> They became popular because teams across the industry analyzed code responsible for bugs/SEVs, and all found high correlation between these metrics and shipping defects.

Yes, based on research of human code. LLMs write code differently. We should question whether the human research applies to LLMs at all. (You wouldn't take your assumptions about chimp research and apply them to parrots without confirming first)

> I think the worst outcome would be throwing out our collective wisdom because the AI labs tell us to.

We don't have to throw it out. But our current use of LLMs are a dramatic change from what came before. We should be questioning our assumptions and traditions that come from a different way of working and intelligence. Humans have a habit of trying to force things to be how they think they should be, rather than allowing them to grow organically, when the latter is often better for a system we don't yet understand.

They write code differently but that doesn't mean that's the kind of code they prefer to read. Don't ascribe too much intention to a stochastic process.

Their coding style is above all else a symptom of their very limited context window and complete amnesia for anything that's not in the window.