Bridging Distant Ideas: the Impact of AI on R&D and Recombinant Innovation
Emanuele Bazzichi, Massimo Riccaboni, Fulvio Castellacci
TLDR
AI's impact on R&D innovation is complex, encouraging distant recombinations but risking incrementalism and originality loss with full automation.
Key contributions
- Higher AI productivity encourages distant knowledge recombinations if direct facilitation dominates competition.
- Increasing AI automation in R&D has a non-monotonic effect on innovation radicalness.
- Beyond a human-AI complementarity threshold, firms shift from radical to incremental innovations.
- Full AI automation could collapse recombination distance, undermining knowledge creation.
Why it matters
This paper models how AI influences firms' R&D strategies, revealing its dual impact on innovation. It highlights the risk of over-reliance on AI leading to less original, more incremental research, which is crucial for future AI policy and R&D management.
Original Abstract
We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.