Lenny's Podcast: Product | Career | Growth - Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO)

Episode Date: March 9, 2025

Anton Osika is the co-founder and CEO of Lovable, which is building what they call “the last piece of software”—an AI-powered tool that turns descriptions into working products without requiring any coding knowledge. Since launching three months ago, Lovable hit $4 million ARR in the first four weeks and $10 million ARR in two months with a team of just 15 people, making it Europe’s fastest-growing startup ever.—What you’ll learn:1. Why you need to be in the top 1% of AI tool users2. Watch Lovable build a functional Airbnb clone in 30 seconds—complete with working features and modern design3. The unconventional hiring approach that helped build a 15-person team capable of extraordinary execution4. How traditional product development will look with AI5. What skills will matter most to product teams going forward6. How Anton’s team discovered a breakthrough in AI “unsticking itself”—Brought to you by:• Sinch—Build messaging, email, and calling into your product• Persona—A global leader in digital identity verification• Fundrise Flagship Fund—Invest in $1.1 billion of real estate—Find the transcript at: https://www.lennysnewsletter.com/p/building-lovable-anton-osika—Where to find Anton Osika:• X: https://x.com/antonosika• LinkedIn: https://www.linkedin.com/in/antonosika/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Anton and Lovable(05:12) Lovable’s rapid growth(09:39) Live demo: Building an Airbnb clone(18:34) Tips for mastering Lovable(21:42) The origin story(26:50) Scaling laws and getting AI unstuck(33:20) Reliability and unique features(36:25) The vision and future of Lovable(38:14) Skills and job market evolution in the age of AI(40:30) Hiring philosophy and team dynamics(46:21) Building in Europe(48:02) Prioritization and product roadmap(51:38) Tools and work environment(53:17) Tactics for moving fast(54:37) Advice for building product teams(57:11) Empowering non-technical founders(58:31) Future developments and user support(01:01:23) Failure corner(01:05:20) Final thoughts and advice—Referenced:• Lovable: https://lovable.dev/• Lovable Launched: https://launched.lovable.app/• Cloudflare: https://www.cloudflare.com/• Supabase: https://supabase.com/• GPT engineer: https://github.com/gpt-engineer-org/gptengineer.app• Microsoft Copilot: https://copilot.microsoft.com/chats/cmFw8dTsGU8D6b9siqQ6U• Fabian Hedin on LinkedIn: https://www.linkedin.com/in/fabian-hedin-2377b0144/• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Replit: https://replit.com/• Cursor: https://www.cursor.com• Bolt: https://bolt.new/• GitHub: https://github.com/• Lane Shackleton on LinkedIn: https://www.linkedin.com/in/laneshackleton/• FigJam: https://www.figma.com/figjam/• Linear: https://linear.app/• Sana Labs: https://sanalabs.com/• Duolingo: https://www.duolingo.com/• Claude: https://claude.ai/• ChatGPT: https://chatgpt.com/• Lovable on X: https://x.com/Lovable_dev—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].—Lenny may be an investor in the companies discussed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

Loveable 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 that's creating software. That's been their absolute bottleneck and let them take their ideas and their beams into reality. 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 love? Is the name? It's so sweet. 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. People wonder just what jobs will be more important, what skills will be less important. Doing a bit of everything, being in generalists is I think much more important than it used to be. If I'm putting it together a product team today, I would really obsess about.

getting as many skill sets as possible for each person I hire. What have you done that has allowed you to grow this fast with so few people? People love the product. That's the driver of the growth. Today, my guest is Anton O.C. Anton is co-founder and CEO of Loveable, 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 Loveable 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, crossed 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 does most enable 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's newsletter.com. With that, I bring you Anton O.C. This episode is brought to you by Cynch, the Customer Communications Cloud. Here's the thing about digital customer communications.

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the adaptable identity platform that helps businesses fight fraud, meet compliance requirements, and build trust. While you're listening to this right now, how do you know that you're really listening to me, Lenny? These days, it's easier than ever for fraudsters to steal PII, faces, and identities. That's where Persona comes in. Persona helps leading companies like LinkedIn, Etsy, and Twilio, securely verify individuals and businesses across the world.

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Learn more at with persona.com slash Lenny. Again, that's with P-E-R-S-O-N-A dot com slash Lenny. Anton, thank you so much for being here. Welcome to the podcast. It's a pleasure to talk to you, Lenny. Great to be here. I don't know how you have time to do this podcast. Your life must be insane these days with the, uh, 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 year time is when we're doing this. I'm a bit tired, yes. Mostly from the crazy pace of everything. We're going to, this is going to be an invigorating conversation. Yes. Not going to be able to sleep.

I'm sure. I'm sure. Okay. So for folks that are maybe a little bit familiar with lovable or not at all familiar, What's just, what is lovable? What's the simplest way to understand it? 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 their teams. and some of them become founders because of the empowerment from this. But also developers themselves, they're actually writing code or creating products much faster. And I mean, the reason it's pretty obvious for me, so I'll spell it about, 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 my friends, almost all my friends throughout my life reached out for help. Like, Anton, I want, I need to build something.

How do I find a great software engineer? And we're 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. That's how is it. I'd love the way that you guys describe it and you didn't mention it, but I think it's like building the last piece of software ever.

How do you phrase that? Yeah, we say we're building the last piece of software. 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? Yeah, so we launched Lava both less than three months ago and now we have 300,000 monthly active users

and 30,000 of those, 30,000 of those are actually paying. And it's growing at the same rates, just almost only through organic, word of mouth. 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 there? 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 just know we have to throw everything away and rewrite it in a more performance way. 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 now are actually companies?

I mentioned designers using Lovable. And one of our early users, Harry, he started shipping real web apps to his clients. He stills just shipping the signs. 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, lets anyone upload their photo library, and then it's like the AI, parses and categories as it. And if you go to launch.org.com, like, this is an app built with lovable, which is, you know, a product hunt version where you can see a lot of businesses or small SaaS featured there.

Okay, cool. So we're going to come back to some of this stuff. But let's get into a, 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 is 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 so Lenny I was thinking um did you ever consider making a copy and build your own Airbnb this I haven't but go on how about you do that Let's do it.

Let's do it. Okay. So we're going to make our own Airbnb. Okay. So I just put in the first prompt for an Airbnb clone. Okay. And what is the prompt? Just for folks that aren't watching.

Two words, Airbnb clones. That's the problem. I like, she starts info. And then what you get is that the AI says, okay, I'm going to, I can go through what does a beautiful Airbnb clone look like? And it goes through a bit of like 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. 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? The first prompt takes 30 seconds.

30 seconds, okay. And it's like a very good copy of Airbnb. I love that you didn't have to show it to design. You just tell it, Airbnb and it knows. Okay, so your question is what 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 like a way to buy a listing? Okay, okay. So let's let's, how about we add, I mean, prompting is important here, so let's be specific, but we would ask creating a

add a button on the listing, which has purchased this Airbnb home. Is that it? Perfect. So add, I've got on this. And I'll be even more specific. It will pop up a model to purchase the listing. 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 ask this AI engineer to build, like it's actually a functioning website that you can browse around. It's not just a design. the say obviously there's no like actual listings here like there's no actual houses here say you were trying to like actually build Airbnb and you wanted to start adding like actual homes that plug into this how does that sort of step work so as you say this is just kind of

the mock up UI but it's also 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 users 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 of 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 really, it was probably surprised by you wanting to buy the listing since it's Airbnb, right? So it still says book the listing. But it shows a pretty model where I can click confirm and pay. And then it says booking confirmed. I'll just say it 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. You know, 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... Hey, explaining exactly what you expect and what you're... you're not getting even more important with AI than with humans. But so I go into hooking up more of the actual functionality. But first, I'll actually show you something that was 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 to select this item and say, change it to. buy now. But what we just released 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 wicks 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. So I think people listening to this and seeing this, if you're not aware, like this is the cutting edge of tools like this. this, no other tool out there lets you generate code from an AI engineer and then actually

just like change a small element of it of every other tool that I'm aware of you have to like 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. Yeah. Now it says by now. Okay. And that's something you just launched. Yeah. Great. You just launched this a few days ago. But I want to 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 like automatically generating and explaining some code and explaining what I can do next. And what I would do now is say, let's let's add login, let's say. That's ad logging. And where is it actually hosted on the back end? Everything in general. Yeah, 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 Cloudflared. And the backend is hosted by also a good cloud provider, which is called SuperBased. Amazing. Okay. Let's wrap up the demo. Unless there's anything else,

was there anything else really important that you wanted to show? I mean, 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.

That was a really good piece to add. So basically, like, 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, like, 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 like it would have been tens of thousands and like weeks and weeks and months even build just a prototype. I mean, these tools, as we see here, they're already very good. Like it looks really good as well. But mainly, I would say they're getting better, very, very fast. And I'd say, like, 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 it seems like getting better so fast. So fast, I think the best thing for people who are interested in this or like 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 be to absolutely set you apart in the coming months. and years. So let's 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? 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 and like to understand like how does this work. I'm not getting what I'm What I want here, am I missing something? What should I do?

Is the best way to be productive? It's 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 engineering 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, I would, if I would sit next to you, I would probably say like, hey, you're not being super clear here. Like, 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 a lot of, that's something that a lot of people don't do naturally. I love that like when you have an engineer you're working with, that is 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 like 30 seconds later, you're like, oh, okay, sorry, that was wrong. And then you could just try again. That's true.

It might be more costly with humans. Okay. And the first step, so the first tip is chat mode. So you could just, so your advice says chat with the, what do you call it? Do you call on an agent? What's like the term for the thing that you were talking with? Yeah, lovable is an agent. Just lovable.

Yeah. Okay. So you're talking about lovable. By the way, where did you? How did you do you? side on lovable is the name it's so sweet i think it's all about building i mean a great product um 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 like emphasize what we should be striving for is building a

minimum lovable product and then building a lovable product and they're building an absolutely lovable product. So I took that jargon with me in the company name. That is great. Absolute level 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 error. Do you share that at this point, or are you keeping that private? We don't anchor on the numbers, but I mean, I could probably do a two-x tweet about this quite soon, yes. Okay. So it's far beyond 10 million error 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? How did it all begin? What was the journey to today? I think I was not impressed by what people were doing with the large language models when after, especially after, I was using them way back. But when chat GPT 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 year. 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 different files, and then opens the snake game. And then I tweeted a video about that.

And GPD 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,000 GitHub stars and like dozen of academic references. And I know that I'll just add that it like GitHub shut you down because I thought it was some kind of attack. how many stars you're getting, how many people were using it? Right. Yeah, so that came later. That's with lovable.

Okay. Lovable earlier was always creating new projects on GitHub when someone used lovable. And we asked them, is it fine? Like, how was the limits here? They said, are there no limits? But once we started creating 15,000 projects per day,

so there were a lot of usage. Then some engineer when it 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. That's similar to the story I heard when ChatGPT was originally being trained. Microsoft servers were blocked it because they thought it was some crawler and it was just actually like the very first version. and chat GPD being trained on data. Anyway, keep going. So I built this tool called GBT engineer,

and I was thinking about, I mean, we're seeing the biggest change humanity will ever see, I think, where like before you had 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 to build 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 business 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. Okay, and then that became lovable. There's like 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, that they started making a million dollars in ARR per week and once you launched lovable is that true yeah so we launched and so we actually called the first version of the product like GPT engineer app and that was that's what it was very different in some ways and we we launched that under a wait list and so like oh yeah we have this weight list and we got a lot of feedback and iterated and 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 like one million error in a week. And then it kept growing at that pace. It's still growing up even faster than that pace. Faster than one million ARR per week. Holy shit. That sounds like product market fit to me. You said that you did a lot of work on the back end. 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? 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 kind of gets stuck in some place. It starts, it's super good in the beginning and that it gets stuck. What we did was to painstakingly identify places where it goes stuck. And there's different approaches, but address different ways how we do it,

but address the places where it gets like tuned, 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. And so when you talk about things getting stuck, it's like the AI agent just saying, like, I don't know what to do from this point. Or like they introduce some kind of bug.

Is that an example of getting stuck? It introduces some kind of bug. and then it's not smart enough to figure out how to get out of that bug. 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. Like here's a bug it's running into where the infrastructure is 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 like 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. 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 to identify the most important areas. So specifically adding login, creating data persistence, adding payment with Stripe. Those are the things that we make sure it doesn't get stuck on, for example. And the places where it gets stuck today is currently something that where you can use being very good at understanding and getting unstuck. But in the future, it won't be so important.

This is just going to not get stuck. 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 to do 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,

have like 15 people, 10 million ARR in two months. It's absurd. It's something. 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? I'd like to take credit of having done everything end to end in the product. But what's, but we're building on top of the oil here, which we've discovered oil, which are the foundation

models, right? And then what you've done is that we've always, 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're mainly been posting what we've shipped on social media. That's how people know about us. So building in public is how people usually describe that.

So it's like, 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 make the product so lovable.

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 had written the code much of the code recently, I would say. And

I mean, you want people who ship really fast and have good taste for like what is simple, what's the right abstraction. and I think that's what we've done differently and have this obsession for just making it better and better and better. This episode is brought to you by the Fundrise Flagship Fund.

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Flagship Fund before investing. Find this information and more in the fund's prospectus at fundrise.com slash flagship. This is a paid ad. Okay, I want to come back to the team because 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? We have set up lovable so that we can change lovable with itself.

We have done that. Since there is a lot of hyper-specific things in terms of running a separate, we spin up as dedicated computer for each user, it doesn't do everything. Lavable 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 a great course for experimentation.

And are the tools like cursor and stuff like that? Like any tools you can share. I think cursor is the one that almost everyone uses in the team. Yeah. Okay, cool. I did a survey recently on tools that my listeners and readers use in cursor. Like 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. he's in the space, so everyone's always wondering. You, bolt, replet, cursors, a different kind of thing.

What's the simplest way to understand maybe how lovable might be different from, say, bolt and replet, which I think are probably the closest. The packaging for non-technical people is what we aim for. And I showed you in the demo that you can edit the text, like, you can reach the colors and so on instantly without having to go into like a code editor, and without having to wait about 30 seconds for the AI to 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 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 slavables. Not getting stuck is, I think, the most important thing for people. And that's why we came, you know, entered the space late.

We haven't done the same type of marketing as many others. And we're still from the people that I talked to ranked as the one that works most reliably. 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 any other does that. I don't let you do that. Amazing. Okay. And then what's kind of like the vision for lovable? Like 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, ten years.

I mean, 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. This is 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 a chat with open AI,

and then you get a chat with open AI in your product. But that's like 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 at an absolutely massive scale and propose changes to a human to say like, oh yeah, that sounds like a good change, make it a bit more

intuitive. And it can also automatically run spin-out AB tests so as you can see with data or these improvements to the product. So I think that's on the horizon as well quite soon. Like 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 case 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 like figuring out what to build. You could sit there and you, you could just tell 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 I just reactions to that and thoughts on what skills will matter more and less. I mean, if you're, if you want to, if you're a founder or you want to build something, yeah, I totally agree that figuring out what are, what are pin points and seeing, like, there are often currently solutions to every, some kind of

solution to everything. What is the, how can you make this 10x better somehow? Like figuring that out is super important. When you have an existing product, then I think taste and like refined tasting what is good is even more of the important

part. The tech, like the engineer skill set is still going to be important because that helps you understand what are the constraints or what you can build. And I just think a lot of for engineers are probably a bit scared now, like, okay, am I out of a job and what's going to happen?

But they should see themselves as the people who translate the problems that are stated by a human probably to technical solutions. But they do have to abstract themselves up a few steps, not just like looking at the in their tech stack, like, oh, I can just do the front end changes. They engineers are technical people are very good at understanding what are the constraints technically. they should see themselves as that translators. Is it almost like you want to be learn the Eng Manager skill of overseeing engineers versus like the actual engineering skill? Or do you think it's still going to be really important to learn how to code and be really good at that? I mean, doing a bit of everything, being in generalists, I think much more important than it used to be.

And the, if I'm putting it together a product team today, I would re-obsess about getting as much of as many skill sets as possible for each person I hire. They should know how architecting a system works preferably. They should know design. They should have product taste. They should know how to talk to users. I think everyone should be able to

know a bit of one of that preferably. 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 some you share? Yeah, now we're at 18. 18, okay, wow. I love that you, it sounded like you're about to say, oh, we have 100 people now. 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? 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 as 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 like absolute, absolute superpower in some dimension. To be able to understand and do as many things as possible, like have this generalist brain that quickly learns any skill, but we're super, super good in one dimension. And that's 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. So how do you actually test for these things? You know, like some of these things describe, I think everyone's looking for. Like they care about the user. They want to collaborate well. Just like when you're, because like you have 18 people building a company that's growing more than a millionaire every week. Like that's an absurd scale.

And the people you've found are clearly world class. And I think a lot of people are going to like 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? 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, I mean, we do the normal thing of giving it, showing a very hard problem that is a bit unorthodox that someone hasn't seen before, preferably,

and see how they think through the thinking and reason 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. Awesome. Okay. So work trial. That's awesome.

So basically they work with the team for at least a day. You said like sometimes a week. Yeah. And I love this point you made about, they showed, they cared deeply about something they previously worked on and you can, you look for just like obsession with the thing that they built last or something they worked on. Like what percentage are engineers of these 18? So 12 at least write code in at least part times.

12 at 18, okay, cool. When we were setting up, you're like, oh, our engineers are creating content now. I think that's a cool example of how people do a lot of different things. Yeah. Also, okay, so I have your job posting that you shared once of like the actual job description. I'm going to read a few lines from it. It's very inspired by Shackleton, right? 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 like create an engineering job description? In fact, let me read it to you. I don't even know. You may not know what I'm you were 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 thank you thoughts yeah so i did i did get some up with the the formatting of this but But then I was mostly me doing the exact tracing of the different sentences. So good. And I love that, you know, to some people it's going to be like, holy shit, I'm not signing for this.

But a lot of people, the people you want is like, yes, this is exactly what I want to be doing. Great. Amazing. 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-est-growing startup in Europe ever. Thoughts on building in Europe slash Sweden versus the U.S. slash San Francisco. Yeah, so this ambition level that you're talking about in the job ad, it's more uncommon in Sweden.

And I think that is the biggest unlock that someone like me will forget. who sees that this is the time in human history when you have the most impact for our work hour. And that's why we have to be super ambitious, like 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 role talent is much more available is a great recipe.

I think that's a great recipe. And that's what I think it's some kind of advantage there. It's a bit of a double-edged sword, but it's some kind of advantage. So I'm hearing it's like there's incredible people in Europe. They're just not, they're harder to find in what I'm hearing is like the key is how do you suss them out and get them? to want to talk to you. Yeah. Most people in Europe, they haven't thought that, oh, do it 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. Awesome. Okay. I want to talk about prioritization. I imagine all these things that I just shared about just like 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 purchase and actually build? Just top line, I think, identifying what is the biggest problem, what's the biggest problem and iterating fast on saying, okay, this is the biggest problem, let's really resolve that problem, and then picking the next one, and not overthinking, not like dreaming out a long roadmap. That's my default. There's a very, very simple algorithm,

understanding what is 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 people do a lot of requests, as you say. And then when we pick one of the problems, we're quite engineering-led. For a product like ours, it's hard to 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.

So, 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. As it should, I'd be worried if you guys had a product manager at this point. That would make no sense right now. I imagine the answer is it's chaos and there's no actual defined process. But just like what does it look like generally? Like what's kind of the cadence you guys operate on? How do you take an idea to build it, spec it, launch it?

Just like, what does that look like if you have something? If you look back like three months, we mainly said, okay, let's do this. Weekly planning, we have, we have like a fig jam board where we have all the main problems and then we have kind of ranked them, which also do we focus, one of the focus on next door this week. And then we have a demo where we say, like, okay, are the things we ship this week. So to get everyone on the same page. And we do have a bit more of a roadmap now.

And where we say, like, here are we going to make so sure you can support custom domains next. They're going to add collaboration after that. And like the biggest problem now or the biggest initiative now that solves the biggest problem is making the system more agendic. And that has a bit of a longer roadmap, but we still do the occasions 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, fixing the bags and polish this week. And that was the planning on Monday.

That was actually this week was Polish. Polish week. I love that. How far is this roadmap that you are now having? I mean, it's clear over the coming month, but it stretches out three months and then, But in one month, it's probably going to look a bit different. Okay.

And then what are the tools used just for folks that want to understand, like, the latest tools? So you said, Fig Jam, what else is in that stack of tools? I mean, we do so many things in our company in linear, because it's just an amazing product. So we do talent application tracking in linear. Oh, wow. And after going through and this thing, a lot of the other made,

custom-made tools for that linear. and then fig jump. So simple. How soon until one of your engineers as an agent engineer, an AI engineer, do you think? Do you have a sense? I love to dig into what does that question actually mean. I think we've been talking about like,

oh, AI, that would require or something playing chess, that's AI. Like if an AI, if a computer can play chess, that's AI. And now that's like, oh, no, that's chess. program and we always shifting this forward and forward. I think anything that a human doesn't do is just a smart computer system, right? So what is when is something, when is a 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, is that going to be an agent under some definition? Yeah, sure, I think so. But that's less important to me. 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,

a big jamboard 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 was? 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. 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 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 like focus or most of the time you should be focused, but you also have this like high bandwidth where everyone has a bit unstructured communication. 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 like 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. Yeah. Okay. You talked about this, kind of on the same thread, you talked about if you were to start in 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 like, what do you think people should be thinking as they build product teams going forward,

knowing tools like Levelable exist and all the other stuff that's going on? I mean, everyone should be excited about using AI. I think that's a pretty big one. And then the team working really well together is, like the lunch. You have to like to sit down and solve problems together. you should

at 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 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. What's kind of like the background of most of the engineers and people you've hired? Are they like, is there anything like in common? Are they just like super impressive humans generally?

Like, you know, champions of programming contest, stuff like that. I don't know. Like, what are some attributes of the folks you've hired so far? I think raw cognitive capability is the strongest, like, the strongest correlate of being at love. lovable, but there is this startup mindset that I think is also very strong. Being a bit more, being much more interested in moving very fast and iterating fast than 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 see myself like wanting to dig into on me. Amazing. Okay. So smart, like very smart, entrepreneurial, acts like an owner. Yeah. It doesn't just, uh, 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, uh, I think is important that one of the things that gets you excited about what you're building is giving people superpowers and especially people that don't out of code, basically 99% of people. Is there anything along those lines that you think is important to share?

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 was close to having people know that this was like to know that it was exist and they work they solve everything and it's going to be an campaign explosion of like 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,

like, okay, I'm going to build this thing and show you that this is the best, 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. What's next for Lovable? What's kind of like the next few things they might launch as this episode comes out? 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 see, like, oh, the tests fail, 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 seamlessly with your team and making that that is here so that are just obvious things and something we're thinking about

is to help just 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? I was just going to say that. That's exactly where my mind went as like everyone's going to be building all these things.

no one's ever going to get any traction with these tools because no one knows how to find users, get anyone to basically go to market and growth is like 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 a CEO? How do we think about word of mouth,

reality referrals? That is very cool. Okay. We already have some playbooks that we help the people building with. How do you do those things that you can find up on a blog? Interestingly, this makes me want to buy some meta-stom. 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 codebase. This is actually a big question for a lot of people. They see all these tools. They're all like amazing for prototypes and concepting. You talked about how you can actually do this within your existing code base. Use lovable.

Let me correct you there. You cannot use it on any existing code base. Got it. We kind of have a research preview of importing your codebase, but what you can do is if you start in Loveable, then you can have engineers editing it, whatever tool they want to use for editing it. 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 Loveable in the future in some form with your existing app and product. Correct.

Wow, that's huge. Okay, because that's basically the most people. So that's going to be a big deal. Okay. Final question. We have a segment on this podcast called Failure Corner. Okay. Where most people come in 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 and to 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? I'm 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 Xana Labs. And the premise was just, okay, so humans learn in different ways,

if you personalize that you get two standard deviations, more effective learning. So there's a lot of, products and like education software that helps you learn, that is not personalized. And we could build, we were building an API to personalize learning. And the, I mean, the AI and so 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 where it be it like English.

and think Duolingo. And then the people that have that product have to use this advanced AI API to start to making it personalized. And it was, it's a very hard like retrofitting. Like, oh, you have to switch out the engine and put in this AI. And it's, well, the big learning here is in 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 like 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 how the what is 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 Sondon Labs is doing great, but it's not on top of that product specifically. I think it's a lot of people hear this and I'm 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, like, no one actually cares if it's not solving a problem for them. You know, there's like a lot of novelty products that, like, everyone want to use for a little bit and then, like, forget it. I don't actually need this often.

And so I, like, with this, 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 maybe we should add like the lenny mode she activates in lovable, it activates like this product, product coach. That would say, internet questions you. I'm not a way.

Hold on it. Why? Let's take a step back. Everyone's going to be like, close, get out of my way. Yeah, exactly. What's your experiment? Yeah, what's your experiment plan? That's actually, I think there's actually a big opportunity there to say people,

because, you know, there's like a play around with this thing. And then there's like, okay, but really is this anything people actually want? I love it. Can we call it Lenny mode? Is that a fine with you? 100%. Awesome. Let's do it.

I'll license you no cost. Sure. 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?

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 the AI tools. So go out there, use lovable, 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.

Okay, well, I've got to try to make this even more specific for people. How do you know if you're in the top 1%? What's like a heuristic almost of like slash how do you get there? Is it just use it 100 times a day? What else can you recommend? Yeah, I think if you spend a full week on trying to reach an outcome, the best way to learn is like, I want to do this thing and then I want to use AI to do that thing. And then you've spent a full week, you're in the top 1% in the global population.

If you have friends that 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%. So what I'm hearing is like find a problem that can be solved. Like find a problem, a pain point for yourself or someone. Yeah. And then end to end, like fully solve that problem. Spend a week getting from idea to like a thing that was actually somebody's actually using. Yeah. And you're in the top 1%.

Yeah. I think at the top, yeah, the top 1% by just spending a full week and making, like, asking AI if you don't understand. So making sure that you can understand. Yeah. Like, that's the thing people forget. You just ask. Like, would you ask the chat feature of lovable in this case? Or would you go to Cloud or ChatGPT to ask for advice?

I mean, my recommendation here, if you're in product, is to use Loveable to build software and learn that AI tool. If you're, and then you should use chat mode. And chat mode, 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 chat mode. If you want to learn some other AI tool, then you should, I mean, ask that tool or ask Claude about how that topic, that domain works.

Okay. Yeah, amazing. Where can people find you? Where can they find lovable? And how can listeners be useful to you? Loveable 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 will software.

So you can follow lovable underscore dev and you can follow me at Anton O'Sika at Twitter. I'd love more feedback on what people, like 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 Loveable and was super useful to me. And feedback. dot lovable.dev, you can ask for new features.

There's a lot of people asking enough about what features you want. Thanks. And that's super useful. That's the most important thing for us. We just want to solve people's problems. Amazing. Anton, you're doing incredible work. What a journey.

I'm excited to have you back some day when we see more chapters of this journey. I have a lot more to learn. As do we all. That's why people listen to this podcast. Anton, thank you so much for being here. Thank you so much, Lenny. Bye, everyone. Thank you so much for listening.

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