[mossgu]

Code quality matters more now, not less — but the definition shifts.

"Good code" used to mean readable, well-structured, maintainable by humans. When AI writes the implementation, those qualities become secondary.

What matters is: was the design clear enough to produce correct code in the first place?

The pattern I keep seeing: teams try to enforce quality after generation — linting, reviews, static analysis. That works for surface-level issues. It doesn't catch architectural drift, because by the time you're reading the code, the design decisions are already baked in and invisible.

Quality has to move upstream.

If you can't make AI-coded apps work without the code being robust and testable, the question is: who's responsible for making it robust and testable? The answer can't be "hope the AI gets it right." It has to be constraints that make wrong output impossible.