Beyond Dataism: Toward a Life-Centered Political Economy

In recent years, the proliferation of data-driven analysis has reshaped the epistemological foundations of economics. Vast computational capacity and algorithmic learning promise to turn uncertainty into calculable probability, and complexity into manageable prediction. Yet, the triumph of data often conceals a deeper conceptual poverty. As economics has increasingly become an empirical technocracy, it risks losing sight of what makes social inquiry meaningful: context, purpose, and human consequence.

The Epistemic Limits of Data

The turn toward “data-driven” reasoning reflects a powerful methodological confidence. From macroeconomic forecasting to behavioral nudging, the assumption is that sufficiently large datasets can reveal the underlying structure of economic reality. However, as Tony Lawson has argued, the economy is not a closed, law-like system where stable regularities can be mechanically extracted. It is an open, emergent, and historically contingent domain shaped by institutions, norms, and evolving power relations.

When treated as a self-sufficient epistemology, dataism turns into a form of empirical reductionism. It equates visibility with knowledge and quantification with truth. The result is what Nancy Cartwright (2007) calls the “illusion of measurement,” the belief that causal understanding can be replaced by statistical correlation. This technocratic reflex breeds epistemic blindness, especially in moments of structural rupture when models trained on the past fail to anticipate the future.

From Technocracy to Hermeneutics

The dominance of data has simply hidden interpretation behind algorithms. Every dataset presupposes acts of selection—what to measure, how to classify, whose behavior counts as relevant. These choices are never neutral. They reflect implicit theories about human motivation, welfare, and rationality. As Amartya Sen expresses, measurement without conceptual clarity leads to misleading inferences about well-being and freedom.

Reclaiming the interpretive dimension of economics does not mean abandoning empiricism. It means redefining its purpose. Data should not dictate what questions matter. They should illuminate the conditions under which meaning emerges. Economics, understood hermeneutically, becomes a dialogue between evidence and understanding, not an asymmetrical domination of one by the other.

The Political Economy of Data

The so-called data revolution has reconfigured capitalism itself. Data have become a new factor of production, a form of capital extracted from human experience and converted into algorithmic control. As Shoshana Zuboff (2019) demonstrates, surveillance capitalism operates by translating lived behavior into predictive value, enclosing social life within proprietary digital infrastructures. This process extends what David Harvey (2014) called accumulation by dispossession into the informational sphere.

A truly critical political economy of data must therefore ask a foundational question: who owns the means of prediction? As the production of economic knowledge increasingly depends on vast digital infrastructures controlled by a handful of corporations, the ability to collect, process, and monetize data has become highly concentrated.

According to UNCTAD’s Digital Economy Report 2021, ten global technology firms account for more than 80% of the world’s cloud computing capacity and over two-thirds of total private investment in artificial intelligence. This concentration allows a few platforms—Google, Amazon, Microsoft, Apple, Meta, and their Chinese counterparts—to dominate the infrastructures through which most of the world’s information flows.

As Nick Srnicek (2017) observes, these firms function as platform monopolies, accumulating not only profits but also epistemic power, the authority to define what counts as knowledge and prediction in a digital economy.

The result is a profound asymmetry between those who generate data and those who control its analytic capacity. Individuals, small enterprises, and even governments increasingly depend on proprietary platforms to access and interpret information about the very societies they inhabit.

Shoshana Zuboff (2019) calls this new regime surveillance capitalism, in which behavioral data extracted from everyday life is transformed into predictive products that serve corporate interests. Similarly, Couldry and Mejias (2019) describe the process as data colonialism: the appropriation of human experience into quantifiable resources that reinforce global hierarchies of wealth and knowledge. In this sense, the issue of epistemic justice, who has the right to know and to interpret, becomes inseparable from that of economic democracy, who benefits from what is known.

Data-driven economics, when governed by private ownership and algorithmic opacity, thus reproduces the very inequalities it claims to measure. What appears as an age of transparency may, in reality, conceal a new opacity: the privatization of knowledge itself under the guise of objectivity.

Toward a Life-Centered Epistemology

The alternative lies not in rejecting data, but in reimagining its role within a broader ontology of life. Data should serve the interpretation of living systems, not their instrumentalization. Its purpose is to deepen our understanding of how economic activity interacts with ecological balance, social well-being, and human creativity, rather than to reduce life’s complexity into variables to be optimized.

A life-centered economics would treat information as relational, emerging from the interaction between human societies and their ecological environments. This echoes the post-Keynesian, evolutionary, and ecological traditions that view the economy as a dynamic, adaptive, and historically grounded system.

Such an epistemology restores the primacy of meaning, ethics, and sustainability. It recognizes that resilience and efficiency are not synonymous with vitality, and that human well-being cannot be computed into existence. Data, in this light, become not an oracle but a language, one that must remain open to interpretation, criticism, and moral reflection.

Markets, Policy, and the Ethics of Interpretation

The differentiation between market agents and policymakers marks a crucial boundary in the discussion of data-driven economics.

Markets are not moral organisms but sociological structures of coordination. Their function is to process information, not to interpret it. As Friedrich Hayek observed, markets are powerful precisely because they enable the “use of knowledge in society” through the price mechanism rather than through central command. Yet, this coordination is epistemic, not ethical. It transforms dispersed data into signals of scarcity and preference, but remains indifferent to justice, culture, and sustainability. Markets are therefore systems of reaction, not reflection.

By contrast, policymaking cannot operate within such moral neutrality. Economic policy is an interpretive act, not merely a computational one. It must translate data into meaning by embedding quantitative evidence within the lived fabric of societies, their institutions, cultures, and histories.

As Dani Rodrik (2015) argues, economic policies are inherently contextual. What works in one institutional setting can fail catastrophically in another. Data can illuminate possibilities, but it cannot define priorities. Policymaking must therefore integrate historical knowledge, cultural diversity, class structures, and ecological constraints into its decision framework, dimensions that elude statistical measurement but define social reality.

This asymmetry between markets and policy is also a matter of responsibility. Market agents are accountable to their shareholders and to short-term efficiency metrics. Policymakers, on the other hand, are accountable to citizens, future generations, and the moral legitimacy of governance.

If both spheres adopt a purely data-driven rationality, political judgment risks collapsing into algorithmic decisionism. Jürgen Habermas warned against such tendencies in his theory of communicative action. When instrumental rationality colonizes the public sphere, deliberation yields to calculation, and democratic legitimacy erodes.

The challenge, then, is not to reject data, but to re-situate it within the ethics of interpretation. Markets may operate on information, but policymaking must operate on understanding.

A life-centered political economy should thus treat data as a guidepost, not a compass, an aid to human reasoning rather than its substitute. Only when data are filtered through the lenses of culture, history, and democratic deliberation can it serve the collective good rather than the computational logic of markets.

Conclusion

To move beyond dataism is not to regress from science, but to recover its philosophical essence: the pursuit of understanding rather than prediction. A data-driven economics that forgets this becomes a sterile machinery of measurement. It might be powerful, but blind.

Reclaiming meaning requires embedding data within the deeper structures of life, history, and value. Only then can economics transcend the algorithmic illusion and rejoin the human conversation it was meant to serve.

The rise of artificial general intelligence (AGI) represents not merely a technical leap but a profound challenge to humanity’s capacity to generate meaning. The issue is whether humanity can preserve its own domain of meaning, ethics, and responsibility.

Technically, AGI may surpass the human mind in computation, learning, and even creativity. Yet, human intelligence rests not only on processing information but on constructing meaning. A machine may manipulate symbols, but it does not understand their historical, cultural, or moral context.

As Hubert Dreyfus (1992) argues, meaning does not emerge solely from the relations between symbols. It requires embodied experience, historical awareness, and social context. Therefore, even if AGI replicates human cognitive functions, it cannot share the existential depth of human understanding.

Hans Jonas (1984), in discussing the limits of technological power, reminds us that humanity’s task is no longer to transform nature, but to understand the limits of its own power.

The rise of AGI carries the risk of losing this sense of boundary. Algorithmic intelligence presents a form of rationality stripped of value judgments, and a rationality devoid of value judgments inevitably creates an ethical void. Within this void, the production of knowledge becomes detached from meaning. Data serve not the interpretation of life, but its instrumentalization.

This development transforms not only production processes, but also the position of the human subject. As Hannah Arendt (1958) writes, what distinguishes the human being is not labor, but action, the capacity to create meaning, to speak, and to make ethical judgments. With the advance of AGI, the sphere of human action may gradually contract. Knowledge production becomes automated while the production of meaning becomes marginal. Humanity may continue to possess knowledge, yet lose the ability to understand.

As Nick Bostrom (2014) emphasizes, AGI is not merely a technological phenomenon. It compels a redefinition of humanity’s system of values. Scientific progress, when detached from its ethical foundation, endangers the very integrity of human existence.

When political authority ceases to serve citizens and instead aligns itself with corporate interests, policymaking becomes an extension of market logic. This phenomenon, evident across many economies and acutely visible in the United States today, transforms democratic governance into a form of regulatory capture. Under such conditions, public institutions adopt the language, metrics, and incentives of business. The state begins to imitate the algorithmic rationality of markets, efficient in form, yet hollow in moral content.

Markets themselves are not self-regulating organisms, as classical theory presumes. They are arenas of power, continuously shaped by political struggles, institutional design, and the distributive outcomes of regulation. Behind the apparent equilibrium of supply and demand lie conflicts between labor and capital, between public welfare and private profit, between democratic accountability and oligarchic influence. As these forces mold economic rules and expectations, what appears as “market balance” is often the outcome of structured power, not spontaneous order.

The consequences are profound. Social needs are redefined as investment opportunities. Public deliberation yields to lobbying, and moral judgment dissolves into cost–benefit calculus. Policy, supposedly and ideally to be tasked with interpreting data for the collective good, optimizes it for capital accumulation. As Jürgen Habermas warned, when instrumental rationality colonizes the political sphere, democracy becomes procedural without being normative, a system that measures, but no longer understands.

A life-centered political economy can offer a new orientation at this juncture. Both AGI and the data economy should be built not solely on efficiency but on the redistribution of meaning and responsibility. The purpose of technology should not be to replace the human being, but to complete the human being. Otherwise, as knowledge accumulates, meaning will be depleted.