Feed rankings decide which creators get seen, and shape what those creators choose to make in the first place.

This explores how feed algorithms act as gatekeepers for creators: they decide who gets seen, and creators adapt what they make to fit. The most striking point in the collection is that this works in both directions. A feed doesn't just distribute content that already exists. It changes what content gets made in the first place. How do recommendation feeds shape what people see and believe? describes recommendation systems as persuasion infrastructure rather than neutral pipes. Their effects build on each other through biased ratings and selection effects, so small ranking choices can grow into large changes in behavior.

The clearest evidence comes from Facebook's emoji reactions. When the platform cut the weight of an 'angry' reaction from five times a 'like' to zero, misinformation dropped, and political parties moved away from negative framing in what they posted How do feed ranking weights shape what content gets produced?. Creators read the ranking rules from their reach numbers and change course. A single number in a ranking formula therefore works like industrial policy: it decides which kinds of content are worth producing.

AI-generated content makes this more pressing. On a Chinese short-video platform, AI creators matched human creators' total engagement mainly by posting more, even though viewers watched less of each AI video Can AI creators match human creators through posting volume alone?. If reach rewards volume, cheap production wins even when audiences like it less. The same study hints that the algorithm can push back: matched human and AI videos were given different amounts of exposure, with less going to AI content Can algorithmic distribution prevent AI content from overwhelming creator diversity?. Treat that as a lead rather than a settled result, since the excerpt in the collection leaves out the full exposure numbers. Whether this balancing happens at all depends on the platform. AI-attributed posts rose to about 38% on Medium and Quora but stayed near 2% on Reddit Is AI-generated content rising faster on some platforms?. On the open web, about a third of new websites are now AI-generated How much of the internet is AI-generated now?.

The less obvious cost is what happens to reputation. AI posts collect likes through polished, comprehensive writing but get few replies, so they win visibility without the back-and-forth that used to signal that a voice was credible Why do AI posts get likes without inviting conversation?. Over time this pushes human creators aside and weakens social media's core job of building lasting reputations for real people Does AI content displace human influencers on social media?. Reach and recognition used to rise together. A feed that rewards engagement alone can separate them.

For newer creators, the useful idea comes from recommender-system engineering. Contextual-bandit methods deliberately give exposure to untested items so the system can learn whether they're good, instead of always promoting what has already worked Can bandit algorithms beat collaborative filtering for news?. How much a platform explores is effectively how much room it leaves for newcomers. A related warning comes from AI personalization research: tuning too closely to each user's past preferences narrows what they see and builds echo chambers Does personalizing reward models amplify user echo chambers?. A creator whose work falls outside a viewer's established taste has a harder time reaching them. The collection covers the platform side well. It has much less direct research on how individual creators experience or work around these systems.