The Fabricated Front: Generative AI and the Opacity of Workplace Performance

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).

Introduction. Workplace trust depends on the everyday interpretation of communicative performances. When a colleague sends an email or delivers a report, we make inferences about the person behind the performance. Word choice signals attentiveness, for instance, and distinctive phrasing indexes personality. These diagnostic cues form the tacit infrastructure of workplace coordination, helping us allocate trust and calibrate expectations. In other words, professional communication functions as a “front,” a patterned performance through which workers make competence and commitment available for interpretation (Goffman 1959). Generative AI (GenAI) disrupts this infrastructure by introducing a new form of mediated communication in which a system can modify, augment, or generate interpersonal messages on behalf of a communicator (Hancock et al. 2020). When outputs can be produced without the engagement they appear to represent, the interpretive framework through which we read professional contributions becomes unreliable. This paper examines how professionals navigate the use of an AI-mediated front.

Discussion / Conclusion. This article has shown how GenAI reorganizes the cue surfaces through which workers become accountable to one another. The most consequential finding is that workers did not treat AI mediation as uniformly problematic. They were more likely to protect identity-bearing cues, especially voice and provenance, while allowing labor-bearing cues such as effort, attention, and uncertainty to disappear into otherwise acceptable outputs. Voice and provenance remained contestable because they attach an artifact to a recognizable source: a colleague can notice that a message no longer sounds like its sender, or a client can question whether a designer produced an image. Effort, attention, and uncertainty, by contrast, receded into the completed task and became accountable mainly when something went wrong. This asymmetry reflects the output-centered organization of contemporary work. Workplaces commonly treat the deliverable as evidence that work occurred: emails sent, tickets closed, tasks completed, and so on.