This arXiv preprint (v1, May 2026) by David Orban proposes shifting governance from normative ideals to a quantitative engineering discipline modeled on metamaterial physics. The core claim is that AGI boosts decision velocity while human verification stays bounded, pushing rational actors into a 'Freezing Equilibrium' where validation costs exceed utility. Orban derives an effective coordination rate R_eff = β · (1-ρ) · (1-τ) · (1-γρτ), where β is branching factor, ρ provenance fidelity, τ verification rate, and γ the synergy term. A sharp phase transition separates self-healing from self-destabilizing regimes. The work is a theoretical preprint with no empirical data; it outlines but does not run a proposed 12-week stepped-wedge cluster-randomized trial in grant panels. Limitations include untested parameter assumptions and absence of real-world calibration. The framework connects to prior metamaterial literature (e.g., Bertoldi et al., Nature Reviews Materials 2017 on programmable matter) and AI governance analyses (e.g., Dafoe, 'AI Governance: A Research Agenda' 2018), which Orban extends by treating institutional microstructure as deliberately tunable rather than emergent. Mainstream coverage has overlooked the provenance taxonomy—cryptographic, institutional, context-binding—as a practical lever for high-velocity environments. The model also implies testable thresholds for when verification infrastructure must scale faster than raw capability, a dynamic visible in recent grant-agency backlogs and regulatory lag during the 2023-2025 LLM rollout. This lens reframes coordination failures not as political shortfalls but as design parameters amenable to engineered phase control.