Can artificial intelligence accelerate technological progress? Researchers' perspectives on AI in manufacturing and materials science

Checked 5 Nov 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

The use of artificial intelligence (AI) in research 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 research processes. It therefore remains unclear how and under what conditions AI-enabled research can accelerate technological progress and innovation, and what limitations might apply. To address this gap, we analyzed results from 32 interviews with U.S.-based academic manufacturing and materials science researchers experienced with AI, including machine learning (ML) techniques (AI/ML). We found that, much like older computational tools, AI/ML 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 computational savings in technology development. However, AI/ML tools were unreliable outside design spaces for which dense data were already available; they required skilled and judicious application in tandem with older research techniques; and concerns were raised about the potential to detrimentally circumvent opportunities for disruptive theoretical advancement. Based on these results, we suggest there is reason for optimism about acceleration that sustains established technological paradigms through the use of AI/ML. However, support for conventional empirical, computational, and theoretical research is required to enhance the likelihood of further disruptive advances in manufacturing and materials.

• 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. Trace →
• Wu et al. 2026 argue that the barriers to agentic AI in biomedical laboratories are organizational rather than model-centric—technical fluency, standard operating procedures and data-governance rules that more capable models alone will not remove—and they built the open-source Murmurent layer beneath the agents to supply them. Nelson et al. 2026's interview study reaches the same conclusion from the innovation side: AI's benefits depend on integration within a robust ecosystem of conventional experimentation. Trace →
• 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. Trace →

• AI for science and agentic research