While I have a similar experience with, hurm, "legacy" codebase, I gotta say, LLM (in my experience) made the "legacification" of the codebase way, way faster.

One thing especially, is the loss of knowledge about the codebase. While there was always some stackoverflow-coding, when seeing a weird / complicated piece of code, I used to be able to ask the author why it was like that. Now, I sometimes get the answer "idk, its what chatgpt gave me".

At least LLM write a huge amount of comments /s

I call this the Peter Principle of Programming:

"Code increases in complication to the first level where it is too complicated to understand. It then hovers around this level of complexity as developers fear to touch it, pecking away here and there to add needed features."

http://h2.jaguarpaw.co.uk/posts/peter-principle/

Seems like I'm not the first one to notice this either:

https://nigeltao.github.io/blog/2021/json-with-commas-commen...

Then you had some very shitty jobs. Good teams put time and effort in architecture before development.

LLMs will produce code with good structure, as long as you provide that architecture before hand.

Most teams are understaffed and under pressure to deliver. Agile is all the rage so why spend time developing architecture when you can yolo it during the implementation phase?

> Good teams put time and effort in architecture before development.

I can only accept that as true if I also accept the fact that the vast majority of jobs won’t be on a good team

Good teams are few and far between. I guess most of them are made over time.

But that is for different reasons. There are definitely human created codebases that are clean, that no AI could match.

    > except one

Please share more!