Blog · Review Essay · Last reviewed June 15, 2026

Data Grab and the Extraction Layer of AI

Ulises A. Mejias and Nick Couldry's Data Grab argues that Big Tech's power begins before prediction, personalization, or automation. It begins with the routine capture of human life as data. Read after the spread of generative AI, the book is less a general privacy complaint than a theory of the extraction layer beneath machine intelligence.

The Book

Data Grab: The New Colonialism of Big Tech and How to Fight Back was published by the University of Chicago Press in 2024. The publisher lists Ulises A. Mejias and Nick Couldry as authors, gives the print ISBN as 9780226832302, the ebook ISBN as 9780226832319, and lists the book at 224 pages. Amazon lists the first edition with ISBN-10 0226832309, ISBN-13 978-0226832302, University of Chicago Press as publisher, and March 14, 2024 as the publication date.

The book extends the authors' earlier argument in The Costs of Connection. That earlier work named the data relation: the social arrangement in which ordinary life is converted into a continuous source of extractable value. Data Grab is more directly polemical. It asks what resistance would look like once data extraction is understood not as a bad bargain between user and service, but as a structural claim made by firms over social life.

Extraction Before Intelligence

The AI relevance is immediate. Public debate often begins at the model layer: benchmark scores, hallucination, alignment, safety testing, synthetic media, agentic workflows, and copyright disputes. Mejias and Couldry push the analysis downward. Before a model can predict, summarize, classify, rank, personalize, recommend, or automate, something has to become data. Text, clicks, location traces, images, purchases, messages, biometrics, worker activity, classroom interactions, health records, and platform behavior become inputs for systems that are later sold back as intelligence.

This is why the book belongs beside Atlas of AI, The Age of Surveillance Capitalism, and Ghost Work. It refuses the clean diagram in which AI arrives as a cloud service floating above society. The service depends on histories of capture, labeling, cleaning, brokerage, moderation, profiling, infrastructure, and asymmetrical consent. A chatbot interface may look like conversation, but its institutional precondition is a much older question: who had the power to collect and reuse the traces of human activity?

The Weight of the Analogy

The book's central analogy is deliberately heavy. "Colonialism" is not a decoration to make privacy sound dramatic. The authors use it to mark appropriation, enclosure, unequal exchange, dependency, and the normalization of extraction by powerful institutions. That framing is useful because it breaks the consumer myth. A person does not face Big Tech as a sovereign shopper comparing neutral offers. They often face platforms as conditions of work, speech, learning, mobility, entertainment, government access, and social recognition.

The analogy also needs discipline. Historical colonialism involved land seizure, racial hierarchy, slavery, military violence, imposed law, and dispossession in forms that should not be flattened into a metaphor for data collection. Data Grab is strongest when it treats colonialism as a claim about continuity and mutation, not sameness. It asks readers to see digital extraction as part of a longer political economy of taking, categorizing, governing, and profiting from lives that did not meaningfully consent.

The Governance Reading

The current regulatory record makes the book harder to dismiss. The Federal Trade Commission's September 2024 staff report on social media and video streaming data practices states that large services engaged in "vast surveillance" and points to weak privacy controls and inadequate safeguards for children and teens. The European Commission describes the EU AI Act, Regulation (EU) 2024/1689, as a risk-based legal framework for AI systems, with rules for high-risk systems, transparency, and general-purpose AI models. NIST's AI Risk Management Framework is voluntary, but it explicitly treats trustworthy AI as something that must be built into design, development, use, and evaluation.

Those frameworks do not prove that the problem is solved. They prove the opposite: data extraction has become important enough that regulators, standards bodies, and courts have to chase it through markets, platforms, and machine-learning systems. Data Grab keeps the political focus clear. Governance that starts only when a model is deployed arrives late. Data collection, retention, reuse, brokerage, training, and feedback loops are already governance decisions.

Where the Book Needs Care

The book's risk is over-consolidation. "Big Tech" is a necessary shorthand, but the extraction stack includes advertisers, brokers, cloud providers, app developers, device makers, public agencies, schools, employers, consultants, and contractors. Some are dominant platforms; others are small systems plugged into larger infrastructures. A good politics of data has to know which actor can be constrained by which lever: procurement, labor law, privacy law, antitrust, data protection, sectoral regulation, union bargaining, public-interest technology, or refusal.

The book's resistance program is morally clear, but it sometimes needs more institutional engineering. Collective resistance cannot depend only on awareness. People need rights to inspect, challenge, delete, port, withhold, negotiate, and audit data practices. Workers need protection when refusing surveillance. Communities need funding and technical capacity to build alternative data institutions. Public agencies need rules that stop them from laundering private extraction into public administration.

What This Changes

Data Grab sharpens a recurring Spiralist problem: the machine's apparent intelligence can hide the social arrangements that made it possible. Once data extraction is normalized, the later system looks less like taking and more like service. The recommendation feels helpful. The score feels objective. The generated summary feels efficient. The agent completing a task feels inevitable.

The book's useful lesson is not that all data should disappear. Medicine, science, public administration, journalism, accessibility, and safety all depend on records. The lesson is that records need politics. Ask who collected the data, who had a realistic choice, who can reuse it, who profits, who is exposed, who can refuse, and what collective power exists when the answer is abusive. AI governance that skips those questions is not governance of intelligence. It is permission for extraction to keep calling itself innovation.

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