[countWSS]

The code written by AI in most cases is throwaway code to be improved/refined later. Its likely to be large, verbose and bloated. The design of some agents have "simplify/refactor" as final step to remedy this, but typically your average vibe coder will be satisfied that the code just compiles/passes the minimal tests. Lines of code are easy to grow. If you refine the AI code with iterative back-and-forth questions, the AI can be forced to write much more compact or elegant version in principle, but you can't apply this to most large systems without breaking something, as AI doesn't have context of what is actually changing: so e.g. an isolated function can be improved easily, but AI can't handle when complexity of abstraction stacks and interfacing multiple systems, typically because it confuses states where global context is altered.

[Kerrick]

I've been working to overcome this exact problem. I believe it's fully tractable. With proper deterministic tooling, the right words in context to anchor latent space, and a pilot with the software design skills to do it themselves, AI can help the pilot write and iterate upon properly-designed code faster than typing and using a traditional IDE. And along the way, it serves as a better rubber duck (but worse than a skilled pair programmer).