Fundamental Physics, Existential Risks and Human Futures

Adrian Kent

quant-phApr 29, 2026

Fundamental Physics, Existential Risks and Human Futures

Authors: Adrian Kent

Organizations: Centre for Quantum Information and Foundations, DAMTP, University of Cambridge and Perimeter Institute for Theoretical Physics · Centre for Quantum Information and Foundations, DAMTP, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom · Perimeter Institute for Theoretical Physics, 31 Caroline Street N, Waterloo, ON N2L 2Y5, Canada

Abstract

Over the past 25 years, I have been involved in some intriguing developments in the foundations of physics, exploring the quantum reality problem, the relationship between quantum theory and gravity and the interplay between consciousness and physical laws. These investigations make it plausible that we will find physics beyond quantum theory, potentially including both new evolution laws and new types of measurement. There is also a significant chance they could have potentially transformative impact on information processing and on the development of and our future with AI.

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Can AI Follow In Einstein's Footsteps?

Michael Shalyt, Nathan Regev, Marin Soljačić, Ido Kaminer

AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful, the most visible AI contributions to physics discovery appear to mirror the historical development of physics, but in reverse. Human discovery in physics progressed, in broad strokes, from ancient pattern prediction, through phenomenological laws such as Kepler's, to principle-based universal theories such as relativity and the Standard Model. On the AI side, prominent contributions to physics discovery point in the opposite direction: early milestones emphasized explicit equation-discovery methods, such as symbolic regression, whereas more recent frontier contributions are powerful predictors such as AlphaFold and GraphCast, which can be remarkably accurate yet do not provide clear theoretical understanding. If this trend continues, AI would become extraordinarily good at prediction but may struggle to ever propose its first serious contender to quantum gravity or other paradigm-level theories. We review the current landscape of AI for physics discovery and highlight a critical missing skill: the ability to pose the right questions or invent the right principles to guide the development of new theories and the tests to falsify them. This mode of discovery has driven many of the deepest advances since the 17th century, where symmetry, simplicity, and new mathematical frameworks guided theory construction before experimental tests. Equipping AI systems with such skills could move them from predicting within known frameworks to proposing the next paradigm-level discovery in physics.

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Abstract

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