2026-10-23 02:40 UTC
Replies (3)
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@alottafagina@lemmy.ml 2026-10-23 04:33
It looks like it was more of a survey and afaict, participants weren’t given an opportunity to distinguish between “AI slop” and high quality AI assisted contributions (so were all AI contributions reported as “slop”?!) To answer your other questions, this is the relevant part: Across the board, genAI reduces estimated task time across all six measured activities in the pooled sample, with the largest gains in code authoring (−52%) and code review & feedback (−44%), and smaller but still meaningful reductions in technical discussion (−20%) and discussion on proposals (−19%) Productivity gains are most often reinvested in addressing technical debt [66%], adding new features [63%], and learning/upskilling [58%] … genAI productivity is invested right back into OSS projects. In other words, genAI is not shrinking the overall OSS workload so much as reallocating it toward highervalue and longer-deferred work, from debt paydown to skill-building. It’s not all clear weather and blue skies though. My interpretation of the findings is that there definitely are challenges to address, but also that those that take the effort to address and integrate AI usage see the most in terms of net positives. You can download the full report here.
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@communism@lemmy.ml 2026-10-23 09:42
They are slop according to the maintainers, which I would say should be the arbiter of that.
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@lengau@midwest.social 2026-10-23 11:21
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.