AI governance doesn’t fail at the model. It fails where “good” policies collide and nothing in the architecture says who wins. @ChrisHood’s weights-and-vetoes framing exposes the real design question: not “what can the AI do?” but “who owns the override when security, ethics, and business value disagree?” In most organizations, that answer is still implicit and political, not explicit and engineered. A practical way forward: - Treat agents as heteronomous by design: every critical action traces back to human-owned policies and named owners per dimension (security, ethics, legal, commercial). - Make vetoes and weights auditable: every veto explainable in business language, every weight change with an approver and trail. - Separate layers: a normative layer (non‑negotiables and trade‑offs), a computational layer (scores and allow/deny/defer), and a political layer (the only group allowed to change the first two). We should stop saying “the AI decided.” The agent executed; the governance stack decided; specific humans designed and still own that stack. The real maturity test is no longer “do you have AI governance?” but “is your override architecture legible, negotiable, and clearly owned?” #AI #AIGovernance #AIEthics linkedin.com/pulse/weights-v…