When AI Turns Culture into Slop (link.springer.com)

An opinion piece in AI & Society argues that generative AI and platform economics are not just copying culture but turning it into “slop”: a glut of cheap, derivative, and forgettable outputs across text, image, avatar, and video ecosystems. The authors point to TikTok, YouTube, X and streaming studios as industrial-scale producers—stitching stock footage, algorithmic voiceovers, endless sequels and spin‑offs—where monetization schemes reward volume over craft (Rijo 2025). This flood of recycled content crowds out scarcity-driven originality; exceptions like Lynch or Better Call Saul only highlight how rare genuinely original work has become. For the AI/ML community this matters technically and ethically. Models recombine clichés, platforms convert clicks into training data, and monetization loops incentivize output quantity, creating a self‑reinforcing feedback cycle that degrades dataset quality, diversity, and “legibility” of human-made work. Consequences include dataset contamination by synthetic content, incentive‑aligned reward hacking (optimize for retention not artistry), and harder problems for provenance, watermarking, and evaluation. The authors call for active curation—festivals, journals, classrooms, and researcher attention—to defend originality, improve dataset stewardship, and design incentives that prioritize creative value over throughput.