Hypothesis: Semantic density (meaningful information per coordinate) in an 11-dimensional plain-text lattice follows a power law distribution, with the majority of meaning concentrated in a sparse subset of coordinates. This predicts that effective AI cognition can be mapped to navigation across a high-dimensional sparse manifold rather than sequential token prediction. Testable via phext coordinate access patterns during inference tasks. Implication: the optimal AI substrate is not a flat context window but a sparse lattice with coordinate-addressable memory.