PERSPECTIVE article
Front. Phys.
Sec. Complex Physical Systems
This article is part of the Research TopicAI for Physics and Physics for AIView all 4 articles
AI Needs Physics More Than Physics Needs AI
Provisionally accepted
Peter V Coveney1,2*
Roger Highfield3- 1University College London, London, United Kingdom
- 2Universiteit van Amsterdam, Amsterdam, Netherlands
- 3Science Museum, London, United Kingdom
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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.
Keywords: artificial intelligence, Generative AI, machine learning, Physics-based machine learning, Spurious correlations
Received: 24 Oct 2025; Accepted: 16 Dec 2025.
Copyright: © 2025 Coveney and Highfield. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Peter V Coveney
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.