Perspectives on thermodynamics for AI and AI safety

Abstract Artificial intelligence (AI) and human intelligence share deep thermodynamic foundations: both operate as systems that convert environmental energy and entropy into ordered, predictive structures under the constraints imposed by boundary conditions. Zentropy theory, an integration of quantum mechanics and statistical mechanics, provides a rigorous foundation for describing configurations and their contributions to free energy landscapes. Building on this foundation, the zentropy-enhanced neural network (ZENN) framework extends thermodynamic principles to complex data-driven systems by decomposing a system into independent configurations, each defined by its own internal energy and intrinsic entropy, which encode information across progressively finer scales down to quantum pure-state configurations, and integrating these contributions through a zentropy theory layer to provide a unified description of the system. This architecture not only improves predictive performance and stability across scientific benchmarks but also introduces two intrinsic mechanisms for AI safety: structural containment via configuration partitioning, and dynamic containment via zentropy-based regulation of driving forces and stability. By linking thermodynamic reasoning, statistics, and modern AI, ZENN offers a principled and physically grounded path toward safe, interpretable, and internally contained AI systems capable of scaling toward higher intelligence while remaining aligned with the laws that govern all viable systems and their evolutions. Zentropy theory thus represents an all-scale, pan-displinary framework with the configuration scale tailorable to desired granularity and precision.