AI writes code faster than ever. But "works on my machine" and "works in prod" are different universes. Here's where vibecoding usually falls apart — and what to build in so you survive real traffic.
The prototype optimizes for the wrong thing
A model optimizes for code that runs right here, right now. Production wants something else: predictability under load, backward compatibility, observability and recovery after failure. Those properties don't appear on their own — you design them in.
A classic failure is data operations without transactions. In a prototype, two queries in a row "just work". Under concurrent traffic a second user slips between them — and inventory goes negative while orders get duplicated.
What to build in from day one
- Versioned schema migrations, not manual edits
- Idempotent deploys and a predictable rollback
- Logs and metrics from the first commit
- Backups and a tested restore
- Explicit module boundaries and API contracts
None of these "slow you down". They move the cost from 3 a.m. to Monday afternoon — where it's far cheaper to pay.
Production isn't the moment you launch. It's the set of properties you consciously decided to guarantee.
Vibecoding is a great accelerator. But you have to point it at an architecture that survives your first thousand users. That's where we start.