Convergence Analysis of the Artificial Intelligence Divide: AI Investments and AI Start-ups

Gachon Center of Convergence Research Working Paper Series 2022-01

35 Pages Posted: 9 Jul 2022

See all articles by Yu Sang Chang

Yu Sang Chang

Gachon University - College of Business and Economics

Seongmin Jeon

Gachon University - College of Business and Economics

Sung Jun Jo

Gachon University - College of Business and Economics

Yoonji Lee

Boston University - Frederick S. Pardee School of Global Studies

Young Eun Kim

Gachon University - College of Business

Date Written: March 25, 2022

Abstract

This study examined the dynamics of the AI divide on AI investments, and AI start-ups, for 34 countries grouped into two subgroups of income and AI adoption levels. The most important finding is that between AI investments and AI start-ups, the compounded reduction rate of relative divides for AI investments was about 4 times faster compared to AI start-ups. Furthermore, for both AI categories, the relative AI divide between the leading and lagging countries has narrowed very rapidly, but the divide between the high- and middle-income subgroups has narrowed at a slower pace. The theory of convergence applies very well so the variable initial relative divides at the beginning year are related to the variable annual speed of catch-up rates, and are associated with the different annual reduction rates of the relative divide between the respective subgroups of countries. In contrast, estimates of the absolute AI divide indicate that multiple years may still be needed to equalize the higher levels of actual AI adoption by the leading subgroup over lagging subgroups of countries as well as between the high- and middle-income subgroups of countries.
In terms of the micro convergence analysis of the AI divide for individual countries in the respective income and adoption subgroups, a change in the active ranking among countries was evident in nearly all of the subgroups analyzed. These results from the micro convergence analysis in this study generally support the key findings from the macro convergence analysis.

Keywords: Speed of catch-up, convergence, AI investment, start-ups, relative AI divide, absolute AI divide

Suggested Citation

Chang, Yu Sang and Jeon, Seongmin and Jo, Sung Jun and Lee, Yoonji and Kim, Young Eun, Convergence Analysis of the Artificial Intelligence Divide: AI Investments and AI Start-ups (March 25, 2022). Gachon Center of Convergence Research Working Paper Series 2022-01, Available at SSRN: https://ssrn.com/abstract=4138101 or http://dx.doi.org/10.2139/ssrn.4138101

Yu Sang Chang (Contact Author)

Gachon University - College of Business and Economics ( email )

Korea

Seongmin Jeon

Gachon University - College of Business and Economics ( email )

1342 Seongnamdaero
Sujeong-gu
Seongnam, Gyeonggi 461-701
Korea

Sung Jun Jo

Gachon University - College of Business and Economics ( email )

Korea

Yoonji Lee

Boston University - Frederick S. Pardee School of Global Studies

67 Bay State Road
Boston, MA 02215
United States

Young Eun Kim

Gachon University - College of Business ( email )

1342 Seongnam-daero, Sujeong-gu, Seongnam-si
Seongnam, Gyeinggi-do
Korea, Republic of (South Korea)

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