Mar 9, 2025 · 1h 9m · lennys-podcast
Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder)
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this episode, Lenny Rachitsky interviews Lovable co-founder and CEO Anton Osika to explore how the AI software engineer startup scaled to $10M ARR in 60 days with just 15 people. Osika provides a live product demonstration, breaks down technical scaling laws, and outlines how AI is elevating product taste, lean team execution, and versatile generalists.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Lenny holds 40.5% of the talking time here. How this is scored →
Lenny as informed peer 4.2 Guest teaching 4.0 Guest disagreement 0.7 Lenny pushing back 0.4
speaking balance: gold is Lenny, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 52:15 Anton rejects the premise of AI agent classification
When Lenny asks when an engineer will become an AI agent, Anton halts the premise, arguing that agent definitions are arbitrary shifting semantics.
Hardest push from Lenny ▶ 1:06:05 Lenny demands tangible metrics for AI masteryLenny challenges Anton's generic top 1% advice by pressing him for specific heuristics on how someone actually achieves and measures that proficiency.
Biggest teaching moment ▶ 1:00:41 Anton corrects Lenny on existing codebase integrationAnton directly interrupts and corrects Lenny's assertion that users can plug Lovable into any existing codebase, explaining the strict technical boundary.
Lenny holds their own ▶ 38:14 Lenny delivers an authoritative breakdown of product skill evolutionLenny articulates a comprehensive thesis on how AI shifts product leverage from raw engineering implementation to problem discovery, taste, and craft.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Lenny as informed peer | Guest teaching | Guest disagreement | Lenny pushing back | Why |
|---|---|---|---|---|---|---|
| Episode Highlights and Preview of Lovable | 0 | 0 | 0 | 0 | Introductory trailer and monologue segment providing an overview of the guest and Lovable's metrics. | |
| Sponsor Message: Customer Engagement with Sinch | 0 | 0 | 0 | 0 | Standard host-read sponsor advertisements for Sinch and Persona. | |
| Defining Lovable and Its Unprecedented Early Growth | 5 | 4 | 1 | 1 | Lenny asks Anton to explain Lovable's core value proposition and shares remarkable growth statistics, which Anton details collaboratively. | |
| Live Demonstration: Building an Airbnb Clone | 6 | 6 | 1 | 1 | Anton conducts a live demonstration building an Airbnb clone. Lenny provides strong product management context while Anton walks through visual editing and backend integration. | |
| Practical Tips for Mastering Lovable and Product Naming | 5 | 5 | 1 | 0 | Anton shares practical advice on using Chat Mode and clear prompting, explaining the coinage of 'Minimum Lovable Product'. | |
| The Origins of Lovable from GPT Engineer | 5 | 4 | 1 | 0 | Anton recounts the transition from GPT Engineer open-source project to founding Lovable, and Lenny draws parallels to OpenAI training anecdotes. | |
| Technical Scaling Laws and Preventing AI Failure Points | 6 | 6 | 1 | 2 | Anton outlines how quantitative feedback loops solve AI bottlenecks, while Lenny references insights from Amjad Masad on keeping AI unstuck. | |
| Drivers of Hypergrowth and Team Product Taste | 5 | 5 | 0 | 0 | Anton attributes rapid scaling to exceptional team taste and building on foundational models with tight product packaging. | |
| Sponsor Message: Long-Term Real Estate with Fundrise | 0 | 0 | 0 | 0 | Sponsor message for Fundrise flagship fund. | |
| Long-Term Vision for Software Creation and Autonomous Optimization | 5 | 5 | 0 | 0 | Anton describes Lovable's integration with Cursor and GitHub and outlines his vision for automated A/B testing and user behavior analysis. | |
| Evolving Skillsets and the Rise of Generalists | 7 | 5 | 1 | 1 | Lenny synthesizes how product management priorities are shifting from engineering execution to problem discovery and taste, which Anton affirms and builds upon. | |
| Hiring Cracked Engineers through Deep Vetting and Trials | 5 | 4 | 0 | 0 | Anton details their hiring process, including week-long paid work simulations and Shackleton-inspired job descriptions. | |
| Cultivating Extreme Startup Ambition in Europe | 5 | 5 | 1 | 0 | Anton discusses startup ambition in the European tech ecosystem and explains how Lovable balances rapid weekly cadence with an engineering-led roadmap. | |
| Defining AI Agents and Leveraging In-Person Collaboration | 4 | 6 | 3 | 1 | Anton pushes back on Lenny's question regarding when engineers will be replaced by AI agents, reframing AI definitions as moving goalposts. | |
| Building Modern Product Teams and Empowering Builders | 5 | 5 | 0 | 0 | Anton explains how in-person collaboration and cognitive capability shape modern builder culture. | |
| Upcoming Product Roadmap and Go-To-Market Support | 5 | 7 | 4 | 1 | Anton explicitly corrects Lenny regarding whether Lovable currently supports existing codebases, clarifying that it applies to Lovable-originated projects. | |
| Failure Corner: End-to-End Solutions and Lenny Mode | 6 | 5 | 0 | 0 | In Failure Corner, Anton reflects on Sana Labs and starting with end-to-end user experiences rather than retrofitting APIs, prompting the joke about 'Lenny Mode'. | |
| Strategies to Reach the Top One Percent in AI | 5 | 5 | 0 | 1 | Lenny pushes Anton for concrete heuristics on reaching the top 1% of AI users, and Anton outlines deep project immersion and Chat Mode usage. | |
| Connecting with Anton Osika and Lovable Community | 0 | 0 | 0 | 0 | Final outro, contact information, and closing remarks. |
Statements from this episode (17)
Osika: Lovable reached 300K MAUs and 30K paying users in three months
“We launched Lavable less than three months ago, and now we have 300,000 monthly active users, and 30 of those, 30,000 of those are actually paying. And it is growing on this at the same rate, it's like, you just, almost only through organic word of mouth.”
Osika: Lovable enables direct visual editing of underlying codebases
“What we just released is that you can actually edit this. Like this is a fully functioning product, but you can edit it visually. Like you can edit, like you do in Squarespace and Wix and so on. So I'll just change the text to buy now. And then it instantly ch…”
Rachitsky: Lovable is the only AI tool with direct visual editing
“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 thi…”
Osika: Effective AI prompting requires specifying exact expected behavior
“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.”
Osika: GPT Engineer has over 50,000 GitHub stars
“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, 50 something thousand GitHub stars and like dozens of academic references.”
Osika: GitHub suspended Lovable after it generated 15,000 daily repositories
“Lovable Earlier was always creating new projects on GitHub when someone used Lavable, and it was, we asked them, is it fine? Like, how, what's the limits here? They said, are there no limits? But once we started creating 15,000 projects per day, so there were …”
Osika: Lovable generated $1M in ARR within its first week post-launch
“Once we launched that, that was the 21st of November, so that's almost three months ago. We just hit like one million ARR in a week, and then it kept growing at that pace. It's still growing at even faster than that pace.”
Osika: The skill of unblocking AI coding tools will soon become obsolete
“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.”
Osika: Lovable is set up to modify its own codebase
“We have set up Lovable so that we can change Lovable with itself. We have done that.”
Rachitsky: 17% of surveyed newsletter readers already use Cursor
“17% of all people that read my newsletter use cursor already, which is absurd.”
Osika: Perfect AI software creation and integration will arrive within two years
“The last piece of software, how I see that is that it's almost instant to go from what you want to change in a product or what you, what product you want to build. To having it fully working end to end, integrated with any of your existing systems or integrate…”
Osika: AI will soon automate product analytics, recommendations, and A/B tests
“AIs can do that on absolutely massive scale and propose changes to a human to say like, oh yeah, that sounds like a good change to make it a bit more intuitive. And it can also automatically run, spin out A-B tests so that you can see with data, all these impr…”
Osika: Versatile generalists are much more important for modern product teams
“I mean, doing a bit of everything, being a generalist is, I think, much more important than it used to be. And the, if I'm putting 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.…”
Osika: Europe’s lower average ambition makes its untapped raw talent highly advantageous
“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, is a great recipe. I think that's a great recipe. So then, and that's what's I think it's some kind of advantage the…”
Osika: Non-engineer PMs struggle prioritizing deeply technical AI products
“For a product like ours, it's hard to be, like 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 like technical details, or they might be entangl…”
Osika: The software bottleneck is shifting from engineering execution to user intuition
“The bottleneck for most products these days, it's not going to be as much on engineering, but having Good taste, good intuition about your users, and that, I mean, engineers and everyone preferably in the team should have that, like, willingness at least to wa…”
Osika: One dedicated week using AI puts you in the top 1%
“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'm going to use AI to do that thing. And then you've spent a full week, you're in the top one percent in the global populati…”