Tensor of Computational Mass: Infrastructure Density, Information-Thermodynamic Potential, and the Mechanics of Algorithmic Gravitation

Contemporary interface design and generative algorithmic systems operate under the ideological illusion of absolute weightlessness and friction-free interaction. In this work, I dismantle this myth by introducing a rigorous physical and field-theoretic framework for the Tensor of Computational Mass (M_comp^{μν}). Synthesizing non-equilibrium thermodynamics, spectral graph theory, and the Free Energy Principle (FEP), I demonstrate that the physical infrastructure of modern digital ecosystems—comprising distributed neural network weight parameters, continuous telemetry lookups, and server-side pre-emption arrays—constitutes a measurable, dense computational mass. By coupling M_comp^{μν} with the information-thermodynamic potential (Ψ_DDM), I prove how infrastructural density deforms the variational phase space of cognitive agents, flattening ecological entropy gradients and inducing a catastrophic collapse of structural plasticity (ϑ → ϑ_min). Furthermore, I establish the mathematical mapping between server-side compute density and the effective "gravitational" pull that traps cognitive systems in pathological attractor basins, proposing the architectural countermeasure of Strategic Friction to ensure cognitive security.