Innovation Mechanisms of Generative AI in Empowering the Creator Economy: Evidence from Digital Content Platforms

Abstract

Generative artificial intelligence is increasingly embedded in digital content production and is fundamentally reshaping how creators generate ideas, produce content, and interact with platform audiences. This paper examines the innovation mechanisms through which generative AI may empower the creator economy, with a particular focus on digital content platforms. Building on a mechanism-oriented analytical framework, the study systematically links generative AI tools with four key dimensions: innovation efficiency, content quality, user feedback, and monetization potential. Two publicly available datasets serve as empirical references: DiffusionDB, which captures prompt-based visual creation activities, and YouTube Trending Video Statistics, which provides observable indicators of platform-level audience feedback. AI-related content is identified through systematic keyword screening across titles, descriptions, tags, and prompts, and is then compared with non-AI content drawn from similar categories and publication periods. The descriptive results indicate that AI-related content achieves higher average views, likes, and comments, although the difference in engagement rate remains relatively modest. The analysis further suggests that generative AI may support creators by reducing creative trial-and-error costs, enhancing textual and visual presentation quality, and increasing platform visibility within certain content contexts. However, because open-source platform data do not contain actual income information, views, likes, comments, and engagement rate are treated as proxies for monetization potential rather than direct evidence of revenue growth. Overall, the findings offer a cautious yet informative empirical basis for understanding the evolving role of generative AI in platform-based creator economy development.