Cybernetic proletarianization: Spirals of devaluation and conflict in digitalized production

Abstract

Drawing on a case study of algorithmically controlled manual labour in German manufacturing and delivery logistics, this article develops the concept of cybernetic proletarianization. It does so by joining an empirical analysis of labour processes with theoretical class analysis. Thus, it reconstructs Marx’s understanding of technical proletarianization as a dialectic between expulsion and reintegration of living labour in production processes. In the cases researched here, a qualitative and quantitative expulsion of living labour could be observed in different forms: First, deskilled flexibilization via digital instructions on working steps; second, a cybernetic mode of work intensification that is based on a permanent digital evaluation of the labour process; third, data-based automation, which builds on the data collected from the labour processes. This expulsion is counterweighted by a process of reintegration of devaluated living labour due to new highly labour-intensive forms of production and distribution, which are enabled by algorithmic work control. However, these processes are highly conflictual, resulting in different ‘technopolitics from below’, in which workers influence or even disrupt the processes of cybernetic proletarianization.

Algorithmic Integration and Precarious (Dis)Obedience: On the Co-Constitution of Migration Regime and Workplace Regime in Digitalised Manufacturing and Logistics

“…On the ‘other side’ of the algorithm (i.e. in the case of manual work in manufacturing and logistics examined here), devaluation trends can be observed in connection with algorithmic work control (Schaupp, 2021a). This is especially the case for migrant workers.…”

The Data-Driven Workplace and the Case for Worker Technology Rights

“…One of the key applications of data-driven technologies is to monitor and manage worker productivity, which is not harmful in and of itself. But when an employer uses electronic monitoring and algorithms to minutely track and relentlessly push workers to achieve greater productivity, negative effects can quickly make themselves felt (Schaupp 2021). In warehouses and distribution centers, handheld or wearable product barcode scanners enable firms to track workers’ scan rates and errors and to send them performance notifications to increase their pace or accuracy (Gutelius and Theodore 2022).…”

“…Employers can use data-driven technologies to routinize jobs and break them into discrete simplified tasks, accompanied by measuring and monitoring of performance. While the employer’s main goal may be to increase efficiency, the result for workers can be job de-skilling, a reduced scope of work, and increased repetition (Schaupp 2021; also see Levy and Barocas 2018). Algorithmic systems designed to manage workers can separate work tasks, shifting decision-making to programmers and computers while leaving task execution to the frontline worker—effectively turning them into a human machine that implements tasks dictated by a machine (Cappelli 2020; Jarrahi et al 2021).…”

“…Workers who previously performed those tasks are relegated to supporting the robots to enable them to function in the complex hospital environment. Task standardization can also lead to partial or wholesale job automation, since the task data gathered in real time from workers can be used to train robots or algorithms to eventually take over (Schaupp 2021). For example, algorithms used in self-driving trucks have been trained using data from hours of monitoring truck drivers (Plus 2021; Viscelli 2022).…”

COVID‐19, economic crises and digitalisation: How algorithmic management became an alternative to automation

“…In contrast to automation, new forms of algorithmic management do not only aim at the expulsion of human labour—in the form of qualitative and quantitative rationalisation—but also at its devalued reintegration (Schaupp, 2021b ). Detailed digital instructions allow for the hiring of unskilled labour.…”