Three dominant themes in the discussion
| Theme | Key points | Representative quotes |
|---|---|---|
| AI‑generated “slop” issues and PRs | Users complain that LLMs produce vague or hallucinated issues that then spawn low‑quality pull requests, wasting maintainers’ time. | “Just show me the prompt.” – anileated “If you don’t have time to write an issue yourself… whom are you trying to fool by making it look pretty?” – anileated “My low‑effort issues were becoming low‑effort pull requests, with AI doing both sides of the work.” – wiseowise |
| Erosion of external contribution value | With code generation becoming trivial, the cost of reviewing and the risk of bad work rise, making outside contributions potentially negative. | “If code is easy to write and bad work is virtually indistinguishable from good, then the value of external contribution is probably less than zero.” – andai “When code was hard to write and low‑effort work was easy to identify… If code is easy to write… the value of external contribution is probably less than zero.” – netcan |
| Management/maintainer attitudes & job‑security anxiety | Many express confusion or frustration over how leaders view AI, fearing that automation will replace human roles and that maintainers must adapt or risk obsolescence. | “I am also confused by corporate or management who seem to think they are immune to AI developments.” – vanillameow “Do you think any of them cares about long‑term? Regardless of AI, your head is always on a chopping block.” – wiseowise |
These themes capture the core concerns: the practical mess of AI‑generated issue content, the strategic question of whether external code still matters, and the cultural shift in how maintainers and managers perceive their own relevance in an AI‑augmented workflow.