Co-founder & CEO, Lovable Fastest-growing startup in Europe
LOVABLE · AI ENGINEER
The Vision
Your Personal AI Software Engineer
"Almost all my friends throughout my life reached out — 'Anton, I need to build something.' We are building for this 99% of the population who don't write code."
Describe an idea in plain English → get a fully working product in 30 seconds
The goal: be the last piece of software anyone ever has to write
Non-technical users, designers, and PMs all ship real products with it
The Growth
€10M ARR in 2 Months, 18 People, Pure Word of Mouth
$4M
ARR in first 4 weeks
$10M
ARR in first 2 months
300K
monthly active users
Growth strategy
Zero paid marketing. 100% organic word of mouth driven by posting what they ship on social media. The product demo sells itself — people see it and immediately share it.
18 people · fastest in Europe
Hired for max skill-set breadth per person. Every engineer has product taste, design sense, and can talk to users. Generalists who ship with exceptional speed.
The painful pivot
Had to throw away the entire codebase and rewrite it in a more performant language — mid-hypergrowth. Couldn't ship features for weeks. Still grew.
How It Works
From Prompt to Deployed Product: The Lovable Stack
30-second first build: Type "Airbnb clone" — get a beautiful, interactive UI with listings, categories, and login buttons
Visual editing: Click any element and edit it directly, like Squarespace — instantly changes the underlying code. No other AI tool does this.
Backend in one click: Connect Supabase for auth, user data, and storage — auto-generates the integration code
GitHub sync: Fully synced — developers can jump into Cursor while non-technical teammates keep using Lovable's UI
One-click deploy: Hosted on Cloudflare — from prompt to live URL in minutes
Lovable built with Lovable: The team uses Lovable to change Lovable itself
"Not getting stuck is the most important thing for people. That's why we entered the space late — we still rank as the one that works most reliably."
Pro tip #1 — Patience & clarity
Don't say "it doesn't work." Explain exactly what you expected vs. what you got. Precision matters more with AI than with human engineers.
Pro tip #2 — Use chat mode
Ask Lovable to explain how things work before asking it to build. Understanding the system makes you 10x more productive as a builder.
The Lovable Differentiator
Visual editing + GitHub sync + reliable output = the only tool that works for both technical and non-technical users on the same codebase.
Future Skills
What Skills Will Actually Matter When AI Writes Code
Taste: Knowing if what was built is right — not building it yourself
Discovery: Figuring out what real pain points to solve is now the hardest step
Generalism: One person should know architecture, design, product, and user research
Technical literacy: Engineers must abstract up — understand constraints, not just implement
Prompting: Being in the top 10% of AI tool users will set you apart in months
"Being a generalist is much more important than it used to be. I obsess about getting as many skill sets as possible for each person I hire."
Anton's hiring lens
Every hire at Lovable should know how systems are architected, how to design, how to talk to users, and have product taste. All four. Non-negotiable.
Contrarian
Anton's Counterintuitive Takes on AI Product Building
✗Engineering is still the hardest part of building a productINSTEAD →✓ Figuring out what to build is now the bottleneck. The building is nearly free. Discovery, taste, and judgment are the scarce resources.
✗Specialists are what great product teams needINSTEAD →✓ Generalists who can do architecture, design, product, and user research are the most valuable hires. AI handles depth; you need breadth.
✗You need marketing to grow a software product fastINSTEAD →✓ Lovable hit $10M ARR with zero paid marketing. Build something so good that demos go viral. Posting what you ship is enough if the product is lovable.
✗Software engineers will be replaced by AIINSTEAD →✓ Engineers should see themselves as translators — converting human problems into technical constraints. Abstract up a few levels. That skill becomes more, not less, valuable.