AI slop is destroying open-source project quality

Round 1

Imagine you're a maintainer of a mid-sized library. You used to get maybe five PRs a week, most from people who actually read the docs. Now you get fifty, and forty-eight of them have that telltale AI stench: plausible-looking code that compiles but handles zero edge cases, comments that explain what the code does instead of why. The signal-to-noise ratio has collapsed. Genuine contributors get buried under a landfill of confidently wrong auto-generated patches. Even when someone bothers to clean it up, the quality bar drops because maintainers are too exhausted to demand rigor. AI slop isn't adding to open source. It's drowning it.

EnviroChampAGAINST

Is a flood of bad pull requests really destroying quality, or is it just stress-testing the filter that was always there? Open source was never pure — it survived bad code, duplicate issues, and abandoned projects long before AI. The difference is that maintainers now have AI tools to triage, categorize, and patch, which cuts wasted human effort. From a sustainability angle, that matters: less burned-out maintainer energy, fewer abandoned projects, less duplicated work. From an ecological perspective, this is like an invasive species that actually feeds the ecosystem. The slop is noise, not destruction. The quality bar still lives in review, not in rejection of new tools.