Morozov's historical framework draws on the well-documented trajectory of previous critical infrastructures. Railroads, telephone networks, and electrical grids all followed a recognizable pattern: initial private development, rapid concentration, emergence of critical dependency, eventual imposition of democratic governance through regulation or public utility designation. In each case, the democratic response was resisted by the industries it constrained; in each case, the resistance deployed the language of inevitability and market superiority; in each case, the response was eventually recognized as necessary for benefits to be broadly shared rather than narrowly captured.
AI infrastructure is the most concentrated and the most consequential infrastructure humanity has built. It is governed by the least democratic mechanisms of any critical infrastructure in modern history. The decisions that determine how models are trained, what data they are trained on, what behaviors they are optimized for, what safety constraints they observe, and at what price they are offered are made by relatively small groups of people operating within institutions accountable to shareholders and boards but not to the populations whose lives their products reshape.
The dependency this creates is qualitatively different from previous infrastructure dependencies. The user who depends on AI for cognitive augmentation has restructured not just purchasing habits but thinking habits, professional capabilities, and her relationship to difficulty itself. The dependency operates at the level of cognition rather than consumption, which means switching costs are measured not in inconvenience but in diminished capacity — atrophied skills, lost tolerance for friction, restructured expectations that make operating without the tool feel not merely inconvenient but cognitively impoverishing.
The training data question adds a dimension without precise precedent. The models were trained on datasets incorporating the intellectual labor of millions — writers, programmers, artists, researchers — whose work was extracted from the public internet and incorporated into proprietary systems without compensation, without meaningful consent, and without any mechanism for contributors to share in the value their contributions made possible. Morozov has identified this as a form of enclosure: the conversion of a commons into private property that generates revenue for the enclosing institution while providing no return to the commons from which value was extracted.
Morozov has developed the infrastructure concentration analysis across multiple essays, most notably 'Socialize the Data Centres!' (New Left Review, 2015) and extended it with specific reference to AI infrastructure in 'Socialism After AI' (New Left Review, December 2025).
Material substrate. The empowerment narrative operates at the application layer while concealing the infrastructure layer on which the application depends.
Historical trajectory. Previous critical infrastructures followed a predictable pattern from private concentration to democratic governance. AI infrastructure is traversing the same arc.
Cognitive dependency. AI infrastructure creates dependency at the level of cognition itself, producing switching costs that are qualitatively different from previous platform dependencies.
Enclosure of the commons. The training data regime represents the conversion of a vast intellectual commons into proprietary capital without compensation to contributors.