Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI

Abstract

Frontier-AI governance today faces a problem structurally analogous to the one banking regulation faced pre-2008, and which post-2008 reforms (Basel III, Dodd-Frank) have since addressed. Two gaps recur: discovering a risk is not tantamount to acting on it, and individual-model review is unlike managing correlated build-up across the sector. Drawing on the Basel III framework and the U.S. financial-stability architecture, I propose a macro-prudential early warning and response system ("MEWRS") for internal frontier AI. These are systems deployed for labs' own internal research, testing, and production workflows, as distinct from externally released products. Layer A adapts the finder-coordinator-defender early-warning model to route structured reports on dual-use capabilities, autonomy indicators, and security compromises through a government clearinghouse to domain-specific defender working groups. Layer B calibrates operational controls via three quantitative buffer metrics, namely Effective Compute-at-Risk (ECAR), Cumulative Red-Team Hours (CRTH), and an Alignment Robustness Score (ARS), so that faster capability scaling automatically triggers stronger safeguards, analogously to how risk-weighted assets drive capital ratios under Basel III. I outline the reporting schema, map six Basel III mechanisms onto AI-governance analogues, identify seven failure modes with concrete mitigations, and sketch an exercise-based validation plan. MEWRS is designed to detect correlated risk build-ups across the frontier-AI sector and create pre-committed off-ramps before a cascade unfolds.