it shouldn’t be that impactful negatively that AI is being used

Doesn’t it invalidate the GPL? Open source licenses depend on copyright, and LLM-generated code isn’t copyrightable.

(Also, it can’t be proven not to contain plagiarised proprietary code, in fact it’s quite likely that it does; between this, the testing / review burden, and the mental decay of developers that use LLMs instead of coding, this pretty much kills open source projects, in several different ways to make sure they stay dead…)

Plus studies on skill decay when using AI. Also the linux maintainer onboarding pipeline—which was already critically endangered—is now no longer building understanding through writing code, just monitoring it’s generation.

I personally think you need to write a language by hand without AI doing it for you to learn in detail. It’s like trying to learn how to do math or how to write mandarin by just supervising an AI doing it… you need to do it yourself. Supervising is fine if you’re already fluent in mandarin and have been writing it for years and years. If you’re just learning it’s less than useless. If anything its directly harmful to learning.

Same with speaking a language yourself without crutching your native language as the best method to learn to speak it (immersion)

Edit: The long term externalities of AI—like pretty much everything nowadays—is going to be made worse by short term thinking. Quarterly profits and yearly results take precedence over long term skill development. Even OSS like linux will eat its seed corn out of expediency because of a lack of maintainers and new candidates being onboarded into the ivory tower mailing lists…

One thing I’m noticing about AI code is it’s very, very task oriented. Like it will produce code, that works for the one objective, but absolutely will not account for other interactions with other parts of the system. It’s really bad at juggling multiple priorities so for every solution it creates, it causes another 2 problems with other parts of the stack. Human coders would design a solution that takes those interactions into account from the ground up. With an AI solution, you kind of have to rebuild those interactions as an afterthought, which is where a lot of failures occur. Since interaction of multiple subsystems is kind of the whole point of a kernel… I have concerns.

Human review of AI is sufficient in theory, but in practice it’s never as thorough as the understanding you get writing it yourself. It’s hard to avoid AI, but it is a constant invitation to laziness.