The risk is not AI code, but unsupervised AI code
AI-written SaaS apps are not automatically doomed. The real issue is who is guiding the and whether that person can judge the result. An app can look convincing because the interface works, the demo feels real, and it has pages, components, database changes, and login screens.
But across the whole product, the may not match the . may exist, while ownership checks are missing in the places that protect user data. , retries, , and idempotency may never have been planned.
The can become accidental instead of intentional, with several half-used patterns instead of one steady approach. AI is useful when it can continue a clear pattern, but it is weaker when no good pattern exists, when patterns are mixed, or when the person using it cannot tell whether the pattern is sound. A stronger workflow starts with a spec for bigger features, covering the data model, API behavior, edge cases, and what must not be changed.
Key points
- AI-written SaaS code is not automatically bad, but it needs active supervision.
- A working demo can hide deeper problems in the and product rules.
- Login screens are not enough if ownership checks are missing where user data is handled.
- , retries, , and idempotency should be designed before they become emergencies.
- For larger features, start with a spec that defines the data model, API behavior, edge cases, and limits on changes.