Reciprocal AI Attribution and Status Equalization in Online Discourse

This work analyzes the emerging use of “AI-generated” as a ritualized form of dismissal in online discourse rather than a technical claim about authorship. Using a micro-ethnographic case study drawn from a public YouTube comment exchange, the paper formalizes the concept of Reciprocal AI Attribution: a reflexive rhetorical maneuver in which an authorship accusation is mirrored back to the accuser, collapsing implied status hierarchies and neutralizing legitimacy policing. The analysis situates this pattern within a broader post-authenticity discourse regime, where tone compliance and stylistic expectation outweigh factual provenance. Implications are discussed for digital ethnography, platform moderation, and alignment research, particularly where attribution labels function as social control mechanisms rather than descriptive categories.