works, which is you don’t have to write the code anymore, but it is useful to understand how software engin- or how building products works. So I think that’s the patience and curiosity is super useful. The second part that we spoke about is that being, if I would sit next to you, I would probably say like, “Hey, you are not being super clear here.” For example, don’t say it doesn’t work. Just explain exactly what you’re expecting and which parts are working and which parts are not working. And that’s something that a lot of people don’t do naturally.

Lenny Rachitsky: I love that when you have an engineer you’re working with that does a very expensive mistake to miscommunicate something, to just forget about a feature, to forget a better requirement, and here it’s… You do that and then 30 seconds later you’re like, “Oh okay, sorry, that was wrong.” And then you could just try again.

Anton Osika: That’s true. It might be more costly with humans.

Lenny Rachitsky: Okay, and so the first step is chat mode. So your advice is chat with the… What do you call it? Do you call it an agent? What’s the term for the thing that you were talking with?

Anton Osika: Yeah, Lovable is an agent.

Lenny Rachitsky: Just Lovable?

Anton Osika: Yeah.

Lenny Rachitsky: Okay. So you’re talking about Lovable by the way. How decide on Lovable as the name? It’s so sweet.

Anton Osika: I think it’s all about building a great product. That’s what I want more people to be able to do and the best word for a great product is that it’s Lovable. A lot of jargon that I like to use to emphasize what we should be striving for is building a minimum Lovable product and then building a Lovable product and then building an absolutely Lovable product. So I took that jargon with me in the company name.

Lenny Rachitsky: That is great. Absolute Lovable product. ALP is the new MVP. Okay, so we talked about this, the scale you guys have hit at this point, I imagine it’s far beyond 10 million ARR. Do you share that at this point or are you keeping that private?

Anton Osika: We don’t anchor on the numbers, but I could probably do a two X tweet about this quite soon. Yes.

Lenny Rachitsky: Okay, so it’s far beyond 10 million ARR at this point. It’s one of the fastest growing startups in history, the fastest growing startup in Europe. I want to zoom us back to the beginning. What is the origin story of Lovable? How did it all begin? What was the journey to today?

Anton Osika: I think I was not impressed by what people were doing with the large language models [inaudible 00:22:21], especially after I was using them way back. But when ChatGPT came out, they were starting to get really good at taking a human instruction and spitting out code and then people in my team, I was the CTO at a YC startup, they felt like, “Oh, Anton, you’re exaggerating. This is not going to change anything in the coming years.” So I wanted to prove a point and I created an open source tool called GPT Engineer where you write something like create a snake game and then it spits out a lot of code, a little of different files and then opens the snake game. And then I tweeted a video about that and GPT Engineer is to date the most popular open source tool to showcase the ability for large language models to create applications and it’s at like 50 something thousand GitHub stars and dozens of academic references.

Lenny Rachitsky: And I know that I’ll just add that it GitHub shut you down because they thought it was some kind of attack, like how many stars you’re getting, how many people were using it,

Anton Osika: Right. Yeah. So that came later. That’s with Lovable. So this is Lovable. Lovable, earlier was always creating new projects on GitHub when someone used Lovable and we asked them, “Is it fine? How was the limits here?” They said, “Oh, there are no limits.” But once we started creating 15,000 projects per day, so there were a lot of usage. Then some engineer when was on call, maybe they woke up in the night and they saw their servers were taking too much load because of us. So then they shut off down completely and we got this email that said, “Oh, you broke some kind of rules and we didn’t know what was going on.”

Lenny Rachitsky: That’s similar to a story I heard when ChatGPT was originally being trained, Microsoft servers blocked it because they thought it was some crawler and it was just actually the very first version ChatGPT being trained on data. Anyway, keep going.

Anton Osika: So I built this tool called GPT Engineer and I was thinking about we’re seeing the biggest change humanity will ever see, I think, where before you had the manual labor being taken over by machines, but now it’s actually cognitive labor being done better than humans by machines and what’s the best way to have some kind of positive impact here? It’s not to make engineers more productive, which there’s a lot of companies using AI to make engineers more productive, Microsoft did with co-pilot and so on. But it is to enable those who have such a hard time finding people who are good at creating software that’s been their absolute bottleneck and let them take their ideas and their beliefs into reality. So enabling more entrepreneurship and innovation by building the AI software engineer for anyone. And then I grabbed a previous colleague of mine who has also been a founder, Fabian, and I said we should build something like GPT Engineer but it has to be for the people who don’t write code and that’s the story.

Lenny Rachitsky: Okay. And then that became lovable? There’s the shift from open source into a product that anyone can use but also pay for. Makes sense. Okay, so from that point I saw a stat that you started making a million dollars in ARR per week and once you launched lovable, is that true?

Anton Osika: Yeah, so launched, we actually called the first version of the product like GPT Engineer app and it was very different in some ways and we launched that under a waitlist and said like, Oh yeah, we have this waitlist and we got a lot of feedback and iterated. Finally, when we thought the product was really good we said okay, now we have a Lovable product. And it was mainly on the AI that we did a lot of improvements, once we launched that, that was 21st of November, so that’s almost three months ago. We just hit 1 million ARR in a week and then it kept growing at that pace. It still growing at even faster than that pace.

Lenny Rachitsky: Faster than 1 million ARR per week. Holy shit.

Anton Osika: Yeah.

Lenny Rachitsky: Okay, that sounds like product market fit to me. You said that you did a lot of work on the backend. I saw you tweet about this that you guys figured out some kind of unlock on scalability, like a new scaling law that allowed you to build something like this. What can you talk about there that on the technical element allowed you to build something new and the successful?

Anton Osika: There are many scaling laws I would say when you build AI systems and this one in particular is about when you put in more work, the product reliably gets better and better. And what you’ve seen generally when you have AI building something is that it can get stuck in some place. It is super good in the beginning and then it gets stuck. What we did was to painstakingly identify places where it got stuck and there is different approaches but address different ways how we do it but address the places where it gets stuck, tune the entire system quantitatively and having a very fast feedback loop to improve it in the areas where it got stuck. The most important areas, it still does get stuck sometimes, but that’s the scaling law and we’re still early in that scaling law, I would say.

Lenny Rachitsky: And so when you talk about things getting stuck, it’s like the AI agent just saying, I don’t know what to do from this point or they introduce some kind of bug. Is that an example of getting stuck?

Anton Osika: Yeah. It introduces some kind of bug and then it’s not smart enough to figure out how to get out of that bug.

Lenny Rachitsky: I see. And this is a common problem people have with tools like this is they get to a certain point and then it’s like, “Well I don’t know what to do. I’m not an engineer, here’s a bug it’s running into or the infrastructure’s built the wrong way.” And so it sounds like one of the paths to solving that is what you’re describing is you make the AI smarter to avoid more and more of these places they get stuck. Another is people just learning how to get AI unstuck. This is something when we had Amjad on the podcast from Replit, he said that this is the main skill that he thinks people need to learn is how to unstuck AI when it runs into a problem. Just thoughts there, I don’t know anything along those lines come up as I say that.

Anton Osika: This is something that is a problem today and the frontier of where this is a problem is very rapidly receding back. So what we did was we identified the most important areas, so specifically adding login, creating data persistence, adding payment with Stripe. Those are the things that we made sure it doesn’t get stuck on, for example. And the places where it gets stuck today is currently something that you can use being very good at understanding and getting unstuck, but in the future it won’t be so important. This experience just going to not get stuck.

Lenny Rachitsky: And I know you’re not talking super in-depth about this because this is one of your unfair advantages, this kind of stuff you figured out. So I’m not going to push too far. I know you want not everyone’s into exactly the same stuff. So I want to zoom back to the pace of growth that you guys have seen. One of the big stories, everyone’s always looking at you guys of like 15 people, 10 million ARR in two months. That’s absurd. I don’t know if it’s ever been done in history. If so, it’s maybe a couple other AI startups recently. How have you been able to do this? What have you done that has allowed you to grow this fast with so few people?

Anton Osika: I’d like to take credit of having done everything end to end in the product, but we are building on top of taking the oil here, which we have discovered oil, which is are the foundation models and then what we’ve done is that we’re obsessed about what’s the right way to present this to a user. What’s the interface for the human to get as much out of this as possible? Packaging together, I showed you in the demo how you can add authentication and making this work seamlessly together as a whole. That’s what we’ve done. And then people love the product. That’s the driver of the growth. For getting awareness, we’ve mainly been posting what we’ve shipped on social media, that’s how people know about us.

Lenny Rachitsky: So building in public is how people usually describe that. So I think it’s like you guys have the advantage of the demos are just like, “Holy shit, you can do that.” And then you guys share the numbers that you guys are growing at. So it’s innately interesting and shareable, but I imagine most people have something interesting to share. I guess is there anything that you think you did that other companies maybe haven’t done that make the product so lovable?

Anton Osika: I mean the team is everything in building a great product, so I just give a big shout-out to the team that has written the code. I haven’t written much of the code recently, I would say. You want people who can ship really fast and have good taste for what this simple, what’s the right abstractions and I think that’s what we’ve done differently and have this obsession for us making it better and better and better.

Lenny Rachitsky:

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Okay, I want to come back to the team. I know you have a lot of thoughts there in terms of writing code, how much do you guys actually use AI to write the code that is building Lovable? How does that work on your team?

Anton Osika: We have set up lovable so that we can change lovable with itself. We have done that. There is a lot of hyper-specific things in terms of running a separate… We spin up a dedicated computer for each user. It doesn’t do everything. Lovable doesn’t do everything. So we use the tools that are for developers, not for the 99%, most of the time. And everyone uses AI all the time in writing code. It’s also in great course for experimentations.

Lenny Rachitsky: And are there tools like Cursor and stuff like that? Like any tools you can share right now?

Anton Osika: Yeah. I think Cursor is the one that almost everyone uses in the team.

Lenny Rachitsky: Yeah. Okay, cool. I did a survey recently on tools that my listeners and readers use in cursor. 17% of all people that read my newsletter use Cursor already, which is absurd and you guys are in there, too. Okay, so kind of along these lines, there’s obviously other competitors and companies in this space, so everyone’s always wondering, you, Bolt, Replit, Cursor is a different kind of thing. What’s the simplest way to understand maybe how Lovable might be different from say Bolt and Replit, which I think are probably the closest.

Anton Osika: The packaging for non-technical people is what we aim for and I showed you in the demo that you can edit the text, you can change the colors and so on instantly without having to go into the code editor and without having to wait about 30 seconds for the AI do the full change. So that’s the big way that we think about packaging it. And then for making sure that this can be used as productively as possible in a larger team. Something that’s different from I think all the other tools is that it is synchronized with GitHub and that means that you can use Cursor, or the people in your team that want to be more low-level, they can use Cursor and while the people who don’t want to mess and set up their local file system and commit to GitHub and so on, they can use Lovable’s.

Not getting stuck is I think the most important thing for people. And that’s why we entered this space late, we haven’t done the same type of marketing as many others and we still, from the people that I talked to, ranked as the one that works most reliably.

Lenny Rachitsky: I love it. Okay. So this point about how you can just use Lovable to build a lot of it for you and then get into Cursor to edit and tweak is a really big point. And you’re saying other companies aren’t as good at that. I don’t know if any other does that.

Anton Osika: Yeah.

Lenny Rachitsky: I don’t think they let you do that. Amazing. Okay. And then what’s the vision for Lovable? What’s the end state of this? Is this everybody can build anything they want sort of thing? What’s the simplest way to understand where you’re going in the next, I don’t know, five, 10 years.

Anton Osika: I have to say. So we’re building the last piece of software and it is inherently very hard to predict how the world looks like in five years. These days it’s very hard. But the last piece of software, how I see that is that it’s almost instant to go from what you want to change in the product or what product you want to build to having it fully working end-to-end, integrated with any of your existing systems or integrated with the very powerful third-party providers. Already today you can just ask, add and chat with OpenAI and then you get the chat with OpenAI in your product. But that’s just working perfectly is something that’s coming in the coming two years, I would say. And then after that there is a lot of things in building a product that is not just the engineering side, right? And I think an AI can be very useful in aggregating and understanding your users.

So, if you use the analytics tools, you know that there is something quite common which is to see how users have interacted with the product. AIs can do that on an absolutely massive scale and propose changes to a human to say, “Oh, yeah, that sounds like a good change to make it a bit more intuitive.” And it can also automatically run A-B tests so that you can see the data, all these improvements to the product. I think that’s on the horizon as well, quite.

Lenny Rachitsky: What’s interesting about this in one way is people wonder just what jobs will be more important, what skills will be less important? Let me share a thought I have and then I want to get your take and see where you go with this. It feels like what is getting more valuable is being good at figuring out what to build and then knowing if the thing you had built is correct and good and ready. So it’s like discovery, ideation, idea, part of the step of launching a product and then it’s like taste and craft. Just like is this the thing? Is this going to solve people’s problems because the building now is being done more and more and it’s interesting, it used to be the reverse engineering was the hardest, most valuable skill and now it’s figuring out what to build.

You could sit there and you could just tell it what to build and a lot of people get to your screen I’m sure and they’re like, “I don’t know what to build, I don’t know what people want.” And it’s like that’s the thing now. So just reactions to that and thoughts on what’s skills will matter more and less.

Anton Osika: I mean if you’re a founder or you want to build something. Yeah, I totally agree that figuring out what are pain points and seeing there are often currently solutions, some kind of solution to everything. How can you make this 10X better somehow figuring that out is super important when you have an existing product. Then I think taste and tasting what is good is even more of the important part. The engineer skill set is still going to be important because that helps you understand what are the constraints, so what you can build and I just think a lot of software engineers are probably a bit scared now like, “Okay, am I out of a job? What’s going to happen?” But they should see themselves as the people who translate the problems that are stated a human, probably, to technical solutions, but they do have to abstract themselves up a few steps, not just looking at in their tech stack like oh I can just do the front end changes. Engineers or technical people are very good at understanding what are the constraints technically and they should see themselves as that translators.

Lenny Rachitsky: Is it almost like you want to learn the eng manager skill of overseeing engineers versus the actual engineering skill or you think it’s still going to be really important to learn how to code and be really good at that?

Anton Osika: I mean doing a bit of everything. Being a generalist is I think much more important than it used to be. And if I’m putting together a product team today, I would re-obsess about getting as many skill sets as possible for each person I hire. They should know how architecting a system works, preferably they should know the sign, they should have product taste, they should know how to talk to users. I think everyone should know a bit of all of that, preferably.

Lenny Rachitsky: Easier said than done. It’s hard to find people that know all these things. So let’s segue to hiring and how you hire. How many people do you have at this point? Is that something you share?

Anton Osika: Yeah, now we’re at 18.

Lenny Rachitsky: 18. Okay. Wow. I love that you… It sounded like you’re about to say, “Oh, we have a hundred people now.” No, 18. Okay, so you went from 15 to 18. Okay, great. So what do you look for when you’re hiring people? The way I saw you describe it on Twitter is you look for cracked engineers, the best crack team in Europe, things like that. I guess just specifically what are you looking for when you’re hiring?

Anton Osika: I think the most important thing is that people care a lot and they’re not just like, “Oh, I’m here for a job. I’m here for being just a passenger on this journey,” but everyone should really care about the product, the users and care a ton about the team, how the team works together and that you’re always contributing to making the team work more productively together and that care or preferably obsession gets you a very long way and then you do often want to have absolute superpower in some dimension to be able to understand and do as many possible things as possible, have this generalist brain that quickly learns any skill but be super, super good in one dimension. And for us that’s mostly cramming as much out of AI, out of the large language models and understanding the entire parameter space of what you can change to make our product perform better.

Lenny Rachitsky: So, how do you actually test for these things? Some of these things describe, I think everyone’s looking for, they care about the user, they want to collaborate well. Because you have 18 people building in a company that’s growing more than a million ARR every week. That’s an absurd scale and the people you have found are clearly world-class and I think a lot of people are going to want to hire the type of people you’re hiring. So when you’re actually interviewing, how do you suss out some of these things like their AI cramming skills, their team building collaboration, what do you actually do?

Anton Osika: I ask people what they’ve done before and these people that I’m describing, they have often done something where they care a lot about what they’ve done before and dig into details about the technical things that they did. And then we do the normal thing of showing a very hard problem that is a bit unorthodox that someone hasn’t seen before preferably and see how they think through thinking research through that. Then something that I think is more uncommon is that we do, I pretty much always have people join the work simulation for at least a day, often a full week.

Lenny Rachitsky: Awesome. Okay, so work trial. That’s awesome. So basically they work with the team for at least a day. You said sometimes a week, and I love this point you made about they care deeply about something they previously worked on and you look for just obsession with the thing that they built last or something they worked on. What percentage are engineers of these 18?

Anton Osika: So 12 at least write code at least part-time.

Lenny Rachitsky: 12 out of 18. Okay, cool. When we were setting up, you’re like, “Oh, our engineer’s creating content now.” I think that’s a cool example of how people do a lot of different things. Also. Okay, so I have your job posting that you shared once of the actual job description. I’m going to read a few lines from it. It’s very inspired by Shackleton, right?

Anton Osika: Yeah.

Lenny Rachitsky: Would you agree? Cool. I love it. By the way, did you write this or did you have AI write this job description where you create an engineering job description? In fact, let me read it to you. I don’t even know, you may not know what M you’re referring to. I’ll read a few lines here. “Long hours, high pace, candidates must thrive under a high urgency under AGI timelines approaching, difficult mission ahead, honor and recognition in case of success, those seeking comfortable work need not apply.” And then there’s a few other things, “Collaboration with other exceptional minds, purpose larger than any normal engineering role, generous share in the venture success.” Amazing.

Anton Osika: Thank you.

Lenny Rachitsky: Thoughts?

Anton Osika: Yeah, so I did get some help with the formatting of this, but then it was mostly me doing the exact phrasing of the different sentences.

Lenny Rachitsky: So good. And I love that to some people it’s going to be like, “Holy shit, I’m not signing up for this.” But to a lot of people, the people you want is like, “Yes, this is exactly what I want to be doing.”

Anton Osika: Great.

Lenny Rachitsky: Amazing.

Anton Osika: Yeah.

Lenny Rachitsky: Okay, cool. So it feels like one of the elements of hiring here is, create a really good filter to be clear about just how intense this is so that the people that want that are the ones drawn to you. Okay. And then you’re also, you’re in Sweden, fastest growing startup in Europe ever thoughts on building in Europe slash Sweden versus the US slash San Francisco?

Anton Osika: Yeah, so this ambition level that you’re talking about in the job ad is more uncommon in Sweden and I think that is the biggest unlock that someone like me, sees that this is the time in human history when you have the most impact for a worked hour and that’s why we have to be super ambitious, just up the ambition level and then we can maybe retire and have AI take care of most things in society and inspiring people to be this ambitious in a place where the average ambition is lower but the talent, the raw talent is much more available, is a great recipe. I think that’s a great recipe. And that’s, I think it’s some kind of advantage there. It is a bit of a double-edged sword but it’s some kind of advantage.

Lenny Rachitsky: So what I’m hearing is there’s incredible people in Europe, they’re just not, they’re harder to find and what I’m hearing is the key is how do you suss them out and get them to want to talk to you?

Anton Osika: Most people in Europe, they haven’t thought that, “Oh, going on an extremely ambitious mission is what I want to do.” So that’s figuring out who those are is a big part of it.

Lenny Rachitsky: Awesome. Okay. I want to talk about prioritization. I imagine all these things that I just shared about just how ambitious this mission is, how much you’re doing the last piece of software, you must have a bazillion things that people ask you to build that you want to build. What’s your approach to deciding what to prioritize and actually build?

Anton Osika: Just top line? I think identifying what is the biggest bottleneck, what’s the biggest problem and iterating fast on saying, “Okay, this is the biggest problem, let’s really, really solve that problem.” And then picking in the next one and not overthinking, not dreaming out the long roadmap, that’s my [inaudible 00:48:41]. There’s a very, very simple algorithm. Understanding what is the, mostly the biggest problem is not always a simple problem I think. Yeah, so we spend time as one should on talking to users, reading up on what people are writing. We have the feature board for where people do a lot of requests, as you say. And then when we pick one of the problems, we are quite engineering-led. For a product like ours, it’s hard to be have product managers that are not engineers say, oh, this is what we should do now because the right solution to the problem might be entangled in things that are technical details.

They might be entangled in technical details of like, “Okay, yes, this is the biggest problem, but we should have this larger technical initiative that’s going to solve all of these problems.” So it’s quite engineering-led compared to many other product companies.

Lenny Rachitsky: As it should. I’d be worried if you guys had a product manager at this point, that wouldn’t make no sense right now. I imagine the answer is it’s chaos and there’s no actual defined process, but just what does it look like generally? What’s kind of the cadence you guys operate on? How do you take an idea to build it, spec it, launch it? Just what does that look like if you have something?

Anton Osika: If you look back three months, we mainly said, “Okay, let’s do this weekly planning.” We do have a big jam board where we have all the main problems and then we have kind of ranked them which else do we focus or when we focus on next or this week? And then we have a demo where we say, “Okay, these are things we ship this week.” So to get everyone on the same page, we do have a bit more of a roadmap now, where we say we are going to make so sure you can support custom domains. Next, they’re going to add collaboration after that. And the biggest problem now or the biggest initiative now that solves the biggest problem is making the system more agentic and that has a bit of a longer roadmap, but we still do the cadence of weekly planning. These are the things we’re focusing on. This week, it’s mostly… There’s a good word for this that I would want your help with, but polish, we were fixing the bugs and polish this week and that was the planning on Monday.

Lenny Rachitsky: That was actually this week was polish, polish week. I love that. How far is this roadmap that you are now having?

Anton Osika: I mean it’s clear over the coming month, but it stretches out three months, but in one month it’s probably going to look a bit different.

Lenny Rachitsky: Okay. And then what are the tools you use just for folks that want to understand the latest tools? So you said FigJam, what else is in that stack of tools?

Anton Osika: I mean we do so many things in our company in Linear because it’s just amazing product. So we do talent application tracking in Linear and after going through and this thing, lot of the other custom-made tools for that Linear and then FigJam.

Lenny Rachitsky: So simple. How soon until one of your engineers is an agent engineer, an AI Engineer, do you have a sense?

Anton Osika: I love to dig into what does that question actually mean? I think we’ve been talking about, Oh, AI that would require something playing chess, that’s AI. If a computer can play chess, that’s AI and now that’s like, Oh no, that’s a chess program and which always shifting this forward and forward. I think anything that a human doesn’t do is just a smart computer system, right? When is an software engineer and agent, I think it’s always going to be just we’re building in… Lovable is just an interface that humans interact with to create the software that they want and then how we solve that, we said going to be an agent under some definition. Yeah, sure. I think so, but that’s less important to me.

Lenny Rachitsky: Okay, I like that. Let me ask this, you guys are moving super fast, scaling like crazy. You described a little bit about your process, weekly planning, FigJam board of ideas and now there’s a roadmap that you’re kind of thinking out in the future. Is there anything else that you found helps you move this fast that gives you a lot of leverage over the small team you have to ship quickly and move fast that you haven’t already mentioned?

Anton Osika: We work from the office most of the time. I think it’s pretty nice. Then you can say like, “Hey, I think we’re thinking wrong about this thing,” or, “Shouldn’t we actually do this other thing?” And especially I think lunch, eating lunch together is a pretty productive hour where you’re cross pollinating. I mean people are constantly thinking subconsciously as well about how to solve these different problems and which the most important ones are. And then being in office has this focus or most of the time usually focus, but you also have this high bandwidth where everyone has to be down structured communication.

Lenny Rachitsky: I love that. The answer to the CEO of a company that’s one of the most advanced AI tools in the world is one of your answers to how to move fast is lunch together. I love that. That’s so human and so it makes all the sense in the world, but I love that that’s still a part of this.

Anton Osika: Yeah.

Lenny Rachitsky: Okay. You talked about this kind of on the same thread you talked about if you were to start a team, like a new product team today, say you were head of product somewhere or head of RPM, VP of product somewhere building a new product team, scaling a product team, what would you do going forward that’s different from what people have done in the past in terms of who you’re hiring, how you’re structuring them, that kind of thing? Just what do you think people should be thinking as they build product teams going forward? Knowing tools like Lovable exist and all the other stuff that’s going on.

Anton Osika: I mean everyone should be excited about using AI. I think that’s a pretty big ones. And then the team working really well together is, like the lunch, you have to sit down and solve problems together. The bottleneck for most products these days is not going to be as much on engineering, but having good taste, good intuition about your users. And that, engineers and everyone preferably in the team should have that willingness at least to want to go through that motion and listen to the users and truly understand what they care about.

Lenny Rachitsky: Well it’s kind of like the background of most of the engineers and people you’ve hired. Is there anything in common? Are they just super impressive humans generally, like champions of programming contests, stuff like that? I don’t know. What are some attributes of the folks you’ve hired so far?

Anton Osika: I think raw cognitive capability is the strongest, the strongest correlate of being at Lovable. But there is this startup mindset that I think is also very strong. Being much more interested in moving very fast and iterating fast, then having a lot of structure, a lot of process and thinking about the business as a whole. More than thinking about my specific profession, my specific craft that I’m seeing myself wanting to dig into on me.

Lenny Rachitsky: Amazing. Okay. So smart, very smart entrepreneurial, acts like an owner, isn’t just like, this isn’t just a job. But they feel like they actually have agency. Okay, this is great. There’s something you said kind of along these lines that I think is important that one of the things that gets you excited about what you’re building is giving people super powers and especially people that don’t add a code, basically 99% of people. Is there anything along those lines that you think is important to share?

Anton Osika: It’s very clear to most people who have been engineers or been founders, that there’s so many that have failed in their endeavors because they didn’t have someone that know how to solve the technical parts. And now that we’re close to having people know that this exists and they solve everything, it’s going to be an Cambrian explosion of entrepreneurship and better software product. We’re not going to settle for all the annoying bad technology that we use today. And everyone who has an idea is going to say, “Okay, I’m going to build this thing and show you that this is the best version of the product or what our company should be doing,” instead of having long meetings or writing up documents. So it’s going to be empowering across a lot of different professions and places in the world.

Lenny Rachitsky: What’s next for Lovable? What’s the next few things they might launch as this episode comes out?

Anton Osika: As I mentioned this agentic behavior, and when I say agentic, what it means is that you give more freedom to the system to decide what happens next. It might want to write a test, run those tests and say like, “Oh, the test failed, let’s fix those.” So that’s one of the big unlocks for getting further faster. And then there’s some more obvious things that you want to do to go all the way to easily go all the way to making money with Lovable. And that’s like how do you set up so that it’s hosted on your specific domain? How do you collaborate there seamlessly with your team and making that is here so that those are just obvious things and something we’re thinking about is to help founders succeed after they built their first version. And how do they get more users? How do they get feedback? How do they get the word out if they build something useful?

Lenny Rachitsky: I was just going to say that and that’s exactly where my mind went is everyone’s going to be building all these things. No one’s ever going to get any traction with these tools. No one knows how to find users, get anyone to basically go to market. And growth is a whole different skill. So that is so cool that you’re thinking about that. How do we run some paid ads for you? How do we think about SEO? How do we think about word of mouth, reality referrals? That is very cool. Okay.

Anton Osika: Yeah, we already have playbooks that we help the people building with how do you do those things that you can find up on our blog?

Lenny Rachitsky: Interestingly, this makes me want to buy some meta stock because all these apps that everyone’s building, they’re going to be running paid ads on Facebook and Google. Oh my god, what a good business those other guys get. I want to come back to, you said that you can work on your existing code base. This is actually a big question for a lot of people. They see all these tools, they’re all amazing for prototypes and concepting. You talked about how you can actually do this within your existing code base, use Lovable.

Anton Osika: Let me correct you there. You cannot use it on any existing code base.

Lenny Rachitsky: Got it.

Anton Osika: We kind of have a research preview of importing your code base, but what you can do is if you start in Lovable, then you can have engineers editing it in whatever tool they want to use for editing it.

Lenny Rachitsky: Okay, cool. That’s great clarification. So I guess just for people, because most listeners here are not building something brand new, they’re working within an existing product. So you’re saying that that is coming, you can use Lovable in the future in some form with your existing app and product?

Anton Osika: Correct.

Lenny Rachitsky: Wow, that’s huge. Okay. Because basically most people, so that’s going to be a big deal. Okay. Final question. We have the segment on this podcast called Failure Corner, where most people come to this podcast, they show all these stories of success and everything’s going great, and here’s all the things always winning. You guys, this is a good example, just up into the right, the fastest growing product ever. What’s an example when something totally failed in the course of your career and what did you learn from that?

Anton Osika: I am a bit hard-pressed to find something that totally failed, but I think there’s a bit of a product lesson where I was the first employee at an AI startup here in Stockholm called Summer Labs, and the premise was just, okay, so humans learn in different ways. If you personalize, then you get two standard deviations more effective learning. So there are a lot of products like education software that helps you learn that is not personalized. And we were building an API to personalize learning and the AI and so on, it was pretty good.

But the thing that we were doing in the end was to say like, Okay, here’s this product. Someone has to build a product or some way to learn or be it like English thing Duolingo, and then the people that have that product have to use this advanced AI API to start making it personalized. And it is very hard retrofitting like, oh, you have to switch out the engine and put in this AI. And the big learning here is that it didn’t work very well for the company. I mean, the company wasn’t super successful in this. The big learning is that you have to start with how is this product working end-to-end and then add AI or think where should we add AI? So that was a big learning for me that you really want to see what does the big picture of the user, what’s the big picture of how do you think the user experience should be? And then add something with AI to solve specific problems. And now Summer Labs is doing great, but it’s not on top of that product specifically.

Lenny Rachitsky: I think it’s a lot of people hear this and they’re like, of course, but I think it’s so hard to actually remember this point when you have some cool tech and you’re like, “Holy shit, everyone needs to try this. They’re going to love it.” And then you don’t realize no one actually cares if it’s not solving a problem for them. There’s a lot of novelty products that everyone want to use for a little bit and then forget instant, I don’t actually need this often. And so what this makes me think about is, there’s all these product lessons for what is likely to help your product be successful. And an app like a tool like Lovable can help you do this because if someone is building something, you can guide them, Okay, what’s the problem you’re solving for somebody? How many people have this problem? How much does this matter to them?

Anton Osika: Maybe we should add the Lenny mode. It activates in Lovable, it activates this product coach. That would be infinite questions, like, “No, no, wait, hold on, why are you doing this?”

Lenny Rachitsky: Absolutely. Let’s take a step back. Everyone’s going to be like, “[inaudible 01:04:55], get out of my way.”

Anton Osika: [inaudible 01:04:57].

Lenny Rachitsky: Yeah, exactly. What’s your experiment plan? I think there’s actually a big opportunity there to save people. There’s a play around with this thing and then there’s like, okay, but really is this anything people actually want?

Anton Osika: I love it. Can we call it Lenny mode? Is that fine with you?

Lenny Rachitsky: 100%.

Anton Osika: Awesome.

Lenny Rachitsky: Let’s do it. I’ll license you no cost.

Anton Osika: Sure.

Lenny Rachitsky: Okay. Okay. We made a deal here. Let’s do it. Okay, Anton, is there anything else that you wanted to share? Anything you want to leave listeners with before I let you go and go to sleep?

Anton Osika: I think, again, the world is changing quickly and it’s very fun. You should see that’s like have fun in all of this change, and the best thing you can do for your current profession or if you want to have a new job is to be in the top 1% in knowing how to use AI tools. So go out there, use Loveable, use other AI tools, and become… Make sure to understand or try to understand as much as possible in how to use them productively. That’s something I tell all my friends generally, and I love the audience to know as well.

Lenny Rachitsky: Okay. Well, I got to try to make this even more specific for people. How do you know if you’re in the top 1%? What’s a heuristic almost slash how do you get there? Is it just use it a hundred times a day? What else? What can you recommend?

Anton Osika: Yeah. I think if you spend a full week on trying to reach an outcome, the best way to learn is I want to do this thing and then I want to use AI to do that thing. And you’ve spent a full week, you are in the top 1% in the global population. And if you surround yourself with friends who have this obsession or they also care a lot about this, then you’d be quickly in the top 0.1%.

Lenny Rachitsky: So what I’m hearing is find a problem that can be solved, find a problem, a pain point for yourself or someone, and then end-to-end fully solve that problem. Spend a week getting from idea to a thing that somebody’s actually using and you’re in the top 1%.

Anton Osika: Yeah. I think… At the top, yeah, the top 1% by just spending a full week and asking AI if you don’t understand. So making sure that you understand.

Lenny Rachitsky: Yeah, that’s the thing people forget. You just ask. Would you ask the chat feature of Lovable in this case or would you go to Cloud or ChatGPT to ask for advice?

Anton Osika: I mean, my recommendation here, if you’re in product is to use Lovable to build software and learn that AI tool and then you should use ChatMode and ChatMode, I have to add, is something you activate in your user profile. It’s not launched in the main product, so it’s in labs, but if you add that flag, then you can use ChatMode. If you want to learn some other AI tool, then you should ask that tool or ask Cloud, ChatGPT about how that topic, that domain works.

Lenny Rachitsky: Okay, amazing. Where can people find you? Where can they find Lovable and how can listeners be useful to you?

Anton Osika: Lovable posts updates, and memes on Lovable underscore dev on Twitter, we post things on LinkedIn as well, and there are a lot of things coming out and changing in how we build software, so you can follow Lovable underscore dev and you can follow me at AntonOsika at Twitter. I’d love more feedback on where people see this is a huge change for them. There are a lot of people posting about that on Twitter, but we have a Discord where you can share like, “Oh, this is how I use Lovable. It was super useful to me.” And feedback.lovable.dev can ask for new features. There’s a lot of people asking and uploading what features you want next. And that’s super useful. That’s the most important thing for us. We just want to solve people’s problems.

Lenny Rachitsky: Amazing. Anton, you’re doing incredible work. What a journey. I’m excited to have you back someday when we see more chapters of this journey.

Anton Osika: I have a lot more to learn.

Lenny Rachitsky: As do we all. That’s why people listen to this podcast. Anton, thank you so much for being here.

Anton Osika: Thank you so much, Lenny.

Lenny Rachitsky: Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny’sPodcast.com. See you in the next episode.

Glossary

English	中文
A/B tests	A/B 测试
Amjad	Amjad
ARR	年经常性收入 (Annual Recurring Revenue)
backend	后端
Bolt	Bolt（AI 应用构建平台）
Copilot	Copilot（微软 AI 编程助手）
Fabian	Fabian
GitHub	GitHub
GPT Engineer app	GPT Engineer app
minimum lovable product	最小可喜爱产品
mockup UI	模拟界面
Replit	Replit
Stripe	Stripe

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Reformatted by reformat_english.py