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Supplementary Section

Alienation in the Age of Artificial Intelligence: From Labor and Data to the Digital Self and Collective Intelligence (2)

An Inquiry into Technology, Ownership, and Human Freedom in A New Social Order in the Age of Awareness

Introduction

One of the most fundamental questions in examining any social order concerns the position human beings occupy in relation to the products of their activity, the production process, other people, and ultimately themselves. Advances in the productive forces have always created new possibilities for expanding human capacities, reducing suffering, and widening freedom, yet no technology by itself guarantees that these possibilities will actually serve humanity. Artificial intelligence is no exception. It can reduce repetitive and exhausting labor, make knowledge available to broader groups, and expand the possibility of conscious participation in production and decision-making. At the same time, it can make surveillance more pervasive, concentrate power, and deepen the distance between human beings and their products, data, knowledge, and will. To understand this duality, Marx’s concept of alienation remains one of our most powerful theoretical tools—provided that we treat it not as a term confined to the nineteenth-century factory but as an analysis of the human relationship to production, ownership, and power under changing historical conditions.

Alienation in Marx

The concept of alienation appeared before Marx in German philosophy, particularly in the work of Hegel and Feuerbach, but in the Economic and Philosophic Manuscripts of 1844 Marx connected it to the material relations of life, labor, and ownership. His concern was not merely poverty, low wages, or workers’ psychological dissatisfaction. The deeper problem was that under capitalist relations, human beings produce something that passes out of their control, becomes the property of another power, and then confronts them as an independent force. Alienation should therefore not be equated with loneliness, depression, or an individual sense of meaninglessness, although such conditions may be among its consequences. Alienation is first and foremost a social relationship in which people lack sufficient control over their products, time, abilities, and conditions of activity, while a power they helped create acquires independence from them.

Marx’s formulation is commonly summarized through four interconnected relationships. First, people become alienated from the product of their labor because what they create passes out of their control. Second, they become alienated from the labor process because its purpose, timing, pace, and organization are largely determined externally, turning work from a field for developing human capacities into an activity performed for survival. Third, people become separated from their creative and conscious human potential—what Marx called their species-being: the capacity to plan, imagine, create freely, and consciously transform the world. Fourth, people become alienated from one another because ownership and competition reduce human relations to those between worker and employer, buyer and seller, owner and dispossessed, or market rivals. These forms do not exist separately; they form a chain in which alienation from the product, the activity, the self, and other people reinforces itself.

From the Industrial Factory to the Digital Platform

Under industrial capitalism, alienation was clearly visible in the factory. Workers produced commodities they did not own, within processes whose organization and purpose were determined by others, and spent much of their time and energy in activity that did not necessarily express their talents freely. In the digital economy these relationships have not disappeared, but production has expanded so far that the boundary between producer and consumer is no longer as clear as it once was. Internet searches, online purchases, map use, video viewing, social networking, music choices, and even pausing over an image generate data. Those data are used for targeted advertising, behavioral analysis, product design, demand prediction, and the training of artificial-intelligence models. Activity experienced as consumption or communication therefore becomes part of the production of economic value.

This situation creates a form of hidden labor. Platform users generally have no employment contract, their activity is not recorded as company working time, and they receive no wage for the value of the data and content they generate. Yet without the continuous participation of millions of users, many platforms would lack sufficient content, behavioral data, and raw material for improving intelligent systems. This does not mean that every click can be measured exactly like an hour of wage labor. It means that social production has expanded beyond the formal workplace. Society produces data, content, relationships, and behavioral patterns on an unprecedented scale, while ownership of the infrastructure, control of the algorithms, and appropriation of most economic value remain concentrated in a small number of corporations. The contradiction between the socialization of production and the private character of ownership now reaches into everyday experience and even unconscious behavior.

The Digital Self and the Return of the Human Product as an Independent Power

Extending Marx’s logic into the digital age reveals a new form of alienation: the alienation of human beings from their own digital selves. This does not add a fifth category to Marx’s historical formulation; it explains how the same separation of the product from its producer is reproduced in the realm of data. Alongside lived and social identity, every person leaves an immense collection of data traces: interests, purchases, searches, locations, contacts, active hours, patterns of choice, reading speed, subjects of attention, and possible emotional responses. A single data point may seem insignificant, but their combination creates a computational portrait capable of predicting behavior, classifying the probabilities of future decisions, and placing individuals into economic or political categories.

The problem begins when this calculated self is not controlled by the person it represents. Individuals may not know what information has been collected about them, how it has been combined with other data, what inferences have been drawn, or which decisions employ those inferences. The data representation becomes separated from the person, passes into organizations with far greater analytical power and resources, and then acts back upon lived experience: shaping what news people see, what products are recommended, which options are highlighted, what political advertising they receive, and how they are classified in credit, insurance, employment, or security systems. A product of human activity has not only become separated from its producer; it has become a force that organizes the field of choice. The classical logic of alienation thus returns in a data-driven and algorithmic form.

Cognitive Alienation and the Algorithmic Direction of Choice

Artificial intelligence carries this process a step further because the issue now extends beyond ownership of data and products into cognition itself. Search engines, recommendation systems, and AI assistants increasingly stand between human beings and much of their informational world, suggesting what to read, watch, buy, write, and sometimes how to analyze a problem. These tools can expand human capability, facilitate translation and access to knowledge, and strengthen research and creativity. Yet when the human relationship to them becomes passive, cognitive alienation emerges: people gradually distance themselves from questioning, searching, comparing, doubting, and judging, replacing conscious intellectual activity with a ready-made result.

Cognitive alienation is not the inherent result of using tools. Books, calculators, and computers have also externalized certain mental capacities. The crucial distinction is between a tool that extends human ability and a system that effectively appropriates the right of judgment. The issue lies in the design of the tool, its transparency, and the power relations within which it operates. When a recommendation system is organized not to deepen understanding but to maximize time on a platform, provoke reactions, and generate advertising profit, what people see does not necessarily reflect their conscious needs. It reflects a mechanism that turns attention into revenue. Under such conditions, preferences may not simply be discovered; they may be constructed, reinforced, and directed. Freedom of choice without awareness of how the field of choice is organized can become only the appearance of freedom.

Alienation from Collective Intelligence

Artificial intelligence is not the product of one individual’s genius or the isolated activity of a single corporation. Generations of researchers, programmers, engineers, teachers, writers, artists, translators, users, data producers, and public institutions have contributed to its development. The language, scientific knowledge, culture, images, texts, and social experiences on which AI models are built are products of humanity’s collective history. We can therefore identify another separation: society’s alienation from its own collective intelligence. Knowledge and culture produced socially across generations become concentrated in privately controlled infrastructures, while access to their products may be conditioned by purchasing power, subscription fees, intellectual-property monopolies, or the decisions of a limited number of companies.

This contradiction becomes sharper in the age of AI. The more socially produced intelligence becomes, the more ownership of computing capacity, data centers, advanced chips, and foundation models may become concentrated. Society provides cultural and intellectual raw material, users help improve systems, and universities and public funding support much foundational research; yet the power to determine the direction of development, the terms of access, and the distribution of benefits may remain with private infrastructure owners. This is not merely a question of income inequality. Society’s collective intellectual power becomes separated from society and confronts it as an economic and political power. Ownership and control of AI are therefore directly connected to democracy and participation in power.

Algorithmic Management and Alienation at Work

In the workplace, algorithms can intensify older forms of alienation. Algorithmic-management systems can continuously record and compare work pace, rest time, routes, number of calls, sales, customer ratings, and even behavioral indicators. Decisions once made by human managers may be delegated to systems whose criteria workers cannot see and whose judgments they have little power to challenge. Workers become distanced not only from the purpose and product of their labor but also from the basis on which they are evaluated, disciplined, or dismissed. The supposedly neutral and technical appearance of the algorithm can conceal the power relationship, as though no human being made the decision, even though the system’s objectives, data, and criteria are all products of social and managerial choices.

Automation does not have to produce this result. The same technologies can reduce dangerous and exhausting labor, shorten working hours, improve safety, and create more time for education, creativity, and social participation. What matters is how the gains in productivity are distributed. If greater productivity leads only to job losses, intensified labor, and higher profits for owners, technology deepens alienation. If it leads to shorter working hours without loss of economic security, stronger public services, opportunities for retraining, and worker participation in decision-making, technology can help create the material conditions for liberation from compulsory labor. The issue is not simply whether to accept or reject AI, but the struggle over ownership, purpose, and the distribution of its benefits.

Reducing Alienation in the Age of Artificial Intelligence

Alienation cannot be reduced through moral appeals for responsible technology use, because its roots lie in structures of ownership and power. Genuine data rights must go beyond lengthy and largely formal consent agreements, giving citizens meaningful rights to know, access, correct, transfer, and delete their data. Systems affecting employment, credit, insurance, education, public services, and fundamental rights must be transparent, auditable, and contestable. Workers and unions must participate in decisions about introducing intelligent systems into the workplace. Public research, nonmonopolistic infrastructure, data cooperatives, and different forms of social ownership can reduce society’s dependence on a handful of private centers of power. No single measure is sufficient, but together they can transform the human relationship to data and technology from passive subordination into conscious participation.

Social and democratic oversight must not mean transferring monopoly power from private corporations to a centralized state. A state without public accountability, freedom of expression, independent media, unions, and the right to protest can turn AI into an even more dangerous instrument of surveillance and repression. Genuine social control requires direct participation by citizens, workers, specialists, and independent institutions in setting the rules. Algorithmic transparency, auditing, a right to explanation, limits on surveillance, privacy protection, and the accountability of both public and private institutions must be embedded in a structure that makes power open to questioning and change. The goal is not to surrender social decisions to experts or machines, but to use specialized knowledge and computing capacity to expand citizens’ ability to make conscious decisions.

From Overcoming Alienation to Social Self-Realization

The aim of a future society cannot be defined simply as the elimination of alienation, because freedom is not merely the absence of domination; it is the presence of conditions in which people can understand their abilities, create, choose, and participate in shaping their social world. In this sense, we can speak of social self-realization. Individuals do not discover themselves in isolation; their capacities, language, consciousness, and identity develop through relations with others and within social institutions. A society that subordinates human time entirely to economic necessity, appropriates personal data without meaningful control, and delegates consequential decisions to unaccountable structures restricts the possibility of self-realization. By contrast, reducing necessary labor, ensuring economic security, providing universal education, widening access to knowledge, and enabling participation in power can allow people to move from being passive producers of value for others to conscious authors of their individual and collective lives.

This perspective is directly connected to A New Social Order in the Age of Awareness. Awareness here is neither a stock of information nor an official truth controlled by a party, leader, state, or AI system. It is an open, social, and continuously revisable process. An aware society can produce data about itself, transform that data into knowledge, test its decisions in practice, evaluate consequences, and correct its course. In such an order, AI does not decide in place of human beings; it helps people understand complex options and consequences and participate more fully in decisions affecting their lives. Moving from the distribution of power to participation in power is essential if society’s collective intelligence is not to become separated from society and turned against it.

Conclusion

Artificial intelligence does not contain a single predetermined future. Within relations of concentrated ownership and unaccountable power, it can intensify labor, expand surveillance, increase cognitive dependency, and concentrate wealth. Within an order based on social rights, democratic ownership, shorter working hours, transparency, and public participation, it can release people from part of compulsory labor and expand their capacities for creativity and self-awareness. The central question is not whether machines will become more intelligent, but what relations human beings will build around the growing power of these machines. The measure of human progress is not merely processing speed, data volume, or productivity growth, but the degree of control people possess over their time, data, work, knowledge, and social destiny. Alienation in the age of AI is therefore ultimately a question of ownership, democracy, human dignity, and society’s capacity to shape its future consciously.

References

Karl Marx, Economic and Philosophic Manuscripts of 1844, especially “Estranged Labour.”

Karl Marx, Capital, Volume I, particularly the chapters on the labor process, surplus value, machinery, and large-scale industry.

Karl Marx and Friedrich Engels, The German Ideology, Part I: Feuerbach.

Harry Braverman, Labor and Monopoly Capital: The Degradation of Work in the Twentieth Century, 1974.

Ursula Huws, writings on labor in the digital economy and the commodification of work and everyday life.

Christian Fuchs, Digital Labour and Karl Marx, 2014.

Shoshana Zuboff, The Age of Surveillance Capitalism, 2019.

Nick Srnicek, Platform Capitalism, 2016.

Kellogg, Valentine, and Christin, “Algorithms at Work: The New Contested Terrain of Control,” Academy of Management Annals, 2020.

International Labour Organization, reports and research on digital labor platforms and algorithmic management.

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