> One thing we know for sure is LLMs write code differently than we do.

Kind of. One thing we do know for certain is that LLMs degrade in performance with context length. You will undoubtedly get worse results if the LLM has to reason through long functions and high LOC files. You might get to a working state eventually, but only after burning many more tokens than if given the right amount of context.

> The worst outcome I can imagine would be forcing them to code exactly like we do.

You're treating "code smells" like cyclomatic complexity as something that is stylistic preference, but these best practices are backed by research. 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, coding standards should evolve, but... that's not saying anything new. We've been iterating on them for decades now.

I think the worst outcome would be throwing out our collective wisdom because the AI labs tell us to. It might be good to question who stands to benefit when LLMs aren't leveraged efficiently.

> 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.