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A Global Labour Regime for Data Work? Exploitation, control, and their convergences across geographies
Søren Bøgh Sørensen feb 2026 · essay · issue 01
John Flamsteed, "The Celestial Atlas of Flamsteed"

The vast networks of global build-outs necessary for the production of digital technologies have prompted extensive discussions on the infrastructural power of digital capital. These debates — often characterising these infrastructures as “megamachines”, or as a “planetary stacking order” — aim to clarify how digital capitalists have gained greater control over society through an increasing concentration of power and capital.1

Such scale of build-outs have led scholars to argue that the “platform capitalism” of the 2010s is now giving way to a new “era of AI”.2 While the age of platform capitalism was characterised by social media companies generating profits through network effects, cross-subsidisation, and micro-targeted advertising, the AI era can be seen as increasingly dominated by “Big AI” companies. These companies — OpenAI, Anthropic, Cohere, Nvidia, and the like — benefit from infrastructural power and control over the architecture necessary to produce AI systems.

This apparent concentration of power has also sparked a parallel debate over whether or not we are witnessing a transition to a new mode of production: a form of techno-feudalism, where tech-conglomerates increasingly rely upon speculative financial valuation and rent-extraction in order to maximise profits.3 While speculation and rent extraction can indeed serve as sources of profit for tech companies, what is often overlooked is the fact that the geographically dispersed infrastructure that undergirds machine learning also relies upon the exploitation of diverse categories of workers, within the international division of digital labour.4 Indeed, the global supply chains of machine learning systems themselves include the work of software engineers and data scientists; the preparation and verification of datasets by human annotators; the free labour of digital media users; the manual assembly of hardware; and the mining and refining of so-called critical rare-earth minerals.5

Particularly with respect to the data required to train machine learning models, these global networks of production involve vast amounts of labour, paid or otherwise.6 In this light, Antonio Casilli argues in his recent book, Waiting for Robots, data work can be viewed as the “basic and constant form” of platform work writ large.7 “Data work” — an umbrella term for data annotation, verification, content moderation, and so on — tends to occur under the auspices of business process outsourcing companies (BPOs), where workers work in physical office settings; or through digital labour platforms (DLPs), where work can be performed from anywhere with a laptop and internet connection.8 And as the hype bubble around AI nears its peak, the need for human data preparation and verification has only grown, prompting large technology companies to outsource increasing amounts of data work through these subcontractors.9

Proclamations of a novel era of AI development in global capitalism may thus be overstated. The strategies for capital accumulation and managerial control that AI firms and their outsourcing partners deploy draw upon platform capitalism’s organisational and management models.10 And these models, too, did not emerge out of nowhere: rather, they followed from the specific lineages of the logistics revolution, and digital service work in call centres.11

The global population of data workers has rightly been theorised as a reserve army of labour, since they constitute an un- and under-employed workforce, intermittently drawn into the digital economy’s circuits of value accumulation.12 In the context of digital labour platforms, the exploitation of this reserve army occurs through a digitally mediated “planetary labour market”.13 The word planetary here does not imply that geographical differences become irrelevant — rather, it signifies how these differences are strategically leveraged by tech companies, through labour arbitrage and cross-border competition. Even in a highly digitised, global labour market, exploitation is always asymmetrically embedded in specific geographical locations, and conditioned by political-economic, environmental, and cultural factors.14

Analysis of digital labour platforms through the lens of labour process theory have tended to elide precisely these socio-political contexts.15 Labour regime analysis provides a useful alternative theoretical lens here, linking micro-level antagonisms in the production process to macro-level dynamics in the global economy.16 It extends labour process theory’s narrow focus on the immediate process of production to encompass surrounding institutional arrangements, geographically anchoring the specificity of determinate labour processes within their local contexts.

Through the lens of labour regime analysis, then, we can begin to describe labour regimes as “invisible infrastructures” that mobilize workers for production, simultaneously extending and intensifying work within the labour process itself.17 Such infrastructures are made up of competitive pressures in the global economy; international regulations such as trade agreements; national organizations and institutions such as trade union federations and social protection systems; and modes of social reproduction, at the household level. Together, these components of labour regimes compel workers to exert greater labour power in the production process, all the while accepting wages and working conditions that are amenable to capital’s dictates. Labour regimes also express specific underlying logics, or operational rationalities, that can adapt to different social, political, and legal context.18 These underlying logics manifest themselves on online platforms in the form of algorithmic work allocation; digital tracking and monitoring; rating systems; independent contractor status; and legal and regulatory arbitrage — together forming a burgeoning “platform management model”.19

These manifestations of platform capitalism are closely tied to historical shifts in the global economy: from de-industrialisation and rising unemployment, to the proliferation of precarious and flexible forms of work.20 The advent of the gig economy in the global North epitomises these shifts, reflecting a restructuring of work in line with the long-standing quotidian reality of informal workers in the global South.21 We can, thus, view this platform management model as an instance of neoliberal globalisation, in which capital’s expansionary logic has made the relations of production more precarious for workers in both the global South and the North.22

It is important here, however, to guard against accounts of capitalist expansion in the global South as unilinear or homogeneising, and against treating their contexts as underspecified “elsewheres”.23 Theorising a single global labour regime risks overlooking the geographically anchored institutional arrangements that regulate local labour markets, framing workers in the global South as passive victims that remain entirely under the control of capital and the state.24 Yet — geographic divergences notwithstanding — we can still identify shared features and tendencies, and conceive of a global labour control regime as a constellation of dynamics and mechanisms that intensify the exploitation of data workers globally. Indeed, in contrast with celebratory but superficial accounts of worker agency, understanding these common tendencies can provide concrete avenues for eliminating constraints and obstacles to the realistic exercise of this agency.

How can we characterise the commonalities and differences between the positions and experiences of data workers across the world today? DLPs and BPOs — where most annotation labour takes place — have traditionally been analysed as separate types of businesses. Whereas the former have been conceived as a “planetary labour market” — since anyone with an internet connection can log onto them and begin performing tasks — the latter are seen more as traditional companies.25 Yet, there is abundant evidence that workers are exposed to similar forms of exploitation and managerial control across both types of enterprises, an expression of the convergent tendencies emerging in the global market for outsourced data annotation services.

For instance, both BPOs and DLPs employ a management model in which workers are integrated into teams composed of data workers (euphemistically referred to as “associates” or “agents”), quality analysts, team leads, and project managers.26 In this setup, data workers send their completed tasks to the QAs, who review the annotations and send their reviews to the team leaders and project managers, after which monthly or weekly spreadsheets of performance ratings are produced.27 In both BPOs and DLPs, this review process is supported by algorithmic management systems that monitor workers’ performance metrics: accuracy, efficiency, productivity, occupancy, and so on.28 And inspired by DLPs, some BPOs also now employ reputational systems, in which workers’ ratings are visible, creating a highly competitive “gamified” environment. All of this is reinforced by the general oversupply of data workers, and by managerial demands for faster and more accurate annotation.29

The Taylorist logics applied here — through the quantification of the labour process, and the production of conditions in which humans are compelled to behave more as machines — also reflect the instrumental rationality prevalent in digital capitalism in a wider sense.30 And in terms of management style, the combination of machinic and human management are deeply reminiscent of the call-centre management model.31 Such convergences have led to the emergence of more and more hybrid organisations, that present themselves as DLPs, while integrating forms of managerial supervision and discipline that we normally find in conventional companies.32

Of course, there are also obvious differences between working for a BPO and on a DLP, as well as differences between specific platforms or companies.33 Standard DLPs tend to offer piece-rate remuneration to workers, classifying them as independent contractors; BPOs, on the other hand, tend to offer full-time (yet temporary or short-term) contracts, with monthly salaries and employer contributions to health insurance schemes.34 Differences such as these can lead to considerable fragmentation amongst workers, according to the informality of their working arrangements. In and of itself, this fragmentation serves as a controlling device in the labour process.35

Yet, ultimately, as digital capitalism’s ambit expands globally, competition drives capitalists to adopt one another’s structures and techniques to control and exploit workers more effectively. It is, paradoxically, precisely this tendency towards convergence that can create opportunities for a collective workers’ consciousness, helping workers find common ground and outrun capitalist control.

On a broader scale, particularly when comparing data workers across the global South and the North, major differences begin to emerge.36 After all, working on online platforms from the slums of Kibera or Mathare in Nairobi presents a rather different reality to doing platform work to supplement your income as a student in Copenhagen. We know, for instance, that a higher proportion of workers in the global South report that online data work is their primary source of income, and that these workers perform twice the amount of unpaid work on platforms compared to those in the global North. And on average, they earn only half the amount that their Northern counterparts do, despite having higher educational levels.37 This shows how the production of machine learning systems reinforces the uneven geographies of extraction produced by historical developments in global capitalism — thus making it inseparable from the legacies of colonial domination of human labour in the global South by capitalist enterprises in the global North.38

Even though the past decades have witnessed a broader global convergence towards platform management, we can still trace a distinct trajectory followed specifically by labour control regimes for data workers in the global South. While the exact form the exploitation of data workers takes might differ across geographies, different fractions of capital do adopt similar methods and tools to perfect this exploitation. In the global South, we observe the most pronounced convergence toward such shared characteristics because the socio-economic and political conditions here are most conducive to the extension and intensification of data worker exploitation.

All of this makes it profoundly important to take seriously the political-economic and social contexts in which data work is carried out. Broadly similar conditions and dynamics are evident across various geographies in the global South, where high unemployment rates result in a chronic oversupply of labour. This — combined with large informal economies, pervasive poverty, limited access to social protections, and weak or corrupt political institutions — creates the perfect set of incentives for companies to outsource data work.39 Within a global data work labour control regime, then, we can observe the emergence of a common set of features. Workers are subjected to human and algorithmic monitoring, evaluation, and discipline; their pay rates are variable, calibrated to both the volume and quality of work performed; unpaid labour proliferates; information asymmetries are entrenched; and workforces dynamically expand and contract in response to client demand.40 These features overlap with more locally determined mechanisms that use the oversupply of labour to extract more labour power — such as regulatory arbitrage across geographical regions according to levels of labour protection, or non-disclosure agreements and anti-mobilisation clauses in contracts.

Understanding these general tendencies in data work labour regimes can elucidate the dynamics through which the contemporary, digitised capitalist totality reproduces and entrenches its power structures. Strategically speaking, it can help foreground the shared interests of digital workers within the global economy, and identify choke points in the global supply chains of machine learning systems — where strikes or sabotage could disrupt the accumulation of capital. Ultimately, a focus on how labour regimes intensify the exploitation of data workers across contexts enables us to map the obstacles and constraints that limit the exercise of collective agency, and to chart a path towards dismantling the hellscape that is digital capitalism.

Notes

  1. Kate Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, 2021; Florian A. Schmidt, “The Planetary Stacking Order of Multilayered Crowd-AI Systems”. In Mark Graham and Fabian Ferrari, eds., Digital Work in the Planetary Market, 2022; Antonio Casilli and Julian Posada, “The Platformization of Labor and Society”. In Society and the Internet: How Networks of Information and Communication Are Changing Our Lives, 2019. [^]

  2. James Muldoon, Callum Cant, and Mark Graham, Feeding the Machine: The Hidden Human Labour Powering AI, 2025; Paul Langley and Andrew Leyshon, “Platform Capitalism: The Intermediation and Capitalisation of Digital Economic Circulation”, Finance and Society, 2017. [^]

  3. Cédric Durand, How Silicon Valley Unleashed Techno-Feudalism: The Making of the Digital Economy, 2024; Evgeny Morozov, “Critique of Techno-Feudal Reason”, New Left Review, 2022. [^]

  4. Christian Fuchs, Digital Capitalism, 2022; Greig Charnock and Ramon Ribera-Fumaz, “What’s Talent Got to Do with It? The Collective Labourer and the Rise of Barcelona’s Digital Economy”, Antipode, 2024. [^]

  5. Kerry Holden and Matthew Harsh, “On Pipelines, Readiness and Annotative Labour: Political Geographies of AI and Data Infrastructures in Africa”, Political Geography, 2024. [^]

  6. James Muldoon, Callum Cant, Boxi A. Wu, and Mark Graham, “A Typology of Artificial Intelligence Data Work”, Big Data & Society, 2024; Lorenzo Cini, “How Algorithms Are Reshaping the Exploitation of Labour-Power: Insights into the Process of Labour Invisibilization in the Platform Economy”, Theory and Society, 2023. [^]

  7. Antonio A. Casilli, Waiting for Robots: The Hired Hands of Automation, 2025. [^]

  8. Paola Tubaro, Antonio A. Casilli, and Marion Coville, “The Trainer, the Verifier, the Imitator: Three Ways in Which Human Platform Workers Support Artificial Intelligence”, Big Data & Society, 2020. [^]

  9. Examples include Alphabet’s Raterhub and Crowdsource platforms, or Microsoft’s internal Universal Human Relevance System (UHRS) platform. OpenAI and Meta have outsourced content moderation and model output verification tasks to low-wage Kenyan data workers through the BPO Samasource. See: International Labour Organization, Digital Labour Platforms in Kenya: Exploring Women’s Opportunities and Challenges Across Various Sectors, 2024. [^]

  10. Ursula Huws, “Where Did Online Platforms Come From? The Virtualization of Work Organization and the New Policy Challenges it Raises”. In Pamela Meil, Vassil Kirov, eds., Policy Implications of Virtual Work, 2017; Jamie Woodcock, “Artificial intelligence at work: The problem of managerial control from call centers to transport platforms”, Frontiers in Artificial Intelligence, 2022. [^]

  11. Sandro Mezzadra and Brett Neilson, “Operations of Platforms: A Global Process in a Multipolar World”. In Sandro Mezzadra et al., eds., Capitalism in the Platform Age: Emerging Assemblages of Labour and Welfare in Urban Spaces, 2024. [^]

  12. Patrizia Zanoni and Frederick Harry Pitts, “Inclusion Through the Platform Economy? The ‘Diverse’ Crowd as Relative Surplus Populations and the Pauperisation of Labour”. In The Routledge Handbook of the Gig Economy, 2022. [^]

  13. Mohammad Amir Anwar and Mark Graham, “The Global Gig Economy: Towards a Planetary Labour Market?”, First Monday, 2019. [^]

  14. Mohammad Amir Anwar, Susann Schäfer, and Slobodan Golušin, “Work Futures: Globalization, Planetary Markets, and Uneven Developments in the Gig Economy”, Globalizations, 2024. [^]

  15. Alessandro Gandini, “Labour Process Theory and the Gig Economy”, Human Relations, 2019; Simon Joyce and Mark Stuart, “Digitalised Management, Control and Resistance in Platform Work: A Labour Process Analysis”. In Julieta Haidar and Maarten Keune, eds., Work and Labour Relations in Global Platform Capitalism, 2021. [^]

  16. Jamie Peck, “Modalities of Labour: Restructuring, Regulation, Regime”. In Elena Baglioni et al., eds., Labour Regimes and Global Production, 2022. [^]

  17. Elena Baglioni, Liam Campling, Alessandra Mezzadri, Satoshi Miyamura, Jonathan Pattenden, and Benjamin Selwyn, “Exploitation and Labour Regimes: Production, Circulation, Social Reproduction, Ecology”. In Elena Baglioni et al., eds., Labour Regimes and Global Production, 2022. [^]

  18. Mezzadra & Neilson, “Operations of Platforms”; Maurilio Pirone, “Out of the Standard: Towards a Global Approach to Platform Labour”. In Sandro Mezzadra et al., eds., Capitalism in the Platform Age: Emerging Assemblages of Labour and Welfare in Urban Spaces, 2024. [^]

  19. Phoebe V. Moore and Simon Joyce, “Black Box or Hidden Abode? The Expansion and Exposure of Platform Work Managerialism”, Review of International Political Economy, 2020. [^]

  20. Jamie Woodcock and Mark Graham, The Gig Economy: A Critical Introduction, 2020. [^]

  21. Alessandra Mezzadri, “Social Reproduction, Labour Exploitation and Reproductive Struggles for a Global Political Economy of Work”. In Mauro Atzeni et al., eds., Handbook of Research on the Global Political Economy of Work, 2023. [^]

  22. Kevan Harris and Phillip A. Hough, “Labour Regimes, Social Reproduction and Boundary‑Drawing Strategies Across the Arc of US World Hegemony”. In Elena Baglioni et al., eds., Labour Regimes and Global Production, 2022. [^]

  23. Philip F. Kelly, “The Political Economy of Local Labor Control in the Philippines”, Economic Geography, 2001. [^]

  24. Neethi P., “Globalization Lived Locally: Investigating Kerala’s Local Labour Control Regimes”, Development and Change, 2012. [^]

  25. Mark Graham and Mohammad Amir Anwar, “The Global Gig Economy: Towards a Planetary Labour Market?”, First Monday, 2019. [^]

  26. Milagros Miceli, Martin Schuessler, and Tianling Yang, “Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision”, Proceedings of the ACM on Human-Computer Interaction, 2020; Milagros Miceli, Julian Posada, and Tianling Yang, “Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?”, Proceedings of the ACM on Human-Computer Interaction, 2022. [^]

  27. Srravya Chandhiramowuli and Bidisha Chaudhuri, “Match Made by Humans: A Critical Enquiry into Human‑Machine Configurations in Data Labelling”, Proceedings of the 56th Hawaii International Conference on System Sciences, 2023; Bidisha Chaudhuri and Srravya Chandhiramowuli, “Tracing the Displacement of Data Work in AI: A Political Economy of ‘Human‑in‑the‑Loop’”, Engaging Science, Technology, and Society, 2024. [^]

  28. James Muldoon, Callum Cant, Mark Graham, and Funda Ustek‑Spilda, “The Poverty of Ethical AI: Impact Sourcing and AI Supply Chains”, AI and Society, 2023. [^]

  29. Alex J. Wood and Vili Lehdonvirta, “Platforms Disrupting Reputation: Precarity and Recognition Struggles in the Remote Gig Economy”, Sociology, 2023; Agnieszka Piasna, “Algorithms of Time: How Algorithmic Management Changes the Temporalities of Work and Prospects for Working Time Reduction”, Cambridge Journal of Economics, 2024; Srravya Chandhiramowuli, Alex S. Taylor, Sara Heitlinger, and Ding Wang, “Making Data Work Count”, Proceedings of the ACM on Human‑Computer Interaction, 2024. [^]

  30. Jernej Amon Prodnik, “Algorithmic Logic in Digital Capitalism”. In Pieter Verdegem, ed., AI for Everyone? Critical Perspectives, 2021; Moritz Altenried, “The Platform as Factory: Crowdwork and the Hidden Labour Behind Artificial Intelligence”, Capital and Class, 2020. [^]

  31. In Kenya, companies such as CloudFactory operate their own online platforms, where taking screenshots and surveilling remote data workers via their laptop webcams is not uncommon. Other platforms, such as the Computer Vision Annotation Tool (CVAT), provide companies with digital means to outsource data annotation, allowing them to integrate workers into their own teams of QAs and team leads, with communication occurring through Telegram, Signal, or Slack channels. Client companies can thus introduce more direct human management and supervision through digital means, circumventing issues related to data quality and security that are associated with outsourcing to an anonymous, global crowd of data workers. See: Muldoon et al., “A Typology of Artifical Intelligence Data Work”. [^]

  32. Clément Le  Ludec, Maxime Cornet, and Antonio A. Casilli, “The Problem with Annotation: Human Labour and Outsourcing Between France and Madagascar”, Big Data & Society, 2023. An example is the DLP Remotasks, which had several physical offices in Nairobi, Nakuru, and Thika — combining features of the BPO and platform management model. [^]

  33. In the Kenyan context, there are also more standard, “pure” online platforms, such as Hive AI, as well as BPOs that do not rely on remote work via online platforms. [^]

  34. Some BPOs also pay workers in cash to avoid paying taxes and social protection. [^]

  35. Nikolaus Hammer and Lone Riisgaard, “Labour and Segmentation in Value Chains”. In Kirsty Newsome et al., eds., Putting Labour in its Place: Labour Process Analysis and Global Value Chains, 2015. [^]

  36. Mark Graham, Isis Hjorth, and Vili Lehdonvirta, “Digital Labour and Development: Impacts of Global Digital Labour Platforms and the Gig Economy on Worker Livelihoods”, Transfer: European Review of Labour and Research, 2017. [^]

  37. Uma Rani and Marianne Furrer, “Digital Labour Platforms and New Forms of Flexible Work in Developing Countries: Algorithmic Management of Work and Workers”, Competition and Change, 2021; International Labour Organization, World Employment and Social Outlook 2021: The Role of Digital Labour Platforms in Transforming the World of Work, 2021 [^]

  38. Kelle Howson, Alessio Bertolini, Srujana Katta, Funda Ustek‑Spilda, and Mark Graham, “The Emerging Geographies of Platform Labour: Intensifying Trends in Global Capitalism”. In Valerio De Stefano et al., eds., A Research Agenda for the Gig Economy and Society, 2022; James Muldoon and Boxi A. Wu, “Artificial Intelligence in the Colonial Matrix of Power”, Philosophy & Technology, 2023. [^]

  39. Mohammad Amir Anwar and Mark Graham, The Digital Continent: Placing Africa in Planetary Networks of Work, 2022; Kelle Howson, Hannah Johnston, Matthew Cole, Fabian Ferrari, Funda Ustek‑Spilda, and Mark Graham, “Unpaid Labour and Territorial Extraction in Digital Value Networks”, Global Networks, 2023. [^]

  40. Valerio De Stefano, “The Rise of the ‘Just‑in‑Time Workforce’: On‑Demand Work, Crowd Work and Labour Protection in the ‘Gig‑Economy’”, SSRN Electronic Journal, 2015. [^]