Position provocations

AI agents mediate not only individual contributions but also the process through which many contributions become a team position. This matters when an AI-generated account of a meeting reaches another team, a manager, or an organization. Such an account may be accurate sentence by sentence while obscuring how the group reached its position, whose contributions shaped it, and which differences remain unresolved.

Proposals for accountability in agent-mediated work—AI use statements, detection, watermarking, content provenance—presuppose that the parties who would rely on them want them. Whether that demand exists has not been measured in the markets where AI-mediated work is exchanged between strangers. Our position is that accountability mechanisms in such markets are selected on cost incidence rather than on design, and that any mechanism imposing a cost on the verifier will not be adopted there.

In spring 2026, our four-person graduate research team spent a quarter interviewing professional visual artists about how they defend authorship when generative AI enters their work. The artists had clear rules about where AI was allowed. We had none, and by the end of the quarter a word none of us could account for had traveled from a coding memo into our final presentation, where our advisor asked why we had chosen it. Nobody could say. I argue that AI does not need to write anyone's work to dissolve contribution; it only needs to be available in the background of each member's individual workflow.

Communities often respond to potentially AI-assisted work by asking three questions: Was AI used? Was that use disclosed? Can hidden use be detected? These questions place AI use itself at the center of accountability while overlooking a deeper problem: unowned judgment. Evaluations, claims, decisions, and creative directions can be shaped by AI with no accountable human or institution prepared to stand behind them.

As AI systems increasingly synthesize team discussions and contributions, they can make a group appear to speak with one voice before its members have developed that voice together. We use contribution dissolution to describe this transformation: individual inputs are reorganized into a shared representation in ways that can obscure how those contributions differed, related to one another, and shaped the resulting position.

Increasingly widespread generative AI adoption is changing the nature of group design work. To understand how ownership and design intention shift, we interviewed eighteen students from a graduate interactive device design course where projects are done in teams, each walking through their own repository and chat logs. First, design intentions can have histories their authors cannot recover. Second, intention forms downstream of the tool. Third, the group record dissolves.