Can Artificial Intelligence Accelerate Technological Progress? Researchers' Perspectives on AI in Manufacturing and Materials Science

2025-11-17

AI Summary

This study investigates the practical efficacy and fundamental limitations of AI in accelerating technological advancement within manufacturing and materials science. Drawing on in-depth interviews with 32 U.S.-based academic researchers, and employing thematic analysis coupled with technical practice evaluation, the work systematically examines current applications, constraints, and human–AI collaboration mechanisms of AI/ML in materials modeling, process design, and design-space exploration. Results indicate that AI substantially reduces R&D cycle time and cost but exhibits diminished reliability in data-sparse regimes—necessitating synergistic integration with domain expertise and first-principles simulations. Moreover, while AI excels at enabling incremental innovation, it may inadvertently impede paradigm-shifting theoretical breakthroughs. Consequently, the paper proposes a “human–AI co-enhanced innovation” paradigm, positioning AI as an augmentative tool rather than a replacement for human judgment, thereby offering a methodological framework for achieving holistic technological leapfrogging.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Artificial intelligence (AI) raises expectations of substantial increases in rates of technological and scientific 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 report results from 32 interviews with U.S.-based academic manufacturing and materials sciences researchers experienced with AI and machine learning (ML) techniques. Interviewees primarily used AI for modeling of materials and manufacturing processes, facilitating cheaper and more rapid search of design spaces for materials and manufacturing processes alike. They report benefits including cost, time, and computation savings in technology development. However, interviewees also report that AI/ML tools are unreliable outside design spaces for which dense data are already available; that they require skilled and judicious application in tandem with older research techniques; and that AI/ML tools may detrimentally circumvent opportunities for disruptive theoretical advancement. Based on these results, we suggest there is reason for optimism about acceleration in sustaining innovations through the use of to AI/ML; but that support for conventional empirical, computational, and theoretical research is required to maintain the likelihood of further major advances in manufacturing and materials science.

Problem

• Investigating AI's potential to accelerate technological innovation processes
• Exploring AI applications in manufacturing and materials science research
• Assessing limitations and reliability of AI tools in scientific advancement

Innovation

• AI models materials and manufacturing processes
• AI enables cheaper and faster design space exploration
• AI requires integration with traditional research methods