Generative AI is changing digital platforms in a very specific way, it explodes content supply but it is not creating more human attention. That matters because platforms do not make money from raw upload volume. Rather, they make money from keeping users engaged with content they actually value. My new paper “The Disruption of Attention Platforms by Generative AI,” with Chenyang Li, Xun Wu and Fei Xiao, argues that this creates a new management problem for attention platforms. The core challenge is no longer just how to attract more creators, but how to govern a flood of cheap AI-generated content when the truly scarce resource is informative user attention.

To study this problem, we introduce a simple but powerful framework. On the platform, both user-generated content and AI-generated content compete for recommendation traffic. Every upload first enters a “training pool,” where the platform uses early views to learn whether the content is good enough to deserve wider distribution. Only the content that proves itself is promoted into a “spotlight pool.” In the model, AI-generated content is cheaper to produce, but user-generated content is more likely to be high quality. Users stay on the platform only if their experience is good enough relative to what they could do elsewhere, so bad content does not just waste impressions today. It can also reduce future attention.

The main result is striking: cheaper AI-generated content can make a platform worse off even while it increases supply. The reason is congestion in quality discovery. When too many low-hit-rate uploads enter the system, the platform must spend more scarce human attention evaluating content. As a result, it has less attention left to amplify the winners. That lowers spotlight exposure, reduces realized average quality, and can even shrink the platform’s effective attention budget as users disengage. In other words, more content is not always better. If supply grows faster than the platform’s ability to identify value, abundance can become a liability rather than an asset.

The practical message is not at all that “AI is bad.” It is that AI changes what good platform governance looks like. The paper shows why tools such as provenance-based payouts, better disclosure, upload fees, and multi-stage screening can all matter. They help platforms allocate scarce attention more intelligently and keep low-signal content from overwhelming discovery. For managers, that shifts the strategic question from “Should we allow AI content?” to “How should we redesign incentives and screening when content is cheap but attention is not?” That is a useful lens not just for social media, but for any digital platform whose value depends on matching users with quality at scale.