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About this paper
Gabriel Manso, Emma Fu, Neil Thompson
A cross-disciplinary analysis of 2,507 method comparisons from 268 papers maps where AI fits relative to traditional statistics and scientific computing. AI usually improved accuracy over statistical methods at roughly tenfold higher cost, while neural surrogates often replaced scientific simulations with comparable or better performance at about 614-fold lower cost; outcomes still varied widely by domain and task.
Key insights
- Against traditional statistics, AI most often delivered higher performance at higher computational cost; 59.6% of comparisons followed this pattern, while 23.6% were both more expensive and worse.
- Against scientific-computing methods, AI more often reduced cost than improved accuracy: 41.1% of cases were win-win and 44.8% prioritized efficiency.
- AI’s position is conditional rather than universal, with outcomes differing across disciplines and improving over time especially for replacements of scientific-computing pipelines.
Summary by alphaXiv.
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