Why doesn’t vibe coding scale well to large applications?

As a system grows, the cost of changing it goes up. A generated rewrite that looks cleaner may accidentally remove old behavior that other parts of the system depend on. Large systems contain years of bug fixes, undocumented assumptions, integrations, and data constraints. Vibe coding can still be part of the workflow, but the developer needs enough architectural knowledge to preserve those contracts and review every significant change.

It’s not really the user count by itself. A small script or proof of concept can be judged by whether it works in a few obvious cases. A real product has many interacting requirements: reliability, security, deployment, monitoring, backups, data migrations, performance, incident recovery, and compatibility with existing systems. AI can help implement pieces of that, but someone experienced still has to identify the pieces and make sure they fit together.

AI is very good at producing code that passes a quick test, but “it works” does not necessarily mean it is safe or correct. It might miss authorization checks, mishandle concurrent requests, leak data, or fail badly under unusual input. An experienced engineer can review the generated code, recognize the likely failure modes, and design tests for cases the original prompt never mentioned.