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

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.