Power Without Ground: A Ground Integrity Framework for Human and Hybrid Governance Systems

Public anxiety about artificial intelligence often misidentifies the underlying governance risk. The most dangerous systems are not necessarily the most intelligent ones, but the ones able to classify people, route decisions, and impose consequences through routinized processes at scale without a legible path back to source evidence, accountable authority, and rapid correction. This commentary introduces \emph{ground integrity} as a framework for analyzing that broader risk across human, automated, and hybrid decision systems. Ground integrity asks whether a coercive effect on a person can be traced through a continuous, externally testable chain from source artifact to enforcement outcome. The paper argues that governance failures commonly arise not from intelligence alone, but from scale without verification: missing provenance, hidden delegation, cross-system identity drift, off-ledger routing, and correction pathways that exist only formally. To make these failures operationally visible, the commentary presents a source-to-enforcement chain, a shadow-stack and money-stack model, a compact family of historical and contemporary test beds, and ten ground tests for continuity, bonding, traceability, and fault-clearing. The contribution is a policy-facing framework that shifts the debate from fear of AI as a novelty to a more durable concern with ungrounded power, wherever it appears.