Why AI-generated apps struggle in production
AI builders are excellent at producing a convincing first version. The weaknesses appear later: authentication checks done only in the browser, database rules left open, API keys committed to the repository, no input validation, duplicated logic, missing error handling, and a data model that cannot survive the first real feature request. Speed of generation also hides cost, because every extra feature added to a weak base increases the final repair bill.
None of this means the prototype was wasted. It proves the idea and often gets customers interested. The task is to separate what is sound from what must change, then repair it in the order that reduces risk fastest. Public exposure of customer data is almost always the first concern. Reviewing carefully before touching anything avoids the common trap of fixing visible bugs while leaving the data exposure open.
