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This paper investigates the interactional dynamics introduced by generative AI (GenAI) in workplace settings, focusing on the concept of effort opacity, which refers to the decoupling of observable outputs from human engagement. By analyzing 1,250 interview transcripts from Anthropic's AI Interviewer dataset through Goffman's dramaturgical lens, the authors identify five mechanisms鈥攙oice, provenance, vulnerability, attention, and investment鈥攖hat contribute to this opacity in workplace interactions. The findings reveal that while professionals actively manage their identities, they often allow opacity to emerge around the labor mechanisms, highlighting the need for nuanced governance in AI workplace policies that account for the variability of inspectability in human involvement.
Generative AI is reshaping workplace interactions by creating a veil of opacity around human effort, complicating trust and collaboration.
Generative AI (GenAI) has become a fixture of workplace life. Current research asks chiefly what this implies for jobs and outputs, measured in productivity, displacement, or bias. What remains underexamined are the interactional reconfigurations that GenAI produces at work. The emerging concept of effort opacity has begun to fill this gap by highlighting the systematic decoupling of observable output from human engagement. When GenAI makes interactional cues less diagnostic, it weakens the reciprocal exchange that sustains collaborative trust. Extending this account of effort opacity, we examine the interactional mechanics that produce opacity in everyday workplace encounters. Drawing on Erving Goffman's dramaturgical framework and 1,250 interview transcripts from Anthropic's AI Interviewer dataset, we identify five opacity mechanisms through which workplace fronts are reorganized: voice (whose stance the words index), provenance (who can stand behind the artifact), vulnerability (whether the worker is uncertain), attention (whether the worker is engaged), and investment (how much labor the output reflects). We show that professionals defend the identity mechanisms while freely producing opacity around the labor mechanisms, and trace this asymmetry to the output-centered organization of contemporary work, where deliverables already stand in for the labor process that produced them. The governance task, accordingly, is one of involvement management: specifying which forms of human involvement (attention, effort, judgment) must remain inspectable, and to whom. Workplace AI policies built on universal disclosure will systematically misrecognize a social field in which inspectability is already audience-relative.
