Paper
The "Gold Rush" in AI and Robotics Patenting Activity. Do innovation systems have a role?
Authors
Giovanni Guidetti, Riccardo Leoncini, Mariele Macaluso
Abstract
This paper studies patenting trends in artificial intelligence (AI) and robotics from 1980 to 2019. We introduce a novel distinction between traditional robotics and robotics embedding AI functionalities. Using patent data and a time-series econometric approach, we examine whether these domains share common long-run dynamics and how their trajectories differ across major innovation systems. Three main findings emerge. First, patenting activity in core AI, traditional robots, and AI-enhanced robots follows distinct trajectories, with AI-enhanced robotics accelerating sharply from the early 2010s. Second, structural breaks occur predominantly after 2010, indicating an acceleration in the technological dynamics associated with AI diffusion. Third, long-run relationships between AI and robotics vary systematically across countries: China exhibits strong integration between core AI and AI-enhanced robots, alongside a substantial contribution from universities and the public sector, whereas the United States displays a more market-oriented patenting structure and weaker integration between AI and robots. Europe, Japan, and South Korea show intermediate patterns.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.05034v1</id>\n <title>The \"Gold Rush\" in AI and Robotics Patenting Activity. Do innovation systems have a role?</title>\n <updated>2026-03-05T10:31:33Z</updated>\n <link href='https://arxiv.org/abs/2603.05034v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.05034v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>This paper studies patenting trends in artificial intelligence (AI) and robotics from 1980 to 2019. We introduce a novel distinction between traditional robotics and robotics embedding AI functionalities. Using patent data and a time-series econometric approach, we examine whether these domains share common long-run dynamics and how their trajectories differ across major innovation systems. Three main findings emerge. First, patenting activity in core AI, traditional robots, and AI-enhanced robots follows distinct trajectories, with AI-enhanced robotics accelerating sharply from the early 2010s. Second, structural breaks occur predominantly after 2010, indicating an acceleration in the technological dynamics associated with AI diffusion. Third, long-run relationships between AI and robotics vary systematically across countries: China exhibits strong integration between core AI and AI-enhanced robots, alongside a substantial contribution from universities and the public sector, whereas the United States displays a more market-oriented patenting structure and weaker integration between AI and robots. Europe, Japan, and South Korea show intermediate patterns.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='econ.GN'/>\n <published>2026-03-05T10:31:33Z</published>\n <arxiv:comment>Revise and Resubmit at Technological Forecasting & Social Change</arxiv:comment>\n <arxiv:primary_category term='econ.GN'/>\n <author>\n <name>Giovanni Guidetti</name>\n </author>\n <author>\n <name>Riccardo Leoncini</name>\n </author>\n <author>\n <name>Mariele Macaluso</name>\n </author>\n </entry>"
}