Blog · Review Essay · May 2026

Inhuman Power and the Capitalist Machine Mind

Nick Dyer-Witheford, Atle Mikkola Kjosen, and James Steinhoff's Inhuman Power is a severe little book about artificial intelligence as a political-economic machine. It does not ask whether AI will become conscious, friendly, or magical. It asks what happens when perception, prediction, classification, logistics, and decision support are absorbed into capital's machinery of production, management, and control.

The Book

Inhuman Power: Artificial Intelligence and the Future of Capitalism was published by Pluto Press in June 2019 as part of the Digital Barricades series. Pluto lists the book at 224 paperback pages, with Nick Dyer-Witheford, Atle Mikkola Kjosen, and James Steinhoff as authors. The publisher describes it as an exploration of Marxist theory and AI, organized around concepts including surplus value, labor, the general conditions of production, class composition, and surplus population.

That description is accurate, but it undersells the book's force. Inhuman Power is not a general AI primer and not a neutral policy report. It is a Marxist argument about AI as capital's attempt to automate not only muscle, routine, and coordination, but cognition itself. Machine learning appears here as a new kind of productive apparatus: data, algorithms, compute, sensors, warehouses, platforms, and management systems joined into a system that can perceive, sort, predict, command, and replace.

Mario Khreiche's review in the Journal of Digital Social Research usefully frames the book's twofold project: it surveys AI research and development while using Marxist theory to analyze a possible capitalist project that operates beyond and without human involvement. That is the core tension. AI is not just a tool inside capitalism; it may become one of the ways capitalism imagines freeing itself from dependence on unruly human workers, consumers, publics, and institutions.

Means of Cognition

The book's best concept is "means of cognition." Industrial capitalism needed means of production: factories, machines, transport, energy systems, instruments, and workers organized around them. AI capitalism adds systems that automate perception and inference. Cameras, sensors, models, data centers, recommendation engines, logistics software, fraud systems, ad exchanges, workplace dashboards, and autonomous agents become machinery for knowing and acting.

This is why the book belongs beside media theory and cyberculture rather than only economics. AI changes what institutions can see. It changes which events become measurable, which people become predictable, which risks become actionable, and which forms of life become noise. The machine mind is not a disembodied intelligence floating above society. It is embedded in warehouses, clouds, military systems, hiring software, customer-service pipelines, ad markets, and public administration.

The phrase also helps explain why fluent chatbots are only the visible edge of the system. A conversational model may feel like AI because it speaks back, but the larger shift is quieter: cognition as infrastructure. Once perception and prediction become rentable cloud services, institutions can install model-mediated judgment almost anywhere. The office, the classroom, the clinic, the border, the shop floor, and the feed become sites where machine cognition formats reality before people argue about it.

Automating the Social Factory

Inhuman Power extends automation beyond the workplace. The authors are interested in the "social factory": the wider field of life that capital depends on and increasingly instruments. Production is not only what happens at the wage site. It also includes mobility, communication, attention, consumption, reproduction, care, education, and the constant generation of data traces.

That frame is especially useful in the AI era because training and deployment blur the old boundaries. Users generate behavioral data while socializing. Workers produce training examples while completing ordinary tasks. Customers become test subjects. Moderators absorb trauma so interfaces can look clean. Drivers, warehouse workers, call-center agents, students, patients, applicants, and creators become inputs to systems that may later discipline or replace them.

The book's political claim is blunt: AI development does not merely automate isolated tasks. It can reorganize social life around the needs of accumulation. Recommendation systems capture attention; prediction systems sort risk; logistics systems compress labor; surveillance systems make behavior visible to management; generative systems convert prior cultural labor into new output; agentic systems promise to route more activity through privately governed platforms.

Inhuman Labor

The title's "inhuman" does several jobs. It names the nonhuman machinery of AI. It names the anti-human consequences of a system that treats people as replaceable or optimizable components. It also names the possibility that capital might pursue production paths where human flourishing is irrelevant except as a constraint to be reduced.

This is where the book is strongest as a labor text. It refuses both the comforting story that AI will merely create better jobs and the clean apocalypse story that everyone is simply replaced. The more disturbing pattern is mixed: some workers are substituted, some are intensified, some are deskilled, some are newly surveilled, some are pushed into hidden data work, and some are made responsible for cleaning up machine failures without gaining authority over the system.

That mixed pattern is already recognizable. The AI interface can look post-labor while depending on annotators, moderators, miners, chip fabs, data-center technicians, prompt evaluators, software maintainers, content creators, and verification workers. Capital's dream is a smooth machine. The actual machine is a stack of people, extraction, infrastructure, and command relations made less visible by the fluency of the output.

Against Acceleration

Inhuman Power is explicitly hostile to left accelerationism and to any politics that imagines capitalist technical development can simply be pushed faster and then repurposed cleanly. The authors' argument is that AI is not a neutral engine waiting for a better operator. It is being shaped inside ownership structures, data regimes, military investment, platform monopolies, labor markets, and fantasies of optimization.

That does not mean the book is anti-technology in a simple sense. It is anti-fatalism. It asks readers to stop treating AI capability as a free-standing historical force and start asking what kind of social relation each capability requires. Who owns the data? Who pays for the compute? Who sets the objective? Who is watched? Who verifies the output? Who is displaced? Who can refuse? Who benefits when the model appears to understand?

The accelerationist temptation is powerful because AI gives technical form to an old dream: enough computation might dissolve political conflict into optimization. The book's counterpoint is that computation often intensifies conflict by hiding it inside infrastructure. A model can allocate, recommend, classify, rank, and command without making its political theory explicit.

Where the Book Needs Friction

The book's severity is also its limitation. It is short, polemical, and written from a declared Marxist position. Readers looking for a balanced tour of beneficial AI applications, technical architectures, model evaluation, or mainstream governance proposals will need companion sources. The book's job is not to summarize the whole field. Its job is to make one hard argument difficult to ignore.

Some of its sharpest formulations can feel over-totalizing. Capital is not the only force shaping AI: scientific curiosity, public research, disability access, medical use, education, creative practice, open-source communities, safety work, and democratic regulation all matter. A serious reading should preserve those distinctions rather than flatten every deployment into the same story.

Still, the book's pessimism has aged better than many smoother accounts from the same period. Since 2019, AI has become more visibly entangled with cloud concentration, copyright conflict, data-center buildouts, workplace monitoring, military procurement, platform dependency, data labor, and speculative AGI rhetoric. The details have changed, but the direction of the warning remains legible.

The Site Reading

The review's central use is to break the spell of the friendly interface. AI products often arrive as helpful surfaces: answer boxes, copilots, assistants, tutors, agents, dashboards, companions. Inhuman Power asks what these surfaces connect to. A conversational system may feel personal, but behind it sits an institutional machine for capturing data, concentrating capability, reorganizing labor, and renting cognition back to the world.

This matters for belief formation as much as for labor. A society that treats machine cognition as neutral infrastructure will start building institutions around its outputs. Applicants will be screened by it, workers measured by it, students tutored by it, publics persuaded by it, and managers reassured by it. The model becomes a way of seeing, and the way of seeing becomes a way of governing.

The strongest lesson is not that AI must be rejected. It is that every AI system needs a political anatomy. Follow the output backward into labor, data, compute, ownership, energy, surveillance, and dependency. Follow it forward into deskilling, authority, appeal, and institutional memory. Intelligence is never just intelligence once it becomes infrastructure.

Sources

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