[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84820-en":3,"doc-seo-84820-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84820,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies","Frontier AI labor-market impacts are uneven across tasks and countries, yet existing evidence often focuses on a limited set of high-income economies. This research introduces a national AI exposure metric that combines occupation-level exposure scores with international employment data for 141 countries. High-income countries show substantially higher exposure than low-income countries, with Europe & Central Asia about 50% higher than Sub-Saharan Africa, and women more exposed than men in 91% of countries. The estimates are validated against national AI adoption statistics and reveal indirect exposure channels through cross-country income dependencies.","The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies  \nArul Murugan  \nUC Berkeley  \nTomás Aguirre  \nUniversity of São Paulo  \nAbhishek Nagaraj  \nUC Berkeley  \nRishi Bommasani  \nStanford University  \narXiv :2607 .05404v 1 [ cs .CY] 8 Jun 2026  \nAbstract  \nFrontier AI’s labor-market effects matter to workers, firms, and policymakers, but current evidence generally comes from a handful of high-income economies. The capabilities of frontier AI are jagged across work tasks and national economies diverge in how they allocate human labor. We introduce a national AI exposure metric that combines occupation-level exposure scores and international employment data for 141 countries. We find that high income countries are substantially more exposed than low income countries and that Europe & Central Asia are 50% more exposed than Sub-Saharan Africa. We also find a gender gap: women are more exposed than men in 91% of countries, driven by their concentration in white-collar and sales occupations. The exceptions are countries where women’s employment remains concentrated in agriculture and household enterprises. We validate our national AI exposure estimates by showing they predict national AI adoption statistics published by Anthropic, Microsoft, and OpenAI. Beyond direct exposure, we identify a new mechanism for indirect exposure due to cross-country income dependencies. Some nations such as Tajikistan depend heavily on foreign workers remitting money back to their home countries: Tajikistan’s direct exposure to frontier AI is below-average but because 37% of Tajikistan GDP is Russian remittance and Russia is very exposed, Tajikistan’s remittance-accounted exposure becomes above-average. Our research shows that national variation in exposure is large enough that policy responses calibrated to U.S. or European labor markets will not generalize.  \n1 Introduction  \nHow frontier AI will reshape national economies is central to workers, firms, and governments. Around the world, the public already expects AI to reshape work: a majority of respondents in the 2025 Stanford AI Index report that AI will change how people do their jobs within five years, while the 2025 Ipsos AI Monitor finds that more people think AI will worsen than improve their local job market [Stanford HAI, 2025, Ipsos, 2025] . Policymakers frame frontier AI as a strategic priority [UK DSIT, 2025, Canada ISED, 2025, European Commission, 2025] and, reciprocally, frontier AI firms target national AI sovereignty agendas [OpenAI, 2025] . AI investment is seen as critical for future growth: the UN projects that the global AI market will expand to $4.8 trillion by 2033 [UN, 2025] .  \nThese effects are likely to be very unevenly distributed across countries. Frontier AI capabilities are jagged across tasks [Dell’Acqua et al., 2026], and national economies differ substantially in how they allocate workers to jobs. Consider call center work. Brynjolfsson et al. [2025a] find that deploying generative AI in a large call center increases issues resolved per hour by 15% with larger gains for less experienced and lower-skilled workers. A productivity shock of this level at scale would disproportionately disrupt nations like India or the Philippines where a large share of the national labor force is tied to call center work. The same frontier AI technologies could complement workers in one nation, reorganize entry-level labor markets in another, and have little effect in a third.  \nPreprint.  \nExposure  \n0.32  \n0.26  \n0.24  \n0.26  \n0.28  \n0.29  \n0.30  \n0.25  \n0.26  \n0.22  \n0.21  \n0.18  \n0.24 0.20  \n0.21  \n0.32  \n0.35  \n0.3  \n0.25  \n0.2  \n0.15  \nFigure 1: National AI exposure varies substantially across countries. Darker countries are more exposed to frontier AI. Countries without 2-digit ISCO-08 employment data are shown in gray.  \nWe introduce a national AI exposure metric to capture this heterogeneity. Our approach combines occupational exposure estimates from the econ","cbCaijvYE9ASwwXR","https://ap.wps.com/l/cbCaijvYE9ASwwXR","pdf",801540,1,21,"English","en",105,"# Introduction\n## National AI exposure metric\n## Cross-country exposure patterns\n## Gender gap and occupational composition\n## Validation and indirect exposure mechanism","[{\"question\":\"What does the national AI exposure metric measure?\",\"answer\":\"It measures how strongly a country’s labor-market occupation mix aligns with the tasks frontier AI can already accelerate or transform, enabling cross-country comparisons rather than forecasts of adoption, wages, or employment losses.\"},{\"question\":\"Which countries are found to be more exposed to frontier AI?\",\"answer\":\"Higher income countries are substantially more exposed, with North America and Europe \\u0026 Central Asia showing at least 50% higher exposure than the least-exposed region of Sub-Saharan Africa.\"},{\"question\":\"Why are women more exposed than men in most countries?\",\"answer\":\"The study finds a pervasive gender gap: women are more exposed than men in 91% of countries, driven by women’s concentration in white-collar and sales occupations, with exceptions where women remain concentrated in agriculture and household enterprises.\"}]",1784198509,53,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"the-jagged-global-economy-frontier-ai-unevenly-exposes-national-economies","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/the-jagged-global-economy-frontier-ai-unevenly-exposes-national-economies/84820/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does the national AI exposure metric measure?","Question",{"text":74,"@type":75},"It measures how strongly a country’s labor-market occupation mix aligns with the tasks frontier AI can already accelerate or transform, enabling cross-country comparisons rather than forecasts of adoption, wages, or employment losses.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which countries are found to be more exposed to frontier AI?",{"text":79,"@type":75},"Higher income countries are substantially more exposed, with North America and Europe & Central Asia showing at least 50% higher exposure than the least-exposed region of Sub-Saharan Africa.",{"name":81,"@type":72,"acceptedAnswer":82},"Why are women more exposed than men in most countries?",{"text":83,"@type":75},"The study finds a pervasive gender gap: women are more exposed than men in 91% of countries, driven by women’s concentration in white-collar and sales occupations, with exceptions where women remain concentrated in agriculture 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