Taming the algorithm: The platform realism of GrabBike delivery workers

Abstract

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, and 10 in-depth interviews. We identify a distinct set of moves that workers perform to survive on the platform, a strategic cluster of practices we term “taming the algorithm.” These practices appear incompatible or contradictory—a bodily enactment of improvising, scrambling, and enduring that nevertheless is registered by the algorithm as routinized productivity. Even if done successfully, taming does not fundamentally disrupt platform logics, but rather makes exploitation more consistent and predictable. Workers adopt what we term “platform realism,” striving for a bleak but concrete agency that maintains their status. The aim is not to disrupt the system or hack the algorithm, but to live with it.