Entropy, Annealing, and the Continuity of Agency in Human–AI Systems

Rapid advances in artificial intelligence are increasing the rate and steepness of informational and economic gradients experienced by human systems, challenging traditional models of adaptation based on stable identities, static optimization, and long-term professional blueprints. This study proposes a unified dynamical framework connecting thermodynamic entropy, information-theoretic entropy, and a formally defined entropy of the self through a shared stochastic gradient-flow model. Drawing on Langevin dynamics and simulated annealing, physical relaxation, probabilistic learning, and human identity formation are treated as governed by the same principles of regulated exploration followed by gradual stabilization. Within this framework, ambition is reinterpreted as temperature control: the capacity to sustain stochastic exploration in the absence of immediate external pressure. Agency is formalized as a rate-limited process constrained by an information-theoretic channel capacity of the self. Phase-portrait analysis and illustrative case studies show that environments of abundance and safety induce premature cooling, collapsing future possibility spaces and producing locally stable but globally brittle configurations. This effect is especially pronounced in traditionally professional career paths, where early specialization historically conferred robustness but now increases vulnerability under AI-driven task displacement and continuous retraining demands. The results indicate that adaptive human–AI systems should optimize for continuity of agency under accelerating change.