I wrote an analysis of why so many platforms have started labeling and limiting AI content. I’d love to get some feedback on it (essay link with all sources and extra graphs that visualize the mechanism at the bottom), here is the full argument. The question During Aug-2026, X announced it is winding down its “Creator Revenue Sharing” program. The replacement, “Original Content Rewards”, explicitly states that any content that was “created or posted using automated means” is ineligible. YouTube enacted a similarly worded policy change in 2025, renaming its “repetitious content” monetization policy to “inauthentic content”. Spotify joined the fray after removing over “75 million spammy tracks”. TikTok, Meta, Amazon and Pinterest added labeling, and Deezer is tagging AI-tracks and removing them from recommendations. Why have so many platforms decided to label and limit LLM-outputs? Corporations don’t change policy based on whimsy, and I’m uncertain that public pressure is the only catalyst - they need something measurable, a mitigation strategy against a concrete risk. So what’s the risk? The mechanism The ubiquity and effectiveness of LLMs has reduced the cost of creating content. What this means is that effort might no longer signal quality. This dynamic was modeled in a 1970 study (Akerlof, “The Market for ‘Lemons’”). A market that can’t tell good used cars from bad used cars defaults to setting the average price for both - low quality is overpriced and good quality is under-priced. Higher quality sellers end up leaving the market and the market size falls. This repeats in a downward spiral until the market itself can unravel. Online platforms deal in attention: a two-sided attention market. Consumers don’t pay - they just pay attention, and that attention is sold to advertisers. The near-zero production cost of AI slop breaks the quality signal, flooding the feeds, reducing attention for high-quality creators. And if the slop-wave is not enough to make people leave, just less engaged, advertisers are still paying, but for users that are less interested, less willing to click, less willing to convert. Then the balance shifts and advertisers pull back. That’s the concrete mechanism: the slop-wave might result in lower revenue for online platforms. The evidence (from different platforms) The first step: on Freelancer.com, after LLMs (the platform also built a native LLM pitch-builder), the polish of a pitch no longer signaled good output, and the price premium clients were willing to pay dropped by ~40%. The study’s model found the top fifth by ability was hired 19% less often, the bottom fifth 14% more. Volume: according to Deezer, fully AI tracks rose from 28% of daily uploads in Sep-25 to over 50% by Jul-26, yet the share of streaming has remained ~3% at most. For long-form text on Reddit during 2022-24, a peak of ~8-9% of posts for certain communities is machine-generated. ~35% of a sample of new Internet-Archive English pages were categorized as AI-generated or AI-assisted by the first half of 2025. Engagement: on TikTok, posts labeled “made with AI” got ~8% fewer likes than the same creator’s other posts, for the same number of views. The authors concluded that perceived effort was the factor that led to reduced engagement. An equivalent “Made with Photoshop” label resulted in no drop. Perceived quality: in a randomized experiment run on a Reddit-equivalent mock website, users given AI writing tools wrote comments that were 50% longer. Readers rated these comments as less informative, dislikes doubled, and users suspected of using AI rose from 14% to 40%. When AI comments were introduced to a thread, that whole thread was perceived as lower quality, even the comments made by non-LLM users. The caveats A true recreation of the model assumptions would have to exist in a single platform to prove it, so this evidence, all from different platforms, is not a final confirmation. I also can’t prove users are leaving; that has to be considered as another potential ramification theorized in the model. Where it goes: a treadmill the platforms will continue to run on, instead of a downward spiral The used-car study points to a remedy: certification. You can’t really certify posts, but you can label (Meta labels AI content using metadata that the LLM suppliers themselves embed in tool outputs). Then the platforms use the labeling to influence exposure, via ranking: Spotify and Deezer exclude the tracks they tag from their recommendation algorithms. But slop just keeps on coming. Deezer’s demotion was in action in September-25, and it did not discourage uploaders: uploads doubled from 30K a day to 60K a day in Jan-26, and rose again to ~90K in Jun-26. Slop is cheap to make, so people keep making it. So my expectation is that we will see a kind of treadmill. Every time slop reaches a tipping point that results in users losing interest, advertisers decreasing their spend and platforms seeing a noticeable loss of revenue, policy changes will be enacted. How it could be wrong If extra slop does not result in decreased ad-spend, if the current rules end up being enough to forestall slop, or if the rate of slop stops increasing, then the theory ends up disproven. There’s also another exit condition: if the quality of LLM-outputs increases to the point where it is no longer distinguishable from human, or human-involved output, then it should no longer be a risk for online platforms to host it. Full essay, with the sources for every number: The War on AI-Slop What do you think? Does the Akerlof equivalency feel far fetched? Does the mapping onto it work for you? if you checked the graphs in the link - do they help? Thank you in advance! submitted by /u/guy6400
Originally posted by u/guy6400 on r/ArtificialInteligence