The paradigm-shift rhetoric meets a ground-level dissent: where Luo et al. 2026 call AI not merely a tool but a paradigm shift, Nelson et al. 2026's 32 interviews with AI-experienced manufacturing and materials researchers find AI mainly reducing the experiment-intensity of searching design spaces—likely facilitating sustaining innovation while possibly undercutting disruptive innovation—more another tool in the research and development toolkit than a radical transformation.
E51 states Luo et al. call AI 'not merely a tool, but a paradigm shift,' and E57/E59 state Nelson et al.'s 32 interviews found AI reduces the experiment-intensity of design-space search, likely facilitates sustaining while possibly undercutting disruptive innovation, making it 'more another tool in the research and development toolkit than a radical transformation.'
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 30 Oct, 04:34
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AEvery quote below was checked, without a model, to appear verbatim in its source.
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“at least thus far, AI/ML is more another tool in the research and development toolkit than a radical transformation of the innovation process”
Full-text passage · p. 2 · evidence E57
The answer to both questions is a qualified “yes.” Our results deepen and refine prior suggestions that AI may help to speed up science, but may raise epistemic risks, by clarifying the role of AI -enabled science in processes of technological change; identifying mechanisms and boundary conditions underlyin g both affordances and downsides of AI; and drawing out implications of these mechanisms for research strategy and policy. Our key, novel findings are as follows: 1) AI’s contributions to technological progress come by extending prior computational utilit ies in reducing the “experiment intensity” of search through technological design spaces; 2) this utility is likely to facilitate sustaining innovation within existing “technological paradigms” (Dosi, 1982; Dosi, 2007; Nelson, 2008b) , but may undercut disruptive innovation; and 3) even sustaining innovation facilitated by AI depends upon integration within a robust ecosystem of conventional experimentation, data collection, analysis, and theory development. Consequently, at least thus far, AI/ML is more another tool in the research and development toolkit than a radical transformation of the innovation process.
Passage read from arxiv.org, which may be a preprint rather than the published version.
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“results from 32 interviews with U.S. - based academic manufacturing and materials sciences researchers experienced with AI and machine learning (ML) techniques”
Full-text passage · p. 1 · evidence E59
1 Can Artificial Intelligence Accelerate Technological Progress? Researchers’ Perspectives on AI in Manufacturing and Materials Science John P. Nelson*,1,2, Olajide Olugbade2, Philip Shapira†,2,3,4, & Justin B. Biddle†,2,5 Abstract—Artificial intelligence (AI) raises expectations of substantial increases in rates of technological progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes. Accordingly, it remains unclear how and to what extent AI can accelerate innovation. To help to fill this gap, we explore and assess results from 32 interviews with U.S. - based academic manufacturing and materials sciences researchers experienced with AI and machine learning (ML) techniques. We found that AI was primarily used for modeling of materials and manufacturing processes, facilitating cheaper and more rapid search of design spaces for materials and manufacturing processes alike. Benefits included cost, time, and computation savings in technology development.
Passage read from arxiv.org, which may be a preprint rather than the published version.
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“AI is not merely a tool, but a paradigm shift”
Full-text passage · no page number · evidence E51
A new framework for AI-based model intercomparison is essential for rigorous benchmark performance. Finally, we contextualize these technical developments by discussing the usefulness and applicability of AI to society, including the improvement of multi-hazard early-warning systems and green energy production. We conclude by envisioning the future of AI agents for Earth science—autonomous, goal-oriented systems capable of designing and running experiments, generating and testing hypotheses, and learning dynamics from multisource data. This synthesis underscores that AI is not merely a tool, but a paradigm shift, which will significantly improve how we understand and adapt to a changing climate. INTRODUCTION Atmospheric and oceanic sciences have progressed through the combined supports of monitoring, theoretical analysis and numerical modeling over the past century. Intensified observations reveal major atmosphere and ocean phenomena—frontal systems, eddies, waves and large‑scale circulations—and make it possible to trace their complete life cycles.
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.