Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.

Introduction. AIGC is restructuring online content ecologies [1–3], propelled by the success of generative models [4–8]. Accordingly, leading content platforms have elevated AIGC as a strategic priority [9, 10]. Notably, a leading Chinese video-sharing platform, with over 400 million daily active users, has officially launched tools to facilitate AIGC creation. By mid-2025, AIGC has accounted for nearly 30% of its Local Life Channel (Fig. 1(a)). Despite this proliferation, the implications of AIGC on human behavior and content distribution remain insufficiently explored. Given that these factors determine the flow of attention and rewards [11, 12], understanding their dynamics is essential for the development of effective distribution algorithms and governance frameworks. To bridge this gap, we conducted a large-scale retrospective analysis encompassing hundreds of millions of videos from this platform, spanning 12 months with substantial AIGC volume. Framed by the understanding that platforms connect creators and consumers with interest-based content distribution algorithms (Fig. 1(b)) [13–16], we investigated the implications of AIGC from both the behavioral and distributional perspectives through matching [17] and temporal analysis [18]. We first characterized the systematic disparities in creation and consumption patterns between AIGC and HGC (RQ1 and RQ2), and subsequently examined how algorithmic distribution mechanisms adapted to these AIGC-driven behavioral shifts (RQ3). Our analyses revealed a scale-over-preference dynamic wherein AIGC creators produce higher volumes of content that achieve overall engagement returns comparable to HGC creators, despite weaker individual consumer preferences for AIGC. This divergence signals competing incentives: creators may maximize engagement returns by proliferating AIGC, thereby diluting the density of content that aligns with consumer preferences. Furthermore, we uncovered that algorithmic distribution mechanisms moderate this tension by dynamically adjusting exposure distributions, preserving balanced engagement for creators as AIGC scales. These results provide a mechanistic understanding of how AIGC reshapes online content ecologies, offering insights for sustaining ecosystem health and guiding platform governance in the generative AI era.

Method. 4.1 Platform Our analysis is conducted on data from the Local Life Channel of a leading shortvideo platform in China (with over 400 million daily active users), where content exposure is primarily driven by algorithmic recommendations [14, 21–23] based on user–content interactions [21]. On this platform, creators can upload content at high frequency and leverage built-in AIGC tools to assist video creation, enabling AIGCbased production to scale at substantially lower marginal effort than HGC and thereby amplifying variation in content supply across creators. User engagement signals, such as valid views and full views, are recorded at scale and serve both as measures of content performance and as feedback signals for subsequent content distribution. These platform characteristics provide a suitable setting for studying AIGC’s behavioral and distributional implications.

4.2 Data Identification of AIGC and HGC. AIGC and HGC are distinguished using platform-generated metadata labels embedded when videos are created with official AIGC tools. Videos carrying this label are treated as AIGC; all others as HGC. Potential misclassification from external AI tools is assessed using Sightengine [24]. Validation indicates that the unlabeled set is predominantly HGC, supporting the reliability of platform labels (SI-1.1).

Data collection. Data were collected from the platform’s Local Life Channel spanning June 2024–May 2025. To reduce confounding from heterogeneous browsing paths, analyses were restricted to impressions delivered through the main feed, where exposure is determined by algorithmic recommendation and user scrolling.

4.3 Evaluation Metrics We use a multi-dimensional set of metrics covering creator-side, consumer-side, and distributional outcomes, derived from the collected interaction/exposure logs. Creator-side metrics. Creation volume is the number of videos uploaded by a creator during the observation period. Creator engagement return is measured by aggregating valid view and full view counts received by all videos uploaded by the creator. Following platform definitions, a valid view is recorded when a video <7s is watched in full, or a video ≥7s accumulates ≥7s playback. A full view denotes completion of the video. Consumer-side metrics. Consumer engagement depth is measured by average view duration, valid-view rate (valid views divided by total views), and full-view rate (full views divided by total views), capturing attention from initial to sustained viewing. Distributional metrics. Algorithmic exposure is measured by show count (impressions delivered by the recommender). We additionally compute time-to-90% exposure—days required to reach 90% of a video’s 31-day cumulative shows; shorter values indicate a more front-loaded exposure lifecycle. Evaluation horizon and data consistency. All metrics are calculated on a 31-day horizon after upload, covering >80% of total activity. Records are deduplicated and filtered for abnormal or non-human traffic prior to computation.

4.4 Analytical Framework The study employs two analytical streams—behavioral analyses and algorithmic response analyses.

To examine the behavioral effects of AIGC on creation and consumption, we employ matching-based analyses to mitigate potential confounding. Because the creation-side and consumption-side analyses focus on different units, we apply distinct matching strategies as described below.

4.4.2 Algorithmic Response Analyses Algorithmic response analyses consist of (i) matching-based analyses to compare AIGC and HGC exposure within fixed periods and compare different algorithms’ effects, and (ii) regression-based time-series analyses examining how system exposure responds to changes in the scale-over-preference dynamic.

Matching-based comparisons. The comparisons first contrast the exposure received by AIGC and HGC within the same time window, aiming to estimate algorithmic distributional differences under comparable conditions. We follow the matching procedures described for the consumption-side behavioral analysis, focusing on videolevel matching because the unit of analysis is the video without conditioning on specific users.

Discussion. 2.1 Scale-over-Preference Dynamics of AIGC AIGC creators produce higher volumes of content with overall engagement returns comparable to HGC creators. Using 2,497 matched AIGC and HGC creators, we compared their content creation behaviour. We plotted the complementary cumulative distribution function (CCDF) of video creation volume for the matched creators. As shown in the left panel of Fig. 1(c), the CCDF curve for AIGC creators lies consistently above that of HGC creators (HL median difference = 4, p < 0.001), indicating higher overall productivity. Further decomposition of AIGC creators’ output shows that this increase is driven primarily by AIGC video creation rather than HGC (SI 2.5). We next assessed the engagement returns using total valid views, views exceeding a minimum watch-duration threshold, and full views, representing completed watches (Methods). The CCDFs of both metrics (Fig. 1(c) middle and right) are similar for AIGC and HGC creators, with Wilcoxon signed-rank tests indicating no meaningful differences (valid views: HL median difference= 0, p = 0.003; full views: HL median difference =0, p = 0.363), indicating comparable engagement returns. These results suggest that AIGC creators produce higher volumes of content with overall engagement returns comparable to HGC creators.

Consumers show weaker preferences for AIGC relative to HGC. Using 47,288 matched user-video interaction pairs, we compared consumer engagement preferences for AIGC versus HGC videos across three measures: valid-view rate (the fraction of interactions exceeding a minimum watch-duration threshold), full-view rate (the fraction of completed watches), and view duration (watch time per interaction). We reported average valid-view and full-view rates, and the distribution of view duration using box plots. As shown in Fig. 1(d) (left panel), consumers display lower valid-view and full-view rates for AIGC videos than for HGC videos (valid-view Scale-over-Preference dynamics of AIGC. The behavioral results reveal a clear SoP dynamic for AIGC: AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. This reflects an ecological tension in the content ecosystem, characterized by an asymmetry between AIGC supply scale and consumer preference. To quantify the tension, we defined a SoP index as SoPI = ln(S/P) , S > 0, P > 0, where S and P denote AIGC’s relative supply scale and relative consumer preference at a given time, respectively, both measured relative to HGC. SoPI increases with larger S and smaller P, corresponding to a larger tension. Fig. 1(e) visualizes the daily SoP index (i.e., SoPI computed at each day) over the two-month observation period (April 1–May 31, 2025). All results cluster in a region of low relative consumer preference (P ≈0.30–0.40) and higher relative supply scale (S ≈0.55–0.70), with most SoPI exceeding ln(1.5), suggesting a persistent tension between AIGC supply and consumer preference over time.

2.2 Algorithmic Content Distribution Mechanism Moderates the Scale-over-Preference Dynamics of AIGC Algorithmic content distribution mechanism assigns lower exposure to AIGC than to HGC. Using the dataset for consumption pattern analysis in Section 2.1, we first examined algorithmic exposure over a 31-day post-upload window. To enable a fair comparison, we matched AIGC and HGC videos on factors like creator characteristics and content categories, yielding 178,854 matched pairs (robustness checks in SI 2.4). We compared exposure along two dimensions: cumulative exposure (total show count) and exposure lifecycle (days to reach 90% of cumulative exposure). As shown in Fig.