What "AI governance" actually is (in practice) It's a control stack that makes digital decisions admissible, retractable, and steerable by the people who write rules. In a low Gross Consent Product world, the goal is order at lower enforcement cost. Core objectives: 1. Attribution & custody: Who touched what data/model/decision, when, under which consent. 2. Revocability: Ability to halt, rollback, or re-score outputs post-hoc. 3. Provenance: Bind content to signed origin; devalue the unsigned. 4. Identity binding: Tie users, developers, data, models, and money to verifiable IDs. 5. Chokepoints: Put rules where few actors can say "no" (chips, clouds, payments, app stores, ISPs). 6. Harmonization: Synchronize standards across blocs so one change moves the world. Call it Policy-as-Parameters: the knobs are legal words (attest, trace, revoke, retain) baked into software defaults. AI governance isn't about ideals; it’s about cheap stability. The stack will bind identity → data → model → output → money into a single admissible loop with revocation on demand. View quoted note →