Algorithmic systems are becoming part of firms' decision-making machinery, distributing economic agency across human–algorithm networks; this 'posthuman' framing reframes organizational design and the production of economic knowledge.

Main Finding

The paper argues that artificial intelligence is reconfiguring economic agency: organizations now operate as hybrid socio-technical systems in which algorithmic systems meaningfully participate in interpreting data, producing predictions, and shaping strategic decisions. The authors introduce “algorithmic agency” to describe the capacity of AI infrastructures to influence economic outcomes and advance the idea of a “posthuman economy” in which decision-making authority is distributed across human and algorithmic actors.

Key Points

• Algorithmic agency: AI systems (e.g., machine‑learning models, recommendation engines, algorithmic trading, predictive supply‑chain analytics, automated pricing, credit scoring) can influence economic coordination and outcomes by producing predictions, risk evaluations, and policy‑relevant signals.
• Posthumanist framing: Drawing on posthumanist and sociomaterial theory, the paper rejects a strict human‑centered account of agency and instead treats decisionmaking as relational and distributed across humans, technologies, and data environments.
• Hybrid intelligence: Organizations increasingly instantiate hybrid environments where human contextual judgement and strategic reasoning combine with algorithmic speed, scale, and pattern recognition; authority and coordination become networked rather than purely managerial.
• Algorithmic governance: Algorithmic infrastructures not only support but help govern economic behavior—shaping pricing, credit access, labour allocation, and other coordination mechanisms in the digital economy.
• Conceptual contribution: The paper offers a conceptual framework where algorithmic agency arises from interactions among digital data infrastructures, algorithmic systems, and human expertise, and it proposes the “posthuman economy” as an analytic lens.
• Normative and empirical concerns: The transformation raises questions about responsibility, accountability, inequality, measurement of firm capabilities, and the need for governance and ethics in algorithmic decision systems.

Data & Methods

• Methodological approach: Conceptual/theoretical paper based primarily on literature synthesis. The authors draw on posthumanist theory (e.g., Braidotti, Hayles), sociotechnical and organization studies (e.g., Orlikowski, Leonardi), digital economy and management scholarship (e.g., Brynjolfsson & McAfee; Mikalef et al.), and work on algorithmic governance and ethics.
• Empirical evidence: Uses illustrative examples from existing AI applications (algorithmic trading, recommendations, predictive maintenance, credit scoring, automated pricing) rather than new empirical data or formal models.
• Analytical output: Development of a conceptual framework linking digital infrastructures, algorithmic systems, and human expertise to explain the emergence of algorithmic agency and hybrid intelligence in organizations.
• Limitations noted (implicit): The contribution is theoretical; empirical validation, measurement strategies, and causal identification are left for future research.

Implications for AI Economics

• Theoretical reframing: Economic models of firms, markets, and organizational decision‑making should incorporate distributed agency—treat algorithms as active decision participants that alter information flows, firm capabilities, and constraints on behaviour.
• Modeling and measurement challenges: New constructs and metrics are needed to capture AI capabilities, algorithmic influence on beliefs/expectations, and the complementarities between human and algorithmic inputs (e.g., hybrid intelligence indices, measures of algorithmic governance power).
• Market dynamics and competition: Algorithmic decision systems can change strategic behaviour (faster pricing, anticipatory supply chains, algorithmic collusion risks), requiring industrial organization models to account for algorithm‑mediated coordination and feedback loops.
• Labor and organizational design: Firms will reallocate tasks between humans and algorithms; studying complementarities, skill demand shifts, governance of human–algorithm teams, and incentive/monitoring mechanisms becomes central.
• Policy and regulation: The distributed nature of agency complicates responsibility and accountability (who is liable for algorithmic decisions?). Regulators must consider transparency, auditability, fairness, and systemic risk from widespread algorithmic adoption (e.g., financial markets, credit markets).
• Empirical research agenda: Important directions include: (a) causal studies of algorithmic influence on firm outcomes; (b) measurement of algorithmic agency and hybrid intelligence; (c) experiments and field studies of human–algorithm decision processes; (d) macro and market‑level analyses of algorithmic governance effects (price dynamics, market entry, inequality); (e) policy evaluation of oversight and accountability mechanisms.
• Normative considerations: Economic analysis should integrate ethical dimensions—surveillance, distributional effects, and power asymmetries arising from firms’ control of data and algorithmic infrastructures.

Overall, the paper calls for AI economics to move beyond treating AI as a simple productivity input and to study how algorithmic systems redistribute agency, reshape organizational capabilities, and alter economic coordination and governance.