Oil & Water? Diffusion of AI Within and Across Scientific Fields
Not on the map. Why?
Abstract (arXiv)
This study empirically investigates claims of the increasing ubiquity of artificial intelligence (AI) within roughly 80 million research publications across 20 diverse scientific fields, by examining the change in scholarly engagement with AI from 1985 through 2022. We observe exponential growth, with AI-engaged publications increasing approximately thirteenfold (13x) across all fields, suggesting a dramatic shift from niche to mainstream. Moreover, we provide the first empirical examination of the distribution of AI-engaged publications across publication venues within individual fields, with results that reveal a broadening of AI engagement within disciplines. While this broadening engagement suggests a move toward greater disciplinary integration in every field, increased ubiquity is associated with a semantic tension between AI-engaged research and more traditional disciplinary research. Through an analysis of tens of millions of document embeddings, we observe a complex interplay between AI-engaged and non-AI-engaged research within and across fields, suggesting that increasing ubiquity is something of an oil-and-water phenomenon -- AI-engaged work is spreading out over fields, but not mixing well with non-AI-engaged work.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 24 May 2024.
Builds on
12 of 31 references.
References · 12
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Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024Confabulation: The Surprising Value of Large Language Model Hallucinationscited by 42
- 2025Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Paperscited by 5
- 2024Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Sciencecited by 3
- 2025Toward Effective AI Governance: A Review of Principlescited by 2
- 2026An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Processcited by 1
- 2026Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditionscited by 0
- 2026An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process13 Mar 2026
- 2026Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditions14 Jan 2026
- 2025Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers21 Nov 2025
- 2025Toward Effective AI Governance: A Review of Principles29 May 2025
- 2024Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science27 Nov 2024
- 2024Confabulation: The Surprising Value of Large Language Model Hallucinations6 Jun 2024