Dynamic effects of artificial intelligence on R&D and innovation

As AI for Science (AI4S) emerges as a new frontier in global technological competition, clarifying the micro-mechanisms of how Artificial Intelligence (AI) reshapes the R&D innovation paradigm has become a critical issue. This paper aims to reveal the dynamic effects and complex mechanisms of AI-driven R&D innovation. This study constructs a “Technology-Task-Ability” (TTA) analytical framework to argue that AI profoundly reshapes the R&D innovation ecosystem by reorganizing, rather than simply accelerating, R&D tasks. Based on patent data from the global carbon nanotube field, this paper employs a combination of methods, including technology co-occurrence network analysis, the time-varying parameter vector autoregression model, impulse response analysis, and wavelet coherence analysis, to empirically examine the dynamic evolution of the technology network before and after the integration of AI. The findings indicate that: first, the integration of AI significantly enhances the overall connectivity of the technology network, with AI itself becoming a key technology spillover source and playing the role of a “leader” in the innovation network. Second, by automating tasks such as materials characterization, AI causes traditional analytical technologies, such as materials testing and analysis, to shift from a “leader” to a “follower” role within the network, providing direct empirical evidence for the task reorganization mechanism proposed by the TTA framework. Third, impulse response and wavelet coherence analyses further confirm that AI has a significant and dynamic positive driving effect on the development of carbon nanotube technology. Furthermore, by analyzing the interactive logic between AI and new materials R&D innovation, the paper proposes principles of “stratified governance” and “segmented governance” for the R&D innovation ecosystem.