I waited a while to respond to this one - partly out of embarrassment, as what I said wasn't exactly what I meant, and partly out of respect for the community rules because I don't want to start a flamewar about it.

I was using "human readability" very liberally. Let me try that statement again: "Code quality" describes a set of standard practices that are largely intended to ensure the resultant code is maintainable by humans. That means it should require minimal cognitive load to understand enough context to make everyday changes, and the most complex areas and overall architecture of the system should be well-documented, consistent, and with enforced rules to ensure that consistency.

AI changes this substantially, because the things that we were previously optimizing for are not longer the constraints that the system is operating under.

Before AI, I placed a much higher value on consistency in design patterns across an organization and even moreso within a single codebase. After AI, I no longer need to think in terms of many levels of abstraction at the same time - I'm working on architecture without needing to look at every line, or I'm focused on a small area with a performance bottleneck or other issue. As long as I'm able to quickly grok what's happening within that single block - defined inputs, outputs, and purpose - that's generally good enough.

In short - our notions of code quality are optimizing for a process that no longer works the same way, and we should be critical when implementing "best practices" and making sure that the problems those practices were intended to mitigate are still actually problems. In most cases I'm finding that they're not, and have come to the conclusion that many of the heuristics that I've developed over 20+ years in this industry aren't much different from a cargo cult.