AI passing and invisible authenticity labor: trust vulnerabilities and inequality in China's short-video creator economy

Generative AI is increasingly embedded in China's short-video production, yet on authenticity-oriented platforms its visible use can be discrediting. Drawing on 16 in-depth interviews with creators active on Xiaohongshu and Douyin, this article introduces AI passing: creators' strategic efforts to conceal and humanize AI-assisted drafts so that outputs plausibly appear human-authored. We show that creators associate legible AI assistance with intertwined trust vulnerabilities, including epistemic unreliability, anticipated relational penalties, and platform authenticity regimes. To manage this legibility, creators perform four recurring forms of invisible authenticity labor: epistemic verification, linguistic naturalization, narrative restructuring, and performative embodiment. These practices reallocate work from generation to downstream repair and performance, complicating claims that AI simply improves efficiency. Finally, we demonstrate that passing capacity is stratified by educational and professional capital, economic resources and team support, and platform position, producing trust-based inequality in who can leverage AI while sustaining credibility, voice, and liveness.