Abstract This work presents a conceptual framework for quantifying intelligence capacity in Electrodynamic Intelligence (EDI) systems composed of memristive cell arrays. We introduce two complementary models, a thermodynamic–electrostatic model and a memory–energy model, and unify them through ionic and electric field dynamics. Intelligence capacity is linked to physical characteristics of materials, network coupling, and energy constraints. A critical threshold is defined, marking the transition from passive memory storage to active cognitive function. When this threshold is surpassed, the system exhibits emergent, quantized intelligence, analogous to a phase transition. This threshold also corresponds to the onset of self-learning, where adaptive behavior arises intrinsically from the physics of the substrate which provides a foundation for engineering physically embodied intelligence in matter.