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 β†’ data β†’ model β†’ output β†’ money into a single admissible loop with revocation on demand.

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