AI Monopoly: Corporate Hegemony and the Future of Science

Document Type : review paper

Author

Associate Professor of Futures Studies, Institute of Cultural and Social Studies, Tehran, Iran,

10.22034/rahyaft.2026.12246.1651

Abstract

The findings indicate that the shift of scientific authority to technology firms is not a temporary fluctuation but a structural paradigm shift. To counter this, scientific institutions, especially in developing countries, must adopt a strategy of "changing the playing field". Instead of futile direct competition in general-purpose foundational models, universities should leverage their deep domain expertise and access to "sovereign data"—longitudinal, micro-level, and governmental datasets in fields like macroeconomics, climate change, and public health—which are less accessible to commercial entities. Strategically, the formation of regional and international infrastructure consortia is essential to aggregate resources and overcome the CapEx wall.
Given the structural CapEx wall identified in this study – where the training cost of a single frontier model such as Gemini Ultra surpasses the annual budgets of leading academic AI labs – the paper argues that universities in developing countries should change the playing field rather than attempting to compete in general-purpose frontier models. This implies a strategic pivot towards deep domain expertise and sovereign longitudinal micro-data in areas such as macroeconomics, climate and public health, where commercial actors face higher entry barriers.
Finally, an aggressive "Open Science" diplomacy is required to defend knowledge as a "public good," promoting open-source models and legal frameworks to combat data colonialism and ensure that AI-driven discoveries serve global sustainable development.
Each of these policy directions is directly anchored in the four empirical dimensions identified in this study (infrastructure oligopoly, talent drain, data colonialism and narrowing diversity), and is informed by emerging international experiments with shared AI infrastructures and open science frameworks.

Keywords

Main Subjects

References

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