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Mapping AI Economic Complexity

Author

Listed:
  • Daeun Moon
  • Yeokyung Hwang
  • Junseok Hwang
  • Dawoon Jeong

Abstract

Green economic complexity provides a generalizable framework for examining countries' productive capabilities in a defined product set. We apply this framework to AI-enabling goods within the full product space, linking current specialization with adjacent diversification opportunities. Using BACI exports for 2007-2023 and 103 AI-enabling goods, we measure complexity-weighted specialization (AECI), product-level adjacent opportunities (AIAP), and average complexity-weighted relatedness of remaining candidates (AECP). In 2023, Japan leads AECI, while China leads AECP; portfolio breadth accounts for much of the variation in raw AECI. Initial raw potential is positively associated with subsequent changes in the AI-enabling export share, but its associations with changes in AECI and specialization counts are not statistically significant at the 5% level. Our contribution is a trade-based assessment of AI-enabling productive capabilities and related opportunities. The results and public dashboard provide a preliminary complement to publication and patent indicators, not a comprehensive measure of national AI performance or a validated forecast of diversification.

Suggested Citation

  • Daeun Moon & Yeokyung Hwang & Junseok Hwang & Dawoon Jeong, 2026. "Mapping AI Economic Complexity," Papers 2609.17200, arXiv.org.
    • Handle: RePEc:arx:papers:2609.17200
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    4. Mealy, Penny & Teytelboym, Alexander, 2022. "Economic complexity and the green economy," Research Policy, Elsevier, vol. 51(8).
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