Beyond the Thermal Wall: The Cognitive Transition Law (CTL) as a Roadmap for Reversible and Neuromorphic AI Sustainability

The contemporary trajectory of artificial intelligence is increasingly constrained not by algorithmic ingenuity but by thermodynamic reality. Over the last decade, progress in AI has been driven primarily by scale: larger models, denser hardware, and rapidly increasing energy consumption. This paper introduces the Cognitive Transition Law (CTL), a thermodynamically grounded conceptual framework describing a transition from energy-dominated computation toward structure-embedded cognition. The central argument is that cognitive efficiency emerges not merely from minimizing energy per operation, but from progressively internalizing information within stable physical structures. As structural internalization increases, inference shifts from an energy-intensive computational process toward architecture-guided cognition. The CTL is proposed as a falsifiable design principle aligning artificial intelligence development with the thermodynamic limits of computation and the structural efficiency observed in biological cognition.