AI changes but does not replace traditional scientific methods
The AI-Enabled Scientific Frontier
Artificial IntelligenceMachine LearningPerformance
Summary
Sometimes people say AI can do all kinds of science tasks better than old methods. The authors collected many studies comparing AI with traditional techniques in many science fields to see if this is true. They found AI often does better than traditional statistics but uses more computing power, and sometimes AI does worse and costs more. AI used to do worse than scientific computing but since 2020 it’s getting better and now beats it more than half the time. This shows AI helps science grow but doesn’t fully replace older methods yet.
What this means in practice
- •For data analysis teams: Choose AI methods over traditional statistics when higher accuracy justifies greater computing resources in scientific data analysis.
- •For scientific software developers: Integrate AI techniques increasingly to outperform traditional scientific computing methods, especially in applications developed since 2020.
Authors
Gabriel Manso, Emma Fu, Neil Thompson
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.