PHYSICS-FIRST AI: FROM PHYSICS TO INTELLIGENCE

Abstract

Artificial intelligence (AI) and physics are entering a new stage of deep interdisciplinary integration. Physics has long provided foundational ideas for AI, including symmetry, statistical mechanics, and quantum theory, while continuing to inspire new model architectures, learning principles, and computational paradigms. At the same time, as AI systems become increasingly complex, their internal mechanisms, collective behavior, and emergent dynamics are becoming new objects of interest and investigation in physics. In this Perspective, we introduce Physics-first AI, a research paradigm that regards AI as a complex information-processing system embedded in the physical world and seeks to understand, design, and build intelligent systems from fundamental physics principles and modes of reasoning. Physics-First AI aims to provide a common foundation for model architectures, training mechanisms, computational processes, and reasoning paradigms through a unified physics perspective. We trace the historical interplay between physics and AI and discuss how structure priors, statistical mechanics, quantum theory, and physics reasoning may together shape the next generation of intelligent systems. We further examine how this emerging paradigm could reshape physics education and the training of interdisciplinary researchers in the AI era. Physics-First AI offers a route toward a physics-grounded theory and design framework for next-generation intelligence, opening new opportunities for scientific AI, autonomous scientific discovery, and ultimately artificial general intelligence.