Artificial Intelligence (AI) should not be reduced to the status of a mere technological breakthrough, as though it were simply another instrument in the long lineage of human inventions. It represents something far more profound: a decisive quantum layer in the ongoing transformation of the global political economy. AI penetrates deeply into every dimension of capitalist reproduction—from the speculative movements of algorithmic finance, to the optimization of automated logistics networks, to the algorithmic governance of populations through predictive policing, and the cultural transformation wrought by generative language models and other creative systems. What AI accomplishes, in essence, is the quantization of labor, data, and decision-making: the breaking down of complex human activities into discrete, machinically processable units of cognition. Yet this very process that yields efficiency and integration simultaneously generates new contradictions—in labor markets, in epistemic trust, in global inequality, and in the relation between human and machine subjectivity.
To understand AI only in technological terms—as an extension of computing power, or as a set of algorithms that mimic intelligence—is to miss its political-economic essence. Likewise, to reduce the discourse to ethical concerns—privacy, fairness, or bias—while important, is insufficient, because such perspectives treat AI as though it were an isolated technology rather than a structural transformation of capital itself. The trajectory of AI must instead be interpreted as a dialectical unfolding of systemic contradictions, where the relations between capital, labor, and knowledge are reorganized at a new historical scale. This is precisely where the framework of Quantum Dialectics becomes indispensable. Unlike linear or moralistic analyses, Quantum Dialectics situates AI within the universal interplay of cohesive and decohesive forces, recognizing that technological change always both stabilizes and destabilizes, integrates and fragments, liberates and controls.
By deploying concepts such as quantum-layer structures and emergent contradictions, Quantum Dialectics enables us to grasp AI not as a neutral tool, nor as a purely external force, but as a material crystallization of capitalism’s inner dynamics. In this view, AI is simultaneously the product of capital’s relentless drive toward accumulation, efficiency, and control, and a potential agent of systemic transformation, carrying within itself the seeds of new contradictions that could destabilize existing structures and open possibilities for alternative futures. It is in this tension—between cohesion and decohesion, reproduction and rupture—that the true political economy of AI must be understood.
The history of political economy is inseparable from the commodification of human labor power. From the earliest phases of industrial capitalism, labor has been progressively abstracted, measured, and reconfigured to serve the imperatives of capital accumulation. The steam engine mechanized muscular effort; electrification harnessed dynamic flows of energy; and Fordism fragmented labor into repetitive, routinized operations. In this long historical trajectory, Artificial Intelligence represents the next decisive moment: it does not merely mechanize the body or streamline energy but reaches into the very sphere of cognition itself, reducing complex mental processes into algorithmic micro-units that can be simulated, optimized, and monetized. What is being quantized here is not muscle or motion but thinking, perception, and decision-making—dimensions once considered irreducibly human.
From a cohesive perspective, AI operates as a synthesizing force. By collecting, analyzing, and patterning immense volumes of data, it weaves fragmented information and dispersed knowledge into coherent outputs that can approximate human reasoning. Through statistical pattern recognition, machine learning, and neural architectures, AI generates a simulacrum of intelligence capable of producing predictions, classifications, and even creative artifacts. In this capacity, AI stabilizes and integrates the otherwise chaotic flows of information characteristic of contemporary capitalism, offering corporations and states a powerful instrument of coordination and control.
Yet, simultaneously, AI embodies a decohesive dynamic. The very same processes that generate synthetic coherence in data streams produce fragmentation within the labor process itself. Stable professions are dismantled into micro-tasks: the work of a radiologist, journalist, or translator can be decomposed into discrete algorithmic functions or outsourced to gig-based platform workers who annotate, label, and verify the data on which AI systems depend. Entire sectors confront redundancy as tasks once central to human expertise are displaced or reorganized by machinic cognition. This dissolution of established labor structures destabilizes social identities, undermines forms of collective organization, and accelerates precarity.
AI, therefore, should not be misinterpreted as the outright replacement of labor. Rather, it constitutes a new quantum dialectical layer of proletarianization, in which human subjectivity itself is partially externalized into machinic substrates. Just as earlier industrial revolutions transformed the body into a mechanical appendage of capital, AI transforms elements of thought into algorithmic functions subsumed under capital’s logic. This process exemplifies the contradictory motion at the heart of capitalism: on the one hand, the drive to minimize labor costs through automation and deskilling (decohesion); on the other, the continuing need to enlist labor—to produce, train, supervise, and interpret machinic systems, thereby generating new forms of value (cohesion).
In this way, AI does not transcend the labor-capital contradiction but intensifies it at a higher quantum layer. It crystallizes the dialectical interplay of cohesion and decohesion in the sphere of cognition, revealing both the potential for expanded capacities of collective intelligence and the dangers of deeper alienation and exploitation.
The history of capitalism has been punctuated by great technological ruptures, each of which reorganized the political economy of labor, capital, and production in profoundly new ways. These ruptures did not merely add new tools to the arsenal of industrial development; they transformed the very quantum layers through which labor was externalized and subsumed into capital. To situate Artificial Intelligence within this lineage requires tracing the dialectical logic of earlier revolutions: the steam engine, which mechanized muscle; electrification, which harnessed energy flows; and now AI, which extends commodification into the sphere of cognition itself.
The steam engine marked the first decisive rupture of capitalist industrialization, a moment when the forces of production leapt beyond artisanal limits and inaugurated the era of large-scale industry.
By introducing centralized, machine-driven production, the steam engine unified the labor process within the factory system. Workers were disciplined into synchronized mechanical rhythms, subordinated to the tempo of steam-driven machinery. This cohesion of labor around industrial capital created the basis for new forms of collective production, but also entrenched new forms of subordination.
At the same time, the steam engine uprooted artisans and peasants, dissolving traditional modes of subsistence and autonomy. Whole communities were dislocated as rural economies collapsed and populations were forced into wage labor in rapidly expanding industrial centers.
From the perspective of Quantum Dialectics, the steam engine represented the externalization of human muscle power into mechanical energy. It was a quantization of labor’s physical force, transforming individual exertion into machinic output. This rupture enabled surplus value extraction at unprecedented scales, binding the body of the worker to the mechanical apparatus of capital while simultaneously undermining older communal forms of cohesion.
The late 19th and early 20th centuries witnessed a second great rupture with the electrification of industry and society. Electricity was not merely another energy source; it revolutionized the very form of work and communication.
Electricity allowed production to become flexible and dispersed, unshackling machines from steam-driven centralization. It enabled continuous operation, global telecommunication, and the rapid expansion of urban infrastructures. In doing so, it knitted capitalism into a planetary network, laying the foundation for Fordist mass production and the new social fabric of modernity.
Yet this revolution also destabilized existing monopolies and created new global struggles. Control over oil, copper, and electrical infrastructure sparked imperial rivalries and deepened geopolitical conflicts. At the same time, electrification facilitated the rise of mass consumer culture, which eroded older social relations and intensified alienation under the banner of consumption.
Dialectically, electrification was the externalization of dynamic energy flows rather than sheer force. It quantized work into continuous current, allowing capital to circulate not only commodities but information and culture at new speeds. In this sense, electrification extended the dialectic of cohesion and decohesion to the global scale, reorganizing both production and everyday life into flows of energy, light, and communication.
Today, Artificial Intelligence stands as the third great rupture, comparable in scope to steam and electricity but situated at a higher quantum layer.
AI integrates global data flows into predictive systems that underpin algorithmic governance, platform monopolies, and financial automation. It consolidates vast amounts of fragmented information into coherent decision-making processes, producing new forms of systemic order and control.
At the same time, AI dissolves long-standing professions in fields such as law, journalism, and education, displacing skilled labor and destabilizing entire labor markets. It undermines epistemic trust, as the boundary between authentic and synthetic knowledge collapses, creating crises of verification and meaning.
If the steam engine externalized muscle and electricity externalized energy flows, then AI externalizes cognition itself. It represents the quantization of decision-making, prediction, and creativity into machinic substrates. At this level, the contradictions of capitalism penetrate directly into the contradictions of consciousness: the alienation of thought, the commodification of knowledge, and the tension between machinic coherence and human subjectivity. This rupture does not simply mechanize or electrify the world—it reorganizes the very conditions of cognition and social existence, creating possibilities for unprecedented control and equally unprecedented liberation.
In the framework of classical political economy, the central source of surplus value was labor: the expenditure of human energy in productive activity that generated value beyond what was necessary for the reproduction of labor power itself. Capital thrived by appropriating this surplus. In the epoch of Artificial Intelligence, however, the locus of surplus extraction undergoes a profound transformation. While labor remains indispensable, surplus is increasingly derived from data flows—the continuous production of digital traces, interactions, and signals that permeate everyday life. These data flows, though immaterial in appearance, are deeply social in origin, produced by the collective activity of billions of individuals in work, communication, consumption, and even leisure.
The paradox lies in the fact that data is socially produced but privately appropriated. Every search query, GPS signal, online purchase, or biometric scan becomes raw material for what can be termed algorithmic capital. These traces, aggregated and refined through machine learning models, are not simply inert inputs but sources of predictive surplus. The surplus generated by AI systems is thus not confined to financial profit in the narrow sense, but extends to the accumulation of predictive power—the ability to anticipate behavior, reorganize markets, restructure labor processes, and even reshape subjectivity itself. In this sense, AI capitalizes not just on what individuals do, but on what they are likely to do, thereby collapsing the distinction between production and prediction.
From the standpoint of Quantum Dialectics, the process of data accumulation operates as a field of entanglement. Each individual’s digital trace is inseparable from the collective informational substrate: my online choices make sense only in relation to yours, just as one user’s data gains predictive value only when contextualized within the patterned behaviors of millions. Yet this collective production is captured and privatized by platform monopolies, who construct vast proprietary datasets and models from what is in essence the social fabric of human life. Here, the contradiction is stark: the socialized production of data—in which all participate—stands opposed to the private appropriation of algorithmic value, concentrated in the hands of a few corporations.
This contradiction mirrors, but also transcends, Marx’s original critique of industrial capital. Whereas industrial capitalism appropriated the physical surplus of labor, AI capitalism appropriates the informational surplus of social life. At this higher quantum layer, exploitation no longer stops at the factory gates but extends into the most intimate dimensions of existence: patterns of speech, gestures, preferences, and relations. What is at stake is not simply who owns the means of production but who owns the means of prediction and cognition. The struggle over data, therefore, becomes a struggle over the future trajectory of political economy itself.
Artificial Intelligence embodies within itself a superposition of contradictory potentials, reflecting the dual character of every major technological rupture in the history of capitalism. On one side, AI functions as a powerful cohesive force of control, consolidating systemic order and enhancing the capacity of capital and the state to monitor, discipline, and direct populations. On the other side, it generates decohesive openings toward liberation, creating new possibilities for collective knowledge, social cooperation, and the emancipation of labor from drudgery. These dimensions coexist not as abstractions but as real, material tendencies that define the contemporary political economy of AI.
In its cohesive dimension, AI extends the apparatus of surveillance and discipline to unprecedented levels. Predictive policing algorithms pre-emptively target communities based on statistical likelihoods, reinforcing structural inequalities while cloaked in the authority of science. Biometric identification systems map bodies into databases, transforming identity into a perpetual object of verification and control. In the workplace, algorithmic management subjects labor to constant monitoring and optimization, dictating pace, output, and efficiency with mechanical precision. Here, AI becomes capital’s most advanced apparatus of order, a cohesive force knitting together fragmented flows of data into structures of dominance. It represents the perfection of disciplinary power, where control is not only exercised over the body but penetrates cognition, behavior, and even affect.
Yet, alongside this tightening grip, AI also possesses a decohesive potential that destabilizes existing forms of domination. Open-source models circulate outside the confines of corporate monopolies, enabling communities, researchers, and activists to experiment with knowledge production beyond capital’s enclosure. AI-driven advances in medical diagnostics, drug discovery, and climate modeling hold the promise of directing technological power toward the collective survival and flourishing of humanity. Moreover, by automating repetitive or dangerous tasks, AI creates the possibility of freeing human beings from forms of work historically associated with drudgery, thereby expanding the space for creativity, education, and social cooperation. In these capacities, AI is not only a tool of exploitation but also a potential instrument of emancipation, capable of unlocking resources for a new organization of social life.
From the standpoint of Quantum Dialectics, these contradictory dimensions cannot be reduced to a simple opposition where one cancels out the other. Rather, they exist in a state of unstable equilibrium, a quantum superposition in which control and liberation, enclosure and emancipation, are entangled within the same technological matrix. The political economy of AI is therefore not a fixed trajectory but an indeterminate field of becoming. It can collapse into deeper capitalist enclosure, consolidating algorithmic monopolies and authoritarian governance, or it can catalyze new forms of social cooperation and collective freedom. Which path materializes depends not on the technology alone but on praxis—on the conscious interventions of social movements, political struggles, and collective decisions that determine how AI’s contradictory potentials are resolved.
The advent of Artificial Intelligence intensifies the contradictions of capitalism across multiple layers of the global system. Far from being a neutral tool of technological progress, AI is a catalyst that deepens structural inequalities while simultaneously creating conditions for new forms of resistance. Its effects must therefore be understood as both extensions of long-standing capitalist dynamics and as qualitatively new ruptures at the quantum layer of cognition and information.
One of the most visible contradictions is labor displacement. Millions of workers across diverse sectors—from manufacturing and transport to journalism, law, and education—face redundancy as tasks once performed by human expertise are increasingly subsumed under algorithmic processes. What emerges is not a utopia of leisure but new forms of digital servitude, where workers are fragmented into invisible contributors to AI’s functioning. Gig-based data labeling, content moderation, and platform-based microtasks constitute a hidden labor force that trains and sustains AI systems. This invisible proletariat is both indispensable and disposable, embodying the dialectic of cohesion (AI’s reliance on labor) and decohesion (the erasure of stable employment).
Equally significant is the dynamic of capital concentration. AI development and deployment require massive computational infrastructure, proprietary datasets, and advanced research capabilities. These resources are monopolized by a handful of corporations, often clustered in a few global centers of technological power. The result is a sharp intensification of capitalist centralization, where control over algorithms translates into control over markets, labor, and knowledge itself. AI, in this sense, becomes both the product and the driver of digital monopoly capitalism, with a small number of firms exercising disproportionate influence over the direction of technological and social development.
The contradictions are further globalized through the axis of inequality between North and South. While the Global North controls the strategic nodes of AI development—cloud infrastructure, high-performance computing, proprietary datasets, and elite research institutions—the Global South is relegated to the role of supplier. It provides the raw materials necessary for computation, such as lithium and rare earth elements; the cheap, precarious labor that labels, moderates, and maintains digital systems; and the mass of social data harvested from expanding internet use. This division reproduces a colonial pattern in digital form: technological sovereignty for the North, digital dependency for the South.
Yet, as always in the dialectic of capitalism, these contradictions generate their own counter-movements. Platform cooperatives experiment with collective ownership of digital infrastructure, challenging monopoly logics. Social movements and academic communities demand algorithmic transparency, pressing for accountability in systems that govern lives yet operate as opaque “black boxes.” At the international level, states in the Global South begin to articulate demands for technological sovereignty, seeking to develop indigenous AI capabilities or to establish rules that resist digital colonialism. These initiatives represent decohesive ruptures within the apparent inevitability of capitalist AI, destabilizing its claim to total dominance.
From the perspective of Quantum Dialectics, the AI revolution cannot be seen as a unilinear march toward capitalist consolidation. Rather, it embodies a field of tensions where cohesion and decohesion operate simultaneously: displacement and new labor formations, monopolization and cooperative experiments, global inequality and emergent struggles for sovereignty. It is precisely within these contradictions that the future trajectory of AI—and indeed, of capitalism itself—remains open.
Artificial Intelligence does not only reconfigure the structures of labor, capital, and markets; it also destabilizes the philosophical ground of political economy itself. The central question of value, long anchored in the labor theory of political economy, must now be re-examined in light of machinic cognition. If processes of reasoning, prediction, and even creativity can be externalized into algorithms, what becomes of the principle that human labor is the fundamental source of value? Does the labor theory of value collapse under the weight of automation, or does it undergo a transformation at a higher dialectical layer?
From the perspective of Quantum Dialectics, this problem does not signal the end of the labor theory of value but its quantum extension. AI systems are not autonomous creators ex nihilo; they are crystallizations of accumulated human knowledge, labor, and cultural practices. Every line of code, every curated dataset, every labeled image or moderated comment carries the imprint of countless hours of human activity. Even the architecture of machine learning models reflects intellectual labor, itself embedded in collective histories of mathematics, logic, and engineering. In this sense, labor remains primary—not displaced, but refracted through a new medium of algorithmic formalization.
At the same time, AI inaugurates a dialectical feedback loop between human subjectivity and machinic cognition. Humans design, train, and interpret AI systems, but these systems, once operational, reshape how humans think, act, and perceive the world. They mediate information flows, influence social decisions, and even participate in the production of culture and knowledge. What emerges is a recursive cycle in which human and machinic forms of cognition co-evolve, generating a new emergent layer of productive force that is neither purely human nor purely artificial but dialectically intertwined.
This development means that consciousness itself becomes entangled in political economy. No longer confined to the realm of subjective or immaterial experience, consciousness appears as a quantized, materialized, and contested field. It is quantized insofar as elements of cognition are broken down into discrete algorithmic operations; materialized insofar as these operations are embedded in infrastructures of computation and energy; and contested insofar as ownership, control, and direction of these processes become sites of struggle. The dialectics of consciousness under AI thus mark a profound philosophical turn: thought is no longer only an attribute of the human subject but also a terrain of capitalist accumulation and, potentially, of collective emancipation.
Artificial Intelligence must not be understood in reductionist terms. It is neither merely a neutral technological tool nor a sinister capitalist conspiracy. To frame it in such binaries is to miss its deeper ontological status. AI is, rather, a quantum dialectical phenomenon—a crystallization of the interplay between cohesion and decohesion at the intersection of technology, labor, and capital. It is at once the product of capitalist development and a force that destabilizes its own foundations, embodying contradictions that cannot be resolved within purely technical or moral categories.
In its cohesive dimension, AI acts as a powerful integrator. It weaves together global supply chains, optimizes financial systems, and embeds itself into governance mechanisms that coordinate everything from public administration to security infrastructures. Through this integrative function, AI generates systemic order, enhancing the capacity of capital and states to manage complexity on a planetary scale. It produces new forms of coherence in an otherwise chaotic global economy, enabling capitalism to adapt to volatility, uncertainty, and fragmentation.
Yet this cohesion is inseparable from AI’s decohesive force. As it penetrates labor markets, AI destabilizes established forms of employment and professional identity. Entire occupational sectors face dislocation, as tasks once rooted in human expertise are outsourced to algorithms. Subjectivities are reshaped, as trust in knowledge systems erodes under the proliferation of synthetic media and algorithmically mediated truths. At the macro-level, AI introduces systemic risks—biases embedded in code, vulnerabilities in critical infrastructures, and cascading instabilities in financial and political systems. Far from guaranteeing stability, AI intensifies uncertainty, revealing the fragility of the capitalist order it appears to reinforce.
At the same time, AI holds the potential for emergent synthesis. By automating repetitive tasks and reorganizing flows of knowledge, it opens horizons for the collective reorganization of production, communication, and social life. Freed from the most alienating forms of labor, humanity could redirect its energies toward creativity, cooperation, and planetary survival. AI, if harnessed beyond the logic of profit and monopoly, could become a tool of universal emancipation—extending not control but freedom, not alienation but solidarity. This possibility is neither guaranteed nor illusory; it is a real, material potential embedded in the contradictory logic of AI itself.
From the standpoint of Quantum Dialectics, the political economy of AI is thus not predetermined. It is an unstable field of becoming, structured by the universal dialectic of cohesion and decohesion. Whether AI consolidates into the machinery of capitalist domination—monopolistic control, algorithmic surveillance, digital colonialism—or catalyzes a socialist emancipation—collective ownership of data, cooperative infrastructures, and democratic governance of technology—depends not on the technology alone, but on praxis. It depends on the conscious interventions of the digital proletariat, the organized efforts of states seeking technological sovereignty, and the mobilizations of global movements striving for justice. In this sense, the future of AI is not written in algorithms but in struggle.
The rise of Artificial Intelligence should not be understood as an external shock imposed upon society from the outside, as though it were a mere technological disruption. Rather, it must be grasped as the inner dialectic of capital unfolding at a new quantum layer. By quantizing labor, data, and cognition, AI reorganizes the terrain of political economy, transforming the very conditions under which value is produced, circulated, and appropriated. Yet in doing so, it also intensifies the contradictions that have always structured capitalist development: between labor and capital, monopoly and cooperation, control and freedom, accumulation and crisis. AI does not escape these contradictions; it amplifies them.
Placed in historical perspective, AI emerges as the third great rupture in the long dialectic of capitalist technological revolutions. The steam engine externalized human muscle into mechanical force, binding the body of the worker to the rhythms of industrial machinery. The revolution of electricity externalized energy into continuous flows, enabling planetary circuits of power, communication, and mass production. Today, AI externalizes cognition itself, breaking down the processes of intelligence, prediction, and creativity into machinic substrates. Each revolution represents a higher level of quantization, extending capital’s reach into ever deeper layers of human existence: from body, to energy, to thought.
Through the lens of Quantum Dialectics, AI reveals itself not as a neutral tool to be judged only in technical or ethical terms, but as a dynamic field of contradictory potentials. On one side, it manifests as cohesion: enabling global integration, algorithmic governance, and the systemic order of capital. On the other side, it manifests as decohesion: generating displacement, precarity, epistemic instability, and social unrest. In its double movement, AI embodies both the machinery of exploitation and the possibility of emancipation, both a tightening of capitalist enclosure and the opening of new collective horizons.
The future of AI is indeterminate. Its trajectory will not be dictated by algorithms alone but by praxis—by the conscious interventions of human collectives who struggle over ownership, control, and purpose. If monopolized by corporate and imperial interests, AI may deepen inequalities, entrench surveillance, and reinforce exploitation. But if seized as a tool of universal emancipation, collectively governed and oriented toward human flourishing, it may contribute to the overcoming of capitalist alienation and the construction of new forms of solidarity and freedom.
Thus, the political economy of AI, when viewed through the lens of Quantum Dialectics, must be recognized as the political economy of humanity’s next possible revolution. It is the site where the contradictions of labor, capital, and consciousness converge at a new quantum layer, demanding not resignation but struggle, imagination, and transformation. AI does not dictate humanity’s future; it crystallizes the contradictions through which humanity must create its future.
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