Is Vibe Coding the Next Unicorn?

At the intersection of creativity, code, and exponential technology, a new kind of builder is emerging — one who doesn’t need to write a single line of code, yet ships full-stack products in days. This phenomenon, known as Vibe Coding, was the centerpiece of a lively and thought-provoking Fireside Chat at the 2025 Build Launch Win competition in Stockholm, hosted by Lovable.

Titled “Is Vibe Coding the Next Unicorn?”, the session brought together leaders from across the AI and startup landscape — including Lovable’s co-founder, researchers from Anthropic, founders of next-gen AI-native startups, and investors shaping the future of software creation. Over 30 minutes, they dove deep into the realities of building with AI today: what's working, what still sucks, and what it really means to be a modern creator in the age of generative technology.

This article captures everything that was said — from practical strategies and bold predictions, to philosophical takes on automation, agency, and ambition. Whether you're a solo founder, an AI enthusiast, or just wondering where product development is headed, this conversation offers a glimpse into a fast-arriving future where vibing with your tools might just be your startup superpower.

🔮 What is Vibe Coding?

Fabian (Lovable) kicked off the chat by demystifying "vibe coding":

“It’s about abstracting away from syntax and thinking purely in product terms. You’re not ‘coding’ per se — you’re just vibing with the AI, steering it toward what you want to build.”

He described the visual: leaning back, letting the AI code while you focus on the idea. Less IDEs, more imagination.

Alexander added humor:

“When I vibe code, my problem is: what do I do while the AI writes my code?”

Fabian’s answer?

“Open 10 tabs. Work in parallel. It’s the new AB testing.”

🛠️ Real-World Vibe Coding Practices

Garvin (Anthropic) noted that elite AI-native engineers are innovating in how they manage context windows:

“When you’re building from scratch, context is manageable. But in mid-sized or legacy systems, the challenge becomes: how do you retain, retrieve, and structure knowledge efficiently?”

He mentioned experimental use of graph-based RAG (retrieval-augmented generation) systems, markdown context caching, and non-standard workflows for memory persistence.

🧠 Running AI-Native Startups

Sarah (Norain) offered a deeply practical perspective. Her company builds an AI platform for compliance and auditing — a far cry from the usual chatbots.

She claimed:

“In an AI-native startup, every team member is expected to produce 10x the output of a traditional hire.”

Why? Because tooling is now a multiplier. Sarah described a culture shift:

• Engineers use AI to do routine tasks — so they can focus on hard problems (e.g., bugs, infra, product architecture).
• Sales and customer success use AI for outreach, transcription, and follow-ups — while humans build real relationships.

👩‍🚀 Founder-Market Fit in the AI Age

Richard (NextML) reflected on who should be building startups today:

“Many people who never thought they were ‘technical enough’ can now create world-class tools. It’s founder-market fit — but redefined.”

He noted the technical bar is dropping, and those with deep problem understanding now have real leverage:

• If you know your customer and pain points, the tools will do the building.
• Programming becomes a “language to talk to the AI.”

⏳ Do Developers Have a Future?

Alexander posed the elephant-in-the-room question:

“How long until technical skills stop being necessary for building great products?”

Richard:

“Three to eight years. Coders will still matter — but they’ll be using AI as extensions of themselves.”

Fabian:

“AI today can’t yet build Lovable. But soon? Maybe.”

🧱 What Can’t AI Do (Yet)?

Garvin emphasized current limits:

• Poor at cross-repo or service-to-service understanding
• Still weak in memory, state management, and retrieval
• Not great at code generation in fragmented environments

But he’s bullish:

“Reinforcement learning is perfect for coding environments. They’re deterministic and deep. It’s inevitable we’ll get there.”

🛠️ The Stack Behind Lovable

Fabian revealed that Lovable itself:

• Uses Supabase for data and auth
• Continuously benchmarks the latest models
• Is building toward AI-managed context execution environments

He hinted Lovable 2.0 will support "editing Lovable inside Lovable" (yes, inception).

🧑‍💻 One-Person Unicorns: Hype or Reality?

Sarah was optimistic:

“Yes, it’s possible — but most of these builds have no moat.”

The real trick, she said, will be:

• Having a differentiated idea
• Building in a market large enough for multiple winners
• Creating defensibility through execution or community

📦 Model Context Packs (MCPs)

Garvin gave a masterclass on MCPs:

“MCPs are a standard to bundle tool calling and code logic together. They make LLM tools reusable, composable, and portable.”

He described how enterprise AI adoption suffers because:

• No shared patterns
• Tool use is too bespoke
• No reusability across teams

MCPs could fix all that.

🎯 Final Advice: Building Now

Sarah closed with a grounded note:

“Founding a startup has always been hard. AI hasn’t changed that — it’s just changed what’s hard.”

You still need:

• Sharp ideas
• Speed of execution
• Deep customer understanding

But now?

“Everyone has the tools. So what matters is your taste and your speed.”

✨ TL;DR Takeaways

• Vibe coding = product-focused creation with AI writing the code
• AI-native teams = 10x output, focus on high-leverage human tasks
• Solo unicorns are coming — but only with strong ideas and fast execution
• The dev role will shift, not vanish — coding becomes orchestrating
• MCPs = emerging standard for reusable LLM tools in enterprise
• Current AI is amazing, but still struggles with memory, cross-system logic, and retrieval