Problem
Research questions and friction points this paper is trying to address.
Artificial Intelligence
Scientific Research
Returns to Science
Research Organization
General-Purpose Technologies
Artificial Intelligence in Science: Returns, Reallocation, and Reorganization
🤖 AI Summary
The impact of artificial intelligence (AI) on scientific production remains unclear. This study leverages data from research proposals funded by a major international agency, combining keyword extraction with large language model–based classification to identify the presence, types, and functional roles of AI in scientific research, while linking these to project budgets, team composition, and subsequent publication outcomes. Findings indicate that, in the short term, AI primarily influences scientific production not by directly enhancing efficiency but by reconfiguring research organization—evidenced by larger teams, greater personnel investment, and broader task scopes—aligning with theories of general-purpose technologies. Moreover, AI adoption shows a moderate positive association with high-impact outputs, particularly when large language models are integrated into research conception and experimental design, revealing significant collaborative potential in these early-stage activities.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI in science
large language models
scientific production
research reorganization
general-purpose technology
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