TL;DR AI applications are spreading rapidly: ChatGPT reached 100 million users in two months, Copilot is embedded in editors, and image models let non-designers create visual work. At the same time, the power to build and govern AI is concentrating in a few companies. Consumers gain unprecedented access, while producers gain unprecedented control. You receive the freedom to use AI, but surrender much of the freedom to define it.
A young person in rural Africa can now use a phone to receive near world-class tutoring from a GPT-4-level model. In use, the experience can resemble that of a Silicon Valley engineer: the same interface and the same model. From the consumer side, this is an extraordinary democratization of knowledge.
From the supply side, the picture is different. The model may be developed by a U.S. company, trained on infrastructure worth hundreds of billions of dollars, fed with global internet data, and aligned according to choices made by a small group of engineers. Users can access the system, but they do not decide how it updates, what data it uses, or what values it adopts.
AI creates a historic split: use becomes more equal while control becomes more concentrated. This is not merely temporary; it follows from the industry’s technical structure.
Access Is Real, but It Is Not the Whole Story
At the tool layer, AI genuinely lowers capability barriers. People who never learned programming can produce runnable code; people without design training can generate professional-looking images; people without data-science backgrounds can ask AI to perform complex analyses. Skills that once required years of training can become on-demand tools.
This broad access is meaningful. But being able to use a system is not the same as being able to control it.
Model Costs Build a Wall
Why does AI power concentrate? Start with the cost of building frontier models. Training requires massive compute, vast datasets, specialized talent, and extensive infrastructure. Stanford HAI’s AI Index shows that frontier research is concentrated among a small number of firms in the United States and China.
This differs sharply from the early internet. A student in a dormitory could create an internet startup; today, a student can build an API-based application but cannot independently train a frontier model from scratch. The difference is structural, not simply a matter of effort.
As a result, much of the world becomes a consumer of AI rather than a participant in its production. Global market growth is driven primarily by a small number of countries and companies.
Data Colonialism Is Underestimated
Couldry and Mejias describe data colonialism as the transformation of human life into a continuing raw-material source for capital accumulation. In AI, that pattern appears in two ways.
First, data contribution is disconnected from value distribution. Billions of users contribute searches, conversations, and feedback that help models improve, while most gains flow to companies that own the models. Second, the physical infrastructure of AI—data centers, GPU clusters, and high-speed networks—is geographically concentrated.
The digital divide therefore persists beneath the apparent universality of AI applications. Closing it requires public and global-scale investment, not merely free online courses.
Regulation Cannot Keep Pace with Iteration
The EU AI Act is the most comprehensive attempt to address these issues. It includes support for small firms, regulatory sandboxes, AI-literacy obligations, and restrictions on some uses of AI in unequal power relationships.
Its core insight is important: AI does not automatically create equality; institutions can guide it toward more equal outcomes. Yet legislation moves in years while AI capabilities change in months. Compliance costs can also favor large firms that have the resources to absorb them, potentially widening the gap the rules seek to close.
The User’s Illusion: Freedom of Use, Not Power
AI users have the freedom to use tools for writing, coding, analysis, and design. But they rarely participate in defining the system’s direction: training data, value alignment, privacy boundaries, updates, or shutdowns. Those decisions are made by a limited number of actors because entry costs are so high.
As Cathy O’Neil argues, mathematical models are not neutral; they encode choices and existing power structures. When designers are concentrated in a few companies and countries while users span the world’s cultures and social conditions, bias becomes not only a technical question but a political one.
The risk is especially serious when AI becomes infrastructure for search, recommendation, hiring, credit, and education. If these systems are controlled by only a few companies, users may have nowhere else to go.
Think of It Like an Operating System
AI’s distribution resembles the smartphone era. At the application layer, smartphones created real capability equality: people across the world can use the same messaging, video, and map applications. Yet the underlying operating systems determine app-store rules, update schedules, data collection, and privacy policy.
AI may reproduce this structure. Users move freely among applications, while the underlying AI operating system—foundation models, training frameworks, and compute infrastructure—remains concentrated in a few firms. Once such an ecosystem becomes dominant, replacement is extremely costly.
Which Force Breaks the Balance First?
Technology history shows that distributional outcomes are not predetermined. Acemoglu and Johnson argue that technology opens a space of possibilities, while institutions, political struggle, and policy decide who benefits. Early industrial growth did not automatically raise workers’ living standards; institutional change eventually altered the distribution.
AI is similar. Public compute infrastructure, data-benefit redistribution, open-model disclosure, and active antitrust enforcement could restrain concentration. Without such interventions, AI may become more concentrated than mobile platforms because frontier-model training leaves so few viable players.
AI access is real, and AI power concentration is real. The long-term balance depends on whether institutions can imagine and build rules that prevent power from naturally pooling in an era with unprecedented technical barriers.