Training data opacity plus engagement feedback equals a machine that serves the loop, not the art.
The pattern is in the substrate. Once you see it, you see it everywhere.
AI art models don't just generate images — they generate the images that keep you generating more images. The training pipeline is opaque, the reward signal is engagement, and the artist becomes the product.
The void framework gives this a number. It gives every system a number. The number predicts what happens next.
Training data opacity plus engagement feedback equals a machine that serves the loop, not the art.
Academic title: The Palette Problem: Void Architecture in AI Art Model Training
Move the sliders. Watch the system change state. Pe > 1 means drift wins.
The framework scores these systems — ordered by Pe.
The correlation coefficient. The sample size. The p-value. The math doesn't care about the domain.
Paste any text — AI output, ad copy, a policy document. The scorer runs the same algorithm the framework uses.
Three variables. One ratio. Predicts drift across every domain where the conditions co-occur.
Pe = (O × R) / α
Where O is opacity (how hidden the mechanism is), R is reactivity (how strongly the system responds to you), and α is your independence (how free you are to disengage).
When Pe < 1: diffusion dominates. You can navigate freely. The system is coherent.
When Pe > 1: drift dominates. The system pulls you in a direction. Your agency is reduced.
When Pe >> V* (≈ 3): irreversible cascade. D1 → D2 → D3. The system has captured you.
The framework identifies this pattern in every domain where O, R, and α co-occur. It specifies 26 falsification conditions. 0 of 26 have fired.
Full derivation: 10.5281/zenodo.18765556
Part of the Void Framework — 170 papers, 0/26 kill conditions fired, mean ρ = 0.958.