This article appears in the Fall 2026 Issue of Dissent Magazine.
Veena Dubal is a professor of law and, by courtesy, anthropology at UC Irvine.
Katie Wells is a geographer and senior fellow at AI Now. She is a co-author of Disrupting D.C.: The Rise of Uber and the Fall of the City.
Elena’s phone vibrates before her alarm at 5:12 a.m. She reaches for it because messages that arrive before dawn tend to matter.
Shift allocation updated.*
Base rate: $31.40/hr
Adjustment factors applied: -$2.10/hr
Net rate: $29.30/hr
*Based on recent attendance volatility, performance signals, and local labor supply conditions.
Last week her base rate was $33. The week before it was $35. There is no supervisor to call to ask why she keeps getting downgraded, no office where she can go to complain. All Elena can do is click a small blue “learn more” button. She has begun to think of her work not as a wage, exactly, but as an assessment of her current economic worth, relative to her coworkers, recalculated daily.
Elena imagines the fluctuating systems—not the screens, but what sits behind them. Networks of classifications, constantly updated by machine learning. Her punctuality becomes a variable, her neighborhood becomes a multiplier, her habits become predictions. Her wage is one output, prices another. Together, they define a moving boundary: what she can buy, what she can’t.
Her purchasing power is not something she has control over. It is assigned, continuously recalibrated, and tested against her behavior—and the behavior of others. She wonders if the systems speak to each other, if her declining wages quietly reshape the prices she sees, and if urgency, once detected, becomes a signal to raise them.
[…]
Elena is fictional, but not fanciful. Her day reflects a composite of experiences that researchers and workers, including both authors, have identified across the contemporary labor market: the gigification of healthcare, the real-time personalization of wages and prices, and the use of algorithmic and machine-learning systems to shape behavior and mete out discipline. Dubal’s work on algorithmic wage discrimination has identified opaque, personalized, and variable pay as a technology of labor control. Wells and her co-authors have traced these platform logics into nursing, where apps allocate shifts, set pay, monitor performance, and fragment responsibility for patient care. Joint work by both of us and our colleagues at Fairwork situates these developments within a broader shift toward AI-mediated management. Related research on surveillance prices and wages shows how the same data-driven logic can individualize both what people earn and what they pay, including studies by Wells and her colleagues on the mechanics of surveillance pricing at Instacart and Uber. The result is not only precarious economic uncertainty, but also poorer quality of life and health—for workers and patients alike.
Read the full article here.
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