{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17200",
  "author_total": 4,
  "authors": [
    {
      "name": "Daeun Moon"
    },
    {
      "name": "Yeokyung Hwang"
    },
    {
      "name": "Junseok Hwang"
    },
    {
      "name": "Dawoon Jeong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3fz3d14c3zdb1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17200",
  "fulltext_url": null,
  "id": "3fz3d14c3zdb1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17200",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "econ.GN"
    ],
    [
      "arxiv",
      "physics.soc-ph"
    ],
    [
      "arxiv",
      "q-fin.EC"
    ],
    [
      "openalex",
      "2002|Economic and Technological Innovation"
    ]
  ],
  "openalex": "W7213409707",
  "pdf_url": "https://arxiv.org/pdf/2609.17200",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3fz3d14c3zdb1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:41Z",
      "native_id": "2609.17200",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17200"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.soc-ph",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Mapping AI Economic Complexity",
  "title_text": "Mapping AI Economic Complexity",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:41Z",
  "url": "https://research.draconitic.com/paper/3fz3d14c3zdb1",
  "venue": "arXiv",
  "versions": []
}