Speed of Catch-Up and Convergence of the Artificial Intelligence Divide: AI Investment, Robotic, Start-Ups, and Patents

Seongmin Jeon, Yu Sang Chang, Sung Jun Jo, Tinovimbanashe Madukuand and Young Eun Kim

Journal of Global Information Technology Management, 2024, vol. 27, issue 1, 63-85

Abstract: With the recent acceleration in AI adoption and significant investments being made by many countries, the concern regarding the AI divide is becoming increasingly prevalent. We attempt to determine whether countries that have fallen behind in AI development can catch up to those leading the charge over time. Specifically, this study examines the dynamics of the AI divide on AI investments, robotics, AI start-ups, and AI patents in 34–57 countries, analyzing the data sets from Stanford AI Index Report, World Robotics Report, and the OECD Statistics website. We have used the methods of the relative AI divide (RAID) and absolute AI divide (AAID) for the two categories of income and adoption levels. Also, we have tested both γ convergence for the AI divide and σ convergence for the divide over time. We find that for all four AI categories, the relative AI divide between the leading and lagging countries has narrowed rapidly and the divide between the high- and middle-income subgroups has also narrowed at a slower pace. In terms of the micro convergence analysis of the AI divides for individual countries within the respective income and adoption subgroups, a change in the active ranking among countries was evident in nearly all of the subgroups. This study contributes to the literature by presenting not only the absolute AI divide but also the relative AI divide clarifying the changing trends systematically.

Date: 2024
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DOI: 10.1080/1097198X.2023.2297636

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