Everything depends on context. Most code written by humans is indeed, garbage.
I think that this is the problem, actually.
It's similar to writing. Most people suck at writing so badly that the LLM/AI writing is almost always better when writing is "output".
Code is similar. Most programmers suck at programming so badly that LLM/AI production IS better than 90+% (possibly 99%+). Remember, a huge number of programmers couldn't pass FizzBuzz. So, if you demand "output", Claude is probably better than most of your (especially enterprise) programming team.
The problem is that the Claude usage flood is simply identifying the fact that things that work do so because there is a competent human somewhere in the review pipeline who has been rejecting the vast majority of "output" from your programming team. And he is now overwhelmed.
a) a pristine, good codebase that follows the best coding practices, but it is built on top of bad specs, wrong data/domain model
b) a bad codebase but it correctly models and nails the domain model for your business case
Real life example, a fintech with:
a) a great codebase but stuck with a single-entry ledger
b) a bad codebase that perfectly implements a double-entry ledger
Fair, by “perfectly implements” I meant to say that it correctly implemented the core invariant of a double entry ledger (debits = credits), not that it was 100% bug free
Many super talented developers I know will say “Make it work, then make it good”. I think it’s okay to do this on a bigger scale than just the commit cycle.
Make it work, make it work right, make it work fast. In that order.
Who is to judge the "good" or "bad" anyway?