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AI as Public Infrastructure: A Critical Review of the Transition from Tool to Societal Necessity

Year 2025, Volume: 3 Issue: 2, 40 - 61, 31.12.2025

Abstract

Artificial intelligence has ceased to be a collection of discrete tools. It has evolved into a general-purpose cognitive infrastructure that mediates learning, resource allocation, decision-making, and power across economies and polities. AI systems now shape both productivity and institutional legitimacy across finance, healthcare, education, defense, and public administration. This paper conceptualizes AI as Public Infrastructure (AIPI), a governance framework for managing the interface between globally produced AI systems and domestic institutions. AIPI operates as a filter-translation membrane. It channels the inflow of capabilities through cultural, legal, ethical, economic, and contractual layers before embedding them in critical national workflows. Drawing on real-world governance practice, we identify three dominant styles: market-led, state-led, and hybrid. These styles are defined by how authority, accountability, and coordination are distributed across public and private actors. Using country cases, we show the conditions under which AI capabilities cross into public-infrastructure status and warrant infrastructure-grade obligations. For small and mid-tier states, the realistic strategic aim is governed dependence rather than unattainable autonomy. Governed dependence involves the deliberate alignment of imported frontier systems with national priorities and assurance capacity. To operationalize this perspective, we introduce the Infrastructure Status Index (ISI) as a jurisdiction and domain-specific metric. ISI scores a given country-sector pairing on four dimensions: Essentiality, Embeddedness, Legitimacy, and Governance. It answers two questions: (1) has the national capability crossed public-infrastructure thresholds? and (2) where does governance lag adoption? Taken together, the AIPI-ISI frameworks form a national and sectoral design and oversight architecture. They translate diagnosis into clear obligations and pathways for implementation. This enables imported AI to be embedded purposefully and accountably.

Ethical Statement

Not applicable. This research does not involve human subjects or primary data collection from individuals.

Supporting Institution

n/a

Project Number

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Thanks

To everybody who encouraged me morally to conduct research and write this paper

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There are 20 citations in total.

Details

Primary Language English
Subjects Computing Applications in Social Sciences and Education
Journal Section Research Article
Authors

Ogtay Ibrahimov

Project Number n/a
Submission Date December 17, 2025
Acceptance Date December 25, 2025
Publication Date December 31, 2025
Published in Issue Year 2025 Volume: 3 Issue: 2

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