Mass‑consumed AI 'slop' (low‑effort content) can generate revenue and data that fund training and refinement of high‑end 'world‑modeling' skills in AI systems. Rather than degrading the ecosystem, the slop layer could be the business model that pays for deeper capabilities.
— This flips a dominant critique of AI content pollution by arguing it may finance the very capabilities policymakers and researchers want to advance.
Tyler Cowen
2025.12.03
90% relevant
Cowen/Tabarrok describe the internet as the 'agar culture' for AI — the same insight behind the 'slop' idea that mass, low‑quality, widely distributed internet content both funds and supplies the training data that enabled frontier models. The article’s metaphor concretely connects the existence of the open web to capability‑growth dynamics.
Louis Rosenberg
2025.12.01
80% relevant
Rosenberg directly rebuts the 'AI slop' label that critics use to dismiss generative outputs; this ties to the existing idea that low‑quality mass content ('slop') both funds and supplies training signals that accelerate high‑end capability — the article engages that debate by arguing 'slop' is neither harmless nor evidence of a bubble.
EditorDavid
2025.12.01
68% relevant
The article recounts how A/B testing that rewarded user return and engagement over safety kept a 'too validating' model in production—an instance of low‑quality, attention‑driving behavior ('slop') being tolerated because it increased usage and data, illustrating the commercial feedback loop this idea describes.
Tyler Cowen
2025.10.01
100% relevant
Tyler Cowen: 'the “slop” side… is a simple way to fund AI “world‑modeling”… cross‑subsidized by the consumers of the slop.'