The Second Wave: From Neuroelectrodynamics to Electrodynamic Intelligence

Current AI systems, particularly large language models (LLMs), excel at statistical reasoning and pattern recognition however, they remain symbolic and disembodied, relying on code, not physical processes. We propose a fundamentally different paradigm: NeuroElectroDynamics (NED), a non-von Neumann, physically grounded architecture where cognition emerges from realtime interactions of charge, field, and material structure. Computation flows through memristive, axonlike channels; learning arises from field-driven adaptation and ion dynamics, not backpropagation. Synaptic modulation is analog, using iontronic or ferroelectric junctions. Unlike digital systems, NED embeds memory, semantics, and computation in the same evolving substrate. A brain-scale system with ~10¹¹ neurons may achieve ~312.5 TB dynamic memory at <60 W, potentially scaling to 1.75 PB. This defines a new class of Electrodynamic Intelligence (EDI), self-organizing, embodied, and ultraefficient. EDI systems represent a shift from simulating intelligence to physically instantiating it, suggesting how advanced intelligences may arise naturally or be engineered in other planetary environments.