Artificial intelligence intervenes in economic systems as a factor of profound reorganization of value, rather than as a simple automation technology. Its diffusion does not merely modify production processes, but redefines what is considered relevant work, what remains necessary yet unrecognized, and what concentrates decision-making power. In this transition, a silent stratification of value takes shape, cutting across firms and institutions without immediately producing visible conflicts, while accumulating structural tensions destined to affect long-term economic stability.
The distinction between augmented labor and residual labor constitutes the backbone of this new configuration. Augmented labor operates in proximity to computational systems, benefits from the cognitive amplification provided by algorithms, and participates, directly or indirectly, in processes of decision and value allocation. Residual labor, by contrast, remains indispensable to the functioning of systems, but loses symbolic and economic centrality. This is not obsolete labor, but labor that does not access the sites where value is defined, measured, and redistributed.
This separation is reflected in global cognitive value chains. While traditional industrial chains organized production according to criteria of cost, logistics, and manufacturing specialization, cognitive chains distribute activities based on access to data, models, and the capacity to intervene in decision criteria. According to estimates by the International Labour Organization, more than 70 percent of activities related to the training, moderation, and maintenance of artificial intelligence systems are performed under fragmented contractual forms, often outsourced and concentrated in areas with low labor protection. This segment sustains the apparent efficiency of AI, yet remains excluded from the dominant narrative of technological progress.
Invisible labor represents the most evident expression of this dynamic. Activities such as data labeling, result verification, contextual adaptation, and error correction constitute an indispensable operational base for intelligent systems. However, this labor is rarely accounted for as strategic human capital. In corporate language, it is treated as a variable cost rather than as critical infrastructure. The consequence is a systemic devaluation that does not depend on the quality of the contribution, but on its peripheral placement with respect to decision-making centers.
This configuration introduces new forms of economic subordination. It is no longer solely a matter of contractual dependence, but of functional subordination mediated by technical architectures. Those operating at the margins of intelligent systems depend on evaluation criteria embedded in models, difficult to negotiate and often opaque. Power is not exercised through direct command, but through the definition of metrics that establish what counts and what can be neglected. In the world of global enterprises, similar dynamics are observable when algorithmic governance reduces the space for organizational mediation and concentrates authority in the nodes that control information flows.
This transformation has significant macroeconomic implications. The concentration of value in cognitive phases with high technological intensity tends to reduce the redistributive capacity of labor. According to the International Monetary Fund, the growth of wage differentials linked to advanced digital skills has contributed significantly to rising inequalities in high-income countries over the past decade. The systemic risk is not only social, but economic: weakened aggregate demand and a devalued labor base reduce the overall resilience of the system.
The thought of Simone Weil offers a particularly incisive interpretative key to understanding this condition. Weil described labor as an experience of attention and a relationship with necessity, emphasizing how dehumanization does not derive from effort, but from the loss of meaning and recognition. In the artificial intelligence economy, invisible labor embodies this loss: it requires concentration, responsibility, and continuity, yet produces neither status nor voice. Technology, far from neutralizing this tension, risks amplifying it, increasingly separating those who benefit from automation from those who sustain its daily functioning.
For firms, this stratification is not an external phenomenon, but the direct result of organizational choices. Deciding where to place human labor within the cognitive value chain means deciding what kind of responsibility the organization intends to assume. Companies that treat invisible labor as a marginal variable obtain cost advantages in the short term, but build opaque dependencies that can compromise operational and reputational stability. By contrast, integrating this labor into a broader strategic vision implies recognizing its infrastructural role, with positive effects on system quality and long-term sustainability.
Decision-making processes mediated by artificial intelligence make this responsibility even more stringent. Human cognitive limits are compensated at the level of calculation, but not at that of judgment. Delegating to models the implicit definition of priorities means accepting that what is not easily measurable will be progressively excluded. The invisibility of labor thus becomes a structural outcome of optimization-oriented decisions, not an accidental effect.
The silent stratification of value in the AI economy is therefore not a simple evolution of the labor market, but a profound transformation of the relationship between labor, recognition, and power. The division between augmented labor and residual labor does not coincide with a distinction between advanced and backward, but between visible and invisible. Making visible what sustains intelligent systems does not mean slowing innovation, but restoring economic and institutional coherence to it.
The labor that is not seen will continue to be an essential component of the computational economy. The open question concerns the capacity of firms and institutions to recognize it as such, integrating it into their architectures of value and responsibility. In this still unstable space, the quality of future growth will depend on the possibility of bridging the distance between what appears efficient and what makes efficiency possible, without ever reducing this awareness to a decorative element of public discourse.
Global AI Observatory