Can algorithmic distribution prevent AI content from overwhelming creator diversity?

The open question is whether the platform's distribution algorithm can keep AIGC's supply-preference tension from costing creators or consumers as AIGC volume grows. The introduction claims it can: the algorithm "moderates this tension by dynamically adjusting exposure distributions, preserving balanced engagement for creators as AIGC scales." Section 2.2 makes a narrower claim, that the mechanism "assigns lower exposure to AIGC than to HGC," across 178,854 matched video pairs measured by cumulative show count and days to reach 90% of exposure. The excerpt then stops mid-sentence, before any exposure result. The moderation claim is stated twice and shown nowhere in the excerpt.

The method describes the loop that would make moderation possible. Exposure is "primarily driven by algorithmic recommendations" based on user–content interactions, and valid and full views "serve both as measures of content performance and as feedback signals for subsequent content distribution." If the loop reads AIGC's weaker view rates as weaker interest, it would hold AIGC exposure down while supply keeps growing. That chain is my reading. The excerpt describes the loop but not the rule that adjusts exposure. Section 4.4.2 also names regression-based time-series analyses of how exposure "responds to changes in the scale-over-preference dynamic," and those results are not in the excerpt either.

Two notes bear on this. Do online ratings actually reflect independent customer opinions? describes a similar loop, in which ratings shape future ratings, and reports that increased opinion variance can mitigate the long-term effect; this excerpt offers no analogous mitigating factor. The Nextdoor note's point that informativeness optimization backfires without engagement-aligned reward models suggests outcomes depend on the optimization target, which the abstract's call for "AIGC-sensitive distribution algorithms" leaves unspecified. The supply-side gain in Can AI creators match human creators through posting volume alone? is what makes the question matter.

What the excerpt does not establish: whether the algorithm moderates the tension or simply tracks other differences, since matching uses creator characteristics and content categories and the robustness checks sit in SI 2.4; whether any adjustment holds as AIGC supply grows beyond the twelve-month window; and whether a different distribution rule would do better, which the abstract advocates without testing in the excerpt. Until the exposure results are read in full, the governance recommendation rests on a stated mechanism and should be treated as a hypothesis.