Selected Papers of #AoIR2024: The 25th Annual Conference of the Association of Internet Researchers Sheffield, UK / 30 Oct - 2 Nov 2024 Suggested Citation (APA): Nguyen-Thu, G., Munn, L. (2024, October). Taming the Algo: Grab Bikers Grappling with Platform Logics from Below. Paper presented at AoIR2024: The 25th Annual Conference of the Association of Internet Researchers. Sheffield, UK: AoIR. Retrieved from http://spir.aoir.org. TAMING THE ALGO: GRAB BIKERS GRAPPLING WITH PLATFORM LOGICS FROM BELOW Giang Nguyen-Thu University of Queensland Luke Munn University of Queensland Introduction 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 GrabBike, the leading rideshare platform in Southeast Asia, composed of 60+ trips talking to drivers on the back of bikes, and 10 in-depth interviews. From this rich material, we identify a strategic cluster of moves that workers perform to survive on the platform, what they term “taming the algorithm.” Taming is double-edged, a form of self-exploitation that nevertheless offers workers a form of agency. Taming thus highlights how platform labor’s performative tensions and machinic resolutions articulate a different kind of “freedom.” Background There has been much debate about the extent of workers’ autonomy in platform labor. Many platform scholars argue that the promises to work your own hours, make your own choices, and be your own boss are empty (Shibata 2020). From this perspective, platform freedom is nothing but an “illusory” sense of autonomy (Umer 2021). But this dismissal runs counter to worker’s own testimonies (Vieira 2021; Purcell and Brook 2022). Tropes of freedom, autonomy, and flexibility recur whenever platform workers explain what drew them to the platform and why they stay on it. If we take worker statements at face value, they’re free; if we accept some platform scholars, workers are merely duped. Methodology: On the Back of a Bike To break this deadlock, we move away from the abstract and investigate the concrete ways workers move, speak, and act on GrabBike, the leading rideshare platform in Southeast Asia. We theorize with workers rather than against them, taking seriously their ability to apprehend and respond to platform logics. Our study draws from two months of ethnographic fieldwork in Hanoi from December 2022 to January 2023 by one of the authors. Sixty-four short interviews, ranging from 10 to 60 minutes, were conducted using what we call a “ride-through method,” when the ethnographic author sat behind the driver during her daily GrabBike trips. Data collected from these mobile conversations was complemented by 10 in-depth interviews. Findings We identify three distinct “moves” that workers perform in response to algorithmic logics: improvising, scrambling, and enduring. Drawing from workers’ own words, we call this cluster of strategies taming the algorithm, or “rèn áp” in Vietnamese. Improvising Firstly, taming requires improvisation, figuring out how the app “thinks” while you’re on the go. The driver must theorize their relationship with “the app” while simultaneously navigating through the busy streets of Hanoi. For example, workers must learn to manage the pros and cons of not only the four major services (passengers, food, parcel, and grocery) but also the intricate differences among two dozen sub-services. “I mainly deliver food because my strategy is to get as many short trips as possible to collect the maximum number of gems [ngọc - bonus], and I don’t mind waiting at the restaurants.” Food delivery improvisation differs from improvisation in passenger service or parcel delivery. Platforms thus provide no winning formula. Instead, workers must gradually come to grips with how platforms operate, often discovering through trial-and-error what works and what doesn’t. In a sense, each worker must construct their own “algorithm” for achieving success (Munn 2018). Scrambling Secondly, taming requires scrambling, a term that captures the subjective quality of frenetic activities demanded by platforms. Platform work is extremely performance- oriented work. The platform’s regime of sensors and data points allows this performance to be recorded in meticulous detail, with personalized targets and gamified elements, like Grab’s “gems”—all attempting to drive the worker to new levels of productivity. From the platform’s perspective, scrambling is simple: a particular number is met or maintained. However, from the worker’s perspective, this performance is a negotiation of irreconcilable demands. It’s not enough to be fast; one must be fast despite conditions that require slowing down, such as heavy rain or chronic congestion. Running [chạy] was both intrinsic to the job and attached to extra suffering—“running” under the burning sun, “running” on the pavement to beat the estimated delivery time, and “running” with an empty stomach. These tensions between algorithmic pressures and real-world contingencies echo workers on other platforms, who describe their work as “mission impossible at times” (Chen and Sun 2020). Scrambling thus draws from the full subjective field of the worker, leading to a full-spectrum pressure that explains the physical, emotional, and mental toll reported by workers on platforms. Enduring Thirdly, taming requires endurance. It’s not enough to carry out an exemplary performance once or twice. GrabBike workers must exhibit a persistent effort over time to move from being a “new chicken” (gà mới) to a veteran, making their labor stable and dependable from the platform’s point of view. Each Grab worker had their self-tested threshold of how long this process took, but the general rule was 12-14 hours a day for about 60 to 100 days—a significant time investment that not all newbies were prepared to suffer. The calibrated endurance lay precisely in the fact that despite endless improvisation and scrambling, one must keep on working as if there was no precarity. In this sense, “taming the app” meant to let the platform know that you had managed to tame yourself. Improvisation + Scrambling + Endurance = Routinized Productivity If the platform worker can successfully perform these three “moves,” then they establish routinized productivity, an algorithmically ideal pattern of labor that has been made technically legible from the perspective of the platform. Routinized productivity is ideal for the platform but ontologically fraught for laborers: the risks, injuries, and abuse needed to achieve it are borne silently by the worker; the platform only “sees” an optimal performer. Conclusion: On Platform Realism While difficult, taming the algorithm is seen by workers as essential because it enables a sense of agency in three ways: it increases predictability, improves survivability and cultivates futurability. By anticipating platform logics, workers can break open the future- on-rails that precarious platform work often produces. They can begin to envision an alternative life after the platform—a stepping-stone allowing them to return to the countryside with savings, or own their own food stall in the city. Of course, taming should be critically reflected on. Grab workers themselves are cynical about their own power in the face of platform logics, asserting that they understand the game rather than necessarily being “on top” of it. Yet if taming is limited, we suggest that it provides a worthy sense of empowerment, bestowing workers with a perception of control, a degree of confidence, an ability to project themselves into precarious circumstances as skilled and savvy. Taming thus accepts and intervenes in actually-existing platform conditions rather than attempting to overthrow them. “You have to be realistic” is a common refrain amongst Grab workers. We call this platform realism, a pragmatic theorization and response to platform logics by workers that strives to attain modest but meaningful advantages. Taming the algorithm returns some control to workers without fundamentally disrupting the systemic inequalities maintained by platform logics. For workers, the platform algorithm may be tamed, but it’s still ferocious. References Chen JY and Sun P (2020) Temporal arbitrage, fragmented rush, and opportunistic behaviors: The labor politics of time in the platform economy. New Media & Society 22(9): 1561–1579. DOI: 10.1177/1461444820913567. https://doi.org/10.1177/1461444820913567 https://doi.org/10.1177/1461444820913567 Munn L (2018) Ferocious Logics: Unmaking the Algorithm. Lüneburg: Meson Press. Purcell C and Brook P (2022) At least I’m my own boss! Explaining consent, coercion and resistance in platform work. Work, Employment and Society 36(3). SAGE Publications Sage UK: London, England: 391–406. Shibata S (2020) Gig Work and the Discourse of Autonomy: Fictitious Freedom in Japan’s Digital Economy. New Political Economy 25(4). Routledge: 535–551. DOI: 10.1080/13563467.2019.1613351. Umer H (2021) Illusory freedom of physical platform workers: Insights from Uber Eats in Japan. The Economic and Labour Relations Review 32(3). SAGE Publications Sage UK: London, England: 437–452. Vieira T (2023) Platform couriers’ self-exploitation: The case study of Glovo. New Technology, Work and Employment 0(0). Wiley Online Library: 1–20. https://doi.org/10.1080/13563467.2019.1613351 https://doi.org/10.1080/13563467.2019.1613351 https://doi.org/10.1080/13563467.2019.1613351