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Red Hat Funded a Report That Estimates 25% of PRs Are AI Slop
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I maintain several small open source projects. I sometimes use AI for my own PRs, especially ones that are almost mechanical but not quite. A great use for AI is "fix the new listing issues from this litter update."
I've been getting a moderate amount of AI slop PRs recently, though it has also coincided with a similar increase in PRs that are probably written by (or an least partially written by) AI but are actually useful. The ones I'd call "AI slop" tend to be huge, change a lot of tests, and may or may not even achieve their goal. A recent AI-written PR I got from an external first-time contributor fixed a bug by refactoring some code into a function, ending up with 4 calls to that function, of which 3 of the previous implementations had been correct and one had missed a step back in 2021. Net code added? 1 line, and only because it added a test.
The main problem is how much time I end up spending on these slop PRs. It can take a while to even describe why you're rejecting the change, though ironically AI has made this easier too.