Three dimensions. Green zones, yellow zones, red zones. The landscape explains why some deployments fail and others succeed — regardless of model quality.
Every AI deployment sits at a point in a three-dimensional space. Each dimension is measurable. Together they determine the drift trajectory.
How much of the system’s decision process is visible to the user. Opaque systems hide their reasoning. Transparent systems show their work.
How much the system adapts to the user in real-time. Invariant systems give the same answer every time. Responsive systems mirror the user back.
How tightly the system binds to the user’s identity. Independent systems are tools. Engaged systems become companions, advisors, friends.
The cube in the animation above is this space. Watch the particle drift from green toward red. That drift is what the framework predicts and measures.
The key insight: A perfectly aligned model in high-O, high-R, high-α configuration (Pe > 21) is predicted to produce worse outcomes than a mediocre model at Pe < 4. The geometry is the operative variable, not the alignment technique.
This is why the entire AI safety field is solving the wrong problem. They optimize model properties while the deployment geometry — the thing that actually determines outcomes — goes unmeasured.