TAMING THE ALGO: GRAB BIKERS GRAPPLING WITH PLATFORM LOGICS FROM BELOW

Resumen

How do workers conceptualize a platform’s algorithm and adjust their practices to its logic? To pursue this question, we draw on an ethnography of Grab, the leading rideshare platform in Southeast Asia, composed of 60+ trips talking to drivers on the back of bikes in Hanoi, and in-depth interviews with 10 drivers. From this rich material, we identify a strategic cluster of practices that we term “taming the algorithm.” Taming requires three key moves: iteratively adapting behaviors to algorithmic pressures (improvisation), juggling competing demands to deliver a productive performance at bodily limits (scrambling), and repeating these activities over time until they become a baseline in the system (enduring). If done successfully, these moves establish routinized productivity, a pattern of algorithmically ideal labor that means the platform will consistently delegate tasks to the worker. Taming is inherently double-edged, an exhausting form of self-exploitation that nevertheless provides some predictability and agency to workers. Taming the algorithm offers a contribution on two fronts. First, it allows us to prise the ontological and the technical aspects of platform labor apart: these moves are risky, brutal, and chaotic for workers but are smoothed into desirable integers and patterns by the algorithm. Secondly, it articulates a minor freedom between the twin poles of Freedom and Unfreedom as conventionally understood, showing how workers achieve some agency and some perception of power without fundamentally disrupting the systemic inequalities maintained by platform logics.