I'm optimistic that AI will actually increase the proportion of good code in the future.

1. IME AI tends to produce good code "in the small." That is, within a function or a file, I've encountered very little sloppy code from AI. Design and architecture is (still) where it quickly tends to go off the rails and needs a heavy hand. However, the bulk of the actual code will tend to be higher quality.

2. Code is now very cheap. And more tests actually results in better results from AI. There is now very little excuse to avoid extensive refactoring to do things "the right way." Especially since there will be a strong incentive to have clean code, because as TFA indicates...

3. Complex, messy code will directly increase token costs. Not just in grokking the codebase, but in the tokens wasted on failed attempts rooted in over-complicated code. Finally, tech debt has a concrete $$$ amount. What can get measured can get fixed, and nothing is easier to measure (or convince execs about!) than $$$.

Right now tokens are extremely cheap because they're heavily subsidized, but when token costs inevitably start ramping up, slop will automatically become less economically viable.

This is my take too - LLMs aren't an excuse to lower our standards, they're the reason to raise them.

Put simply LLMs perform better on better code.