Advanced artificial intelligence today operates as a systemic infrastructure that simultaneously reconfigures the relationships between economy, institutions and power. It is not a simple technological evolution, but a structural shift in the decision-making center of gravity. The ability to orient cognitive processes, evaluation criteria and interpretative frameworks becomes a primary strategic resource, comparable to financial capital, raw materials or logistical networks. In this context, power is exercised not predominantly through normative or coercive acts, but through the prior definition of what appears rational, efficient and legitimate within algorithmically assisted decision systems.

Artificial intelligence systems do not merely support decisions that have already been formulated, but intervene at an earlier stage, where the problem is defined and delimited. The selection of relevant variables, the weighting of risk factors, the construction of probabilistic scenarios constitute cognitive acts that profoundly influence economic and institutional outcomes. In this sense, AI operates as a device for the organization of meaning, establishing interpretative frameworks that tend to consolidate over time through repeated use and the standardization of practices. The resulting dominance does not take the form of an explicit command, but of a progressive convergence toward decision-making models perceived as neutral and technically optimal.

This dynamic finds empirical confirmation in the growing concentration of computational capabilities and advanced models in a limited number of actors. According to estimates by the OECD and the European Commission, more than 80 percent of the computing power required to train large language models is today controlled by a few global firms, predominantly based in the United States and China. This concentration concerns not only hardware, but also access to data and the capacity to define technological standards that are then adopted across both public and private sectors. The result is a form of cognitive hegemony exercised through the very infrastructure of decision-making.

From an economic perspective, this transformation directly affects the production of value. Competitive advantage no longer resides exclusively in productive efficiency or product innovation, but in the ability to influence upstream decision processes. AI-based decision support systems are employed to allocate capital, assess investments, manage supply chains and define pricing strategies. In such contexts, the algorithm does not merely calculate, but incorporates a specific vision of economic rationality, often oriented toward the maximization of short-term quantitative indicators. This orientation tends to reproduce itself as an implicit norm, reducing the visibility of alternatives that privilege different criteria, such as long-term resilience or institutional impact.

Decision-making labor undergoes a similar transformation. Cognitive delegation to algorithmic systems modifies the role of managers and public officials, who are increasingly called upon to validate outputs generated by opaque models. Recent studies by the Bank for International Settlements highlight how the extensive use of predictive systems in financial markets contributes to a growing homogeneity of behavior, amplifying pro-cyclical dynamics and reducing the ability to identify weak signals. Cognitive standardization thus becomes a factor of systemic risk, despite the apparent increase in efficiency.

Public institutions find themselves operating in a context where computational power conditions the capacity for governance. The adoption of AI systems for the management of public policies, from taxation to healthcare, introduces decision criteria that often escape traditional democratic control. The formal transparency of institutional processes does not automatically guarantee the intelligibility of the underlying algorithmic logics. In the absence of cognitive counterpowers, the risk is a progressive technicization of political choices, presented as inevitable outcomes of objective calculations.

This configuration recalls the notion of hegemony as the capacity to render particular interests universal through the naturalization of categories of thought. Artificial intelligence models incorporate cultural assumptions, economic priorities and worldviews that reflect the contexts in which they are developed. Once disseminated on a global scale, these models tend to standardize practices and languages, reducing interpretative pluralism. The loss of cognitive diversity does not occur through explicit censorship, but through the progressive marginalization of approaches that do not conform to dominant algorithmic standards.

The geopolitical dimension of this process is evident. Control over AI infrastructures becomes a central element of competition among states, not only in terms of security or industrial development, but of symbolic influence. The ability to define how global problems are represented, from climate change to financial stability, constitutes a form of leadership that precedes political action. Geopolitics thus shifts from direct confrontation between states to competition for interpretative hegemony mediated by technology.

Within this framework, cognitive power emerges as a structural component of the economy of artificial intelligence. It is not a side effect, but the mechanism through which AI most deeply affects institutions and markets. The reduction of decision-making complexity can favor coordination and scalability, but exposes the system to latent fragilities, linked to the homogenization of perspectives and the loss of adaptive capacity. Economic and institutional resilience depends on the ability to maintain interpretative plurality within decision processes, preventing algorithmic efficiency from becoming the exclusive criterion of legitimation.

Recognizing the hegemonic nature of cognitive infrastructures means reintegrating artificial intelligence within a framework of economic and institutional responsibility. The central issue does not concern the adoption of technology, but the ways in which it is integrated into processes governing value, labor and money. In a context where consensus tends to form even before formal decision-making, the capacity to preserve spaces of interpretation and cognitive conflict becomes an essential condition to prevent computational power from translating into a form of silent and hardly reversible domination.

Global AI Observatory