Untangling the Algorithmic Leviathan: Palantir, the Post-Factual Polity, and the Infrastructural Crisis of AI Governance in Public Administration

This paper extends the post-factual polity framework into AI infrastructure and public administration systems theory. It asks how proprietary analytical platforms alter the state’s capacity to produce, audit, and contest the categories through which risk, threat, eligibility, fraud, and deviance become actionable. Using a structured documentary case analysis of Palantir Technologies across United States agencies and allied jurisdictions, the study applies three diagnostic markers—categorical opacity, contestation displacement, and substitutive dependency—to examine the migration of sovereign classification into vendor-controlled infrastructure. The research gap was identified through an integrative review of public administration, AI governance, algorithmic accountability, systems theory, surveillance studies, and Palantir scholarship. The analysis distinguishes AI epistemic capture from ordinary IT vendor lock-in: the former concerns not merely technical dependence or high exit costs but the loss of public capacity to define and contest consequential administrative categories. The paper argues that administrative law, procurement reform, and algorithmic impact assessment remain necessary but insufficient when agencies lack substitutive capacity. It specifies untangling as a systems-level task involving capacity reconstruction, categorical repatriation, contractual restructuring, and procurement reorientation. Hybrid intelligence is advanced as a post-untangling architecture that embeds machine processing within contestable, accountable, and legally governed human judgment. The contribution is diagnostic, methodological, and design-oriented for AI systems governance.