Making intelligence public: Thresholds of policy, demand, and AI-readiness

AI is emerging as a general-purpose infrastructure whose technical capabilities and governance institutions co-evolve. Societies are increasingly embedding algorithmic decision support across public administration, resource allocation, and production. This produces divergence in outcomes: effective integration yields compounding improvements in efficiency and productivity; ineffective integration risks persistent capability gaps. This paper develops the Societal Intelligence Thresholds (SINT) framework, a diagnostic model that explains when AI systems—and the AI-intensive digital infrastructures surrounding them—become functionally non-optional under sustained human governance. Building on companion studies of Cultural–Technological Synergy (CTS), which conceptualizes culture as adaptive coordination infrastructure, and AI as Public Infrastructure (AIPI), which defines measurable infrastructural maturity through the Infrastructure Status Index (ISI), this paper isolates the missing transitional layer: the Policy–Demand equilibrium, modulated by AI-readiness, that governs AI threshold dynamics. SINT formalizes how policy intent, societal demand, and AI-readiness interact to determine the pace of threshold crossing and the persistence of infrastructural dependence. Societies oscillate across four characteristic quadrants—Dormant Drift, Mandate Compliance, Grassroots Pull, and Convergent Momentum—each associated with distinct fragility patterns. Cultural architectures (heritage adaptability, cross-civilizational competence, innovation ethos, strategic determination) modulate these trajectories by influencing legitimacy, trust, and learning capacity. An interpretive application to Azerbaijan (2012–2025) illustrates pre-threshold alignment and AI-readiness asymmetries typical of transitional economies. The paper concludes with a typology of AI thresholds, a sequencing model for policy interventions, and a research agenda for comparative validation. Recognizing threshold mechanics clarifies that sustainable AI integration depends less on technology supply than on governing how societies build, coordinate, and institutionalize AI capacity—the collective ability to turn technological possibility into stable, legitimate infrastructure.