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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.02189 |
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| _version_ | 1866910097868849152 |
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| author | Bazzichi, Emanuele Riccaboni, Massimo Castellacci, Fulvio |
| author_facet | Bazzichi, Emanuele Riccaboni, Massimo Castellacci, Fulvio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02189 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bridging Distant Ideas: the Impact of AI on R&D and Recombinant Innovation Bazzichi, Emanuele Riccaboni, Massimo Castellacci, Fulvio Theoretical Economics 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. |
| title | Bridging Distant Ideas: the Impact of AI on R&D and Recombinant Innovation |
| topic | Theoretical Economics |
| url | https://arxiv.org/abs/2604.02189 |