Digital capitalism must not be regarded as a marginal or transitional supplement to the existing capitalist system, but rather as its full-scale reconstitution on a qualitatively new quantum layer. Industrial capitalism drew its life-blood from the extraction of surplus value out of material labor, organizing production around factories, machinery, and wage-work. Late capitalism, by contrast, turned culture, signs, and information into key sites of commodification, shaping entire industries of media, advertising, and spectacle. With the emergence of the digital paradigm, however, we enter into a new historical phase where the very foundation of accumulation shifts toward data. Data is no longer a by-product of social life but the principal resource through which capital expands, circulates, and monopolizes. It serves at once as capital, functioning as an asset that drives accumulation, concentration, and predictive control, and as commodity, fragmented into discrete units that can be exchanged, traded, and sold across global markets.
This dual nature of data is not a technical curiosity or accidental feature of digital capitalism but a profoundly dialectical structure. From the perspective of Quantum Dialectics, data embodies a layered material reality governed by the interplay of cohesion and decohesion. As capital, data coheres into vast infrastructures of platform monopolies, algorithmic architectures, and predictive analytics that centralize power and enable systemic accumulation. As commodity, by contrast, data undergoes processes of decohesion: it is broken down into user profiles, behavioral traces, transactions, and modular datasets that circulate in multiple markets, divorced from the contexts of their production. The dynamic tension between these two processes—the integrative force of cohesion and the fragmenting force of decohesion—does not remain at a single level but operates across multiple quantum layers of digital society. It is precisely this multi-layered dialectic that generates the distinctive contradictions and instabilities of digital capitalism.
The purpose of this chapter is to map out these contradictions in their concrete historical forms by focusing on four central dimensions of digital accumulation. The first is surveillance capitalism, where data is harvested and centralized as a mechanism of predictive control and behavioral surplus extraction. The second is algorithmic governance, through which data becomes the basis for regulating and administering not only markets but also social and political life. The third is the digital commons, a sphere where data and knowledge circulate as collective resources, simultaneously challenging and being appropriated by capital. Finally, the fourth dimension concerns AI labor, in which data becomes the basis of machine intelligence, reshaping the conditions of work, value, and human creativity itself. Each of these domains exemplifies a specific manifestation of the contradiction between data-as-capital and data-as-commodity. Taken together, they reveal both the exploitative core of digital capitalism and the emancipatory openings that may point beyond it.
At the core of the capitalist system lies the logic of capital as self-expanding value: money begets more money, commodities circulate to generate surplus, and labor is harnessed to reproduce accumulation on an ever-expanding scale. In the era of digital capitalism, this classical function is no longer limited to physical labor power or tangible commodities but is increasingly embodied in data itself. The digital giants of our time—Google, Meta, Amazon, Microsoft, Tencent, and others—illustrate this transformation with clarity. They capture unending streams of data produced through user interactions, search queries, consumption habits, biometric signals, geolocational movements, and complex social networks. This seemingly mundane flow of digital traces becomes the raw material of an immense productive process. It is processed through machine learning systems, predictive analytics, and recommendation algorithms, which convert heterogeneous fragments of human activity into highly structured, actionable intelligence.
In this sense, data does not simply act as a supplement or auxiliary to traditional forms of capital. It is not merely a new “input” comparable to raw materials or tools; rather, it becomes capital itself, assuming the very function of accumulation and expansion. Data is gathered, stored, monopolized, and strategically deployed to generate surplus value, not once but in recursive cycles of feedback and reinforcement. The dynamic is clear: the more data that platforms succeed in extracting, the more precise and adaptive their algorithms become. More effective algorithms, in turn, attract greater numbers of users, whose activities generate even larger quantities of data, which again refine the algorithms. This recursive loop represents a digital analogue of the classic industrial circuit of capital, but with a new twist: here, it is not machines or factories that primarily expand value, but vast data infrastructures that continuously reconfigure themselves through learning and iteration.
When viewed through the lens of Quantum Dialectics, this recursive cycle takes on a deeper ontological meaning. Each individual data point is a fragment decohesed from the immediacy of lived activity, a minute abstraction severed from its original social and qualitative context. Yet these fragments do not remain isolated; they are drawn together into cohesive wholes within the higher-level infrastructures of platform capital. This act of cohesion is not simply additive—it generates new emergent properties that are irreducible to the sum of individual elements. Through aggregation, data acquires predictive power, algorithmic intelligence, and monopolistic strength. These emergent properties transform data from passive record into active force, a dynamic medium capable of reorganizing behavior, shaping markets, and producing surplus value.
Thus, in digital capitalism, data becomes the paradigmatic expression of capital’s self-expansion. It is a resource that not only circulates but also evolves; it is both the means and the product of accumulation. The dialectical process of decohesion and cohesion transforms scattered traces of human activity into a coherent system of control and profit. Data, in this framework, must be understood as a qualitatively new form of capital, one that embodies the recursive, self-augmenting logic of accumulation at the level of the digital quantum layer.
Alongside its function as capital, data assumes a parallel role as commodity, entering into circulation as a discrete good with measurable exchange value. In contemporary data markets, everything from personal profiles and browsing histories to biometric information, geolocational records, and even algorithmically generated predictions can be packaged, priced, and sold. Companies specializing in “data brokerage” exemplify this process: they collect fragments of digital life, abstract them from their original contexts, and reassemble them into datasets that are then marketed to advertisers, insurers, financial institutions, or political campaigns. What was once lived experience—an online search, a conversation, a bodily rhythm, or a movement through space—becomes an object of exchange, a tradable unit within a global digital economy.
The key to this transformation lies in the processes of abstraction and reification. Data commodities are not simply “found” but actively produced through techniques of standardization, categorization, and quantification. The qualitative richness of human activity is stripped away and replaced with variables, tags, and metrics that can be combined and compared across populations. In this way, lived practices are turned into standardized datasets capable of being circulated independently of the subjects who originally generated them. Here we see a clear contrast with data-as-capital: while capital demands cohesion—integration into monopolized infrastructures and centralized systems of accumulation—commodification demands fragmentation. It is only by being broken down into discrete, exchangeable units that data can circulate as commodity.
From the standpoint of Quantum Dialectics, this commodification of data exemplifies the principle of decohesion at a lower quantum layer. Each fragment of data, once severed from the totality of lived experience, becomes a particle circulating within markets, where it may be recombined, repurposed, or resold without regard for its origin. Yet this fragmentation is not independent of cohesion; rather, it exists in a dialectical relation with it. Data must first be commodified—broken into fragments and rendered exchangeable—in order to be aggregated and capitalized by platforms. Conversely, once aggregated as capital, data is again fragmented, packaged, and returned to the marketplace in the form of predictive products, targeted advertising, or behavioral scores.
The contradiction here is therefore structural and recursive: data must be commodified to become capital, yet it must be capitalized to sustain commodification. This oscillation between fragmentation and integration is not a flaw in the system but its very engine. It is through this constant movement—decohesion feeding cohesion, cohesion producing new forms of decohesion—that digital capitalism reproduces itself as a dynamic and unstable totality. Data-as-commodity, in this sense, is not a secondary function but a constitutive pole of the dialectic, without which data could not serve as capital at all.
The concept of surveillance capitalism, made widely known through the work of Shoshana Zuboff, names one of the most striking and disturbing features of the digital age. Unlike earlier forms of capitalism that relied primarily on the direct exploitation of labor or the commodification of cultural products, surveillance capitalism extracts value by continuously monitoring, recording, and analyzing user behavior. What distinguishes it is not merely the collection of data explicitly provided by users—such as searches, posts, or purchases—but the capture of what Zuboff calls behavioral surplus: the hidden, often unconscious patterns of attention, affect, and intention embedded in everyday digital interactions. Platforms increasingly harvest not only what we say or do, but also how long our gaze lingers, how our emotions shift in response to content, or how our choices might be predicted before we are even aware of them ourselves.
From the perspective of Quantum Dialectics, surveillance capitalism represents the cohesive pole of data accumulation. Here, fragmented traces of human activity are drawn together into massive integrative infrastructures that operate with systemic force. Algorithms no longer merely register reality as neutral observers; they intervene directly in its becoming. They anticipate user behavior, modulate preferences, and strategically nudge decisions toward desired outcomes—whether in consumer purchases, political attitudes, or social relationships. In this way, surveillance capitalism transforms data from passive representation into active command, fusing knowledge with power in a way that reshapes the very field of social motion.
At the heart of this system lies a profound contradiction between visibility and invisibility. On one hand, surveillance mechanisms render users hyper-visible as digital profiles, behavioral patterns, and predictive models. Every action becomes a data point, every affective response a signal to be recorded, categorized, and monetized. On the other hand, the operations of the algorithms themselves remain cloaked in opacity. Their criteria, methods, and systemic biases are hidden from users and often even from regulators, creating a black-boxed infrastructure of control. This duality reflects the dialectical interplay of cohesion and decohesion: cohesion through the centralization and integration of data into monopolized infrastructures of surveillance, and decohesion through the fragmentation of the self into commodified traces that circulate independently of the person who produced them.
Surveillance capitalism therefore exemplifies the double movement of digital accumulation: data is capitalized through predictive infrastructures while simultaneously commodified through its fragmentation into tradable behavioral surplus. The result is an intensification of alienation in a uniquely digital form. Users are transformed into sources of surplus without consent, awareness, or control; their subjectivity itself becomes raw material for accumulation. What was once private, lived experience now circulates as both commodity and capital, producing profit for others while estranging individuals from their own informational being.
The reach of digital capitalism extends far beyond the boundaries of economic accumulation. Increasingly, it penetrates the sphere of governance, where algorithms operate as gatekeepers of opportunity, access, and legitimacy. From the credit-scoring systems that determine one’s eligibility for loans, to hiring algorithms that filter candidates, to welfare systems that decide who qualifies for social benefits, algorithms regulate access to some of the most vital resources of contemporary life. Their influence is not limited to economic transactions; it extends to political visibility and social recognition, as search engine rankings, news feed algorithms, and content recommendation systems determine which voices are amplified and which are silenced. Importantly, algorithmic governance is not confined to corporate platforms. States themselves increasingly adopt algorithmic systems for policing, predictive crime mapping, border control, and the allocation of social services, embedding digital infrastructures at the very core of governance.
From the standpoint of Quantum Dialectics, algorithmic governance can be understood as an emergent property of data-as-capital operating at a higher systemic layer. Data, once aggregated into predictive infrastructures, achieves a level of cohesion where it functions not merely as raw material for economic accumulation but as a meta-institution capable of coordinating flows of capital, labor, and social life. Algorithms become more than tools; they assume a quasi-sovereign role, regulating the distribution of life chances and organizing collective motion in ways that were once the prerogative of political institutions. This marks a dialectical transformation in the nature of governance: it is no longer exercised solely through laws and policies but increasingly through code, data architectures, and algorithmic protocols.
Yet, this transformation is marked by a central contradiction between automation and autonomy. On the one hand, algorithms promise efficiency, objectivity, and precision. They appear to eliminate human error, accelerate decision-making, and standardize outcomes in the name of fairness. On the other hand, these very processes often reduce human agency, entrench systemic biases, and obscure lines of accountability. Automated decision-making, presented as neutral and scientific, frequently reproduces historical inequalities encoded in the data it learns from. What appears as cohesion—the seamless functioning of algorithmic systems—thus produces new forms of decohesion: the erosion of democratic participation, the marginalization of dissenting voices, and the displacement of human judgment by opaque technical processes.
Algorithmic governance therefore illustrates how digital capitalism is not confined to the marketplace but extends into the political superstructure itself, reshaping the modalities of power and authority. Governance, once mediated through visible institutions, becomes increasingly exercised through invisible infrastructures of computation. In Quantum Dialectical terms, the cohesion of algorithmic centralization encounters its opposite in emergent struggles for transparency, fairness, and democratic oversight. Civil society movements, critical scholarship, and policy debates around algorithmic accountability represent the decohesive forces pushing against centralized algorithmic power. The future of governance in the digital age will depend on the outcome of this struggle: whether algorithmic systems remain instruments of domination under capitalist accumulation, or whether they can be reappropriated and democratized as tools for collective self-governance.
Class struggle under digital capitalism assumes forms that both extend and transform the dynamics of earlier epochs. In industrial capitalism, the central antagonism unfolded at the point of production, where labor confronted capital within the factory system. In digital capitalism, however, the extraction of value occurs not only through waged labor but also through the continuous generation of data by users, workers, and citizens. This shift produces new terrains of struggle, where contestation emerges around issues of visibility, autonomy, ownership, and control of data infrastructures.
One major form of struggle arises around the question of data sovereignty. Users generate immense volumes of behavioral data through everyday life, yet this data is expropriated and monetized by platforms without consent or compensation. Movements demanding data rights, privacy protections, and collective ownership of digital infrastructures represent attempts to negate this alienation. These struggles echo earlier battles for control over the means of production, but the “means of production” here are algorithmic systems and data architectures. The demand is not simply for higher wages but for recognition of data labor and for democratic governance of the infrastructures through which data circulates.
Another form of struggle is visible in the rise of platform labor resistance. Gig workers—drivers, delivery couriers, online freelancers—confront algorithmic management systems that govern their access to work, pay scales, and ratings. Their resistance takes novel forms: digital strikes coordinated through apps, collective manipulation of algorithms, legal challenges to platform classification schemes, and demands for algorithmic transparency. These conflicts demonstrate how the classical antagonism between labor and capital persists, but now mediated through digital infrastructures that conceal exploitation behind the veneer of “autonomous contracting.”
A third form of class struggle unfolds in the digital commons. Open-source projects, cooperative platforms, peer-to-peer networks, and community-driven knowledge initiatives attempt to reclaim digital resources for collective use. These efforts embody the decohesive force pushing against platform monopolies, seeking to transform digital production into a commons-based alternative to private accumulation. Yet this terrain is also marked by contradiction: capital often appropriates commons-based innovations, reabsorbing them into circuits of accumulation. The struggle here lies in defending the autonomy of the commons and building institutional forms that resist enclosure.
Finally, a profound struggle is emerging around the question of AI labor. As artificial intelligence systems trained on vast datasets increasingly substitute for human work, the value-creating role of living labor is destabilized. Workers demand protections against displacement, recognition of their role in training and refining AI models, and a share in the benefits produced by automation. This conflict reopens the Marxian question of labor’s centrality to value, now refracted through the dialectic of human and machine intelligence. It is here that the deepest contradictions of digital capitalism crystallize: the potential to liberate humanity from toil through automation exists, but under capitalist conditions this potential is inverted into unemployment, precarity, and intensified exploitation.
Together, these forms of class struggle reveal how digital capitalism reconfigures the terrain of antagonism without abolishing it. The fundamental contradiction between capital and labor persists, but it is mediated by data, algorithms, and platforms. Struggles over wages and working conditions coexist with struggles over data sovereignty, algorithmic transparency, and collective control of digital infrastructures. In the language of Quantum Dialectics, the cohesion of platform capital continuously generates decohesive forces in the form of resistance, solidarity, and emergent demands for democratic control. These struggles point toward the possibility of a new synthesis: a digital order organized not around surveillance and exploitation but around commons, cooperation, and collective emancipation.
Amid the many contradictions of digital capitalism, there also emerges the possibility of a digital commons, a sphere in which data and knowledge are collectively produced, shared, and maintained outside of—or in opposition to—the logic of capitalist commodification. Unlike physical resources that are depleted through use, data and knowledge are inherently non-rivalrous and infinitely reproducible. One person’s access does not diminish another’s, and duplication can occur at virtually no cost. This ontological quality of digital resources gives them a unique potential to serve as commons, fostering collaboration rather than competition. Projects such as open-source software, Wikipedia, Creative Commons licensing, peer-to-peer networks, and cooperative data initiatives embody this potential by organizing production and distribution around collective use-value rather than private exchange-value. They demonstrate that digital production can operate under a logic of abundance rather than scarcity, providing an alternative vision to the extractive monopolies of platform capital.
Within the conceptual framework of Quantum Dialectics, the digital commons represents the decohesive force that destabilizes the cohesion of monopolized platforms and proprietary enclosures. Capital thrives on cohesion, drawing disparate fragments of data into centralized infrastructures that sustain accumulation. The commons, by contrast, disperses and opens these resources, refusing commodification and undermining exclusivity. By asserting data as use-value rather than exchange-value, commons-based initiatives challenge the structural logic of capitalist accumulation at the digital quantum layer. They point toward forms of collective subjectivity and cooperation that emerge from the very infrastructures capitalism has built, but which move in directions that contradict its logic.
Yet, the digital commons is not a pure or uncontested realm. It remains profoundly entangled with capital, and is often subject to processes of parasitic appropriation. Corporations routinely exploit open-source innovations, incorporating them into proprietary products without reciprocating the labor of the community. Platforms profit from user-generated content, transforming collective creativity into advertising revenue and marketable data streams. Even Wikipedia, a symbol of the digital commons, is structurally dependent on donations from technology giants who benefit indirectly from its existence. This dynamic reveals the contradictory motion of the commons: it oscillates between being a seed of post-capitalist transformation and being subsumed back into capitalist accumulation. The commons embodies both resistance and vulnerability, both emancipatory potential and the risk of exploitation.
The dialectical motion of cohesion and decohesion in this sphere highlights the revolutionary potential embedded within digital capitalism itself. Just as industrial capitalism produced the proletariat as the bearer of a new social order, so too does digital capitalism produce digital subjects whose cooperative production of knowledge and data points toward new forms of solidarity and social organization. Whether in collaborative coding, community-driven archiving, or peer-to-peer networks of exchange, digital subjects generate practices that exceed the logic of commodification and accumulation. The challenge lies in defending, expanding, and institutionalizing these practices so that they are not continually reabsorbed by capital but can instead provide the basis for a genuinely new mode of production. In this sense, the digital commons is not simply an alternative to digital capitalism but a terrain of struggle within it—a space where the contradictions of cohesion and decohesion can be harnessed to imagine and enact new social formations.
One of the most profound and disruptive contradictions of digital capitalism manifests in the domain of AI labor. Artificial intelligence systems, trained on vast reservoirs of data, have begun to automate tasks that were once the preserve of human workers—translation, design, coding, legal and financial analysis, customer service, logistics coordination, and increasingly, creative practices such as writing, image production, and music composition. In this context, data plays a dual role. On the one hand, it functions as capital, serving as the indispensable training resource through which AI models are built and refined. On the other hand, it functions as commodity, since the outputs of these AI systems are packaged, marketed, and sold across diverse industries. The result is that AI labor embodies the dual character of data more sharply than perhaps any other domain, crystallizing both its role as a means of accumulation and its fragmentation into exchangeable goods.
From the perspective of Quantum Dialectics, AI labor represents the emergence of a new quantum layer of contradiction. At the level of cohesion, the aggregation of immense datasets into machine-learning models condenses collective human knowledge into operational systems of unprecedented productive capacity. These models can generate language, analyze patterns, and solve problems with a speed and scale that no individual worker could match, embodying in digital form centuries of accumulated human labor. At the level of decohesion, however, this very process fragments labor markets: it displaces workers, undermines professional stability, and devalues hard-won skills. Entire industries face the erosion of established career paths as AI substitutes for or supplements human effort at a fraction of the cost, producing new forms of precarity and structural unemployment.
The dialectic of AI labor, however, extends beyond the economic sphere into the ontological foundations of value itself. For centuries, labor has been understood within Marxian political economy as the basis of value creation—the substance through which capital expands. Yet AI systems embody what might be called datafied labor: the crystallized accumulation of past human labor encoded into datasets and algorithms. This raises a fundamental contradiction between living labor and datafied labor. Human activity continues to generate the raw materials that train AI systems, but once absorbed, these systems appear to displace the very workers whose past contributions made them possible. The paradox is that AI both depends on and erodes human labor, embodying its past while undermining its present.
Resistance to these contradictions is already emerging in diverse forms. Workers and social movements call for universal basic income as a buffer against mass displacement, seek recognition of data labor as a legitimate source of value deserving compensation, and demand stricter regulation of AI monopolies that consolidate power in the hands of a few corporations. Cooperative experiments in collective ownership of AI systems are also taking shape, exploring how models trained on shared data might be governed and used for collective benefit rather than private profit. Each of these struggles represents an attempt to reassert human agency within a system that increasingly externalizes cognition and creativity into algorithmic form.
AI labor thus crystallizes the deepest contradiction of digital capitalism. On the one hand, it contains the potential to abolish scarcity, automate drudgery, and liberate human beings from toil, opening unprecedented horizons of social development. On the other hand, under capitalist appropriation, this potential is inverted into heightened exploitation, unemployment, and inequality. The dialectic of cohesion and decohesion is laid bare: the same technological force that could serve as a foundation for collective emancipation becomes a tool for intensified domination. The outcome of this contradiction will depend not on technology itself but on the struggles that shape its use—whether AI remains an instrument of capitalist accumulation or becomes reappropriated as a commons for human flourishing.
Digital capitalism is not a stable or unified system but one structured by a profound dialectical contradiction: the dual role of data as both capital and commodity. On one side, data coheres into infrastructures of accumulation, feeding platform monopolies, algorithmic architectures, and predictive systems that centralize control and expand capitalist power. On the other side, data simultaneously decoheres into commodified fragments—user profiles, behavioral traces, biometric records—that circulate in markets detached from their origin in lived human experience. This dual motion of cohesion and decohesion does not simply coexist but actively generates the systemic forms through which digital capitalism operates: surveillance capitalism, algorithmic governance, the digital commons, and AI labor. Each of these domains represents a crystallization of the contradiction between data’s function as capital and as commodity, producing new forms of accumulation and new sites of resistance.
Seen through the lens of Quantum Dialectics, these contradictions appear not as technical accidents or anomalies but as layered manifestations of the universal interplay of cohesive and decohesive forces. Digital capitalism is but one expression of the deeper ontological logic by which all systems organize, evolve, and transform: the tension between integration and fragmentation, between totality and particularity. At the micro-level, individual data points are extracted from lived contexts; at the meso-level, these fragments are aggregated into infrastructures of control; at the macro-level, they circulate as commodified goods; and at the systemic level, they generate emergent contradictions that destabilize the entire order. In this sense, digital capitalism must be understood as a transitional phase, not an endpoint—one that embodies both the intensification of exploitation and the opening of emancipatory possibilities.
The historical task, then, is to reorient the role of data: to transform it from an instrument of capitalist domination into a basis for collective flourishing. Just as industrial capitalism generated the working class as the agent of transformation by bringing labor together in the factory system, so digital capitalism generates digital subjects as potential agents of a new synthesis. These subjects—gig workers, open-source contributors, users demanding data sovereignty, and communities defending the digital commons—are already engaged in struggles that point beyond the capitalist appropriation of data. Their collective practices suggest the embryonic forms of a new mode of production, one organized not around the commodification of information but around its cooperative use and democratic governance.
In this light, digital capitalism must be read as both culmination and opening: the culmination of capital’s drive to colonize every aspect of life, and the opening of new horizons where data’s non-rival, infinitely reproducible nature makes alternative social arrangements materially possible. The future will depend on whether the contradictions of digital capitalism are resolved in favor of deeper monopolization and control, or whether they are negated and transcended through struggles that reclaim data as a commons for humanity. Within this unfolding dialectic lies the possibility of a new social synthesis—one in which digital technologies, liberated from the imperatives of capital, serve as instruments for human emancipation, cooperation, and collective self-determination.
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