Mapping AI Economic Complexity

@misc{moon2026mapping,
  title = {{Mapping AI Economic Complexity}},
  author = {Daeun Moon and Yeokyung Hwang and Junseok Hwang and Dawoon Jeong},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.17200},
  eprint = {2609.17200},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.17200},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - Mapping AI Economic Complexity
AU  - Daeun Moon
AU  - Yeokyung Hwang
AU  - Junseok Hwang
AU  - Dawoon Jeong
PY  - 2026
DA  - 2026/09/15
PB  - arXiv
DO  - 10.48550/arxiv.2609.17200
UR  - https://arxiv.org/abs/2609.17200
AB  - 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.
ER  -