The DOGE Experiment: Algorithmic Governance, Operational Capture, and the Case for Hybrid Intelligence

ABSTRACT This study examines the Department of Government Efficiency (DOGE) as a case of algorithmic governance whose stated purpose, improving federal efficiency, diverged systematically from its operational effect: enabling unprecedented private‐sector access to federal data systems while dismantling the oversight and civil‐service infrastructure that constrains such access. Drawing on state capacity, bounded rationality, organizational learning, and public value theory, the analysis demonstrates that the conventional efficiency‐failure reading, though correct on its own terms, obscures what made DOGE structurally distinct from the Grace Commission, the National Performance Review, the President's Management Agenda, and Program Assessment Rating Tool, and the Government Performance and Results Modernization Act. The article advances hybrid intelligence, structured human‐AI collaboration with protected institutional capacity, as a paradigm resisting both reform failure and algorithmic capture. International implementations operationalize it. The analysis contributes a stated‐versus‐operational‐goal analytic to the AI governance literature and identifies legislative grounding as a precondition for durable reform.