A finite-machine inference model uses cost geometry to quantify belief transitions, combining optimal transport with Fisher information. The framework reveals a wall, honesty, and rigidity in belief spaces, with the Gaussian belief achieving maximal hyperbolic curvature. Thermodynamics sets the cost unit, and the geometric floor of precision diverges at certainty, with the value -1/4 representing a key scale.