Artificial intelligence intervenes in productivity as a frontier technology, capable of redefining not only how much an economic system produces, but who can produce, under what conditions, and with what capacity to shape the future. This is not a simple increase in efficiency, but a structural transformation of the relationship between resources, decisions, and value. In this context, productivity ceases to be a technical variable measured ex post and becomes a strategic function, linked to selective access to computational infrastructures, data, and automated decision-making capacities.
Asymmetries in access to artificial intelligence constitute the first element of discontinuity. According to the OECD, more than 80 percent of global investments in advanced AI are concentrated in fewer than ten economies, which possess integrated ecosystems of research, venture capital, and large-scale computing infrastructure. This concentration is not neutral. It transforms innovation into a structural barrier to entry, in which productive capacity no longer depends solely on capital and labor, but on the ability to participate in the layers where models, standards, and optimization criteria are defined.
The productivity differentials that result are qualitative before they are quantitative. The adoption of artificial intelligence systems makes it possible to compress decision-making times, anticipate macroeconomic and microsectoral scenarios, and allocate resources with a precision previously inaccessible. Competitive advantage lies not so much in producing more, but in producing with greater adaptive capacity. Organizations that integrate AI into their strategic processes develop a form of productive elasticity that allows them to react to shocks with greater speed. By contrast, those that remain excluded experience increasing rigidity, similar to that observed in economies that failed to interpret the industrial transformations of the last century.
This dynamic generates new economic dependencies. Where artificial intelligence becomes critical infrastructure, access to platforms, proprietary models, and advanced skills translates into functional subordination. Less equipped economies end up adopting solutions developed elsewhere, implicitly accepting their decision-making logics and priorities. According to the World Bank, countries that import advanced AI systems without autonomous adaptation capabilities show a progressive reduction in their strategic autonomy, even in sectors traditionally considered sovereign, such as industrial planning and labor management. It is a form of cognitive dependence that directly affects the ability to define development trajectories.
The redefinition of competitive advantage thus takes on more complex contours. It is not sufficient to possess the technology, nor to adopt it at scale. Algorithmic productivity requires capacities for governance, audit, and interpretation of systems. Companies that have integrated AI into decision-making processes without adequate safeguards of responsibility have often amplified design errors, crystallized biases, and rendered chains of accountability opaque. The McKinsey Global Institute has observed that a significant share of high-impact AI projects fail not because of technical limitations, but due to deficiencies in governance and organizational integration. Productivity, in this sense, is never separable from the quality of the institutions that support it.
On a theoretical level, the reading of Karl Polanyi offers a particularly relevant interpretative key. In his analysis of the great transformations, Polanyi shows how the economy, when it attempts to detach itself from social and institutional contexts, produces imbalances that require subsequent corrective interventions. Artificial intelligence, if conceived exclusively as a productivity multiplier, risks accentuating this fracture, separating technological efficiency from the social conditions that make it sustainable. Productive capacity then becomes a partial indicator, reflecting value choices and power relations before economic outcomes.
In the world of global enterprises, these tensions manifest themselves with increasing clarity. Investing in AI implies a profound restructuring of value chains, with a shift of decision-making weight toward central functions with high cognitive intensity. Organizations that interpret AI as a purely competitive lever often achieve advantages in the short term, but expose their ecosystems to systemic fragilities. By contrast, companies that embed the technology within a strategy of resilience and institutional balance build a less conspicuous, but more stable productivity over time.
The technological asymmetries that emerge are not a side effect of innovation, but one of its structural outcomes. They can be mitigated through policies of investment, cooperation, and governance, but are unlikely to be eliminated. Artificial intelligence acts as an amplifier of existing trajectories, making latent inequalities more visible and accelerating processes of concentration of economic power.
In this context, productivity ceases to be a stable datum and becomes a moving boundary, redefined by the interaction between technology, institutions, and governance capacity. Understanding this mobility is essential for those who lead organizations and economic systems in an environment characterized by structural uncertainty. Algorithmic productivity does not represent a definitive achievement, but a temporary configuration of economic power, whose sustainability depends on the ability to hold together efficiency, decision-making autonomy, and institutional cohesion.
Global AI Observatory