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Artificial Intelligence in Science: Returns, Reallocation, and Reorganization

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  • Moh Hosseinioun
  • Brian Uzzi
  • Henrik Barslund Fosse

Abstract

Investment in artificial intelligence (AI) has grown rapidly, yet its returns to scientific research remain poorly understood. We study how AI reshapes the production of science using a comprehensive dataset of research proposals submitted to a large international funding agency, including both funded and unfunded projects. Combining keyword extraction with large language model classification, we identify the presence, type, and functional role of AI within each proposal and link these measures to detailed budget allocations, team structure, and subsequent publication outcomes. We find that, in the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail. Instead, its primary effects arise in the organization of research: AI-enabled projects reallocate resources toward human capital, involve larger teams, and undertake a broader set of tasks. These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies. Task-level analyses further show that activities expanded in AI-enabled projects, particularly ideation and experimentation, are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature.

Suggested Citation

  • Moh Hosseinioun & Brian Uzzi & Henrik Barslund Fosse, 2026. "Artificial Intelligence in Science: Returns, Reallocation, and Reorganization," Papers 2603.27956, arXiv.org.
    • Handle: RePEc:arx:papers:2603.27956
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    References listed on IDEAS

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    1. Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2022. "Artificial intelligence in science: An emerging general method of invention," Research Policy, Elsevier, vol. 51(10).
    2. Stefano Bianchini & Moritz Müller & Pierre Pelletier, 2022. "Artificial intelligence in science: An emerging general method of invention," Post-Print hal-03958025, HAL.
    3. Ryan Hill & Yian Yin & Carolyn Stein & Xizhao Wang & Dashun Wang & Benjamin F. Jones, 2025. "The pivot penalty in research," Nature, Nature, vol. 642(8069), pages 999-1006, June.
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