The Costs of Connection and the Colonialism of Data
Nick Couldry and Ulises A. Mejias's The Costs of Connection gives a name to the bargain hidden inside ordinary digital convenience. The book argues that platforms, apps, sensors, smart objects, and data brokers do not merely observe social life. They reorganize life so it can be continuously captured, quantified, processed, and returned as a marketable service. Read after the rise of generative AI, the book becomes a theory of the extraction layer beneath model culture: before a system can predict, personalize, rank, or automate, the world must first be made into data.
The Book
The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism was published by Stanford University Press in August 2019. Stanford lists the book at 352 pages, in the Culture and Economic Life series, with hardcover ISBN 9781503603660, paperback ISBN 9781503609747, and ebook ISBN 9781503609754.
Couldry is a professor of media, communications, and social theory at the London School of Economics and Political Science. Mejias is a professor of communication studies at SUNY Oswego. Their collaboration matters because the book is not a narrow privacy complaint. It is a media-theory, political-economy, and decolonial account of why connection has become an extraction regime.
The book's central claim is that the digital economy treats human life as raw material. Social needs are routed through connective systems; those systems turn action, movement, attention, relation, labor, consumption, health, learning, and expression into data; data becomes the basis for profit, prediction, influence, management, and institutional classification.
Data Colonialism
The book's strongest move is to refuse the language of harmless exchange. The user does not simply trade data for convenience in a clean bilateral bargain. The social world is being rebuilt so that nonparticipation becomes costly. Work requires platforms. Care requires portals. Education requires accounts. Public speech requires privately governed channels. Everyday life becomes difficult to conduct without passing through systems designed to record it.
Couldry and Mejias call this condition data colonialism. The analogy is deliberately heavy. Historical colonialism appropriated land, labor, bodies, resources, and knowledge systems while normalizing the right of distant powers to extract value. Data colonialism does not repeat that history in identical form, and the distinction matters. Its object is not territory in the older sense. Its object is the ongoing flow of human life rendered as data.
That is why the book is useful for thinking about legibility. A colonial system does not merely take what already exists. It changes maps, categories, names, incentives, records, and institutions so that extraction appears administratively natural. The data economy does something similar when it persuades people that every gesture should be measured, every relation should be mediated, every process should be optimized, and every gap in capture is inefficiency.
Cloud Empire
The publisher's table of contents identifies "Cloud Empire" as the chapter where the authors examine the social quantification sector: apps, platforms, smart technologies, data processing, artificial intelligence, and the infrastructure that turns life into monetizable information. This is where the book's AI relevance becomes obvious.
AI is often discussed as if models arrive first and data follows. The Costs of Connection reverses the order. Data relations come first: the social arrangements that normalize capture. The model is downstream of a world already reorganized for measurement. Personalization, recommender systems, targeted advertising, workplace analytics, credit models, predictive policing, automated eligibility systems, and generative AI all depend on prior infrastructures of extraction, cleaning, labeling, storage, and access.
The phrase "cloud empire" also keeps the analysis institutional. The cloud is not just a technical architecture. It is a political geography of data centers, account systems, developer platforms, APIs, procurement contracts, content-delivery networks, model hosting, workplace suites, surveillance products, payment rails, and terms of service. To live under cloud empire is to live inside privately administered conditions for being seen, served, ranked, remembered, and refused.
The Hollowing of the Social
One of the book's most important warnings is that data extraction changes what counts as social knowledge. Institutions once gathered social information through censuses, surveys, professional records, public research, hearings, journalism, audits, local administration, and social science. Those systems were imperfect and often unjust, but they were at least partly visible as institutions. Datafied knowledge is more difficult to see. It is assembled through platforms, brokers, background trackers, smart devices, and proprietary analytics.
The result is a quiet shift in authority. The social world becomes knowable through proxies owned by firms. A person becomes a profile, a behavior pattern, an inferred interest, a risk segment, a churn probability, a fraud score, a productivity signal, or a training example. These abstractions can then circulate between institutions as if they were neutral facts.
This is where the book intersects with the site's recurring concern about recursive reality. Once a proxy enters an interface, it can shape the world it claims only to describe. A ranking changes visibility. A score changes access. A dashboard changes managerial attention. A recommendation changes desire. A model output changes the next record. The measurable social world feeds back into the lived social world until the distinction becomes hard to recover.
Autonomy Under Continuous Capture
The chapter on autonomy gives the book its human stakes. Surveillance is not only harmful when a record is used against someone. Continuous monitoring changes the background condition under which people think, speak, gather, search, experiment, and refuse. It erodes the practical sense that some parts of life belong first to the person living them.
That point is sharper now than in 2019. AI systems promise to observe more context, remember more interaction, personalize more deeply, and act across more tools. The assistant, tutor, therapist, work copilot, shopping agent, hiring screen, classroom platform, and medical scribe all present themselves as convenience. Each also extends the domain in which human action becomes machine-readable.
The danger is not that every data collection is equally abusive. The danger is that the default social order moves toward total availability. Once life is presumed capturable, the burden shifts to individuals to justify opacity, silence, refusal, slowness, local memory, and unoptimized relation.
The AI-Age Reading
In the generative-AI era, The Costs of Connection reads like a prehistory of the training set and the agent platform. Large models need text, images, code, speech, behavior traces, user feedback, workplace documents, browsing patterns, and labeled examples. The public debate often begins at the model layer: alignment, hallucination, bias, capability, benchmark performance, safety, openness. Couldry and Mejias point to the layer beneath: how did so much human life become available as input?
This matters for labor. Data colonialism includes not only passive capture but also the hidden work of cleaning, labeling, moderating, rating, and maintaining systems. It also includes the conversion of work itself into measurable signals. The AI workplace does not begin when a model writes a summary. It begins when ordinary work becomes exhaust: chats, tickets, commits, keystrokes, calls, meetings, badge swipes, location traces, documents, and task outcomes flowing back into management and automation systems.
It matters for belief formation too. A platform that captures behavior can optimize the environment that produces the next behavior. A model trained on that environment can then generate speech, recommendations, summaries, and simulated social cues that feel native to it. The system observes the user, shapes the user, learns the shaped user, and presents the result as personalization.
The authors' related Internet Policy Review article frames data colonialism as a social, economic, and legal transformation built on the large-scale appropriation of social life through data extraction. That is a useful frame for AI governance. Regulating outputs without regulating extraction leaves the deeper order intact.
Where the Book Needs Friction
The book's central analogy is also its hardest burden. "Colonialism" can clarify extraction, domination, unequal knowledge, and the naturalization of appropriation. It can also flatten differences if used carelessly. Historical colonialism involved conquest, slavery, displacement, racial rule, resource seizure, and state violence in specific forms. Data colonialism is not the same event with new gadgets. The analogy works only when it sharpens attention to structures of appropriation and unequal power rather than turning history into a slogan.
The book is also more convincing as diagnosis than as institutional design. Its call to decolonize data is morally clear, but the practical path is hard: privacy law, data minimization, public-interest technology, collective data rights, labor power, procurement rules, antitrust, audit access, public compute, open standards, local governance, and refusal rights all address different parts of the system. No single reform dissolves cloud empire.
Still, that difficulty is not a failure of the book. It is part of the diagnosis. The data economy is powerful because it is infrastructural. It lives in devices, contracts, defaults, business models, workplaces, schools, hospitals, public agencies, and habits of convenience. A serious response has to be equally infrastructural.
The Site Reading
The practical lesson is to ask what a system had to take from the world before it could appear intelligent. A chatbot's fluency depends on archives. A recommendation depends on tracked behavior. A workplace dashboard depends on making labor measurable. A predictive system depends on past classifications. An agent depends on permissions, identity, memory, and logs. The smooth interface is the final surface of a much larger extraction machine.
The Costs of Connection helps explain why AI governance cannot stop at model behavior. The question is not only whether a model answers accurately, refuses dangerous requests, or cites sources. The question is what life has been made available to it, under what consent, through what labor, for whose profit, with what right of refusal, and with what capacity for people to contest the categories that return to govern them.
The book's enduring value is that it makes connection morally noninnocent. A connected system may be useful, generous, even necessary. But connection always has a political economy. It decides what becomes visible, what becomes extractable, what becomes profitable, what becomes governable, and what kinds of unmeasured life remain possible.
Sources
- Stanford University Press, The Costs of Connection, publisher page with publication date, page count, series, ISBNs, description, and author biographies, reviewed May 20, 2026.
- Stanford University Press, table of contents for The Costs of Connection, chapter summaries, reviewed May 20, 2026.
- Sara Schoonmaker, review of The Costs of Connection, Social Forces 99, no. 1, September 2020, DOI: 10.1093/sf/soz172.
- Ben Pettis, review of The Costs of Connection, Critical Studies in Media Communication 37, no. 2, 2020, pp. 204-206, DOI: 10.1080/15295036.2020.1718835.
- Nick Couldry and Ulises A. Mejias, "Making data colonialism liveable: how might data's social order be regulated?", Internet Policy Review 8, no. 2, 2019, DOI: 10.14763/2019.2.1411.
- Nick Couldry and Ulises A. Mejias, "Datafication", Internet Policy Review 8, no. 4, 2019, DOI: 10.14763/2019.4.1428.
- Berkman Klein Center for Internet & Society, "Colonized by Data: The Costs of Connection with Nick Couldry and Ulises Mejias", transcript, September 19, 2019.
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