AI as Amplified Intelligence: Compute Access as the New Displacement Divide

We apply the Displacement Framework to artificial intelligence access inequality, formalizing compute access as a new axis of displacement. Intelligence amplification through AI is modeled as displacement-rate reduction: those with high compute access accumulate less cognitive Phi over time. Eight formal propositions are derived covering: AI access as displacement-rate modifier, the compute-inequality ratchet (wealth buys compute, compute reduces displacement, reduced displacement preserves wealth), attention as the scarce resource captured by engagement-optimized AI, digital literacy as basin depth for AI return paths, algorithmic wrong attractors in recommendation systems, surveillance capitalism as systematic displacement of epistemic ground state, open-source AI as democratized return path, and universal compute access as displacement equity principle.

The paper argues that AI inequality is not merely economic but ontological: those without AI access accumulate displacement at systematically higher rates across cognitive, economic, and social domains.