As the AI landscape rapidly evolves, I've been exploring various coding platforms, including the newly minted Lovable—a browser-based AI coding agent. Here's my review.
The "great for MVPs" framing is honest but I think it undersells the real problem. It is not that AI coding tools are only good for prototypes. It is that they are non-deterministic, so every output requires full human review regardless of complexity.
With Lovable, Cursor, or any of the current tools, you give the same prompt and get structurally different code each time. For an MVP where you are just trying to validate an idea, that is fine because you are throwing the code away anyway. But the moment you want to build something you can maintain, the review tax kills the speed advantage.
The missing piece is determinism. If AI generated the same output for the same input every time, you could review a pattern once and then trust every future instance of that pattern. That is the difference between "great for MVPs" and "great for production."
We are building a frontend coding agent specifically around this idea. Deterministic generation so the review burden does not scale linearly with the amount of code you generate.