The Entropic Lattice Hypothesis

Could AI Learn Through Controlled Thermodynamic Drift Rather Than Optimization?

Abstract

Current artificial intelligence systems primarily rely on optimization techniques, gradient descent, back-propagation, and reinforcement learning, based on minimizing error or maximizing expected reward. These techniques assume the existence of a stable equilibrium or target function. However, biological cognition emerges not from static optimization, but from dynamic disequilibria maintained by continuous energy flow and entropy production. This article introduces the Entropic Lattice Hypothesis, a speculative framework suggesting that advanced AI systems could be built not as static optimizers, but as thermodynamically active lattices, dynamical structures whose “learning” occurs via controlled drift within a non-equilibrium energy landscape. Rather than minimizing error, such systems evolve to sustain specific entropy gradients, enabling them to generate and refine knowledge structures adaptively, without convergence. This reframes intelligence as a thermodynamic process, with profound implications for AI architecture, self-modeling, and autonomy.