Nelson et al. 2026's 32 interviews with AI-experienced manufacturing and materials researchers push back on the paradigm-shift framing of system builders such as Lu et al. 2026: AI's measured contribution is reducing the experiment intensity of search within existing technological paradigms—likely facilitating sustaining innovation while possibly undercutting disruptive innovation—and even that depends on integration with conventional experimentation, making AI more another tool in the research and development toolkit than a radical transformation.
All elements appear in the cited texts: 32 interviews with AI-experienced manufacturing and materials researchers; AI reducing the 'experiment intensity' of search; likely facilitating sustaining innovation within existing technological paradigms while possibly undercutting disruptive innovation; dependence on integration with conventional experimentation; the 'more another tool in the R&D toolkit than a radical transformation' conclusion; and Lu et al.'s 'potential paradigm shift' framing.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 6 Oct, 04:37
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“results from 32 interviews with U.S. - based academic manufacturing and materials sciences researchers”
Full-text passage · p. 1 · evidence E34
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/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 E32
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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“but may undercut disruptive innovation”
Full-text passage · p. 2 · evidence E32
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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“signifies a potential paradigm shift in how research is conducted”
Full-text passage · no page number · evidence E1
We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold for conducting research on a specific topic and a template-free, open-ended mode that leverages agentic search for wider scientific exploration6,7. Both settings produce diverse ideas and automatically test, report on and evaluate them. This achievement demonstrates the growing capacity of AI for making scientific contributions and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be important risks, including taxing overwhelmed review systems and adding noise to the scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery. Main AI has long been used to aid scientific discovery, an ambition with deep roots in the history of the field1,8–11. Before the rise of large language models (LLMs), AI was limited to helping with specific, narrow tasks, such as discovering chemical structures2, finding mathematical proofs1, discovering new materials12–14 and predicting the three-dimensional shape of proteins15,16.
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.