LLMs Flood Journals and Open Source With Slop as Submissions Jump 42%
Updated
Updated · O'Reilly Media · Jul 23
LLMs Flood Journals and Open Source With Slop as Submissions Jump 42%
2 articles · Updated · O'Reilly Media · Jul 23
Summary
Open source maintainers and academic journals are being swamped by plausible-looking AI output that is cheap to produce but still costly to verify by hand.
Daniel Stenberg of curl says maintainers are "drowning" in LLM-written bug reports, some of which flag real issues but most of which waste scarce expert review time.
Academic journal submissions have risen 42% since ChatGPT's arrival, while writing quality has declined, illustrating the same imbalance between near-zero generation costs and unchanged validation costs.
The report argues existing digital gates such as popularity algorithms are poorly suited to filter this surge, while heavier reliance on reputation or AI reviewers risks entrenching incumbents or reproducing model blind spots.
Its broader point is that AI has severed the link between credible appearance and real quality, creating pressure for new institutions to decide what deserves human attention.