Law underpins surveillance capitalism: through legal frameworks that routinize data production, ownership and commercialization, personal information is transformed into commodified inputs for algorithmic governance, reshaping power and producing novel inequalities.

Main Finding

The article argues that surveillance capitalism is not merely a technological or market phenomenon but a distinct politico-economic regime whose core enabling condition is a transformed legal dispositif. Contemporary law — through property, intellectual property, trade secrecy, liability, and data governance arrangements — institutionalizes the production, circulation, ownership, and commercialization of behavioral data, thereby turning human experience into a raw material for prediction markets and algorithmic governance. From a Foucauldian perspective, algorithmic governmentality converts persons into data-objects that produce behavioral surplus, reorganizing power, knowledge, and inequality.

Key Points

• Conceptual framing
  • Draws on Shoshana Zuboff: surveillance capitalism harvests behavioral surplus (extraction imperative), turns it into predictive products (prediction imperative), and deploys interventions to shape future behavior (economies of action).
  • Uses Michel Foucault: governmentality, biopolitics, dispositif — to map how power now operates by producing and managing populations as data rather than through classic juridical sovereignty or disciplinary institutions.
  • Engages Julie E. Cohen’s “biopolitical public domain” to analyze how law reconceptualizes personal information as economically productive material.
• Legal dispositif as constitutive
  • Law does more than regulate platforms; it actively structures incentives and means for data extraction, commodification, and enclosure (e.g., IP, trade secret, contract, corporate form, weak transparency/consent doctrines).
  • Ordoliberal and neoliberal legal rationalities — emphasis on legally structured markets and minimal substantive constraints — help explain why legal form accommodates platform power and data assetization.
• Algorithmic governmentality & subjectivity
  • Algorithms and legal regimes together convert subjects into data-producing objects: the monitored subject becomes simultaneously a market input.
  • This produces new asymmetries of knowledge and power (the “Big Other”), eroding democratic accountability and individual autonomy.
• Political-economic consequences
  • Surveillance capitalism remakes accumulation: value accrues from proprietary prediction products and behavioral modification capabilities, favoring concentrated platform oligopolies.
  • Social harms include threats to privacy, autonomy, democratic processes, and new inequality forms that are legally enabled and protected.
• Normative/policy stance
  • Legal countermeasures must go beyond incremental privacy regulation; Zuboff’s view (adopted in discussion) suggests treating covert data extraction as an illegitimate practice (even “theft”) and rethinking legal regimes that legitimize enclosure of behavioral data.

Data & Methods

• Methodology: theoretical, interpretive, and conceptual analysis.
  • Primary tools are Foucauldian genealogy and dispositif analysis applied to contemporary digital capitalism.
  • Extensive literature synthesis and critique: draws centrally on Zuboff, Foucault, and Cohen, plus secondary work on platform capitalism, neoliberal legal theory, and informational capitalism.
• No original quantitative or empirical datasets are used; the article is normative-analytic, mapping conceptual relations among law, power, and data-driven markets.
• Situates the argument within the Turkish academic context, noting a gap in Foucauldian legal analyses of surveillance capitalism in Turkish-language literature.

Implications for AI Economics

• Legal regimes are endogenous to AI markets
  • Economists should treat law as a constitutive input shaping data availability, exclusivity, transaction costs, and market structure — not as exogenous regulation alone.
  • Property/IP/trade-secret rules change the marginal cost and access of training data, affecting model scale economies, concentration, and entry.
• New sources of economic rent and market power
  • Behavioral surplus and proprietary prediction products constitute intangible assets that generate rents; models of market power and competition must incorporate data assetization and prediction-based returns.
• Measurement and empirical priorities
  • Need methods to quantify behavioral surplus, data rents, and the welfare distributional impacts of prediction markets and behavioral modification.
  • Empirical work: map data flows, estimate the value of behavioral predictions, measure effects of differing legal regimes (privacy, IP, data portability) on firm concentration, innovation, and consumer welfare.
• Labor and production accounting
  • AI economics should incorporate unpaid data production into production functions: individuals produce extractable value (data-as-labor) that traditional GDP/labor accounts miss.
  • Study effects of recognizing data production as compensated labor or collective asset on incentives, innovation, and distribution.
• Policy and design implications
  • Regulations that alter legal ownership/access (data portability, rights to algorithmic explanations, limits on trade secrecy for training data) will shift firm incentives and the returns to scale in model training.
  • Antitrust interventions, data trusts, mandated interoperable data access, and stronger limits on covert behavioral profiling can reduce concentration and curb harmful behavioral modification markets.
  • Design of AI governance must consider democratic constraints: transparency, contestability, and public oversight because informational asymmetries translate into asymmetric power.
• Research agendas
  • Comparative legal-economy studies: how different legal systems (e.g., EU vs. US vs. Turkey) produce varied AI market outcomes.
  • Causal evaluation of regulatory experiments (e.g., data access mandates) on innovation, competition, and welfare.
  • Development of models that integrate legal endogeneity: equilibrium models where legal rules are strategic variables shaping firm behavior in data markets.
  • Field and lab experiments on behavioral modification externalities and consumer surplus losses from opaque prediction markets.

Overall, the paper signals that AI economists must explicitly model law and legal change as central determinants of data supply, market structure, welfare outcomes, and the political economy of algorithmic governance.