More is Different in AI - More is the Same in Physics

More is Different in AI - More is the Same in Physics

A new paper by Prof. Ido Kanter explores the relevance of Philip W. Anderson’s influential concept “More is Different” to modern artificial intelligence. The work compares macroscopic physical systems with machine-learning architectures through the phenomenon of spontaneous symmetry breaking, a central concept in statistical physics.

The analysis shows that, from an information perspective, physical systems often exhibit a principle that can be described as “More is the Same”: the macroscopic state cannot necessarily be inferred from a small number of microscopic components. In contrast, AI models demonstrate the “More is Different” principle. Even a single node in a trained neural network carries information about the global task, while cooperation among multiple nodes produces computational capabilities that exceed the sum of their individual contributions.

These findings highlight a fundamental difference between physical systems with fixed interactions and adaptive AI architectures that evolve through learning. 

📄 Full paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6376578