The economy mediated by artificial intelligence is structurally redefining the very concept of value, affecting the decision-making mechanisms that regulate markets and institutions. The classical categories through which political economy has interpreted value creation, labor, scarcity, utility and exchange presupposed an observable relationship between human activities, resources and economic outcomes. In the current phase of technological development, this relationship weakens. Value continues to be produced, measured and accumulated, but increasingly takes shape in spaces mediated by algorithmic systems that operate before explicit decision-making and outside the direct perception of traditional economic actors.
The main sources of value shift from material production to the ability to organize information, anticipate behavior and reduce uncertainty. Prediction becomes an autonomous economic resource. Models capable of inferring future demand, optimizing complex logistics networks or suggesting a choice before it is consciously formulated generate immediate competitive advantages. According to analyses by the OECD and the World Bank, a growing share of the capitalization of large technology firms is explained not by physical assets or direct labor, but by the ability to extract value from data and automated decision processes. Value is no longer linked to what is produced, but to how the system as a whole is oriented.
In this context, algorithmic mediation between production and exchange assumes a central function. The market, understood as a space of encounter between supply and demand, is progressively complemented or replaced by computational infrastructures that coordinate, synchronize and preconfigure transactions. Recommendation systems, digital platforms and dynamic pricing mechanisms do not merely facilitate exchange, but determine its conditions. Studies by MIT and the European Commission show how, in numerous sectors, from commercial distribution to financial services, a significant share of purchasing and investment decisions is now influenced by algorithms operating in real time, reducing the role of direct negotiation among actors.
This transformation produces a growing detachment between value and direct labor. Human labor does not disappear, but its contribution to final value becomes fragmented and difficult to reconstruct. The training of models, the maintenance of digital infrastructures and the supervision of systems involve global labor chains that are often poorly visible and weakly remunerated. In parallel, a significant portion of value is extracted from users’ daily interactions with platforms. Ordinary behaviors are transformed into economic inputs without corresponding remuneration, as highlighted by numerous reports of the International Labour Organization on digital labor and platform economies.
Traditional economic metrics struggle to capture this reality. Indicators such as productivity, value added or employment appear partial in economies where capitalization growth is not accompanied by a proportional increase in material production or employment. Data from the International Monetary Fund show that, in recent years, profit growth in high algorithmic intensity sectors has been significantly higher than the growth of average wages. This gives rise to a structural paradox: the economy appears dynamic on the financial and technological level, while large segments of the population experience stagnation, precarity and a reduction in prospects for economic mobility.
For firms, this configuration imposes a profound rethinking of value creation strategies. The optimization of internal processes is no longer sufficient. The ability to design and govern automated decision chains becomes central. Organizations that integrate algorithmic mediation as a strategic lever acquire an advantage that depends less on the products offered than on the capacity to orient their own and others’ choices. Economic leadership shifts from punctual decision-making to the design of systems that decide, redefining the perimeter of managerial responsibility.
This delegation raises questions that go beyond efficiency. Hannah Arendt distinguished between labor, work and action, highlighting the risk of a society in which human agency is absorbed by automatic processes devoid of political responsibility. Algorithmically mediated economies approach this scenario. Value is produced by distributed decision sequences that no individual fully controls, while the power to orient outcomes concentrates in those who design and control infrastructures. Responsibility tends to disperse, making it more difficult to attribute decisions and consequences.
On the social and institutional level, the detachment between value and direct labor fuels distributive tensions. When value is no longer clearly attributable to a visible human contribution, its allocation becomes more difficult to legitimize. Firms face a structural dilemma. Continuing to operate according to algorithmic efficiency logics maximizes short-term results, but risks eroding trust, which becomes a strategic resource as important as data or capital. Reports by the World Economic Forum indicate that the perception of fairness in automated decision processes is now a determining factor for the long-term sustainability of organizations.
Within organizations as well, delegation to predictive systems modifies the quality of judgment. Human cognitive limits are compensated by algorithms, but also amplified when these incorporate implicit and undisclosed assumptions. The capacity to question the meaning of decisions risks diminishing, especially in contexts where algorithmic output is perceived as technically incontestable. The issue does not concern a confrontation between human and artificial, but the balance between automation and responsibility.
Value creation in algorithmic economies does not mark a rupture with political economy, but highlights its profound transformation. Value does not disappear, but becomes less visible, moving upstream of human decision-making and sedimenting within the cognitive infrastructures that orient the system. Understanding these mechanisms becomes a necessary condition for interpreting the new forms of economic and institutional power that are emerging, in a context in which decision increasingly precedes the awareness of having made it.
Global AI Observatory