AI Needs Physics More Than Physics Needs AI

TL;DR

Despite AI's hype, its real-world impact remains limited, and physics can significantly contribute to advancing AI through scientific insights and new computational paradigms.

Contribution

The paper argues that physics offers more valuable insights and opportunities for AI development than current AI architectures provide for physics.

Findings

Current AI models depend on meaningless parameters and lack scientific understanding.

Physics can enhance AI through quantum computing and analogue methods.

AI's impact outside specialized fields remains modest.

Abstract

Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning.