The AI-Enabled Scientific Frontier

Abstract:As artificial intelligence's capabilities improve, it is increasingly viewed as a general scientific method. But how true are these claims? Does AI outperform all techniques, or only some, and how is this changing? To assess the claims, we assemble a corpus of 2,507 head-to-head comparisons between AI and other scientific analysis techniques across 27 scientific disciplines from papers published between 2000 and early 2025. We find a profound dichotomy. Relative to traditional statistics, AI often outperforms, but at a significantly higher computational cost. But there are also nearly a quarter of cases where AI is both more expensive and performs worse than traditional statistical techniques and this fraction has been stable for a decade. Relative to scientific computing, AI often underperforms, but at lower computational cost. This has begun to change: since 2020, AI's performance against scientific computing has notably strengthened and it now outperforms on more than half of comparisons. These patterns suggest that AI is therefore not a universal replacement for existing methods, but rather a valuable -- and improving -- part of a new AI-enabled scientific frontier.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF); General Economics (econ.GN)

arXiv:2609.16258 [cs.AI]

(or arXiv:2609.16258v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.16258 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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