From Vibecoding to Production: The 20% That AI Can't Do

The 80/20 Problem

Cursor, Bolt, Lovable — the new generation of AI coding tools is genuinely impressive. A founder with a clear idea can go from zero to working prototype in a weekend. We've seen it happen. We've helped founders who got there.

But here's the pattern we see over and over: the prototype works beautifully in a demo. Then real users show up, and everything breaks.

The first 80% — the happy path, the core screens, the basic CRUD operations — AI handles well. It's the remaining 20% that separates a demo from a product.

What's in the 20%

Error handling that doesn't crash. AI-generated code tends to handle the happy path. When a payment fails, when an API times out, when a user submits unexpected input — the code doesn't degrade gracefully. It breaks.

Security that passes audit. SQL injection, XSS, insecure direct object references — AI models know about these vulnerabilities in theory but regularly produce code that's vulnerable in practice. For a fintech or healthtech product, this is a non-starter.

Performance at scale. The prototype queries the database 47 times to load a dashboard. With 10 test users, nobody notices. With 1,000 real users, the page takes 30 seconds to load.

State management across sessions. The prototype works when you click through it linearly. Real users open multiple tabs, hit the back button, leave for three days and come back. The state handling falls apart.

Edge cases in business logic. The prototype handles the standard flow. But what about partial refunds? Timezone-crossing appointments? Users who are in two roles simultaneously? These edge cases are where the real complexity lives.

What We Do With Vibecoded Codebases

When a founder comes to us with an AI-generated codebase, our process is straightforward:

1. Audit — we review the code for security vulnerabilities, architectural issues, and performance problems. We deliver a prioritized report.
2. Stabilize — we fix the critical issues first. Security holes get patched. Error handling gets added. The database queries get optimized.
3. Harden — we add the infrastructure that production requires: CI/CD, monitoring, alerting, logging, and automated testing.
4. Scale — we refactor the architecture to handle real load. This often means rewriting the data layer and adding caching.

The Honest Assessment

Not every AI-generated codebase is worth saving. Sometimes the architecture is so fundamentally flawed that rewriting is faster than refactoring. We'll tell you that honestly.

But in most cases, the prototype has real value. The UI/UX decisions are sound. The feature set is validated. The core idea works. What's missing is the engineering depth to make it production-ready.

That's the work we do. Not replacing what AI built — building on top of it with the security, performance, and reliability that real products require.