Issuu

CIGI Papers No. 361 — September 2026

Middle-Income Economies: Navigating Digital Dependencies in AI Douglas Lippoldt

CIGI Papers No. 361 — September 2026

Middle-Income Economies: Navigating Digital Dependencies in AI Douglas Lippoldt

About CIGI

Credits

The Centre for International Governance Innovation (CIGI) is an independent, nonpartisan think tank whose peer-reviewed research and trusted analysis influence policy makers to innovate. Our global network of multidisciplinary researchers and strategic partnerships provide policy solutions for the digital era with one goal: to improve people’s lives everywhere. Headquartered in Waterloo, Canada, CIGI has received support from the Government of Canada, the Government of Ontario and founder Jim Balsillie.

Research Director, Digital Economy Odun Olowookere Director, Programs Dianna English Program Manager Morgan Fox Publications Editor Christine Robertson Manager, Publications Jennifer Goyder Graphic Designer Sepideh Shomali

À propos du CIGI Le Centre pour l’innovation dans la gouvernance internationale (CIGI) est un groupe de réflexion indépendant et non partisan dont les recherches évaluées par des pairs et les analyses fiables incitent les décideurs à innover. Grâce à son réseau mondial de chercheurs pluridisciplinaires et de partenariats stratégiques, le CIGI offre des solutions politiques adaptées à l’ère numérique dans le seul but d’améliorer la vie des gens du monde entier. Le CIGI, dont le siège se trouve à Waterloo, au Canada, bénéficie du soutien du gouvernement du Canada, du gouvernement de l’Ontario et de son fondateur, Jim Balsillie.

Human Analysis Standard

This publication’s analysis was developed and approved by the named human authors under documented review controls; AI assistance was judicious, disclosed and verified.

Copyright © 2026 by the Centre for International Governance Innovation The opinions expressed in this publication are those of the author and do not necessarily reflect the views of the Centre for International Governance Innovation or its Board of Directors. For publications enquiries, please contact publications@cigionline.org.

The text of this work is licensed under CC BY 4.0. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. For reuse or distribution, please include this copyright notice. This work may contain content (including but not limited to graphics, charts and photographs) used or reproduced under licence or with permission from third parties. Permission to reproduce this content must be obtained from third parties directly.

Centre for International Governance Innovation and CIGI are registered trademarks. 67 Erb Street West Waterloo, ON, Canada N2L 6C2 cigionline.org

Table of Contents vi

About the Author

Acronyms and Abbreviations

Executive Summary

Overview

AI in Middle-Income Countries: Sectoral Growth and Dependency

AI Sector Leadership Context

AI Trade Dependency

AI Start-Ups in 10 Middle-Income Economies

Investors in AI Start-Ups

AI Policy and International Economic Relations

Thinking Outside the Box: Advanced Economy Partners and the Path to Managed Interdependence

Conclusions

Appendix 1: Key Data Sources

Works Cited

About the Author Douglas (Doug) Lippoldt is a non-resident CIGI senior fellow and an international trade economist based in Claremont, California. He served as chief trade economist at HSBC Global Research in London from 2014 to 2020. Previously, he served in various roles as a senior economist at the Organisation for Economic Co-operation and Development in Paris, France, during a tenure of 22 years. Doug’s early career included seven years as an international economist with the US Department of Labor. He has published extensively on trade topics as well as on related aspects of economic development, labour market adjustment, innovation and intellectual property. Doug holds a Ph.D. in economics from the Institut d’études politiques de Paris (Sciences Po), an M.A. in international studies from the University of Denver and a B.A. in international studies from Washington College in Maryland. He was a Fulbright Scholar at the University of Cologne, Germany, and a Peace Corps volunteer in Burkina Faso. During 2015–2019, he represented HSBC as the deputy delegate on the Business 20 Trade and Investment Task Force. He has been a contributing author on two Think20 trade task force teams.

Acronyms and Abbreviations

Digital Economy Partnership Agreement

DNS

domain name system

ERIA

Economic Research Institute for ASEAN and East Asia

FCDO

Foreign, Commonwealth and Development Office

fintech

financial technology

Group of Seven

GENIAC

Generative AI Accelerator Challenge

GPUs

graphics processing units

HHI

Herfindahl-Hirschman Index

harmonized system

ICT

information and communication technology

IDRC

International Development Research Centre

IMF

International Monetary Fund

intellectual property

ISIC

International Standard Industrial Classification of All Economic Activities

information technology

LLM

large language model

METI

Ministry of Economy, Trade and Industry

OECD

Organisation for Economic Co-operation and Development

PCB

printed circuit board

Sida

Swedish International Development Cooperation Agency

ADP

automatic data processing

artificial intelligence

AI4D

Artificial Intelligence for Development

AIPI

AI Preparedness Index

APIs

application programming interfaces

ASEAN

Association of Southeast Asian Nations

BaTIS

Balanced Trade in Services

TPU

tensor processing unit

CPTPP

Comprehensive and Progressive Agreement for Trans-Pacific Partnership

United Nations

venture capital

WCO

World Customs Organization

WTO

World Trade Organization

DEPA

concentration ratio (market share)

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Executive Summary Middle-income economies are active participants in the global artificial intelligence (AI) economy, yet their engagement rests on foundations they do not control. This paper examines the AI dependency risks facing a sample of 10 middle-income countries — Brazil, Colombia, India, Indonesia, Kenya, Mexico, Nigeria, South Africa, Thailand and Vietnam — and considers the policy responses available to them. The analysis draws on five lines of evidence: → the AI leadership context of the United States and China; → trade dependency in AI-related goods and services; → AI start-up and investor activity; → domestic AI policy development and performance; and → advanced-economy partnership initiatives. The empirical picture is consistent across all five lines. The United States and China dominate the frontier of the AI economy at every layer — services, hardware supply chains and investment networks. Thirty of the 42 most heavily used generative AI service providers globally are US-headquartered; China accounts for 33 of 50 first-place supplier positions for five AI-relevant imported product groups in the sample countries. Official trade in services statistics understate the US position: Ireland appears as the leading information technology (IT) services supplier for five of the 10 sample economies, a statistical artifact of US technology multinationals routing revenues through Irish subsidiaries. In the AI investment ecosystem, the US-China asymmetry is especially striking — China is nearly absent as a start-up equity investor across the sample, while the United States dominates the cross-border accelerator channel that most shapes which start-ups get funded, mentored and connected to global networks. Despite this dependency, the sample economies field some 9,554 active AI startups, with AI start-up employment estimated at 7.1 percent of the information and communication technology (ICT) sector total across nine of the sample economies — a figure that gives dependency management real political and economic weight.

Full technological independence is neither achievable nor desirable for these economies. The appropriate strategic target is managed interdependence — meaningful participation in the AI economy while preserving sufficient policy autonomy to avoid lock-in to either dominant power. Institutional trust — predictable rule of law, evidence-based regulation and appropriate, reliable intellectual property (IP) protection — is the foundation on which this strategy rests, functioning simultaneously as a domestic development priority and an international market-access enabler. Four recommendations for the sample countries and their peers follow from this assessment: → Invest in domestic AI governance and capability: Close documented shortfalls in AI infrastructure, human capital and resilience while building the regulatory foundations that make a country a viable partner for cross-border AI investment and collaboration. → Maintain access to frontier technology while actively diversifying: Continue engagement with US and Chinese sources of frontier models, semiconductors, equipment and cloud infrastructure, while systematically building trade and investment ties with third-party advanced economies. → Engage in international fora to help shape the emerging AI governance architecture: Participate actively in the World Trade Organization (WTO), United Nations and Organisation for Economic Co-operation and Development (OECD) processes, and leverage available trade instruments including as appropriate the Agreement on Electric Commerce (negotiated via the WTO Joint Statement Initiative); the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) with its e-commerce disciplines; regional digital economy agreements (for example, the Digital Economy Partnership Agreement [DEPA]); and — for Commonwealth members — the Commonwealth Model Law on Digital Trade. → Deepen strategic engagement with advancedeconomy partners seeking comparable diversification: Canada, the European Union, Japan and the United Kingdom each offer differentiated AI contributions — from Canada’s leading AI research, to the European Union’s governance template and shared compute

Middle-Income Economies: Navigating Digital Dependencies in AI

infrastructure model, Japan’s domain-specific foundation models and Association of Southeast Asian Nations (ASEAN) co-creation cooperation, and the United Kingdom’s commercial strength in AI — that together support a viable managed interdependence architecture.

Overview Middle-income economies are not generally the focus in Western media coverage of AI issues. But these economies are indeed engaging in the turbulent rise of this sector (Lippoldt 2025). This paper primarily highlights the situation as of Q1 2026 for a sample of 10 middle-income countries: Brazil, Colombia, India, Indonesia, Kenya, Mexico, Nigeria, South Africa, Thailand and Vietnam (see Figure 1).1 Each has experienced a wave of AI entrepreneurial activity, sought to advance domestic AI policy and coordinated to some extent via international organizations. Yet these developments have not taken place in isolation. While many start-ups are wholly homegrown, the underlying technological foundations, managerial insights and often a substantial share of financial capital are drawn from abroad. There is significant dependence on imported inputs from the leading AI economies. The interconnection with the leading AI suppliers — especially China and the United States — provides middle-income economies with technological and economic advantages. In light of international policy uncertainty, it also raises the question of whether the resilience of these developing nations may be improved by diversification in sourcing and exporting, and by strengthening international engagement and cooperation. Broader representation of middle-income country interests in the emerging international governance framework — and better alignment of these countries’ domestic policies with international norms and standards — may play an important role in their AI development trajectories.

World Bank classification framework, demonstrated engagement in the AI economy as evidenced by each having at least 100 active AI start-up enterprises and participation in the OECD.AI Policy Observatory. Geographic diversity across the Global South was an additional consideration, with representation across Latin America, Sub-Saharan Africa and South and Southeast Asia. In considering the sample countries, this paper draws on five lines of evidence.2 These were selected to provide an illustrative sketch of the dependency chain from its structural origins, to its manifestation in the sample country economies, to its domestic and international policy contexts and responses. The first line — AI sector leadership context — establishes the foundational position of the United States and China, whose dominance at every layer of the AI stack sets the terms of engagement for other economies. The second and third lines — trade dependency in goods and services, and local AI start-up and investor activity — capture dependency as it is actually experienced in middleincome economies: through imported hardware and cloud services; the technology choices embedded in platform relationships; and the sources of capital, technology transfer and mentorship shaping domestic start-up ecosystems. The fourth line — AI policy development and performance — assesses how effectively the sample countries are building adequate domestic institutional capacity for engagement with the global AI economy. This could determine whether engagement generates durable local gains rather than merely deepening structural reliance. The fifth line — advanced-economy partnership initiatives — examines the international diversification options available, reflecting the view that managed interdependence (see Box 1), rather than either withdrawal or passive dependency, is the appropriate strategic target for middleincome economies navigating this landscape. The conclusions draw these strands together and offer four recommendations for middle-income economies and their peers navigating digital dependency in AI.

The 10 sample countries were selected based on their standing as middle-income economies in the 1

Core data sets were extracted in April 2026 and generally reflect conditions through Q1 2026 or the latest available year. In a few areas, it was feasible to include subsequent developments as late as July 2026 (for example, with respect to the UN initiatives on AI, as cited below).

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

See Appendix 1 for background information and commentary on data sources employed in this paper.

Box 1: Managed Interdependence In the context of AI start-ups and economic development, managed interdependence is a deliberate strategic approach in which a country engages the global AI economy while actively strengthening its domestic institutions and diversifying its sources of technology supply, capital and partnership.3 This strategy aims to move a country toward a condition in which external dependencies, though they persist, are distributed rather than concentrated, cushioned by institutional capacity and visible to the policy makers responsible for national AI strategy. An indication of status and progress over time may be developed from consideration of the concentration in relevant goods and services supply, the diversity of investment relationships and their quality (for example, inclusion of access to technology and management advisory services) and the strength of domestic governance and regulatory capacity.

AI in Middle-Income Countries: Sectoral Growth and Dependency The emerging literature on AI adoption in middleincome economies identifies a consistent tension between the gains available through engagement with global AI networks and the structural vulnerabilities that engagement creates. Nishith Reddy Mannuru et al. (2023) document how limited access to cutting-edge technology and infrastructure risks deepening inequality in developing countries, while identifying pathways toward inclusive development through domestic support mechanisms, public-private partnerships and investment in education and infrastructure. Analysis by an International Monetary Fund (IMF) team sharpens this picture; Eugenio Cerutti et al. (2025) show that AI and robotics advances may widen the gap between advanced and developing countries, particularly where automation displaces unskilled labour. Closing this gap requires robust digital infrastructure, human capital investment, inclusive STEM4 education and regulatory frameworks able to accommodate digital business models. The World Bank’s work on global value chains (World Bank Group 2020) reinforces the point: while global value chain participation has enabled significant economic convergence, the resulting

Variations on this term and its usage in the AI context have appeared in recent economic literature such as Tanner et al. (2026), Joshi (2026) and Singh and Sengupta (2025).

STEM refers to science, technology, engineering and mathematics.

interdependence also concentrates vulnerability, as supply chains tend to be both highly importdependent and geographically concentrated. Yet dependency is not the whole story, nor is avoidance of dependence a viable strategy. Matthias Bauer et al. (2025), examining 100 leading multinationals operating in Europe, argue that competitiveness depends not on corporate nationality but on the depth of industrial and technological interconnections. While Bauer et al.’s paper is focused on Europe, it does point to lessons that could be helpful in reframing the challenge for middle-income economies: the goal is not to avoid dependency on foreign technology leaders but to pursue managed interdependence that strategically builds domestic capabilities while engaging international partners and securing complementary resources. Douglas Lippoldt (2025), drawing on a case study of 10 middle-income developing countries, finds that successful domestic AI sector promotion tends to combine national AI strategies and consultative regulatory frameworks with appropriate guardrails, AI education and training, international co-operation and digital infrastructure development — with openness to foreign capital and technology transfer playing a constructive role when managed carefully. The research collectively indicates that successful navigation of the AI transition requires middle-income economies to move beyond defensive postures toward active engagement with global technology ecosystems, using policy tools to ensure that integration generates local spillovers, skills development and innovation capacity, rather than merely serving as a passive market for AI deployment (Lippoldt 2024).

Middle-Income Economies: Navigating Digital Dependencies in AI

AI Sector Leadership Context In terms of scale and intensity, there is wide variation in the internet economy around the world. Drawing on recent Cloudflare data, Figure 1 presents overall country shares of global internet traffic via Cloudflare’s network (a rough approximation of global traffic).5 Setting aside the United States as an extreme outlier, the map reveals India and Brazil as having the next most sizeable known internet traffic volumes globally. Table 1 provides a more detailed picture. It reveals the substantial scale of the internet flows for several other sample countries such as Indonesia, Vietnam, Thailand and, to a lesser extent, Mexico. Their shares of internet traffic fall in the same range as for the Group of Seven (G7) economies. Notwithstanding possible biases in the source data, China’s falls within this range as well.6 Table 1 also includes an approximate measure of internet intensity by considering each country’s share of internet traffic relative to its share of global population. By this indicator, Thailand, Vietnam and Brazil still fall within the range for the G7 countries.7 The other middleincome countries fall below the G7 countries in this comparison. The key point here is that whether by scale or intensity of internet traffic, several middle-income countries have achieved G7 levels of activity for this dimension of the digital economy. With respect to provision of AI services, however, the traffic appears comparatively narrowly targeted. As can be seen in Table 2, globally, the 42 most heavily used generative AI services8 originate in just 10 nations.9 Google (Alphabet) is the parent company for four of the services. In other cases, some generative AI service providers are known to rely on models by other leading suppliers. For

Cloudflare’s network handles approximately 20 percent of global web traffic (Borisov 2025; Robinson 2023), providing a useful rough approximation of internet traffic patterns. See Appendix 1 for details.

For data on China, Cloudflare relies on enterprise services data relayed via JD Cloud, its commercial partner in China. See Appendix 1 for details.

China does as well, despite the undercounting inherent for Cloudflare’s data on China’s domestic internet traffic.

Generative AI refers to AI systems that produce novel content — text, images, code and other outputs — responding to user-specified inputs or instructions. They draw on learning from data sets. Generative AI services provide access to generative AI capabilities, generally over the public internet (though not exclusively). In their operation, such services tend to generate internet traffic, which makes domain name system (DNS) data useful in assessment of their reach and utilization.

Recall that there is limited data availability for China via the Cloudflare Radar tracker. This data point is as of April 8, 2026.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

example, Perplexity AI and Microsoft provide services that draw upon OpenAI’s ChatGPT model. Perplexity AI also offers services that draw upon Anthropic’s Claude. There appears to be a number of other such cases, though the specifics are not readily known via the sources underlying the table. Overall, for the generative AI services shown in the table, the United States hosts the headquarters for 30, China hosts four and the other economies each host one (Australia, Israel, Malta, Singapore, Spain, Sweden, Taiwan and the United Kingdom). Middle-income economies are not hosts to any of the top generative AI service providers. Table 3 considers the activity of AI bots and web crawlers that explore websites globally on behalf of AI providers for user-defined tasks (so-called “mixed use”), collection of AI training data or AI-driven search purposes. As shown in the table, the Cloudflare data on the leading AI bots and web crawlers reveals that all but one are of US origin; one originates from China. The activities of bots and crawlers have historically been mutually beneficial for website operators because identification of a website by bots and crawlers often led to future web referrals. However, according to a team from Cloudflare, this symbiosis is breaking down as AI models generate far fewer referrals back to the data source websites (Lee et al. 2025). The relationship risks becoming exploitative from the perspective of some website operators, including in middle-income economies.

AI Trade Dependency Internationally, dependence can be defined as arising “when some countries (the dominant ones) can expand and can be self-sustaining, while other countries (the dependent ones) can do this only as a reflection of that expansion, which can have either a positive or a negative effect on their immediate development” (Dos Santos 1970, 231). Openness can facilitate technology transfer, access to capital and access to markets for competitive imports, as well as economies of scale from expansion into international markets, among other benefits. Yet there can be an asymmetry where the dominant players are able to extract substantial rents in exchange for access to needed inputs or export markets, thereby undermining

Middle-Income Economies: Navigating Digital Dependencies in AI

Ecuador 0.10%

Chile 0.30%

Peru 0.30%

Colombia 0.40%

Argentina 0.60%

Venezuela 0.20%

Brazil 4.40%

Morocco 0.20%

Portugal 0.40%

Ireland 0.70%

Belgium 0.40%

Spain 1.40%

Nigeria 0.20%

Algeria 0.40%

Albania 0.10%

Slovakia

Austria 0.30%

South Africa 0.50%

Israel 0.20%

Egypt 0.60%

Jordan 0.10%

Türkiye 2.00%

Iran 1.00%

Azerbaijan 0.10%

Saudi Arabia 0.90%

Iraq 0.30%

Kenya 0.10%

Georgia <0.10%

Russian Federation 2.00%

Bulgaria 0.30%

Ukraine 0.50%

Belarus <0.10%

Romania 0.50%

Lithuania 0.10%

Finland 0.50%

Greece 0.20%

Hungary 0.20%

<0.10%

Poland 0.90%

Czech Republic

Sweden 0.60%

0.30%

Serbia 0.10%

Italy 1.20%

Germany 4.40%

Croatia 0.10%

France 2.60%

Netherlands 2.10%

Norway 0.20%

Uzbekistan 0.10%

Kazakhstan 0.10%

Pakistan 0.50%

Kyrgyzstan 0.10%

India 5.10% Bangladesh 0.60%

Myanmar 0.30%

China 3.20%

Indonesia 2.60%

0.80%

Vietnam 2.60%

Cambodia Malaysia 0.10%

Thailand 2.00%

Philippines 1.80%

Taiwan 0.80%

South Korea 1.90%

Australia 1.10%

Japan 3.60%

New Zealand 0.20%

Source: The data on internet traffic is from Cloudflare (https://radar.cloudflare.com/traffic?dateRange=52w); the map framework is copyright © Australian Bureau of Statistics, GeoNames, Microsoft, Navinfo, Open Place, OpenStreetMap, Overture Maps Foundation, TomTom, Zenrin; the Excel Map function is powered by Bing; map is based on map conception and labels provided by the author. Notes: The map presents percentage shares, by country, of global internet traffic via the Cloudflare global network over the 12 months ending on April 8, 2026, based on data originally measured in bytes. Cloudflare’s network handles approximately 20 percent of global web traffic (Borisov 2025; Robinson 2023) and provides a useful approximation of internet traffic patterns, though its coverage rate is not uniform. In particular, much of China’s domestic traffic does not route through Cloudflare (see Appendix 1). The shares of the countries shown in colour range from 0.1 percent (lightest colour) to 24.8 percent (darkest colour). The economies shown are the 74 economies each accounting for 0.1 percent or more of the total. Not shown are 126 others, each accounting for less than 0.1 percent of total global traffic.

Mexico 1.20%

United States 24.80%

Canada 2.30%

United Kingdom 4.00%

Figure 1: Internet Traffic Around the World, Shares of Total Bytes (%), with Names of the Sample Countries Highlighted

Table 1: Shares and Intensity of Internet Traffic, 12 Months Ending April 8, 2026 Share of Global Internet Traffic (%)

Share of Global Population (%)

Disproportionate Share of Traffic By Population? (Yes if >100%)

I. Middle Income Economies (Sample)

Brazil

4.4

2.7

164.8

Colombia

0.4

0.7

59.7

India

5.1

18.5

27.6

Indonesia

2.6

3.6

72.2

Kenya

0.1

0.7

13.7

Mexico

1.2

1.7

72.3

Nigeria

0.2

3.0

6.6

South Africa

0.5

0.8

61.0

Thailand

2.0

0.9

224.7

Vietnam

2.6

1.3

203.1

Subtotal

19.1

33.9

56.5

Canada

2.3

0.5

451.0

France

2.6

0.8

313.3

Germany

4.4

1.0

423.1

Italy

1.2

0.7

162.2

Japan

3.6

1.5

235.3

United Kingdom

4.0

0.9

459.8

United States

24.8

4.4

568.8

Subtotal (ex. US)

18.1

5.5

327.9

3.2

17.7

18.1

II. G7 Countries

Memo item: China

Source: Data on internet traffic is from Cloudflare (https://radar.cloudflare.com/traffic?dateRange=52w); Population data is from World Population Review, 2026, “Total Population by Country 2026” (https://worldpopulationreview.com/countries, accessed April 8, 2026); author’s table design, tabulation and review. Note: The table presents percentage shares of global internet traffic via the Cloudflare network over the 12 months ending on April 8, 2026, as measured in bytes. Cloudflare’s network accounts for a large share of global web traffic (approximately 20 percent of the global total per Borisov [2025] and Robinson [2023]), though its coverage rate is not uniform. For example, much of China’s domestic traffic does not route through Cloudflare (see Appendix 1). For each economy covered in this table, the internet traffic share is compared with the population share as an approximation of intensity of internet usage.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Table 2: Generative AI Services, Usage Ranked, with Illustrative Parent Company Characteristics Service

Parent Company

HQ Country

Ownership Type

Category

Model Lineage

Confidence Score (for Model Lineage)

OpenAI API

OpenAI

Independent/private

Model provider

GPT (OpenAI)

High

Chai ML

Chai Discovery

Independent/private

Model provider

Unspecified/mixed

Low

Runway ML

Runway AI

Independent/private

Model provider

Unspecified/mixed

Low

Leonardo

Leonardo.Ai

Australia

Independent/private

Model provider

Unspecified/mixed

Low

Candy AI

EverAI Limited

Malta

Independent/private

Model provider

Unspecified/mixed

Low

Tabnine

Israel

Independent/private

Model provider

Unspecified/mixed

Low

Novel AI

Independent/private

Model provider

Unspecified/mixed

Low

HeyGen

Independent/private

Model provider

Unspecified/mixed

Low

Sources: For the list of generative AI services: Cloudflare (2026); for parent company and headquarters information: Crunchbase (www.crunchbase.com/home), with data parsed by Anthropic, 2026, Claude.ai, Sonnet 4.6, https://claude.ai/new; for assistance in data-set enrichment, including classification of services by ownership type, category and model lineage, OpenAI, 2026, ChatGPT, GPT-5.3, https://chat.openai.com/; author’s table design, edits, updates and review. Sources were accessed on April 9, 2026. Notes: Cloudflare’s generative AI service data covers the week ending April 9, 2026. The ranking is based on Cloudflare’s data on connection frequency of unique users (see Appendix 1). A generative AI service may represent one or more domains. “Ownership Type” reflects whether the parent company is a major publicly traded technology firm (“big tech”) or an independent start-up or private entity. “Category” reflects whether the generative AI service provider develops foundational models (e.g., OpenAI, Anthropic) or primarily leverages third-party application programming interfaces (APIs) (e.g., Poe, Sider, Cursor). OpenAI’s “Model Lineage” identifies the underlying large language model (LLM) model architectures and covers: proprietary foundation model providers (e.g., GPT, Claude, Gemini, Llama, Grok); multi-model aggregators; applications that leverage fine-tuned or domain-specific variants. Assignments are based on best efforts using publicly available documentation and structured synthesis by ChatGPT. The OpenAI Confidence typology: high confidence = the model lineage is explicitly documented (e.g., OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA, xAI Grok); medium confidence = platforms are known to aggregate or provide access to multiple models (e.g., Poe) or rely on open-source ecosystems; low confidence = model lineage is not publicly disclosed or appears to involve proprietary fine-tuning or third-party APIs (smaller start-ups, niche applications). The author made some category reassignments based on company documentation. *For details of WindsurfAI’s history, see Horsey (2025). **Replit and Cursor provide substantial proprietary elements as part of their services. ***GliaCloud primarily packages cloud-based video-creation and editing tools with third-party AI services (e.g., generative media). ****Founded in China, Manus relocated to Singapore and was acquired by Meta Platforms in 2025 (AP News 2025). In April 2026, the Chinese authorities ordered the Manus acquisition to be unwound (Financial Times 2026). ***** Source material indicates Sider AI as being of Chinese origin, now domiciled in the United States.

Middle-Income Economies: Navigating Digital Dependencies in AI

Table 3: AI Bot and Web Crawler Activity, Year Ending April 9, 2026, Shares by Purpose, Percentages Crawl Purpose (Origin)

Mixed Purpose

Training

Bot (right)

Googlebot

Bingbot

MetaExternal Agent

GPT -Bot

Claude -Bot

Applebot

Bytespider

Amazonbot

OAISearch -Bot

Perplexity -Bot

ClaudeSearch -Bot

YouBot*

Originator’s home country (right)

China

75.3

24.7 30.8

30.2

22.7

8.5

6.8

70.5

23.5

4.2

1.3

0.4

74.7

20.3

3.7

0.7

0.4

65.5

28.0

4.7

1.4

0.3

Sector (below) Business and Industry Mixed (51.5% of traffic volume) Training (35.9% of traffic volume) Search (8.9% of traffic volume) Computer and Electronics Mixed (59.4% of traffic volume)

83.8

16.2

Training (29.7% of traffic volume)

25.9

30.7

15.1

9.2

17.1

Search (6.9% of traffic volume) Internet and Telecom Mixed (60.1% of traffic volume) Training (28.9% of traffic volume)

77.9

22.1 29.3

27.4

Search (6.7% of traffic volume)

20.2

10.5

10.9

Source: Cloudflare (https://radar.cloudflare.com/ai-insights?dateRange=52w&industrySet=Internet+%26+Telecom&crawlPurpose=Mixed+Purpose); author’s table design and review. Notes: For information on Cloudflare’s China data limitations, see the notes to Table 1 and Appendix 1. In methodological notes on the Cloudflare Radar AI Insights web pages (https://radar.cloudflare.com/ai-insights), AI bots are described as automated scripts that perform tasks across the internet, scanning public websites to collect data used in search engines, AI model training and other data processing tasks. Crawlers specifically index or scrape website content. Cloudflare’s AI bot and web crawler tracking is focused on those advertised as AI-related or identified via open-source data collection. The three-way purpose taxonomy considers mixed-purpose AI bots as those that access web content as part of a task or action directed by a user; training refers to data collection for use in AI model development and search is for use in refining AI search operations. It should be noted that absent a global standard for declaration of purposes by bot and crawler operators, Cloudflare’s data rely partly on selfreporting by the bot operators. *Founded in 2020, You.com — the developer of YouBot — is a US-based AI search start-up focused on business AI tools and APIs.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

development prospects in the dependent economies. This type of sustained dependency is a structural challenge needing to be addressed if development is to take off. It is differentiated from mutually beneficial interdependence where some short-term variance and shifting in the balance of benefits is a natural fluctuation associated with changing market conditions. In such conditions, disruptions — such as natural disasters, changes in a partner’s trade or industrial policies or geopolitical tensions — can be quite costly. One common risk management strategy has been to strive for diversification in sourcing and sales. Such optionality can help improve resilience against disturbances. Especially where security is at stake, policy makers have increasingly turned to industrial policy measures (Evenett et al. 2025). While such policy may be appropriate, Przemyslaw Kowalski and Andrea Andrenelli (2025) argue for a careful consideration of costs and benefits before making interventions. Getting policy settings wrong could expose supply chains to risks or lead to high economic costs arising from undue trade impediments or other market distortions. AI poses a distinctive challenge in this regard. Development of certain hardware elements required for AI systems — such as high-capacity graphics processing units (GPUs) or other advanced logic chips — is extraordinarily costly and technologically out of reach for most firms. Firms in the United States are uniquely skilled in design of such logic chips, while firms in Taiwan and Korea are leaders in their fabrication, and firms in the Netherlands, Japan and the United States are leaders in specialty machinery to produce the chips. Japan is a leading supplier of certain chemical inputs. The United States constrains overall access to this production capacity via its export control regime. Meanwhile, China has high aspirations as regards such chip production and is mobilizing extremely large investments as it strives to advance its capabilities.10 On the software side, frontier models require computing capacity and data availability on a massive scale, which is extraordinarily challenging for new entrants. Leading technology firms in the United States and China are unparalleled in these capabilities; only a limited number of other economies are able to muster the requisite 10 In 2024, China launched the third phase of its National Integrated Circuit Industry Investment Fund (known as “Big Fund III”), a roughly US$48 billion state-backed investment vehicle directed at accelerating the domestic chip ecosystem (Liao 2025).

inputs for the original development of such models. Moreover, for firms looking to leverage AI development investments via exports of generalpurpose AI model services, access to the Chinese domestic market is impeded.11 In the United States, President Donald Trump recently issued an executive order providing for a voluntary framework for pre-screening of frontier models and for certain other cybersecurity measures to be developed, which may have the potential to constrain some access to the US market (Trump 2026).12 Thus, taking account of the full AI stack, including hardware and software, regulatory constraints make it difficult or effectively impossible for most AI firms to operate fully, freely and simultaneously across both the US and Chinese AI ecosystems. Leading AI firms in the United States and China are operating at the frontier. Via access to their offerings, these AI firms provide a shortcut in their own domestic markets and in other markets for startups interested in finding a place in the AI economy. Start-ups can build apps or devices that draw upon the services of existing AI models and inputs. Yet this strategy has its limitations in that it can create dependency and even lock-in. For example, developer choices in middle-income economies may result in limitations on their future operations if: → they draw on proprietary API access to foundation models that usually requires a contractual and commercial relationship, not just a product purchase; → they employ export-controlled inputs governed by use limitations; or → they contract for on-cloud compute infrastructure, which is often controlled by one

China’s regulatory framework for generative AI requires approval of mandatory pre-deployment filings with the Cyberspace Administration of China before any model — domestic or foreign — can be offered to the public. As of early 2026, no foreign frontier model provider had cleared this process, effectively excluding nonChinese operators from China’s domestic AI services market (Yin et al. 2026). See also Xinhua (2025).

12 This comes on top of restrictions already in place in the United States. Under the Export Administration Regulations, the US Commerce Department’s Bureau of Industry and Security has restricted certain trade in advanced semiconductors and related products. Then, in June 2026, the Commerce Department issued an exportcontrol directive, citing national security authorities, requiring Anthropic to suspend all access to its Fable 5 and Mythos 5 models by any foreign national, whether inside or outside the United States — including the company’s own non-citizen employees (Anthropic 2026). A US official confirmed that the Commerce Department issued the letter, which Anthropic reported did not specify the underlying national security concern (Eastland and Lowenkron 2026). Press coverage characterized the order as unprecedented in barring foreign-national access to a deployed commercial AI model (Eastland and Lowenkron 2026; Compliance Hub Wiki 2026).

Middle-Income Economies: Navigating Digital Dependencies in AI

of a small number of hyperscalers13 and may entail contractual-use commitments. With such considerations in mind, next to examine are empirical indications of AI sector dependence in the middle-income sample economies. First, dependency is reviewed for an illustrative set of AI-related goods. Then, an illustrative set of AI-related traded services is considered.

Merchandise Trade, AI and Dependency As a way of illustrating international interdependence in IT equipment related to AI, trade dependency is evaluated for five AIrelated, traded product groups. These each play an important role in the operation of a layer of the AI hardware stack shown in Figure 2.14 The layers range from upstream materials shown in the bottom layer to the completed AI compute infrastructure that constitutes the top layer. An overview of the selected product groups, classified according to the Harmonized System (HS),15 is presented in Table 4. As can be seen in the snapshot for each product group, there are various constraints that may limit alternatives available to middle-income countries seeking diversification to manage risks. While the selected products are not necessarily representative of all traded elements of each layer, they do provide an illustration of the types of dependency patterns that can emerge. Note that the products in these AI-related categories are generally not exclusively employed in the AI sector, but may also have more general application in IT. They provide a useful indication but may also reflect influences of other IT developments (for example, memory upgrades for legacy systems or inputs for multifunctional components such as video panels).

13 A hyperscaler is a company that operates massive-scale computing infrastructure — data centres, cloud platforms and networking capacity — at a global level, with the ability to scale that infrastructure rapidly and efficiently to meet demand. Examples include Amazon (AWS), Microsoft (Azure), Google (GCP), Meta and Alibaba. 14

Consideration of an illustrative product group from each layer of the AI stack was an additional consideration in development of this sample.

15 Developed by the World Customs Organization (WCO), this is the standard taxonomy for classification of traded goods (see www.wcoomd.org/en/topics/nomenclature/overview/what-is-theharmonized-system.aspx).

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Figure 2: Situation of Five Import Product Categories in the AI Hardware Stack Illustrative AI hardware stack Supply chain layers, with ﬁve HS import categories annotated . arrows show production ﬂow (bottom top)

AI compute infrastructure Servers, GPUs, AI accelerator hardware, edge devices

System assembly and interconnects Wiring, PCB connectors, data cables, power conductors

Integrated circuits

HS 8544

Wire & cable

HS 8542

Logic chips, processors, AI accelerators (GPUs, TPUs)

Semiconductor fabrication equipment Lithography, deposition, etch, inspection tools

Upstream materials Doped silicon wafers, semiconductor-grade chemical

upstream inputs

HS 8471

Computers & ADP

Integrated circuits

HS 8486

Manufacturing equipment

HS 3818

Doped elements

deployed systems

Source: Author’s compilation of illustrative, traded, AI-relevant products with assistance of Claude.ai for research and graphical presentation. Notes: The HS product categories cited here for illustrative purposes are examples of required inputs at each stage. They are not necessarily representative for dependency for all the components employed at each layer of the AI hardware stack. ADP refers to automatic data processing. GPU refers to graphics processing unit. PCB refers to printed circuit board. TPU refers to tensor processing unit (a type of AI accelerator chip developed by Google).

Table 4: Snapshots of the AI Sample Product Groups, by HS Category AI-Stack HS Product Categories

HS 3818

Doped semiconductor elements Chemically doped silicon and semiconductor-grade elements; upstream materials in chip fabrication. Supply is shaped by geographic concentration in raw material extraction and purification, with China dominant in gallium and germanium supply and Japan leading in high-purity silicon wafer production. Export restrictions on critical minerals add a further layer of supply risk. Top producers: Shin-Etsu Chemical (JPN), SUMCO (JPN), Siltronic (DEU) and SK Siltron (KOR).

HS 8471

Computers and ADP machines Laptops, desktops, servers and tablets — the compute hardware layer. For AI applications, GPU-equipped servers and high-density data centre hardware are a critical subsegment, with demand driven by hyperscaler infrastructure build-out. Assembly is heavily concentrated in China and Taiwan. Advanced chip inputs are subject to export controls. Top producers: Dell (USA), HPE (USA), Lenovo (CHN) and Supermicro (USA).

HS 8486

Semiconductor manufacturing equipment Tools for lithography, deposition, etch, inspection/metrology and test, wafer handling and assembly. Supply constrained by extreme capital intensity, long qualification cycles, production backlogs, export controls and high supplier concentration. Top producers: ASML (NLD), Applied Materials (USA), Lam Research (USA), Tokyo Electron Limited (JPN), and KLA Corporation (USA).

HS 8542

Electronic integrated circuits Logic chips, memory, processors and AI accelerators — the most AI-critical goods category. Supply of leading-edge chips is highly concentrated, with Taiwan Semiconductor Manufacturing Co. (TSMC) in Taiwan fabricating the majority of advanced nodes; memory is dominated by three Korean and US firms. US export controls specifically target advanced AI accelerator chips, restricting supply to China and certain other markets. Top producers: NVIDIA (USA), Intel (USA), Samsung (KOR), SK Hynix (KOR) and Micron (USA); fabricated primarily by TSMC (TWN).

HS 8544

Insulated wire, cable and connectors Electrical conductors, high-speed data cables, fibre-optic cables and PCB connectors — the physical interconnect layer. Less AI-specific than other categories in this table, this segment is nevertheless critical infrastructure for data centres and device assembly. China accounts for a dominant share of global production capacity. Less subject to export controls than other categories, but volume-intensive and sensitive to raw material price volatility (copper, rare earths). Top producers: Prysmian (ITA), Nexans (FRA), Sumitomo Electric (JPN) and Hengtong (CHN).

Source: WCO (2022); author’s tabulation; compilation supported by Claude.ai. Notes: TSMC = Taiwan Semiconductor Manufacturing Co.; CHN = China; DEU = Germany; FRA = France; ITA = Italy; JPN = Japan; KOR = South Korea; NLD = the Netherlands; TWN = Taiwan. ASML is the Dutch manufacturer of the most advanced semiconductor lithography equipment in the world. TEL, Tokyo Electron Limited, is a Japanese semiconductor equipment manufacturer.

Middle-Income Economies: Navigating Digital Dependencies in AI

Box 2: HHI In the formulation employed in this paper, the HHI measures import market concentration by summing the squared market shares of all suppliers. Scores below 1,500 denote a competitive market; 1,500–2,500 indicate moderate concentration; above 2,500 signals high concentration and elevated dependency risk. (In the literature, the index is also sometimes normalized to score in the range between 0 and 1.) In the context of AI goods, a high HHI reflects structural vulnerability to supply disruption or geopolitical leverage by a small number of exporting countries. Squaring market shares ensures the HHI is sensitive to the difference between concentrated and distributed supply structures — not just the number of suppliers, but how unequally they are weighted. This makes it particularly suited to measuring dependency risk, where a single dominant source country poses risks that a simple count or arithmetic average would obscure. Source: U.S. Department of Justice and the Federal Trade Commission (2010, 1819); AI-assisted synthesis (Claude.AI, Anthropic); author’s restatement.

Goods Trade Dependency Assessment Table 5 considers AI-related dependency by looking at indicators of market concentration in relevant imports with respect to suppliers’ country of origin. The primary tool in this regard is the HerfindahlHirschman Index (HHI), which is presented in Box 2. Other indicators include market-share concentration ratios for the leading import supplier nation of each HS product group (concentration ratio [CR]1) for each importing nation, and for the combined share for the top three suppliers for each HS product group for each importing nation (CR3). Across all of the products and countries, the proportion of relatively high trade concentration is quite striking. Of the 50 product-country pairings (five product groups for each of 10 countries), nearly three-quarters of the cases are identified as having high concentration. However, a number of the associated import flows are quite modest, which may bias the results. Focusing only on the large import flows valued at US$1 billion or more annually, the proportion of flows with high concentration scores is still 59 percent (13 of 22 product cases), while 36 percent of the flows exhibit moderate levels of concentration. China was the top supplier in all but two of these cases of high concentration. (The United States and

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Taiwan each also held one of the top spots for a high concentration, large value import flow.) Among the large import flows, high concentration most affects computers and ADP (HS 8471) with six instances and wire and cable (HS 8544) with four instances. A further two instances in each of these sectors scored in the moderate concentration range. In the event of supply chain disruptions, this pattern of high and moderate concentration may affect resilience in efforts to build-out compute capacity, which is important for scaling AI. The semiconductor manufacturing equipment (HS 8486) and doped semiconductor elements (HS 3818) product groups only have one large import flow each, but these two flows as well experienced high concentration. In semiconductor trade — all important for AI — import flows (electronic integrated circuits, or HS 8542) experienced high concentration in only one case among the six nations with imports over US$1 billion. The other concentration scores for large import flows in this product group were moderate (four instances) or low (one instance). It appears that the six middle-income economies have a measure of diversification in supply for this

product group (though it should be kept in mind that not all of this trade is destined for AI use).16 At the national level, Tables 6A and 6B highlight the changing pattern of leading suppliers of AI-relevant imports in the five sample product groups to the sample of 10 middle-income developing countries. The tables consider counts of which supplier economies are ranked among the top three suppliers for each of the product groups in each of the 10 importing countries. In Table 6A, China and the United States together account for 48 percent of leading supplier slots for the latest year, up from 41 percent in 2019. China claims overall top position for the recent year (and 33 of 50 first-place positions). The United States ranks second, South Korea third, Other Asia (largely Taiwan) fourth and Japan fifth. In Table 6B, the same basic methodology is used but a filter is applied to only take into account import flows valued at US$1 billion or more. The overall dominance of China persists (including claim to 16 of 22 first-place slots in the recent year17). The United States ranks second, South Korea third, Other Asia (largely Taiwan) fourth and Malaysia fifth. In a notable development, two of the case study countries, Thailand and Vietnam, moved up to tie at sixth place in the recent year. The table reflects the dominance of China and the United States as leaders in the supply of these AI-relevant products to the 10 middle-income economies covered in the paper’s sample. China is often the top supplier. Indeed, as noted in Table 5 with respect to the CR1 measure, China accounts for more than half of the imports in 24 cases out of a possible 50. That is quite a significant proportion. But it is not a monolith. In Table 6B, 13 suppliers place among the top three alongside China and the United States. Moreover, three of the middle-income economies appear in Table 6B as suppliers earning silver and bronze medals — evidence that supply-chain dependency can run in more than one direction.

16 The mean HHI score for six countries with large import flows for HS 8542 was 2,028, whereas the mean score for countries with large import flows for HS 8471 was 3,528. For those with large import flows for HS 8544, the score was 2,900. Lower scores indicate less supplier concentration. 17

As an AI-related goods supplier in the sample markets, China leads with 11 of 13 large, highly concentrated flows (Table 5) and 16 of 22 large flows overall (Table 6B). The difference in the counts for China concerns five markets where China leads as an import supplier, but where there is a moderate concentration in overall supplier mix.

Middle-Income Economies: Navigating Digital Dependencies in AI

Table 5: Country Dependency Profiles, AI-Stack Products, Latest Available Year (2024 or 2023) Brazil (2024) HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

Product

2,647

High

31.2%

88.3%

Japan

6,256,918

HS 8471 — Computers and ADP

1,636

Moderate

29.3%

63.4%

China

1,885,834,005

HS 8486 — Semi mfg equipment

3,244

High

54.7%

71.1%

Japan

14,175,806

HS 8542 — Integrated circuits

1,923

Moderate

28.5%

70.7%

China

5,929,148,905

HS 8544 — Wire and cable

1,761

Moderate

35.6%

62.6%

China

108

1,525,015,666

Colombia (2024) HHI

Classification

CR1 %

CR3 %

Top Suppliers

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

Product

8,444

High

91.5%

100.0%

China

151

HS 8471 — Computers and ADP

5,247

High

71.3%

88.0%

China

1,363,212,016

HS 8486 — Semi mfg equipment

5,194

High

62.2%

99.4%

Germany

387,833

HS 8542 — Integrated circuits

3,097

High

53.4%

71.3%

China

80,971,248

HS 8544 — Wire and cable

2,348

Moderate

43.8%

69.3%

China

368,630,031

HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

9,502

High

97.5%

99.1%

China

189,815,051

HS 8471 — Computers and ADP

4,241

High

63.7%

79.4%

China

10,475,547,422

HS 8486 — Semi mfg equipment

6,485

High

79.7%

92.6%

China

1,025,577,413

HS 8542 — Integrated circuits

2,987

High

45.0%

87.0%

China

23,445,487,574

HS 8544 — Wire and cable

1,997

Moderate

42.0%

57.5%

China

115

1,407,649,013

HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (USD)

HS 3818 — Doped semi elements

8,326

High

91.1%

98.7%

China

327,934,189

HS 8471 — Computers and ADP

3,803

High

55.3%

86.2%

China

3,716,272,379

HS 8486 — Semi mfg equipment

7,939

High

89.0%

94.3%

Other Asia, nes

186,147,525

HS 8542 — Integrated circuits

1,829

Moderate

26.3%

70.4%

Other Asia, nes

4,056,321,629

HS 8544 — Wire and cable

4,295

High

64.8%

75.6%

China

106

1,354,160,993

India (2024) Product

Indonesia (2024) Product

Kenya (2024) HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

Product

10,000

High

100.0%

Romania

290

HS 8471 — Computers and ADP

2,914

High

52.1%

67.4%

China

108

139,365,895

HS 8486 — Semi mfg equipment

4,608

High

58.5%

95.8%

Benin

274,038

HS 8542 — Integrated circuits

4,379

High

60.5%

91.7%

China

18,713,899

HS 8544 — Wire and cable

3,587

High

58.0%

76.4%

China

69,498,415

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Table 5: Country Dependency Profiles, AI-Stack Products, Latest Available Year (2024 or 2023) (Continued) Mexico (2024) Product

HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

6,208

High

74.6%

100.0%

United States

509,811

HS 8471 — Computers and ADP

1,832

Moderate

29.4%

64.7%

China

13,347,625,960

HS 8486 — Semi mfg equipment

1,679

Moderate

21.6%

60.8%

Malaysia

333,246,116

HS 8542 — Integrated circuits

1,364

Low

23.4%

55.9%

Malaysia

25,739,189,809

HS 8544 — Wire and cable

2,593

High

42.5%

77.1%

United States

7,935,204,473

HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

3,712

High

49.2%

100.0%

United States

2,232

HS 8471 — Computers and ADP

3,124

High

53.2%

73.8%

China

92,124,801

HS 8486 — Semi mfg equipment

8,242

High

90.6%

97.0%

China

57,886,619

HS 8542 — Integrated circuits

4,846

High

68.7%

83.1%

China

22,294,537

HS 8544 — Wire and cable

4,665

High

66.9%

83.0%

China

103,650,276

Nigeria (2024) Product

South Africa (2024) HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

Product

3,764

High

52.4%

90.9%

United States

32,724

HS 8471 — Computers and ADP

3,660

High

59.1%

72.5%

China

131

1,993,103,241

HS 8486 — Semi mfg equipment

3,584

High

52.3%

91.6%

China

2,554,977

HS 8542 — Integrated circuits

1,457

Low

30.8%

52.1%

China

144,379,607

HS 8544 — Wire and cable

1,550

Moderate

27.9%

59.1%

Botswana

132

516,195,111

HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

4,816

High

57.8%

99.5%

China

807,962,583

HS 8471 — Computers and ADP

3,512

High

46.5%

88.4%

Other Asia, nes

100

8,281,799,875

HS 8486 — Semi mfg equipment

3,504

High

55.9%

79.1%

China

176,550,963

HS 8542 — Integrated circuits

2,259

Moderate

43.6%

62.5%

Other Asia, nes

105

24,427,995,898

HS 8544 — Wire and cable

3,013

High

53.0%

68.8%

China

122

2,364,037,224

HHI

Classification

CR1 %

CR3 %

Top Supplier

No. of Suppliers

Total Imports (US$)

HS 3818 — Doped semi elements

9,985

High

99.9%

100.0%

China

1,245,417,106

Thailand (2024) Product

Vietnam (2023)* Product

HS 8471 — Computers and ADP

4,296

High

64.2%

79.0%

China

3,592,760,618

HS 8486 — Semi mfg equipment

5,111

High

69.9%

90.1%

Rep. of Korea

580,333,322

HS 8542 — Integrated circuits

1,803

Moderate

30.5%

67.5%

Rep. of Korea

48,094,143,394

HS 8544 — Wire and cable

3,741

High

58.7%

79.0%

China

2,632,332,457

Sources: UN Comtrade database (https://comtradeplus.un.org); author’s table design and tabulation; Claude.ai assisted with computation and graphical presentation. Data are current as of April 26, 2026. Notes: nes = not elsewhere specified. Semi mfg = semiconductor manufacturing. CR1 = concentration ratio, the market share for the top supplier in the market; likewise CR3 = concentration ratio, combined market share for the top three suppliers in the market. From the literature, it is known that for HS 8542 Taiwan is the leading supplier globally and also, specifically, among suppliers in the UN Comtrade classification for “Other Asia, nes” (Gaulier and Zignago 2010; Jones and Krulikowski 2024). *Data for Vietnam for 2024 was not yet available at time of drafting.

Middle-Income Economies: Navigating Digital Dependencies in AI

Table 6A: Top Three Supplier Country Rankings (10 Reporters, Five Products, All Import Flows) Supplier

2019

Latest Year (2024/2023*)

Change in Count

#1 (Gold)

#2 (Silver)

#3 (Bronze)

Total

% of Max

#1 (Gold)

#2 (Silver)

#3 (Bronze)

Total

% of Max

China

28.0%

31.3%

United States

12.7%

16.7%

Rep. of Korea

8.0%

6.7%

-2

Other Asia, nes

6.0%

Japan

8.7%

5.3%

-5

Germany

4.7%

Singapore

6.7%

4.0%

-4

Malaysia

4.0%

3.3%

-1

Vietnam

0.7%

3.3%

Thailand

1.3%

2.7%

Mexico

2.7%

2.0%

-1

Italy

2.0%

1.3%

-1

China, Hong Kong SAR

1.3%

India

1.3%

Paraguay

0.7%

Romania

0.7%

Poland

0.7%

Nicaragua

0.7%

Netherlands

0.7%

Botswana

0.7%

Benin

0.0%

0.7%

United Rep. of Tanzania

0.0%

0.7%

United Arab Emirates

0.0%

0.7%

Türkiye

0.0%

0.7%

Spain

0.0%

France

2.0%

0.0%

-3

Slovenia

0.7%

0.0%

-1

Czechia

0.7%

0.0%

-1

United Kingdom

0.7%

0.0%

-1

Nigeria

0.7%

0.0%

-1

Saudi Arabia

0.7%

0.0%

-1

Luxembourg

0.7%

0.0%

-1

Philippines

0.7%

0.0%

-1

TOTAL (all suppliers)

148

98.7%

146

97.3%

-2

Sources: UN Comtrade database (https://comtradeplus.un.org); author’s table design and tabulation; Claude.ai assisted with computation and graphical presentation. Data are current as of April 26, 2026. Notes: Maximum count per year: 150 = 10 importers × 5 products × 3 ranks. * Latest year = 2024 for all except Vietnam = 2023. “Other Asia, nes” is retained as reported; for HS 8542 and HS 8471, this likely represents Taiwan. Cases with no third supplier for HS 3818: in 2019, Colombia and Kenya; in 2024, Colombia, Kenya and Mexico.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Table 6B: Top Three Supplier Country Rankings (10 Reporters, Five Products, Import Flows ≥ US$1 Billion) Supplier

2019

Latest Year (2024/2023*)

Change in Count

#1 (Gold)

#2 (Silver)

#3 (Bronze)

Total

% of Max

#1 (Gold)

#2 (Silver)

#3 (Bronze)

Total

% of Max

China

28.1%

33.3%

United States

15.8%

13.6%

Rep. of Korea

12.3%

10.6%

Other Asia, nes

7.0%

10.6%

Malaysia

7.0%

6.1%

Thailand

3.5%

4.5%

Vietnam

0.0%

4.5%

Singapore

7.0%

3.0%

-2

Japan

5.3%

3.3%

-1

Mexico

3.5%

3.0%

Paraguay

1.8%

1.5%

Nicaragua

1.8%

1.5%

Germany

0.0%

1.5%

Netherlands

0.0%

1.5%

Spain

0.0%

1.5%

China, Hong Kong SAR

3.5%

0.0%

-2

Poland

1.8%

0.0%

-1

Philippines

1.8%

0.0%

-1

TOTAL (all suppliers)

100.0%

Sources: UN Comtrade database (https://comtradeplus.un.org); author’s table design and tabulation; Claude.ai assisted with computation and graphical presentation. Data are current as of April 26, 2026. Notes: * Latest year = 2024 for all except Vietnam = 2023. Maximum count per year: 57 in 2019 (19 import flows above US$1 billion x top 3) and 66 in 2024/2023 (22 import flows above US$1 billion x top 3). “Other Asia, nes” is retained as reported; for HS 8542 and HS 8471, this likely represents Taiwan. Cases with no third supplier for HS 3818: in 2019, Colombia and Kenya; in 2024, Colombia, Kenya and Mexico.

Middle-Income Economies: Navigating Digital Dependencies in AI

Service Sector AI Dependence Turning now to AI-related dependency with respect to services, the focus turns to middleincome economies and their importation of “telecommunications, computer and information services.” Lacking a more focused statistical product group for tracking AI services trade, this category — as specified in the IMF’s Balance of Payments Manual (BOP6) (IMF 2009) — captures provision of cloudbased AI model services even as it captures much else traded in the IT and communications sector.18 Table 7 considers dependency in the middleincome country sample as measured by indicators of import concentration for the broad services category of telecommunications, computer and information services. The HHI scores are fairly moderate considering the strength of industry leaders in services exports, in particular the United States. For the service sector considered here, the United States does come through with an import market share above 30 percent for Brazil, Colombia and Mexico. And Brazil and Colombia do have moderately high HHI scores, indicating some meaningful concentration occurring. Only Vietnam registers a high level of concentration according to its HHI score. Surprisingly perhaps, Vietnam’s high HHI score is largely driven by imports from its top-ranked telecommunications, computer and information services supplier, Ireland, with a market share of some 51 percent in 2024. This is not an isolated case. In fact Ireland, with a population of approximately five million, emerges as the nominal leading source of IT service imports for five of the 10 sample economies — India, Kenya, Nigeria, South Africa and Vietnam — and as a significant supplier to the remaining five. The bilateral trade data reveal a striking statistical anomaly: This is not a reflection of Irish productive capacity but of the European headquarters structures of major US technology multinationals, which book their non-US revenues and service contracts through Irish-registered subsidiaries (see Box 3).

18 An important reference in this regard is United Nations Conference on Trade and Development (2023), an interagency methodological publication that confirms several aspects of digitally delivered computer services as encompassing AI services trade. The challenge of specifically tracking AI in the economy is an ongoing statistical challenge. But the importance of cloud delivery of AI services and the representation of major AI service providers in the trade rankings for telecommunications, computer and information services qualify it as a potential indicator for pathways open for AI digital services trade.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Sections B and C of Table 7 shed light on further dimensions over time. Both panels reveal a fair amount of year-to-year volatility, reflecting the lumpy nature of trade (which may vary in part in response to large investment projects, for example). Nevertheless, comparing the end points shown in these sections of the table, 2019 and 2024, may provide a starting point for further exploration. In Section B, there appears to be a tendency for concentration as measured by the HHI to increase across the majority of the sample countries. Eight of the countries experienced increases, while Brazil and Colombia experienced a drop in concentration. In Section C, most of the sample experienced a drop in the US-China combined share in imports for this service sector. India, Indonesia and Mexico bucked the trend and experienced marginal increases of less than one percentage point each. The upshot here is that while concentration is rising in most of these countries, it is not directly driven by the United States or China. China’s shares of this import trade remain modest, generally well below 10 percent in each sample country. But the rise of Ireland (the number one supplier in five of the markets with rising HHI scores), appears to be masking an important vector of potential increases in American influence, especially in Africa and Asia.

Summing Up: AI Trade Dependency Middle-income countries face a structural AI dependency rooted in the extreme concentration of frontier capabilities among a handful of firms in the United States and China. While China is a leading supplier for much of the hardware, an important portion — advanced logic chips, specialty fabrication machinery and chemical inputs — is controlled by a relatively small number of firms across the United States, Taiwan, Korea, the Netherlands and Japan, with US export controls acting as the primary regulatory chokepoint. On the software side, the computational scale and data requirements needed to develop frontier models place original LLM development effectively out of reach for most economies. This creates a two-tier dynamic: dominant economies expand on their own terms, while dependent economies tend to participate as a reflection of that expansion — along the lines of the asymmetric relationship Theotonio Dos Santos (1970) identified. Yet, at the firm level, there is active competition among individual AI frontier model developers.

Box 3: Ireland and US Services Exports When a firm in Vietnam or Nigeria pays for US Google Workspace, Microsoft Azure or AWS services, the counterparty in the transaction may be an Irish-registered entity.19 If so, then the payment is likely recorded in balance-of-payments statistics as an import from Ireland rather than from the United States. The scale of this effect is well documented. Ireland’s computer services exports have grown from approximately EUR32 billion in 2015 to EUR196 billion in 2022, which does not correspond proportionally to domestic factor inputs or measured productive capacity.20 A substantial share of Ireland’s external accounts reflects multinational profit shifting and IP-related balance sheet relocations, as noted by institutions such as the IMF and OECD and discussed in Cole Frank (2018). Brad W. Setser (2019) found that Ireland appeared in official statistics as the United States’ single largest export market for certain software and IP-related services, confirming the structural link between US technology multinationals’ headquarters arrangements and Ireland’s apparent export dominance (NB, a portion of this reflects intra-firm flows and accounting structures). Foreign multinationals account for the vast majority of Irish corporate tax receipts, with a small number of large US technology firms contributing a disproportionately large share. The implications for the dependency analysis are significant in two respects. First, the direct US share reported in the WTO Balanced Trade in Services (BaTIS) data used to measure the services imports of the sample economies may constitute a lower bound rather than a ceiling. True US-origin exposure, properly attributed to the ultimate beneficial owner of the service relationship, would be substantially higher once Irish-booked revenues of US affiliates are taken into account. Vietnam appears to be an extreme case, with Ireland accounting for 51.5 percent of reported IT service imports in 2024 against a direct US share of only 3.6 percent — a disparity that seems implausible on economic grounds given Vietnam’s deep integration into US-linked technology supply chains. While Bureau of Economic Analysis data permit identification of a portion of the flows, it is not with sufficient granularity for this type of assessment. Comprehensive attribution of digitally delivered services to ultimate beneficial owners remains an unresolved methodological challenge (Bureau of Economic Analysis 2026).

Middle-income start-ups do have fairly liberal access to these models and others arriving in a wave just behind the frontier. For example, various so-called small language models are pitched at specialty or niche users that may not require the span of capabilities in an LLM. As will be seen in the next section of this paper, there are thousands of start-ups in the middle-income economies building on existing AI models and cloud infrastructure. They do this despite the risks, such as dependency through proprietary API relationships, export-controlled inputs and cloud hyperscaler lock-in.

Of course, not all such transactions go through Ireland: some customers contract with US entities directly. Others are billed through different regional hubs operated by or on behalf of the US suppliers (such as Singapore, the Netherlands, Luxembourg).

20 Ireland does have some real domestic activity in the sector, some of which is exported.

The geopolitical fragmentation of the AI ecosystem compounds this challenge: regulatory constraints make it effectively impossible for most firms to operate freely across both the US and Chinese ecosystems simultaneously, reducing optionality for dependent economies. Diversification — the standard risk management response to supply chain concentration — is structurally impeded in some dimensions of AI in ways that it is not in conventional goods trade. The empirical analysis of traded IT services across the 10 middle-income sample economies bears out this picture, revealing rising concentration in most sample countries and significant US sourcing shares partially obscured by routing through Irish intermediaries. Taken together, the evidence, on balance, points to a dependency that is not the benign interdependence of fluctuating market conditions, but a structural asymmetry with significant implications for development prospects and economic sovereignty.

Middle-Income Economies: Navigating Digital Dependencies in AI

Table 7: Telecommunications, Computer and Information Services Import Dependency, 2024 SECTION A: Cross-Country Snapshot — 2024 (Most Recent Year) Total Imports (US$ m)

US Imports (USD$ m)

CN Imports (US$ m)

US Share

CN Share

US+CN Share

CR1 (top partner)

CR3 (top 3)

CR5 (top 5)

HHI (0-10,000)

Brazil

11,688

4,663

189

39.9%

1.6%

41.5%

39.9%

58.4%

64.5%

1,873

United States

Colombia

2,673

963

36.0%

2.0%

38.0%

36.0%

55.7%

68.1%

1,629

United States

India

21,655

3,170

756

14.6%

3.5%

18.1%

24.0%

44.8%

53.8%

953

Ireland

Indonesia

4,961

564

379

11.4%

7.6%

19.0%

17.7%

44.2%

58.0%

842

India

Kenya

644

5.3%

7.3%

12.6%

27.3%

57.9%

70.5%

1,380

Ireland

Mexico

7,154

2,245

126

31.4%

1.8%

33.1%

31.4%

52.8%

65.9%

1,361

United States

Nigeria

1,785

142

8.0%

2.5%

10.5%

22.9%

54.4%

68.6%

1,169

Ireland

South Africa

7,315

531

242

7.3%

3.3%

10.6%

39.4%

62.1%

71.3%

1,923

Ireland

Thailand

4,551

655

377

14.4%

8.3%

22.7%

19.2%

48.5%

62.6%

965

Singapore

Vietnam

4,264

155

220

3.6%

5.2%

8.8%

51.5%

66.4%

75.4%

2,855

Ireland

Country

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

CR1 Partner (top supplier, 2024)

Table 7: Telecommunications, Computer and Information Services Import Dependency, 2024 (Continued) SECTION B: HHI Trend (2019–2024) — Higher = More Concentrated 2019

2020

2021

2022

2023

2024

Change 2019–2024

Brazil

1,949

1,736

1,659

1,551

1,639

1,873

-76

Colombia

2,241

1,946

1,822

1,630

1,860

1,629

-612

India

923

957

1,014

935

952

953

+30

Indonesia

743

707

763

824

808

842

+99

Kenya

962

1,079

1,011

1,043

1,224

1,380

+418

Mexico

1,308

1,165

1,370

1,355

1,314

1,361

+53

Nigeria

953

903

1,233

1,141

962

1,169

+216

South Africa

1,263

1,524

1,419

1,565

1,983

1,923

+660

Thailand

823

832

922

989

972

965

+141

Vietnam

2,042

2,129

3,421

1,458

1,712

2,855

+813

Country

SECTION C. Combined US + China Import Share Trend (2019–2024) 2019

2020

2021

2022

2023

2024

Change 2019–2024

Brazil

43.1%

40.4%

38.8%

36.5%

38.3%

41.5%

-1.5%

Colombia

43.1%

42.5%

41.7%

38.8%

41.2%

38.0%

-8.6%

India

18.0%

14.9%

14.5%

15.7%

17.2%

18.1%

+0.1%

Indonesia

18.3%

17.5%

18.5%

18.6%

19.2%

19.0%

+0.7%

Kenya

15.0%

13.3%

15.2%

17.4%

15.2%

12.6%

-2.5%

Mexico

32.7%

28.0%

32.8%

31.8%

33.1%

+0.4%

Nigeria

17.2%

14.7%

11.9%

12.0%

12.9%

10.5%

-6.7%

South Africa

13.0%

11.5%

12.7%

12.3%

11.0%

10.6%

-2.4%

Thailand

24.8%

20.8%

17.6%

21.0%

23.4%

22.7%

-2.2%

Vietnam

11.5%

10.1%

7.1%

13.2%

13.5%

8.8%

-2.7%

Country

Source: WTO Stats, “BOP6 Telecommunications, Computer & Information Services,” OECD-WTO BaTIS, https://stats.wto.org, downloaded in April 2026; author’s tabulations and table design with assistance of Anthropic’s Claude.ai (Sonnet 4.6). Note: The China shares may be understated due to Hong Kong and Singapore re-routing of Chinese services exports. See Box 2 for information on the HHI scoring and interpretation. The HHI is tabulated based on imports from all trade partners indicated in the BaTIS data, not just those shown in this table.

Middle-Income Economies: Navigating Digital Dependencies in AI

AI Start-Ups in 10 MiddleIncome Economies The paper now turns to consider AI start-up developments across the 10 sample countries. One of the pathways toward diversification in AI is to be found via development of domestic AI capability and pursuit of domestic innovation. There are gaps in the product offering from the advanced economies, and emerging market entrepreneurs are moving to seize the opportunities. Drawing on the Crunchbase enterprises database during Q1 2026, some 9,554 currently active start-ups were identified that had declared an AI focus in their mission statements or other identifying materials (such as industry tags, website address top-level domains — as in .ai21 or business model descriptions) (see Table 8). This section considers their areas of activity, geographic concentration, scale and investment profiles, including international partners. The data in this section is drawn from Crunchbase, so it should be kept in mind that they are illustrative, depending in part on crowdsourced information rather than representative surveys or official administrative data. Due to gaps in coverage for some firms for some variables, coverage information is included where shares of missing data are substantial. (See Appendix 1 for more details on the Crunchbase data.) Table 8 focuses on scale, size, outside investment, geographic concentration and operations as being most relevant to the dependency assessment. The AI start-up activity varies widely in scale across the sample: India’s ecosystem, at 5,458 active firms, dwarfs all others and is nearly 2.8 times the size of the next largest (Brazil, n=1,964), while Kenya (n=115) and Thailand (n=132) represent considerably smaller but still analytically meaningful start-up populations. The difference in size is partly a reflection of the scale of each host country, but it also appears to be influenced by such dimensions as the domestic policy environment, capital markets and availability of complementary IT resources (for example, human capital and telecommunications infrastructure),

“.ai” is a country-code top-level domain assigned to Anguilla, a British overseas territory in the Caribbean. It is now widely used as a vanity domain by AI companies worldwide and generates tens of millions of dollars annually in registration and licensing fees for the territory (Moreau 2024).

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

among other factors. For statistics based on the pooled sample, note that India and Brazil together account for more than three-quarters of the total population of AI start-ups in the sample. With respect to size, across all 10 countries, the modal employment band is 11–50 employees, pointing to a sector dominated by small but no longer micro-scale firms. These are firms large enough to have a product and a customer base, but almost certainly too small to train or fine-tune large models independently. That places them structurally in the application and integration tier — which is precisely the tier most exposed to the platform dependency. The notable exceptions are Kenya and Nigeria, where the 1–10 band is modal, suggesting a higher share of nascent or sole-trader operations. Reported outside investor engagement — a rough proxy for firms that have progressed beyond early self-funding — ranges from a high of 28.7 percent in Brazil to just 8.7 percent in India (where Crunchbase coverage extends deep into the micro-firm tier), and another low of 11.7 percent in Colombia. The figure for Brazil may be in line with expectations given that the AI ecosystem in Brazil is maturing (with a comparatively high-average firm age) and supported by well-developed financial institutions. The figure for India may seem low in light of the scale and maturity of the Indian IT economy. But bootstrapping (or self-mobilized start-up resources) has proven highly feasible for start-ups in India due to a combination of low-cost structures (including salaries for IT professionals), dense social-capital and family-capital networks, active government subsidies for some start-ups facing early-stage risk at Indian incubators22 and a large pool of technically credentialed mid-career professionals with accumulated savings (Korreck 2019; Wadhwa et al. 2026). Geographic concentration is pronounced in all 10 countries. A single hub city or region accounts for between 33 percent (South Africa: Johannesburg) and 87 percent (Thailand: Krung Thep/Bangkok) of national firms, underscoring the degree to which AI start-up activity in middle-income 22 Even where a start-up accepts government-sponsored partner investment, many of the “investors” are actually government incubators and schemes — MeitY Startup Hub, Indian Institute of Management Ahmedabad Ventures, Small Industries Development Bank of India, Atal Incubation Centres, the Department for Promotion of Industry and Internal Trade and the Technology Development Board. These provide non-dilutive or lightly dilutive capital that keeps founders out of the venture capital (VC) market entirely while still being technically “funded.”

economies clusters around one or two dominant metropolitan centres. These AI start-up firms appear to find value in proximity to weighty urban agglomerations. This is perhaps not surprising as such agglomerations may provide access to talent, research centres, public infrastructure, networking and collaborative opportunities, and financial networks (Krugman 1995). The scale and proximity of start-ups appears to reflect the emergence of AI ecosystems in areas such as São Paulo in Brazil or Karnataka (Bangalore) in India, and potentially in some of the other areas of concentration. With respect to operations, core technology in these start-ups is dominated by the AI-softwareIT triad. The leading industry tag claimed among the pooled start-ups is “artificial intelligence (AI)” at 7,991 firms (83.6 percent of the pooled 9,554). “Machine learning” (2,036 firms/21.3 percent) is the first genuinely discriminating technical signal in that a majority of the start-ups do not claim it. Vietnam is the partial exception, where web development and mobile apps rank close behind machine learning, signalling a somewhat more services-oriented profile relative to its peers. Sector vertical tags exhibit clusters around health care (424/4.4 percent), education (415/4.3 percent) and fintech (335/3.5 percent) — a consistent pattern across the sample. Business model tags are led by consulting (999/10.5 percent) and web development (546/5.7 percent). This is potentially an important point in that these tags give the associated startups the appearance of being IT service firms with AI skills, rather than purpose-built AI developers. Overall, from the operations tagging, it appears that the domestic dynamism is real, but it is largely downstream dynamism: firms building on top of infrastructures they do not control. These are firms integrating AI capabilities developed elsewhere into locally relevant applications. That is valuable, and it does create switching costs and local embeddedness that provide some comparative advantage. But it is a different kind of resilience from genuine technological autonomy.

Middle-Income Economies: Navigating Digital Dependencies in AI

Brazil

314

Colombia

8.7%

5,458

India

24.8%

318

Indonesia

23.5%

115

Kenya

18.7%

332

Mexico

18.3%

240

Nigeria

17.3%

324

South Africa

16.7%

132

Thailand

22.7%

357

Vietnam

Table 8: AI Start-Ups Overview — Baseline Profile, 10 Middle-Income Countries, N=9,554 Start-Ups, Q1 2026

1,964 11.8%

1. Overview Total firms (active) 28.7%

#3 region/city

#2 region/city

#1 region/city

10.1

Parana (n=132, 6.7%)

Minas Gerais (n=141, 7.2%)

São Paulo (n=1, 091, 55.5%)

18.2%

9.7

Valle del Cauca (n=32, 7.3%)

Antioquia (n=141, 22.6%)

Distrito Especial (n=186, 59.2%)

15.9%

7.8

Telangana (n=430, 7.9%)

Maharashtra (n=842, 15.4%)

Karnataka (n=1,282, 23.5%)

13.5%

8.2

Yogyakarta (n=18, 5.7%)

Jawa Barat (n=84, 18.2%)

Jakarta Raya (n=197, 61.9%)

13.0%

6.4

Coast (n=2, 1.7%)

Rift Valley (n=3, 2.6%)

Nairobi Area (n=180, 93.9%)

16.3%

10.5

Nuevo Leon (n=27, 8.1%)

Jalisco (n=30, 9.0%)

Distrito Federal (n=165, 49.7%)

19.2%

6.4

Oyo (n=12, 5.0%)

Lagos (n=185, 65.8%) Federal Capital Territory (n=33, 13.8%)

15.1%

9.6

Johannesburg (Gauteng) (n=22, 6.8%)

Cape Town (n=99, 30.6%)

Johannesburg (n=118, 36.4%)

15.9%

9.2

Nonthaburi (n=4, 3.0%)

Samut Prakan (n=5, 3.8%)

Krung Thep (n=115, 87.1%)

10.9%

7.6

Da Nang (n=16, 4.5%)

Ho Chi Minh City (n=165, 46.2%)

Hanoi (n=170, 47.6%)

2. Geographic Concentration (top three regions/cities by firm count)

Share with ≥ known outside investor (%)

Average age (years) 12.0%

1–10 (35.6% of reported) 51–100 (11.0% of reported)

11–50 (43.6% of reported)

4.1%

1–10 (40.2% of reported) 51–100 (9.3% of reported)

11–50 (45.5% of reported)

8.2%

1–10 (34.8% of reported) 51–100 (10.8% of reported)

11–50 (44.3% of reported)

4.1%

1–10 (28.5% of reported) 51–100 (14.1 of reported)

11–50 (49.5% of reported)

7.8%

11–50 (38.7% of reported) 251–500 (2.8% of reported)

1–10 (55.7% of reported)

5.4%

1–10 (34.4% of reported) 51–100 (9.6% of reported)

11–50 (48.1% of reported)

6.7%

11–50 (38.4% of reported) 51–100 (5.4% of reported)

1–10 (50.4% of reported)

7.4%

11–50 (39.3% of reported) 51–100 (6.3% of reported)

1–10 (49.0% of reported)

9.1%

1–10 (25.0% of reported) 51–100 (16.7% of reported)

11–50 (48.3% of reported)

5.0%

51–100 (17.4% of reported) 1–10 (15.6% of reported)

11–50 (45.7% of reported)

4. Employment Size (Crunchbase standard bands)

3. Firm Age

Share missing founding year coverage

Modal employment range

5.0%

$1M to $10M (67.7% of reported)

$1M to $10M (74.4% of reported)

$1M to $10M (73.3% of reported)

$1M to $10M (68.8% of reported)

Less than $1M (31.9% of reported)

$1M to $10M (68.1% of reported)

Less than $1M (20.7% of reported)

$1M to $10M (71.7% of reported)

Less than $1M (24.7% of reported)

$1M to $10M (70.8% of reported)

Less than $1M (18.2% of reported)

$1M to $10M (76.6% of reported)

$10M to $50M (9.1% of reported)

$1M to $10M (83.3% of reported)

Less than $1M (17.2% of reported)

$1M to $10M (62.9% of reported)

5. Estimated Revenue Range (Crunchbase standard bands)

2nd employment range 3rd employment range Share missing employment data (%)

Modal revenue range

Less than $1M (15.6% of reported)

$10M to $50M (15.5% of reported)

Less than $1M (16.0% of reported)

$10M to $50M (7.6% of reported)

67.5%

Less than $1M (16.9% of reported)

$10M to $50M (4.5% of reported)

50.0%

Machine leaming (n=47, 13.2%)

Less than $1M (21.6% of reported)

$10M to $50M (3.4% of reported)

Machine leaming (n=19, 14.4%)

Web development (n=46, 12.9%)

2nd revenue range

$10M to $50M (6.5% of reported)

52.5%

Analytics (n=18, 13.6%)

Mobile apps (n=42, 11.8%)

$10M to $50M (14.2% of reported)

62.9%

Machine leaming (n=97, 29.9%)

Consulting

$10M to $50M (7.9% of reported)

44.6%

Machine leaming (n=36, 15.0%)

Consulting (n=52, 16.0%)

(n=16, 12.1%)

$10M to $50M (5.6% of reported)

59.1%

Machine leaming (n=60, 18.1%)

SaaS (n=26, 10.8%)

Analytics (n=35, 10.8%)

$10M to $50M (8.5% of reported)

Machine leaming (n=24, 20.9%)

Consulting (n=41, 12.3%)

EdTech (n=23, 9.6%)

3rd revenue range

Machine leaming (n=56, 17.8%)

Internet of things (n=13, 11.3%)

SaaS (n=39, 11.7%)

55.7%

Machine leaming (n=1,298, 18.9%)

Consulting (n=36, 11.3%)

Consulting (n=12, 10.4%)

59.1%

Machine leaming (n=56, 17.8%)

SaaS (n=568, 10.4%)

Analytics (n=36, 11.3%)

49.0%

Machine leaming (n=339, 17.3%)

Analytics (n=50, 15.9%)

Analytics (n=555, 10.2%)

57.9%

#1 industry tag Consulting (n=221, 11.3%)

Consulting (n=43, 13.7%)

6. Top Industry Tags (excluding AI, software, information technology)

#2 industry tag

Analytics (n=194, 9.9%)

Share missing revenue data (%)

#3 industry tag

Sources: Crunchbase (www.crunchbase.com/home), firm-level data set; author’s table design and tabulation; Claude.ai (Anthropic), Sonnet 4.6, supported data parsing, tabulations and table construction. Notes: The table only includes active firms, excluding those that are closed or whose status is unknown. The coverage for India excludes 185 firms with unknown operating status. Location generally refers to region or state, except for Vietnam and South Africa which report at city level. For South Africa, most firms are reported by city, but 22 firms are recorded as “Gauteng, South Africa” (province only, no city); these were consolidated under “Johannesburg (Gauteng),” as they are overwhelmingly Johannesburg metro-area firms. Average age computed from founding year. For employment and revenue, the exact data are not reported. The Crunchbase standard bands are carried over for employment and revenue, as reported. Industry tags covered here exclude AI, software and IT, which are widely claimed by most start-ups.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Investors in AI Start-Ups Investors, of course, provide an important channel for the supply of capital to start-up firms that have promising growth paths. And, as foreshadowed above, some also provide complementary services such as connections to potential markets or experts, managerial guidance or even access to new technology. Foreign investors with ties to AI technology leaders and their global networks can play an especially important role in enabling technology transfer to AI start-ups (Lippoldt 2025). This can be positively transformational for a start-up operation but — depending on the commitments it entails — it may also pose potential risks in terms of technological lock-in and dependency. Where start-ups in middle-income economies find a healthy balance, they may be able to leverage the ideas and counsel of investors in combination with their own local knowledge. A start-up may thereby develop a measure of competitive advantage from its unique position — and, potentially, improve its resilience — thanks to innovation in tailoring AI solutions to conditions in developing countries (Mayer 2021). Across 9,554 start-ups in the sample for this study, some 1,447 (15 percent) have at least one known investor listed in the Crunchbase database. Nearly 60 percent of these firms have more than one recorded investor. Table 9 provides an overview of the pool of known investors servicing AI start-ups in the sample. Across all 10 countries combined, there are 3,497 unique investors that together have made a total of 3,705 known top-five investment placements in the start-ups covered here.23 Brazil and India together account for about three quarters of these investment placements, a figure that is in line with their share of start-ups in the pool. Most of the known investors in the sample are focused on a single target country among the selection of middle-income economies. Only five percent of these investors are active in more than one country in the sample. In this sense, the investment landscape is predominantly local in orientation — even foreign investors tend to concentrate on a single market in this sample — with a thin but highly visible layer of

23 An investment placement is defined here as a cumulative investment that places the investor among the top five investors in a sample firm. The Crunchbase data at the author’s disposal is censored in that placements below the top five are not recorded.

multi-country, cross-border investors at the top. Among the foreign investment leaders, Techstars (United States) leads with presence in eight of the 10 sample countries, followed by Google for Startups (United States) at seven countries, AWS (United States) and Y Combinator (United States) at six each, and 500 Global (United States), Antler (Singapore), FasterCapital (United Arab Emirates), and Startupbootcamp (United Kingdom) at five each. Across the whole group of active cross-border investors, a substantial portion is US-headquartered. This reinforces the risk of a structural dependency in the sense that advice on technology choices and network referrals, for example, may have an implicit bias toward American solutions. Across all types of start-up investment covered here, US dominance is sharpest in Mexico and Nigeria (both 41 percent) and most muted in Thailand (10 percent) and Indonesia (12 percent), where regional Asian investors — Singapore especially — fill more of the foreign investor roles. Accelerators such as Y Combinator and Techstars offer capital and structured cohort-based support programs over three–six months in exchange for an equity stake in a start-up. Incubators tend to offer longer-term arrangements, often anchored by a university or public entity, without requiring equity. Both play important roles in shaping start-up management and technology strategies. In the Crunchbase tagging, sometimes both accelerator and incubator tags are cited for an investor, and the distinction between the two channels of investment may be blurred. In terms of international dependency, the crossborder accelerator segment is a key channel. For this paper’s sample, the United States leads in overall accelerator-type investment activity, accounting for 109 accelerator investors that generated 251 investment placements — 31 percent of all accelerator-type activity across the 10 sample countries, including both domestic and foreign investors. When looking specifically at cross-border accelerator placements, US dominance is even more pronounced: among foreign accelerator investors only, the United States accounts for nearly 60 percent of all such placements. The United Kingdom (6.7 percent of foreign placements) and Singapore (6.2 percent) are the other two main international providers of accelerator investment. By simple counts of the overall “accelerator” tagging, India and Brazil rank second and third, respectively.

Middle-Income Economies: Navigating Digital Dependencies in AI

But their accelerator activity is largely conducted within their own domestic AI ecosystems: India with 76 investors, 172 placements; and Brazil with 28 investors, 149 placements. Together, India and Brazil absorb about 71 percent of all accelerator placements (international and domestic) across the 10 sample countries. The smaller ecosystems are proportionally more dependent on cross-border accelerator pipelines: Kenya, Colombia and South Africa each received between 23 and 26 accelerator placements, and in those countries the top accelerators are predominantly foreign — Techstars, Y Combinator and Google for Startups recur across all three. These smaller nations access program-based investment largely through US-managed pipelines, which risks creating a structural dependency that goes beyond mere financial flows. The relationships grounded in these accelerators shape which founders get visibility, which networks they join and implicitly which technology stacks and market assumptions get validated. India is the only sample country with an overall majority of top five investors being domestic. Brazil comes closest with a plurality of domestic investors. That is, in both cases their domestic investors account for the largest shares of investor, by investor headquarters location. Brazil’s modal type is VC (domestic dominant); India’s modal type is individual/angel (1,216 domestic investors). Both nations can point to the existence of deep investment networks that compare favourably to others in the sample nations. China is almost entirely absent from the AI startup investor ecosystems in the sample economies. Zero Chinese AI start-up investors appear in Brazil, Colombia, Kenya, Nigeria or Vietnam; just nine in Indonesia (7.9 percent), six in India (0.3 percent), two in Mexico and one in Thailand. This is a striking asymmetry compared to the United States. The financial investment side of the dependency relationship for AI start-ups in these middle-income economies is largely USdominated, with essentially no Chinese counterpart at the equity level. While biases in Crunchbase data collection could potentially lead to some undercounting of Chinese investment, the general characterization appears to be confirmed by other reporting. For example, it appears to be consistent with the main thrust in approach for

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Chinese outbound investment such as under the Belt and Road Initiative, which has particular strengths in other areas such as infrastructure and industrial development (Moradi Haghighi and Augustín 2026). Chinese outbound investment in the technology sector has grown significantly, but this often takes the form of large-scale mergers and acquisitions of established firms and of data centre infrastructure deployment (Chow 2026).24 This is a fundamentally different modality from the early stage start-up equity investment captured in the Crunchbase data. Thus, it is not surprising to find that the Chinese investment approach operates largely outside the middle-income AI start-up ecosystems examined here. Chinese entities are present in the AI landscape of several of these countries as infrastructure providers and platform operators. But China does not appear to be a strong venture investor shaping which AI start-ups in middle-income economies get funded, mentored and connected to global networks. A lack of diversity in VC relationships may constitute a risk for the middle-income countries themselves. Early stage capital is an important vehicle not only for funding but for the transfer of soft technology, as has been noted. The choices that become embedded in a young firm — default toolchains, equipment and platforms, along with practices in market strategy, security, standards and internal governance — are often shaped through the investor relationship and its associated mentorship and contact networks. Cumulatively, these influences condition the local AI sector’s preferences, technological compatibilities, network ties and external dependencies. Such transfers can accelerate development, but their direction is not automatically aligned with a country’s own priorities. Care is therefore needed at the level of national policy to ensure that framework conditions preserve appropriate investor access, balance and consistency with national AI strategy, and that the technology choices being embedded remain visible to the policy makers responsible for governance.

24 For example, see Loletta Chow (2026, 20). Chow reports that Chinese technology giants are expanding through greenfield investments and mergers and acquisitions with a focus on “AI applications and cloud infrastructure” and “AI-driven data centers.” This reflects an infrastructure-deployment and product-export model, rather than a local start-up equity investment approach.

Middle-Income Economies: Navigating Digital Dependencies in AI

2.4 (n=563 funded firms)

57 (8.9%)

Accelerator (8.1%)

Venture capital (31.4% of investors)

FCJ Venture builder (BRA) n=21 firms

Google for Startups (USA) n=33 firms

Bossa Invest (BRA) n=63 firms

2.0 (n=37 funded firms)

20 (15.4%)

Individual/angel (27.7%)

Venture capital (46.2% of investors)

Startupbootcamp (GBR) n=2 firms

Y Combinator (USA) n=2 firms

3.2 (n=476 funded firms)

219 (10.0%)

Venture capital (28.9%)

Individual/angel (43.7% of investors)

LVX Venture (IND) n=19 firms

Inflection Point Ventures (IND) n=20 firms

Venture Cataysis (IND) n=25 firms

6 (0.3%)

435 (19.8%)

180 (8.2%)

869 (39.5%)

1, 150 (52.3%)

2,199

India

2.3 (n=79 funded firms)

17 (15.0%)

Accelerator (14.2%)

Venture capital (68.1% of investors)

East Ventures (SGP) n=7 firms

Startup Studio Indonesia (IDN) n=9 firms

Antler (SGP) n=11 firms

9 (8.0%)

14 (12.4%)

0 (0.0%)

70 (61.9%)

43 (38.1%)

113

Indonesia

2.0 (n=27 funded firms)

28 (47.5%)

Venture capital (33.9%)

Accelerator (37.3% of investors)

Google for Startups (USA) n=2 firms

Techstars (USA) n=2 firms

QuantumSavanna VC (KEN) n=3 firms

0 (0.0%)

17 (28.8%)

0 (0.0%)

45 (76.3%)

14 (23.7%)

Kenya

2.7 (n=62 funded firms)

30 (14.0%)

Individual/angel (19.5%)

Venture capital (56.7% of investors)

Y Combinator (USA) n=3 firms

500 Global (USA) n=5 firms

MassChallenge (USA) n=7 firms

2 (0.9%)

89 (41.4%)

15 (7.0%)

162 (75.3%)

38 (17.7%)

215

Mexico

2.2 (n=44 funded firms)

30 (22.6%)

Accelerator (17.6%)

Venture Capital (46.6% of investors)

Techstars (USA) n=3 firms

NCAIR-Google AI Fund (NGA) n=4 firms

Ventures Platform (NGA) n=4 firms

0 (0.0%)

55 (41.4%)

5 (3.8%)

98 (73.7)

30 (22.6%)

133

Nigeria

2.1 (n=56 funded firms)

28 (21.4%)

Accelator (17.6%)

Venture capital (42.0% of investors)

Alphawave Group (ZAF) n=3 firms

Google for Startups (USA) n=3 firms

Technology Incubator (ZAF) n=3 firms

Savant

0 (0.0%)

36 (27.5%)

9 (6.9%)

69 (52.7%)

53 (40.5%)

131

South Africa

2.0 (n=22 funded firms)

8 (16.3%)

Individual/angel (20.4%)

Venture capital (53.1% of investors)

SCB 10X (THA) n=2 firms

InnoSpace Thailand (THA) n=3 firms

NVEST VENTURE (THA) n=4 firms

1 (2.0%)

5 (10.2%)

3 (6.1%)

32 (65.3%)

14 (28.6%)

Thailand

1.7 (n=81 funded firms)

22 (20.6%)

Individual/angel (15.9%)

Venture capital (43.0% of investors)

Antler (SGP) n=6 firms

VSV Capital — Vietnam Silicon Valley (VNM) n=6 firms

Antler (SGP) n=7 firms

0 (0.0%)

17 (15.9%)

16 (15.0%)

60 (56.1%)

31 (29.0)%

107

Vietnam

Sources: Crunchbase (www.crunchbase.com/home), firm-level data set; author’s table design and tabulation; Claude.ai (Anthropic), Sonnet 4.6, supported data parsing, tabulations and table construction. Notes: Investor counts are based on Crunchbase investor records linked to the Crunchbase firm-level data set (April 2026). The domestic/foreign classification takes into account the investor headquarters country. “No location data” = some investor records lack country data. Top three investors are ranked by the number of local AI firms backed. The investor home country is shown in parentheses as the ISO three-letter code. The modal investor type is based on Crunchbase type tags; investors may carry multiple type tags. The average number of investors per funded firm is computed from start-up files based only on active firms with ≥one outside investor

Average investors per funded firm (funded firms only)

5. Investment Intensity

Accelerators and incubators (n, % of total)

2nd investor type

Modal investor type

4. Investor Type Profile

#3 investor

#2 investor

#1 investor

Acelera Región (COL) n=3 firms

0 (0.0%)

China-based investors (n, % of total)

3. Top Three Investors in Terms of the Number of AI Sample Firms Backed

33 (25.4%)

8 (6.2%)

95 (73.1%)

27 (20.8%)

130

97 (15.2%)

201 (31.4%)

155 (24.2%)

284 (44.4%)

640

Colombia

US-based investors (n, % of total)

2. Investor Origin

No location data (n, % of total)

Foreign investors (n,% of total)

(n, % of total)

Domestic investors

Total active investors

1. Investor Counts

Brazil

Table 9: AI Start-Up Investment Overview, 10 Country Comparison, Q1 2026

AI Policy and International Economic Relations Across the sample countries, AI policy is at various stages of development. But the AI sector is already having a positive economic impact in some areas, which may help to raise the priority of sorting out policy on a range of issues, from safety guardrails and trustworthiness to AI infrastructure and social impacts.25 The paper next takes a preliminary look at AI start-up employment, which is often a priority issue in the minds of many stakeholders and consequently policy makers. It then turns to consider indicators of policy development and implementation.

Employment Zeroing in on the AI start-up topic, one issue to consider concerns employment in the AI sector. This is rising. The firm-level data does not provide precise employment data but rather includes employment range data for most firms. Taking those estimates and summing them up across the AI start-ups in each country, one can develop an estimate of employment range for the entire group. The actual number is likely to be somewhere in between, so a mid-point was also calculated. Table 10 presents estimates for employment in the AI start-ups that have provided employment ranges. The total estimate for the entire sample ranges between roughly 440,000 to nearly 1.3 million. Even in the low case, the national totals range from low thousands in Kenya, Nigeria and Thailand to hundreds of thousands in India and tens of thousands in Brazil, Indonesia and Vietnam. Across all sample countries, midrange estimates put AI start-up employment at 7.1 percent of total ICT sector employment. It is still very early days but AI start-ups are starting to have weight in the economy, including via the politically notable dimension of employment. The number of stakeholders is rising.

Policy Development Table Table 11 presents the state of play in AI policy development as reported for key AI policy elements via the OECD.AI Policy Observatory, the IMF AI Preparedness Index and the 25 For background on responsible AI, see Maslej et al. (2024), chapter 3.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Oxford Insights Government AI Readiness Index. The table compares the performance of the G7 countries and China with the performance of the 10 sample countries. With respect to the OECD’s tracking of AI policy, all of the sample countries are participating in the database. Action on each of four policy elements is tracked and the start year for policy work in each area noted for each country generally based on documentary submissions. For comparability, the table is based on standardized approaches employed by the OECD. But there is some variation in the diligence of the reporting nations. As a consequence, start years for the various elements may depend on how far back reporters were willing to document. Confirmation of a policy’s actions relies on the timely submissions from reporters in each country. With those caveats in mind, the G7 countries and China performed relatively well. Action in each of the four policy elements was generally in place or under way: → national strategy or action plan in place; → national-level AI governance bodies and mechanisms; → national-level regulations, guidelines and standards; and → AI policy initiatives, programs and projects. The only gap in submissions for these countries is Italy’s failure to submit documentation on nationallevel regulations, guidelines and standards. Among the sample countries, Brazil, Colombia, India, Mexico, South Africa and Thailand could tick all of the boxes. Indonesia, Kenya, Nigeria and Vietnam were each missing one or more policy elements. But all of the sample countries had in place a nationallevel strategy or action plan, which is a critical step in identification of strengths, weaknesses and next steps toward improved performance. The performance indicators provide insights into how the policy is working on the ground. Two sets of indicators are drawn upon. First, the IMF AI Preparedness Index (AIPI) is considered, which is based on a large data set reflective of digital infrastructure, human capital and labour market policies, innovation and economic integration, and regulation and ethics. The scoring range is from 0 to 1, with higher values representing more favourable AI preparedness. The middle-

income economies all significantly underperform the G7 average score (by a substantial margin of between 0.181 and 0.381 points on a scale of 0 to 1). Thailand, followed by Mexico, scored highest among the sample countries. Nigeria scored lowest by a wide margin. China underperformed the G7 average by a small margin (0.082) and actually had a better score than Italy. A second set of performance indicators zeroes-in to focus specifically on government AI readiness with an index score from 0 to 100, with higher scores representing better readiness. By this indicator, the sample countries performed a bit better than with the AIPI. Brazil achieved an overall ranking within the range for the G7 countries, as did China. Italy’s overall ranking fell below that of Brazil. Brazil, India, Thailand and Vietnam each had scores above the G7 average in one or two elements of the six in the index. China beat the G7 average for four of the

six elements. But all of the 10 sample countries fell well below the G7 average for economy-wide AI infrastructure, a critical bottleneck for development of the AI sector. Similarly for resilience (which includes safety and security and risk management), all of the sample countries lagged the G7 average by a substantial margin. The sample countries all performed poorly on AI diffusion and development, which encompasses human capital development and the use of AI in the real economy by industry, researchers and civil society. The bottom line is that while the scores for this set of indicators for the sample countries may vary, there are shortfalls in some important areas. Performance could be improved. And a close study of the table provides some helpful indications of priority action areas for AI policy makers to consider.

Table 10: AI Start-Up Employment Estimates, 10 Middle-Income Countries, Q1 2026

Country

Total Firms

Firms Counted

Firms Excluded

Employment Low

Employment Mid

Employment High

ISIC-Rev.4 Sect. J Employment (total)

Brazil

1,964

1,865

93,195

182,110

282,890

1,779,471

2025

10.2%

Colombia

314

301

6,571

13,815

21,360

406,199

2025

3.4%

India

5,458

5,006

452

263,966

496,390

773,820

6,756,306

2024

7.3%

Indonesia

318

305

23,165

37,290

56,720

1,091,177

2023

3.4%

Kenya

115

106

1,466

2,975

4,590

125,447

2022

2.4%

Mexico

332

314

7,574

15,695

24,130

526,954

2025

3.0%

Nigeria

240

223

3,033

6,345

9,880

163,924

2024

3.9%

South Africa 324

300

16,330

29,775

48,520

N/A

Thailand

132

120

3,950

8,515

13,200

184,284

2024

4.6%

Vietnam

357

339

22,439

41,140

60,180

334,675

2024

12.3%

TOTAL: 10 Countries

9,554

8,879

675

441,689

834,050

1,295,290

11,368,437

Various

7.1%

ILOSTAT Data Year

AI Start-Up as % of ISIC-J

Source: Crunchbase (www.crunchbase.com/home), with data parsed by Claude.ai (Anthropic), Sonnet 4.6, (for start-up firm-level data); International Labor Organization, ILOSTAT, LFS database, indicator “EMP_TEMP_SEX_ECO_NB_A,” “Employment by sex and economic activity,” ISIC-Rev.4 Section J, “Information and communication, total,” thousands, downloaded April 2026, https://ilostat.ilo.org/data/?cat_mode=subject; Claude tabulations (Anthropic); author’s table design, tabulations and review. Notes: Crunchbase provides employment range estimates for most firms. The aggregated AI start-up employment totals reflect sums of the minimum, midpoints and maximum employment ranges for each firm. Low = band minimum; mid = band midpoint; high = band maximum. N/A = not available. ISIC = International Standard Industrial Classification of All Economic Activities. “Firms Excluded” = firm-level employment data is missing or incomplete for these records. ILOSTAT does not cover data from South Africa with respect to ISIC-Rev.4 (they have broad sector aggregates only). Nigeria has a series break in 2023, with a decline that likely reflects survey methodology change. India has a sharp rise in 2018–2024, partly reflecting a switch to an alternate methodology in the domestic statistics tabulations. The AI start-ups employment as a percentage of sectoral employment was calculated using the mid-point of the recorded AI start-up employment ranges for each country. In calculating the sample average AI employment share, the total mid-point employment for the 10 countries was adjusted to exclude South Africa, given that the corresponding Section J employment data was not available for that country. The ISIC-Rev.4 Section J total employment data lag the start-up data, which may create a modest upward bias in the AI share calculations. Middle-Income Economies: Navigating Digital Dependencies in AI

Table 11: Comparative AI Policy Development Status as of Q1 2026

Yes

National Strategy or Action Plan In Place

2025*

2017

Start Year

Yes

NationalLevel AI Governance Bodies and Mechanisms

2019

2018

2017

Start Year

Yes

NationalLevel Regulations, Guidelines and Standards

2016

2018

Start Year

Yes

AI Policy Initiatives, Programmes and Projects

2000

1981

Start Year

0.713

0.771

0.635

Global Rank (n=195)

84.50

77.50

92.52

Policy Capacity

73.28

76.27

72.32

91.77

78.36

AI Infrastructure (Economy-Wide)

91.17

90.00

76.85

82.50

91.14

Governance

82.89

97.18

78.60

92.87

49.02

Public Sector AI Adoption

66.24

68.20

69.03

88.16

74.67

AI Diffusion and Development

73.37

88.63

67.57

78.28

77.34

Resilience

Government AI Readiness Index 2025

China

2017

OECD.AI Reported AI Policy-Relaated Initiatives 2025

United States Yes

0.753

0.698

IMF AI Preparedness Index (AIPI) 2023

Canada

1956

2010

72.28

Yes

61.38

Yes

70.24

2016

85.77

2016

63.56

Yes

69.50

Yes

2018

0.621

2022

1969

Yes Yes

Yes

2020

2018 Yes

2018

N/A

Yes No

Yes

2022

Germany Yes

France

Italy

77.74

80.75

67.88

66.13

69.66

50.28

72.71

80.49

35.33

46.57

80.46

85.57

70.70

94.78

59.46

70.62

74.33

67.13

78.18

58.27

70.00

83.79

53.74

65.50

54.85

70.74

80.50

88.00

79.07

44.09

32.22

0.733

64.03

73.77

34.07

35.48

2012

63.31

61.25

66.11

62.14

Yes

0.501

60.28

67.64

70.63

2003

0.489

96.00

39.17

Yes

2013

2018

77.00

52.10

2023

Yes

0.493

35.00

69.00

Yes

2009

0.516

42.66

2019

2025

0.532

0.445

40.81

Yes

2020

Yes

2013

39.45

37.35

Japan

Yes

2018

47.46

37.28

43.28

83.30

Yes 2017

Yes

78.13

64.93

47.03

52.58

68.46

2021

n/a

Yes

76.58

73.21

85.86

68.93

2020 Yes

2018

39.10

70.44

92.27

Yes

2018

80.50

60.05

88.05

72.49

Yes 2017

Yes

43.00

57.41

56.38

100.00

2021

2025

Yes

0.336

53.50

88.00

2025 Yes

n/a

2020

0.497

0.731

Yes

2023

Yes

2006

0.482

0.536

2000

Yes 2018

2022

Yes

2017

2019

Yes

Brazil

Yes 2020

Yes

2016

Yes

2018

Colombia

Yes

2018

2025

n/a

Yes

n/a

2017

Yes

India

Yes

2019

Yes

2018

Indonesia

Yes 2025 Yes

n/a

2018

Yes

Kenya

Yes 2024

Yes

2021

Mexico

Yes**

2021

2022

Yes

Nigeria

Yes

United Kingdom

South Africa

Yes

0.717

Thailand

G7 Average

Vietnam

Sources: OECD.AI Policy Observatory (2025), “The OECD.AI Policy Navigator,” https://oecd.ai/en/dashboards/national, accessed on April 10, 2026; Cazzaniga et al. (2024); Iida et al. (2026); author’s tabulations. Notes: n/a = not applicable. Bold index numbers indicate areas where the developing country performance exceeds the G7 average. See Appendix 1 for details on the OECD AI Policy Navigator. The AIPI assesses the level of AI preparedness across countries, based on a rich set of macro-structural indicators that cover the countries’ digital infrastructure, human capital and labour market policies, innovation and economic integration, and regulation and ethics. The range is [0,1] with higher values representing more favourable AI preparedness. For the Government AI Readiness Index, policy capacity includes policy vision and policy commitment; AI infrastructure is considered economy-wide and includes compute capacity, enabling technical infrastructure and data quality; governance includes governance principles and regulatory compliance; public sector AI adoption includes government digital policy and AI delivery; AI diffusion and development includes human capital, AI sector maturity (ability to meet local client needs) and AI technology diffusion (among industry, researchers, civil society); resilience includes social transition and safety and security (risk management). The Government AI Readiness Index takes into account these six elements. The scores for each element can range from 0 to 100, taking into account a range of underlying indicators. The index methodology is empirically grounded and draws on secondary sources, original desk research and direct submissions from countries that are able to satisfy the indicator requirements. *The United States’ entry in the OECD.AI database for national AI strategy reflects the current administration’s 2025 executive order. But it is worth noting that the United States has had at least three significant predecessor documents: National Science and Technology Council (2016); Trump (2019); Biden (2023). ** South Africa has a national AI policy framework in place. A national AI policy is being prepared for public consultation this year. The draft was published and then reportedly withdrawn due to alleged AI hallucinations in the text (Frank 2026).

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Thinking Outside the Box: Advanced Economy Partners and the Path to Managed Interdependence The foregoing analysis establishes that the 10 sample middle-income countries will remain structurally dependent on the leading AI economies — particularly the United States and China — for access to frontier AI models, advanced semiconductor equipment and hyperscaler cloud infrastructure for the foreseeable future. In view of development constraints in these sample countries, full technological independence in the AI sector appears neither realistic nor, on efficiency grounds, desirable. However, under present geopolitical conditions, domestic capacity development and international supplier diversification may be prudent as part of a national strategy to improve resilience while benefitting from engagement in the AI economy. This section argues that managed interdependence — a condition in which these countries participate meaningfully in the AI economy, capturing growth and productivity benefits while preserving sufficient policy autonomy to shape their own governance choices and avoid lock-in to the dictates of either dominant power — is achievable, and that its architecture depends on two complementary strategies. These two strategies should reinforce each other. The first is domestic: strengthening the institutional foundations for AI governance, infrastructure and human capital development, the shortfalls in which are visible in Table 11. The second is international: cultivating a set of alternative partnerships with advanced economies that share aligned interests in a more diversified AI ecosystem. For example, as discussed below, advanced economies such as Canada, the European Union, Japan and the United Kingdom have strengths in various dimensions of the AI economy and are striving for diversification, including with partners in the Global South.

The New Geography of AI Partnerships The dominant logic of the previous era of globalization was geographic: countries traded with nearby partners, multinationals organized supply chains around regional hubs, and policy influence followed manufacturing clusters. As the intangible economy grows in importance, that logic is being supplanted by a new organizing principle in which physical distance has lost much of its constraining force. Erik van der Marel (2026, 7) demonstrates empirically that in the intangible economy — defined by data flows, IP, AI-enabled services and cross-border research collaboration — a doubling of physical distance reduces goods trade by roughly 40 percent but reduces cross-border data flows by only three percent. What has replaced geographic proximity as the binding constraint on cross-border exchange is geopolitical distance: the degree of political trust, institutional compatibility and shared regulatory norms between countries. Critically, countries with weak domestic institutions face roughly double the adverse impact of geopolitical distance on intangible trade flows, amplifying their exclusion from the networks of knowledge exchange that drive AI development (van der Marel 2026, 10). This finding connects directly to the performance gaps visible in Table 11. The shortfalls of the sample countries on AI infrastructure and resilience are not merely operational deficiencies to be remedied through spending; they represent the institutional gaps that may reduce the probability of the crossborder investment and partnership flows necessary to participate in the AI economy. For example, Daniel Hain, Sofia Johan and Daojuan Wang (2016) demonstrate this mechanism specifically for VC using the case of China as an example. In emerging economies, unlike in developed ones, institutional trust — defined as exante confidence in the legal framework, its enforceability and the honest behaviour of societal actors — is a necessary precondition for attracting foreign VC and for enabling cross-border syndication between foreign and domestic investors. The authors note that relational trust built through personal networks is insufficient as a substitute for institutional trust. However, as Hain, Johan and Wang (2016, 744) note, “even in the absence of relational trust, we expect countries with high institutional trust to hold higher VC inflows and syndication activities, despite potential social and geographical

Middle-Income Economies: Navigating Digital Dependencies in AI

distance.” The implication for AI policy is direct: the national AI strategies, risk-based governance frameworks and data protection regimes being developed across the sample countries — and documented in Table 11 — are not administrative exercises in regulatory compliance. They are the institutional trust infrastructure that makes crossborder AI investment and partnership feasible. In this respect, institutional quality investment is simultaneously a domestic AI development priority and an international market-access strategy. The global regulatory landscape is converging around three broad governance models: an open model led by the United States, which relies primarily on firm-level accountability with limited state oversight; a conditional model pioneered by the European Union, which treats privacy as a fundamental right and conditions cross-border data flows on legal and institutional compatibility; and a control model associated with China, which links data governance closely to national security and state oversight (van der Marel 2026; Ferracane and van der Marel 2021; Lippoldt, 2024). Crucially, alignment with these models does not follow regional manufacturing patterns. Countries deeply integrated into Factory Asia do not automatically adopt Chinese data norms; Canada and Mexico, core members of Factory North America, follow the EU conditional model (van der Marel 2026).26 The sample countries’ AI governance choices are not predetermined and may be strategically consequential. Brazil’s pursuit of EU-compatible governance through its AI bill (PL 2338/2023), which adopts a riskbased architecture broadly aligned with the EU AI Act, is not merely a technical regulatory choice; it is a decision that may drive which networks the country’s AI economy will be positioned to participate in over the long term.

Canada: Research Depth and Middle-Power Positioning Canada’s claim to AI relevance rests on solid foundations, having launched one of the world’s first national AI strategies in 2017. Three internationally recognized institutes — Mila in Montreal, the Vector Institute in Toronto and the Alberta Machine Intelligence Institute in Edmonton — anchor Canada’s AI research ecosystem, together claiming approximately 10 percent of the world’s leading AI researchers and a top-five global ranking for highly cited AI research (The Hub Staff 2026). However, those foundations face documented commercial challenges. Hossein Rahnama (2025) notes that despite nearly a decade of national AI strategy, Canada’s social and economic impact in AI is lagging behind its peers, with talent retention and weak commercialization performance as primary constraints.27 This illustrates the limits of research leadership without sufficient pathways to AI implementation in the real economy — a lesson potentially applicable to countries with AI research ambitions such as Brazil, India and Vietnam. A Canadian-British initiative illustrates how research depth can translate into engagement with the sample economies directly. Through the Artificial Intelligence for Development (AI4D) program — launched in 2020 as a CDN$20 million International Development Research Centre (IDRC)Swedish International Development Cooperation Agency (Sida) partnership28 and expanded in 2024 into a CDN$100-million-plus partnership with the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO) — Canada has funded responsible AI policy research hubs, multidisciplinary university AI laboratories, and locally led innovation across Africa, with the second phase extending into Asia (IDRC 2025; FCDO 2025). As a funder-collective model pairing two advanced-economy partners with local institutions, AI4D is a working example of the partnership-based capacity building this section advocates and one that has been most active in precisely the lower-income end of the sample.

27 For example, Ryan Khurana (2025) notes that Denmark’s business AI adoption rate was roughly twice that of Canada. 26 Van der Marel (2026, 5) uses the terms “Factory Asia” and “Factory North America” to refer to “clusters anchored by a major economy and surrounded by neighbouring countries specializing in intermediate goods.” He further references Richard Baldwin (2016) in this context.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

28 The first phase of AI4D was a partnership between IDRC and Sida, which jointly provided the initial funding (see https://sida.se/en/for-partners/researchpartners/research-calls-and-grants). The 2024 second phase is led by IDRC and the FCDO.

Canadian Prime Minister Mark Carney’s articulation of “value-based realism” at Davos in early 2026 — calling for middle powers to build coalitions based on institutional reliability and rules-based engagement rather than waiting for the restoration of the previous world order — provides an ideological frame that resonates with the managed interdependence argument of this section (Muñoz Sanchez 2026). Canada signed AI cooperation memoranda of understanding with Germany, the United Kingdom and the European Union at the G7 digital ministers’ meeting in Montreal in December 2025. Canada’s leadership in developing the MultiParty Interim Appeal Arbitration Arrangement at the WTO provides a functioning dispute resolution mechanism in light of the suspension of the Appellate Body, which may prove broadly valuable for AI-related goods, services and potential new digital trade commitments. In relation to Japan, Canadian research collaboration under the Multimodal Foundation Model Development Project for AI Robots and Physical AI would fit well with a managed interdependence strategy.

European Union: Governance Template, Infrastructure Model and Trade Vehicles The European Union’s contribution to the managed interdependence architecture operates across governance, infrastructure and trade policy dimensions, each with specific relevance to the sample of middle-income countries.29 The EU AI Act30 entered into force in August 2024 with obligations phasing in through 2027. It represents the world’s most developed risk-based AI governance framework. The act’s influence already extends beyond EU borders. Brazil’s senatepassed AI bill (PL 2338/2023) adopts a risk-based architecture and transparency obligations broadly compatible with the EU approach, aligning Brazil 29 In the context of balancing, it is worth noting that the European Union and other advanced economies have strong bilateral trade accords that contribute to their mutual resilience. Japan has a bilateral trade agreement with the European Union and a digital trade agreement with the United States. Canada has similar deals with the European Union and United States. The United Kingdom has in place a trade and cooperation agreement with the European Union as well as a less-comprehensive “economic prosperity deal” with the United States. 30 EC, Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act), [2024] OJ, L 2024/1689, online: <https://eur-lex.europa.eu/eli/reg/2024/1689/oj>.

with European governance norms while adapting them to the local institutional context (Barbieri Advogados 2026). This dynamic — the tendency for EU regulatory frameworks to diffuse globally as third countries align standards to preserve market access — is a mechanism by which regulatory alignment with the European Union’s data governance model creates durable access to EU research “idea” partnerships and investor networks, as described by van der Marel (2026).31 The European Union’s infrastructure investment model offers a more immediately replicable contribution. The AI Continent Action Plan of April 2025 targets €200 billion in AI investment across five pillars, centred on 13 AI factories — shared computing facilities using Europe’s supercomputer network — and up to five AI gigafactories under the €20 billion InvestAI facility (European Commission 2025a). The principle of publicly funded shared compute infrastructure as a means of reducing hyperscaler dependency is being demonstrated at scale. It provides a concrete model for the sovereign AI infrastructure sought in the sample countries’ national AI strategies. At the trade policy level, the EU-Mercosur agreement, in provisional application since May 2026, creates a framework with digital trade dimensions directly relevant to Brazil and the other active Mercosur parties (Argentina, Uruguay and Paraguay).32 While not a traditional trade agreement, the EU-OACPS (Organisation of African, Caribbean and Pacific States) partnership anticipates cooperation in digital capacity building, infrastructure and skills development, providing a channel for AI-relevant support for Kenya, Nigeria and South Africa and potentially influencing future regulatory alignment. The CPTPP-EU Trade and 31

Two qualifications apply. First, the EU framework is itself in evolution: the Digital Omnibus proposal of November 2025 defers the AI Act’s high-risk compliance deadline to December 2027, and a wider digital fitness check of the entire EU digital regulatory architecture is planned for Q1 2027 (European Commission 2025b). Countries seeking alignment with the European Union should focus on the AI Act’s core principles rather than specific compliance timelines that the European Union itself is revising. Second, as Andrea Dugo, Fredrik Erixon and Dyuti Pandya (2026) highlight, even a well-designed EU regulatory framework such as the Digital Markets Act can generate regulatory incoherence when enforcement expands prescriptively beyond its original basis. These authors advocate for governance frameworks that are pragmatic, proportional and predictable. For background on the AI Act’s origins, see Barbara Moens and Melissa Heikkilä (2025).

32 The agreement provisionally applied from May 1, 2026, removes cross-border barriers for cloud services, financial services and software, but leaves data localization, data transfer and digital regulation alignment for future negotiation. Venezuela, a Mercosur member suspended since 2016, is not a party to the agreement. Bolivia, which joined Mercosur in July 2024 after negotiations were concluded, is similarly not a party.

Middle-Income Economies: Navigating Digital Dependencies in AI

Investment Dialogue, inaugurated at the Melbourne ministerial in November 2025, establishes a nascent but real bridge between the two international frameworks most relevant to the sample group, covering Mexico and Vietnam already, while Colombia and Indonesia are exploring CPTPP membership (CPTPP Commission 2025).33

Japan: Equipment Leadership, Domain-Specific AI and Regional Governance Architecture Japan occupies a distinctive position in this landscape, combining world-class strengths in AI-related specialist manufacturing — advanced semiconductor equipment, robotics, and precision hardware — with a deliberately engineered domestic AI ecosystem and an increasingly active international governance role. The institutional foundations for Japan’s current AI capacity include the 2004 incorporation reform of national universities, which granted institutions independence from government management and established an open innovation model linking universities with industry. Kazuyuki Motohashi (2024) documents that this produced durable gains in university patenting and start-up formation without compromising research quality — a model of university-industry-government coordination potentially instructive for middle-income countries building their own AI innovation ecosystems. Japan’s domestic AI policy is structured around the full stack from computing infrastructure through foundation models, domain-specific models and applications, with an explicit overseas expansion vector at each layer (Ministry of Economy, Trade and Industry [METI] 2026). The Generative AI Accelerator Challenge (GENIAC), launched in February 2024, supports Japanese AI firms through subsidized computing resources, facilitation of collaboration with AI users and data holders, and community exchange among developers. Its coverage spans agriculture, manufacturing, health care, finance and services — economic priority sectors for most of the sample countries. While GENIAC support requires Japan-based location, international collaboration is explicitly welcomed.34 33 This agreement covers 11 Pacific Basin countries and the United Kingdom. For details on the agreement, see https://apfccptppportal.ca. 34 Hibiki Tsuruoka, Deputy Director, AI Industry Strategy Office, IT Industry Division, Commerce and Information Policy Bureau, METI, meeting with author and Devin Woods, Tokyo, March 22, 2026.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

A significant new initiative, a five-year multimodal foundation model development program (FY2026– 2030) with a budget of approximately ¥380 billion (around US$2.5 billion), focuses on physical AI — factory automation, robot automation and autonomous vehicles — and will make open-weight models available to users through research and development collaboration with global partners including Canada (METI 2026).35 Japan’s AI Act, enacted by the Diet in June 2025, establishes an AI strategic headquarters chaired by the prime minister, provides for formulation of an AI basic plan and mandates guidelines aligned with international norms (METI 2026; Gehrke 2025). It enshrines international cooperation as a basic principle. Japan leads the Hiroshima AI Process, whose Friends Group had grown to 58 countries and regions by October 2025 (Yoshinaga 2025). Japan’s safety philosophy is distinctive: METI’s approach focuses specifically on user safety and guardrails for physical AI applications, rather than the national security and cybersecurity framing that characterizes the United Kingdom’s approach.36 This focus on applied, user-facing safety is arguably better suited to the immediate concerns of middle-income countries deploying AI in health care, agriculture and public services. Japan’s partnership architecture for several of the sample countries is already operational. The ASEAN-Japan Co-Creation Initiative for AI, proposed at the 28th ASEAN-Japan Summit on October 26, 2025, covers AI solution co-creation, governance capacity building including AI strategy formulation support, human resource development and infrastructure support, such as local-language model development (METI 2026; Ministry of Internal Affairs and Communications of Japan 2026). METI has explicitly stated its ambition to extend this cooperation to other parts of the Global South beyond ASEAN.37 Concrete steps are already in evidence. In 2025, METI conducted opinion exchange events in Indonesia through the Economic Research Institute for ASEAN and East Asia (ERIA). METI also conducted business-matching events with India, connecting Japanese and Indian AI firms. ERIA

35 Ibid. 36 Ibid. 37 Ibid.

also produced policy recommendations for the Indonesian and Indian governments (METI 2026).

United Kingdom: Capacity, Safety Infrastructure and Commonwealth Channels The United Kingdom’s contribution to the managed interdependence framework is best understood not as a regulatory model-setter but as a capacity and ecosystem partner. The government’s AI Opportunities Action Plan, one year into implementation as of January 2026, reported a tenfold increase in AI compute capacity between 2024 and 2025, on a trajectory toward potentially 20 times more by 2030. This comes alongside £68 billion in investment pledged since January 2025. Some one million AI training course places have already been delivered against a target of 10 million workers upskilled by 2030 (Department for Science, Innovation and Technology and Number 10 Data Science 2026). The United Kingdom hosts Europe’s largest AI sector, with AI accounting for one-third of all UK VC investment. The United Kingdom stands out as an important source of AI services, investment resources (including VC), technical expertise and start-up ecosystem connectivity for partners seeking alternatives to US and Chinese hyperscaler models. One concrete example of capacity-oriented engagement for case study countries in Africa and Asia may be found in the Canadian-British AI4D initiative discussed above (IDRC 2025; FCDO 2025). The United Kingdom is a leader on AI safety. The government convened a well-attended intergovernmental conference at Bletchley Park in 2023 (GOV.UK 2023). That event culminated in a declaration for co-operation on AI risk assessment and response, supported by cooperation in relevant scientific research. In addition, the UK AI Security Institute maintains active engagement with the G7’s Hiroshima AI Process and bilateral partnerships with AI safety institutes in multiple jurisdictions. In contrast to Japan’s user-safety focus, the United Kingdom’s approach tends to emphasize the national security and cybersecurity dimensions of AI risk. This is a complementary orientation that reflects the different threat environments each country addresses and together they may provide the sample countries with access to a broad range of safety methodologies.

The Commonwealth channel has gained some AIrelevant institutional traction with the adoption of the Commonwealth Model Law on Digital Trade in September 2025, launched at the Caribbean Telecommunications Union’s ICT Week in Jamaica, with St. Kitts and Nevis as the first pilot jurisdiction (Commonwealth Secretariat 2025). This model law provides legal recognition for electronic transactions, signatures and transferable records — the foundational legal infrastructure for AI-enabled digital commerce — across the Commonwealth’s 56 member states. Kenya, Nigeria, South Africa and India are all Commonwealth members, giving them direct access to this instrument and to the Secretariat’s implementation support program. While the model law addresses domestic legal recognition rather than cross-border data flow taxation, it has the potential to create transactional legal certainty that lowers barriers to AI service delivery across Commonwealth jurisdictions and complements broader data protection frameworks.

International Coordination: CPTPP, the Commonwealth and International Organizations The international architecture open to the sample countries for coordination and development of the AI economy may be richer than is commonly appreciated, though its coverage remains uneven. This section of the paper provides a few illustrative examples relevant to the sample countries including with respect to the international digital economy and policy formation and governance. The CPTPP’s e-commerce chapter provides a relatively stringent international instrument for digital trade disciplines, permanently prohibiting customs duties on electronic transmissions and extending that prohibition to content transmitted electronically — going beyond the WTO moratorium38 by making the commitment permanent and binding (Schweitzer and Saccomanno 2024).39 Mexico and Vietnam are full CPTPP members; Indonesia has been 38 Launched in 1998, the WTO moratorium committed WTO members not to impose customs duties on electronic transmissions, thereby preserving duty-free, predictable cross-border trade in digitally delivered products (WTO 1998). This arrangement required periodic renewal and lapsed on March 30, 2026. 39 Japan, Indonesia, Thailand and Vietnam are also parties to the Regional Comprehensive Economic Partnership in Asia where China is also a member (India notably opted out at a late stage). That agreement includes some important digital trade facilitation provisions, but also significant exemptions from commitments in cases of public policy concern including with respect to national security.

Middle-Income Economies: Navigating Digital Dependencies in AI

formally identified as an aspirant aligned with the Auckland Principles, with its accession process to commence in 2026 (CPTPP Commission 2025). The November 2025 general review committed to enhanced negotiations on the e-commerce chapter, with Vietnam as 2026 chair wellpositioned to advance developing country interests. The WTO Joint Statement Initiative on E-Commerce offers another example.40 Its 66 concluding parties — notably not including any of the sample countries — agreed in March 2026 to proceed with implementation of the recently concluded Agreement on Electronic Commerce with a view to future integration into the WTO framework.41 This provides a complementary vehicle covering digital transaction facilitation, consumer protection guardrails and cross-border data flows. For the three African countries in the sample and India, the Commonwealth Model Law may provide a further relevant channel for establishing AI-compatible digital transaction legal infrastructure across Commonwealth jurisdictions. The OECD.AI Policy Observatory provides an online forum for tracking global AI developments, research and governance matters; all of the middle-income sample countries are active participants along with 79 other jurisdictions (see Appendix 1). This structure offers internationally comparable baseline information for use in policy formation. Over the past year, the United Nations has successfully launched a two-track AI initiative that may prove complementary in providing the sample countries with opportunities for direct engagement in shaping the emerging international governance framework (United Nations 2026). First, aiming to provide an ongoing scientific stocktaking for use in AI policy deliberations, the United Nations established the Independent International Scientific Panel on Artificial Intelligence.42 Second, the Global Dialogue on Artificial Intelligence Governance was established to provide a globally inclusive platform for 193 UN member states, as well as private sector, civil society, academic and technical experts, to

40 See www.mti.gov.sg/trade-international-economic-relations/agreements/digitaleconomy-agreements-dea/wto-e-commerce-agreement/. 41 Seven of the sample countries had participated in the WTO Joint Statement Initiative e-commerce negotiations process: Brazil, Colombia, Indonesia, Kenya, Mexico, Nigeria and Thailand. However, none endorsed the concluded text or joined the group proceeding with implementation. 42 The Independent International Scientific Panel on AI published its first report on July 1, 2026. For more information on the panel, see the corresponding UN website here: www.un.org/independent-international-scientific-panel-ai/en.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

exchange information on best practices in relation to governance and support capacity building.43

Mutual Benefit Scenarios and the Integrative Recommendation The case for AI partnerships rests ultimately not on abstract alignment of interests but on concrete scenarios of mutual benefit. A few illustrative, hypothetical, examples follow. Japan’s GENIACsupported agricultural AI firms engaging with Indonesian agribusiness through ERIA-facilitated business-matching, with established trading company networks potentially facilitating onward market access in Latin America, is one such scenario. A UK-based AI safety testing entity potentially providing model evaluation and certification services for AI systems deployed in Nigerian financial technology (fintech) — building local regulatory capacity while creating British export revenue in a high-growth market — is another. A collaborative program between Mila’s Portuguese-language AI capabilities and Brazilian public service delivery applications, reducing dependence on US language model intermediaries, might be a third. Japan’s openweight multimodal foundation models for physical AI — made accessible for fine-tuning on locally relevant manufacturing and agricultural data sets in Vietnam, Thailand or Indonesia — could be a fourth. These illustrative scenarios aim for mutual benefits even if they may not be symmetric. In each, the advanced-economy partner will tend to bring more mature capabilities, deeper capital and established networks to the relationship than the sample-country partner can yet reciprocate. Some imbalances will persist for now. Yet the value for middle-income economies lies in capturing real gains while building the institutional foundations that improve their relative standing over time. None of these scenarios requires frontier model independence. All of them require institutional trust — which is to say, the governance frameworks, data protection regimes and rule-of-law foundations that this section has identified as simultaneously the necessary domestic precondition and the international partnership-enabling infrastructure.

43 The Global Dialogue on AI Governance convened for its first session on July 6–7, 2026. For more on the initiative, see the corresponding UN website here: www. un.org/global-dialogue-ai-governance/en.

The policy recommendation that follows is therefore integrative rather than sequential: investment in AI governance architecture is not a precondition to be completed before partnership engagement begins, but the mechanism by which cross-border AI investment, research collaboration and market access become possible in parallel. As Hain, Johan and Wang (2016) note for VC flows, and as van der Marel (2026) demonstrates for intangible trade more broadly, institutional trust is the irreducible foundation on which all other forms of AI economy participation ultimately rest. The national AI strategies, risk-based regulatory frameworks and data governance regimes documented in Table 11 are not merely policy outputs to be noted — they are the building blocks of the idea-bloc membership that will determine, over the coming decade, which of the 10 sample countries graduate from managed dependency to genuine AI economy participation.

Conclusions This paper has focused on the situation of 10 middle-income countries in the AI economy, their dependency risks, and elements for an appropriate policy response. Five lines of evidence have been examined concerning: → the AI leadership context of the United States and China; → AI-related goods and services trade dependency among the sample countries; → AI start-ups and investors in the sample countries; → AI policy development and performance in the sample countries; and → advanced economy AI partnership initiatives. The empirical picture that emerges across these lines is consistent and mutually reinforcing. The United States and China command the frontier of the AI economy at every layer: 30 of the 42 most heavily used generative AI services are USheadquartered, and none originate in any of the 10 sample economies. With respect to trade, China accounts for 33 of 50 first-place supplier positions in the AI-relevant merchandise trade analysis.

For AI-related services trade, the United States is a leader, serving as top supplier to the three sample countries in the Americas and an important supplier elsewhere. But the picture is starker than official statistics suggest: Ireland emerges as the nominal leading IT services supplier for five of the 10 sample economies, a statistical artifact linked to US technology multinationals booking revenues through Irish-registered subsidiaries. The true USorigin exposure — attributed to ultimate beneficial owners rather than immediate counterparties — would be substantially higher than OECD-WTO BaTIS data report. This methodological finding, confirmed from the literature, has direct policy implications for any dependency assessment relying on bilateral trade data alone. Turning to the AI sector in the sample countries reveals a dynamic picture, with some 9,554 active AI start-ups as of Q1 2026. Many of these firms are engaged in consultancy or advisory services or application-layer products. Most appear to have launched based on self-arranged financing; only about 15 percent have one or more known outside investors. On the investment side, the USChina asymmetry is perhaps the sharpest finding: Chinese entities are present in the AI landscapes of several sample countries as infrastructure providers. But as equity investors shaping which start-ups get funded, mentored and connected to global networks, China is nearly absent. The United States occupies that formative role by a wide margin. In particular, in the investment channel perhaps most formative for a start-up — the cross-border accelerator — the United States dominates, accounting for nearly 60 percent of foreign accelerator placements across the sample. Meanwhile, the stakes of this dependency are not abstract. Estimates of AI start-up employment indicate that this may already account for 7.1 percent of total ICT sector employment across nine of the sample economies (excluding South Africa44) — reaching 12.3 percent in Vietnam and 10.2 percent in Brazil — a figure large enough to command political attention and raise the cost of getting dependency management wrong. Yet this does not support a case for strategic withdrawal. Full technological independence in the AI sector is neither achievable nor, on efficiency grounds, desirable for middle-income economies at their current stage of development. The benefits

44 South Africa was excluded due to lack of comparable data.

Middle-Income Economies: Navigating Digital Dependencies in AI

of technology transfer in all its forms can be significant, not only for the start-up recipient but also eventually for other firms across the economy via spillovers. The appropriate strategic target is managed interdependence: a condition in which these countries participate meaningfully in the AI economy — capturing growth, productivity and employment benefits — while preserving sufficient policy autonomy to shape their own governance choices and avoid lock-in to the dictates of either dominant AI power. The architecture of managed interdependence rests on a foundation that the analysis of Hain, Johan and Wang (2016) and van der Marel (2026) together identify as irreducible: institutional trust. Predictable rule of law, evidencebased regulatory frameworks, balanced and reliable IP protection and participation in the rulesbased international system are the mechanism by which cross-border AI investment, research collaboration and market access become possible in parallel. The national AI strategies, risk-based governance frameworks and data protection regimes documented in Table 11 are therefore not merely policy outputs — they are the institutional infrastructure on which managed interdependence is built and the signal to potential partners that the sample countries are ready to engage. Four AI development recommendations, for the sample countries and their peers, follow from this assessment: → Invest in domestic AI governance and capability: Close the documented shortfalls in AI infrastructure, human capital development and resilience visible in Table 11. Strengthen regulatory capacity, not merely as a compliance exercise but as the institutional trust foundation that makes the country a viable partner for cross-border AI investment and collaboration. This is simultaneously a domestic development priority and an international market-access strategy. On the policy adoption side of the table, drawn from the OECD.AI Policy Observatory, the sample countries compare well with the advanced economies: all have adopted a national AI strategy, and six of the 10 could document action across all four tracked policy elements. The contrast emerges on the implementation side. The Government AI Readiness measures cited in Table 11, which gauge realized capacity rather than formal adoption, reveal the sample economies to be widely lagging. While the gap between commitment and capacity appears in

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

milder form even among some G7 economies and China, it is most pronounced for the middleincome sample, which underperforms most consistently on economy-wide AI infrastructure, AI diffusion and development, and resilience. The implication is that the binding policy constraint is increasingly execution rather than intent. Closing it calls for targeted investment: in human capital within government to administer AI policy, in public-information capacity to support adoption, in infrastructure seeding to relieve identified bottlenecks and in systemic resilience — from power-supply back-up to contingency arrangements for cases where a key supplier fails or is impeded. → Maintain access to frontier technology while actively diversifying: Preserve trade and investment relationships with both the United States and China as sources of frontier model services, advanced semiconductor inputs and hyperscaler compute capacity. At the same time, systematically build out AI-relevant trade and investment ties with third-party advanced economies — particularly those with complementary strengths and aligned interests in diversification — in order to reduce structural dependency and improve supplychain resilience. Concretely, this includes treating the early stage investment channel as a diversification priority in its own right, not only the goods-trade channel. US investment accounts for roughly 60 percent of foreign accelerator placements across the sample. Deliberately cultivating non-US and nonChinese sources of seed capital, mentorship and accelerator relationships diversifies the formative layer where technical and governance norms are first embedded. → Engage in international fora to help shape the emerging AI governance architecture: Participate actively in OECD, UN and WTO processes to ensure middle-income nation concerns are taken into account, track AI economy developments, advance regulatory alignment, develop institutional capacity and contribute to international AI safety coordination (among other potential benefits). The Agreement on Electronic Commerce negotiated under the WTO Joint Statement Initiative on E-Commerce, now concluded and proceeding toward implementation, and the CPTPP’s e-commerce disciplines offer concrete vehicles supporting

AI-related digital trade (among other things). They may be appropriate points for sample country engagement. Other opportunities may exist via regional digital economy agreements (for example, the DEPA45). For Commonwealth members in the sample — Kenya, Nigeria, South Africa and India — the Commonwealth Model Law on Digital Trade provides an immediately accessible instrument that could potentially serve to guide establishment of AI-compatible legal infrastructure across 56 jurisdictions. A further specific action could be to build the statistical and analytical capacity to monitor dependency by country of ultimate beneficial owner rather than by country of immediate counterparty — the Ireland-Vietnam routing anomaly (documented above in Box 3) shows that counterparty-based trade statistics can mask the true concentration of reliance on a small number of IT service suppliers. Clear statistics help to ensure that delegations enter these fora negotiating based on an accurate picture of their own exposure. → Deepen strategic engagement with advancedeconomy partners seeking comparable diversification: Canada, the European Union, Japan and the United Kingdom each offer differentiated and complementary contributions to a managed interdependence architecture for AI, including (among other attributes): Canada contributes research depth through internationally recognized AI institutes and middle-power diplomatic positioning; the European Union provides a digital economy governance template increasingly influential beyond its borders and a domestic market of tremendous scale (with a suite of preferential trade instruments), as well as experience with an infrastructure investment model centred on shared compute facilities that reduces hyperscaler dependency; Japan brings AI equipment leadership, domainspecific foundation models and an established co‑creation framework with ASEAN partners; the United Kingdom offers scaled compute capacity, safety institute infrastructure and a Commonwealth channel for alignment. Bilateral and regional accords — the CPTPP, 45 The DEPA is an accord engaging Chile, New Zealand, Singapore and South Korea. It covers data flows, digital ID, AI governance, fintech, and small and medium-sized enterprise digital inclusion. For more details, see the DEPA webpage of the Ministry of Trade and Industry of Singapore: www.mti.gov.sg/trade-international-economicrelations/agreements/digital-economy-agreements-dea/depa/.

digital economy agreements and region-toregion arrangements (such as the EU-Mercosur accord, currently provisionally applied) — provide a legal architecture that can serve to anchor these relationships. In practical terms, the highest-value early moves may be sectorand partner-specific rather than diffuse: for an economy with agricultural-AI potential such as Indonesia or Vietnam, the concrete opening may be the ASEAN-Japan co-creation framework and Japan’s open-weight physical AI models available for local fine-tuning; for a fintech-oriented market such as Nigeria, UK AI-safety model-evaluation services may offer a clear complementarity. Depending on each nation’s circumstances, development of direct collaboration with such leading economies may provide a means of accelerating diversification, as a complement to more generalized approaches. These four recommendations are subject to an important caveat. Some of the international frameworks cited here — the WTO Joint Statement Initiative, the CPTPP’s e-commerce disciplines and the OECD.AI Policy Observatory — were shaped substantially by advanced economies whose dominance the foregoing analysis documents. A critic might reasonably ask whether deeper engagement with institutions shaped by the dominant economies can truly serve as a path to promote diversification and resilience, rather than reinforcing the very dependencies the strategy seeks to manage. This tension is real but not disqualifying. Alternative paths — such as attempting development of sovereign AI stacks, parallel South-South institutions or strategic disengagement from existing frameworks — carry substantial risks of their own: higher costs, slower capability development and reduced access to the technology transfer channels that, whatever their dependency implications, currently deliver measurable productivity and employment benefits. Managed interdependence is not a claim that existing institutions are neutral or optimal. It is a claim that middle-income economies are better served by engaging those institutions strategically — shaping their evolution from within, while simultaneously building the domestic capacity and diversified partnership networks that reduce structural exposure over time. In this, the recommendations are not strictly sequential. Initial steps for all four can be launched in

Middle-Income Economies: Navigating Digital Dependencies in AI

parallel. But given the challenging international context, their implementation may benefit from prioritizing early progress in reinforcing domestic foundations, while preparing the groundwork for further diversification, international engagement and strategic partnership in turn. This points toward a more fundamental conclusion about policy autonomy. For middle-income economies, genuine autonomy in the AI era is unlikely to come from technological selfsufficiency. Complete AI self-sufficiency is not, in fact, available to any country. Even the United States and China depend, for example, on Taiwan for advanced fabrication, the Netherlands for lithography and globally concentrated supply chains for essential minerals — as well as on each other. A degree of autonomy is more likely to come from institutional strength. As Douglass C. North (1997) argued, the institutions a society builds — the rules that structure human interaction and underpin low-cost transacting and credible commitment — are fundamental determinants of its economic trajectory, and the high transaction costs imposed by weak institutions are a principal reason developing economies have struggled to realize the productive potential of new technology. For the middle-income economies, advancing institutional quality may be the most sustainable form of leverage available: it is largely within their own control, it cannot be withheld by a foreign supplier and it shapes the terms on which they engage external partners. Even in a period of geopolitical tension and institutional fragmentation, the space for broadening strategic engagement in the international AI economy remains real. The sample countries that invest now in the institutional foundations of managed interdependence will be best positioned to convert that space into durable gains — in productivity, in employment and in the policy autonomy to shape their own AI futures.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

AI Disclosure AI Tools: Anthropic’s Claude Sonnet 4.6 (adaptive) and previous editions, and also Opus 4.7 for some large data tabulations: these served as the primary AI tools in this project. From January to May 2026, the author used Anthropic’s AI tools for research assistance, literature synthesis, data tabulations, table compilation based on the author’s specific instructions, review of selected draft paragraphs for error checking and logic, and some limited drafting of text or restatement of text. ChatGPT 5.3 (OpenAI), from February to May 2026, supported research for specific data points, search for references, and factchecking, as well as error-checking on specific lines of text. Also used to verify Claude’s output in some specific cases, in addition to the human reviews that were always conducted. Use of this tool was limited. Qwen3.6Plus (Alibaba Cloud), from February to May 2026, supported a limited amount of fact-finding on specific points and error-checking on specific lines of text. Use of this tool was very limited. Across all AI deployments — whether research, literature synthesis, data and analytical operations, and proposals for draft text or text restatement — outputs were human reviewed and revised as necessary. From conceptualization to final draft, the author maintained primacy. The author took into account the risk of hallucination and computational errors, and made deletions or corrections of these points where these errors were found, as appropriate.

Acknowledgements The author gratefully acknowledges exchanges on the project at its inception with Paul Samson, president of CIGI, and a discussion of the work in progress with Odun Olowookere, director of research, Digital Economy, CIGI. He appreciates the constructive feedback on the draft from two anonymous reviewers. He thanks Anna Kassman-McKerrell — library and information services manager at the Chatham House Library in London, UK — for research assistance on two occasions during the project. He expresses his deep appreciation for contacts who generously took the time for informative background discussions related to the project including: Helen Royal, head of World Trade Organization Trade and Negotiations at the UK Department for Science, Innovation and Technology in London, UK; Kazuyuki Motohashi, professor at the Research Center for Advanced Science and Technology at the University of Tokyo; Hibiki Tsuruoka, deputy director of the AI Industry Strategy Office at METI of Japan; Osamu Onodera, director general of the Beijing Office, Japan External Trade Organization; Crawford Falconer, former chief trade negotiations adviser, UK Department for Business and Trade in London; and Devin Woods, senior research associate at the Reischauer Center for East Asian Studies, Washington, DC.

Information Collection: Claude Sonnet 4.6 was used to suggest references from the literature, review the author’s reference selections, and support literature synthesis. Any reference suggestions were human verified prior to inclusion in the text. Data Curation: Claude Sonnet 4.6 (adaptive) was used to support data-cleaning and error-checking. Each record was human reviewed (some 15,000). Data Analysis: Claude Sonnet 4.6 (adaptive) and Opus 4.7 handled data operations in cases involving more than a simple spreadsheet. Opus 4.7 was used primarily for the larger data operations. Human spot checks were used to verify inputs and outputs when AI was employed.

Middle-Income Economies: Navigating Digital Dependencies in AI

Appendix 1: Key Data Sources Crunchbase (www.crunchbase.com/home) Crunchbase is a continuously updated global database on firms, investors and deals. The company has developed partnerships with a diverse range of companies and investors across the globe. Rather than relying on web searching or scraping the web, the vast majority of data is crowdsourced directly from Crunchbase’s venture partners (more than 4,000) and active community of contributors (more than 600,000). Crunchbase then uses a large suite of AI and maching learning algorithms to search and validate data using other published sources, with an in-house human data team providing manual verification for daily updates. The community of users provides a further layer of verification. (Crunchbase claims to be secure in its online operations, being Service Organization Control 2 [SOC 2] Type II compliant.) In this manner, Crunchbase has developed a unique and substantial resource for firm-level research. There are some risks in this approach as well. The system relies in part on self-reported data, which can introduce reporting biases and accuracy issues into the database. It may also result in coverage bias whereby technology companies and technology-intensive regions are overrepresented, while some other sectors or regions potentially could be underrepresented (den Besten 2020). The reliance on crowdsourcing at times appears to result in some fields being inconsistently completed or at risk of becoming outdated, and there may be some variation from country to country in the data quality. As a result of these challenges, the results of research based on Crunchbase data should be considered with care and caveats noted. In the course of the research for the present project, each of the selected Crunchbase records was reviewed to extract the desired fields, screen for obvious anomalies (in some cases seeking confirming information from alternative sources) and address formatting issues. Claude.ai was employed to assist in data compilation and to provide an additional layer of quality control. The original data was generally in very good shape and every effort was made to ensure a clean

data set for this project. The present analysis also employs a holistic approach, drawing on complementary sources from the literature and other references in order to check for consistency, avoid the introduction of biases and develop the storyline in an appropriately balanced fashion.46 Cloudflare Radar (https://developers.cloudflare.com/ radar/) This resource provides information on global internet traffic, attacks and technology trends. It is powered by data from Cloudflare’s global network, as well as aggregated and anonymized data from Cloudflare’s 1.1.1.1 public DNS resolver. Cloudflare’s tracking is sufficiently representative for the purposes of this paper. But note that it undercounts China’s domestic internet traffic, as its ability to operate behind the Great Firewall is limited to enterprise services provided via its partner JD Cloud. For details, see the Cloudflare FAQ: https://developers.cloudflare.com/china-network/faq/. OECD.AI Policy Navigator (https://oecd.ai/en/) This database covers 89 countries, with information and documentation concerning various aspects of national AI strategy, policy and regulation. It is updated regularly by official contact points from countries, OECD.AI experts and international organizations. Reporting timeliness varies, depending on correspondents. UN Comtrade (https://comtradeplus.un.org) This resource has data on AI-relevant merchandise trade. It is a well-known central statistical repository for standardized trade data and a go-to reference for international trade economists. OECD-WTO BaTIS (https://stats.wto.org) The Balanced Trade in Services database provides standardized data on bilateral services trade. Due to the time required for data collection and harmonization, the data may be subject to a time lag in availability. Still, it is a valuable resource for consistent services trade data across countries.

46 For more on the use of Crunchbase in economic research, see den Besten (2020).

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Works Cited Anthropic. 2026. “Statement on the US government directive to suspend access to Fable 5 and Mythos 5.” June 12. www.anthropic.com/news/fable-mythos-access. AP News. 2025. “Meta buys startup Manus in latest move to advance its artificial intelligence efforts.” December 30. https://apnews.com/article/meta-manus-purchaseai-agents-aaf01029923011a403ceeb949cf3db5e. Baldwin, Richard. 2016. The Great Convergence: Information Technology and the New Globalization. Cambridge, MA: Harvard University Press. https://doi.org/10.2307/j.ctv24w655w. Barbieri Advogados. 2026. “Brazil’s AI Act: PL 2.338/2023 vs. EU AI Act Comparison.” Barbieri Advogados Legal Update, March 23. www.barbieriadvogados.com/brazil-ai-act/. Bauer, Matthias, Andrea Dugo, Dyuti Pandya and Elena Sisto. 2025. “100 leaders in Europe’s economies: Benchmarking investment, innovation and impact in Europe.” European Centre for International Political Economy Occasional Paper No. 13/2025. www.econstor.eu/ bitstream/10419/338995/1/1941608949.pdf. Bureau of Economic Analysis. 2025. “Activities of U.S. Multinational Enterprises (MNEs): Tables 4.1–4.4, Services Supplied to Foreign Persons by US MNE Foreign Affiliates.” US Department of Commerce. www.bea.gov/data/intl-tradeinvestment/activities-us-multinational-enterprises-mnes. Biden, Joseph R. 2023. “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.” Executive Order 14110, Federal Register 88, 75191, November 1 (rescinded on January 20, 2025). www.federalregister.gov/ documents/2023/11/01/2023-24283/safe-secure-andtrustworthy-development-and-use-of-artificial-intelligence. Borisov, Bobby. 2025. “Global Internet Traffic Rose 19 Percent in 2025, Cloudflare Data Shows.” Linuxiac, December 16. https://linuxiac.com/global-internet-traffic-rose19-percent-in-2025-cloudflare-data-shows/. Cazzaniga, Mauro, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J. Panton, Carlo Pizzinelli, Emma Rockall and Marina M. Tavares. 2024. “Gen-AI: Artificial Intelligence and the Future of Work.” IMF Staff Discussion Note SDN2024/001. www.imf.org/-/media/files/ publications/sdn/2024/english/sdnea2024001.pdf.

Cerutti, Eugenio, Antonio Garcia Pascual, Yosuke Kido, Longji Li, Giovanni Melina, Marina M. Tavares and Philippe Wingender. 2025. “The Global Impact of AI: Mind the Gap.” IMF Working Paper WP/25/76. April. www.imf.org/en/-/media/files/ publications/wp/2025/english/wpiea2025076-print-pdf.pdf. Chow, Loletta. 2026. “Overview of 2025 China outbound investment.” Ernst & Young, February 6. www.ey.com/ en_cn/insights/china-overseas-investment-network/ overview-of-2025-china-outbound-investment. Commonwealth Secretariat. 2025. The Commonwealth Model Law on Digital Trade and Guide to Enactment. September. https://comsec-web-static.s3.eu-west-1.amazonaws. com/s3fs-public/2025-09/d20215-v7-edti-cca-modellaw-digi-trade-guide-enctmnt-r-babrooram.pdf. Compliance Hub Wiki. 2026. “When Washington Switched Off an AI Model: Fable 5, Mythos 5, and the ExportControl Precedent That Should Worry Every Compliance Team.” June 13. https://compliancehub.wiki/fable5-mythos-5-export-controls-ai-compliance/. CPTPP Commission. 2025. “Joint Ministerial Statement on the occasion of the Ninth Commission Meeting of the CPTPP.” November 21. www.dfat.gov.au/trade/ agreements/in-force/cptpp/joint-ministerial-statementoccasion-ninth-commission-meeting-cptpp. den Besten, Matthijs L. 2020. “Crunchbase Research: Monitoring Entrepreneurship Research in the Age of Big Data.” SSRN, November 3. https://ssrn.com/abstract=3724395. Department for Science, Innovation & Technology and Number 10 Data Science. 2026. “AI Opportunities Action Plan: One Year On..” January. https://delivery.ai.gov.uk. Dos Santos, Theotonio. 1970. “The Structure of Dependence.” The American Economic Review 60 (2): 231–36. http://digamo.free.fr/dosantos70.pdf. Dugo, Andrea, Fredrik Erixon and Dyuti Pandya. 2026. “Europe’s Digital Markets Act Under Review: Ways to Make it More Pragmatic, Proportional, and Predictable.” European Centre for International Political Economy Policy Brief No. 07/2026. https://ecipe.org/publications/dma-under-review/. Eastland, Maggie and Hadriana Lowenkron. 2026. “Anthropic Shuts Down Mythos Access After Sweeping US Order.” Bloomberg, June 13. www.bloomberg.com/news/articles/2026-06-13/ anthropic-says-us-limits-foreign-access-to-fable-5-mythos-5. European Commission. 2025a. The AI Continent Action Plan. April. https://digital-strategy.ec.europa.eu/ en/library/ai-continent-action-plan.

Middle-Income Economies: Navigating Digital Dependencies in AI

———. 2025b. “Simpler EU digital rules and new digital wallets to save billions for businesses and boost innovation.” Press release, November 18. https://ec.europa.eu/ commission/presscorner/detail/en/ip_25_2718.

Horsey, Julian. 2025. “The Windsurf Story So Far, Windsurf's Wild Ride: OpenAI, Google and Acquisition By Cognition.” Geeky Gadgets, July 16. www.www. www. geeky-gadgets.com/windsurf-story-so-far/.

Evenett, Simon, Adam Jakubik, Jaden Kim, Fernando Martín, Samuel Pienknagura, Michele Ruta, Sandra Baquie, Yueling Huang and Rafael Machado Parente. 2025. “Industrial Policy Since the Great Financial Crisis.” IMF Working Paper WP/25/222. October. https://doi.org/10.5089/9798229027786.001.

IDRC. 2025. “Artificial Intelligence for Development.” https://idrc-crdi.ca/en/initiative/artificial-intelligencedevelopment.

FCDO. 2025. “AI for Development: Responsible AI, Empowering People (‘AI4D’).” Programme GBGOV-1-400191. https://devtracker.fcdo.gov.uk/ programme/GB-GOV-1-400191/summary. Ferracane, Martina Francesca and Erik van der Marel. 2021. “Regulating Personal Data: Data Models and Digital Services Trade.” World Bank Policy Research Working Paper 9596. March. https://documents1.worldbank.org/curated/ en/890741616533448170/pdf/Regulating-PersonalData-Data-Models-and-Digital-Services-Trade.pdf. Financial Times. 2026. “Beijing’s blunt message to its tech sector.” Financial Times, April 28. www.ft.com/content/5e29c2c6c433-4296-b40c-4bb61cd3242f?shareType=nongift. Frank, Cole. 2018. “Tax avoidance and the Irish balance of payments.” Council on Foreign Relations, April 25. www.cfr.org/articles/tax-avoidanceand-irish-balance-payments. Gaulier, Guillaume and Soledad Zignago. 2010. “BACI: International Trade Database at the Product-Level. The 1994-2007 Version.” CEPII Working Paper N°2010-23. www.cepii.fr/ CEPII/en/publications/wp/abstract.asp?NoDoc=272. Gehrke, Norbert. 2025. “Policy Briefing: The Artificial Intelligence Basic Plan.” Japan FinTech Observer, December 21. www.fintechobserver.com/policybriefing-the-artificial-intelligence-basic-plan/. GOV.UK. 2023. “The Bletchley Declaration by Countries Attending the AI Safety Summit, 1–2 November 2023.” Prime Minister’s Office. www.gov.uk/government/ publications/ai-safety-summit-2023-the-bletchleydeclaration/the-bletchley-declaration-by-countriesattending-the-ai-safety-summit-1-2-november-2023. Hain, Daniel, Sofia Johan and Daojuan Wang. 2016. “Determinants of Cross-Border Venture Capital Investments in Emerging and Developed Economies: The Effects of Relational and Institutional Trust.” Journal of Business Ethics 138 (4): 743–64. https://doi.org/10.1007/s10551-015-2772-4.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Iida, Kate, Sulamaan Rahim, Chris Hutber, Gonzalo Grau, Caroline Moss and Oliver Douglas. 2026. Government AI Readiness Index 2025. Oxford Insights. https://oxfordinsights.com/ ai-readiness/government-ai-readiness-index-2025/. IMF. 2009. Balance of Payments and International Investment Position Manual. Sixth Edition. www.imf.org/ external/pubs/ft/bop/2007/pdf/BPM6.pdf. Jones, Lin and Sara Krulikowski. 2024. “Taiwan — The Silicon Island.” Executive Briefing on Trade, U.S. International Trade Commission, Office of Economics. February. www.usitc.gov/publications/332/executive_briefings/ ebot_silicon_island_taiwan_semiconductor.pdf. Joshi, Shreya. 2026. “Early Lessons in the Pursuit of Sovereign AI.” Carnegie Endowment for International Peace, June 17. https://carnegieendowment.org/research/2026/06/ early-lessons-in-the-pursuit-of-sovereign-ai. Khurana, Ryan. 2025. “Unleashing AI: Canada’s blueprint for productivity, innovation, and workforce integration.” Commentary, Macdonald-Laurier Institute, May 22. https://macdonaldlaurier.ca/unleashing-ai-canadas-blueprintfor-productivity-innovation-and-workforce-integration/. Korreck, Sabrina. 2019. “The Indian Startup Ecosystem: Drivers, Challenges and Pillars of Support.” Observer Research Foundation Occasional Paper. September. www.orfonline.org/research/the-indian-startupecosystem-drivers-challenges-and-pillars-of-support. Kowalski, Przemyslaw and Andrea Andrenelli. 2025. “Economic security and vulnerabilities in international supply chains.” In Economic Security in a Changing World , 12–30. Paris, France: OECD Publishing. www.oecd.org/en/publications/ economic-security-in-a-changing-world_4eac89c7-en/ full-report/economic-security-and-vulnerabilities-in-internationalsupply-chains_dc88aefa.html. Krugman, Paul. 1995. Development, Geography and Economic Theory. Cambridge, MA: The MIT Press. https://doi.org/10.7551/ mitpress/2389.001.0001.

Lee, Jin-Hee, Dipunj Gupta, Brian Mitchell, Reid Tatoris and Henry Clausen. 2025. “Control content use for AI training with Cloudflare’s managed robots.txt and blocking for monetized content.” Cloudflare Blog, July 1. https://blog.cloudflare.com/control-content-use-for-ai-training/. Liao, Andy. 2025. “China’s Semiconductor Investment Defies Economics — But Makes Perfect Strategic Sense.”Sino-Southeast Initiative, July 26. https://sinosoutheastinitiative.com/2025/07/26/ chinas-semiconductor-investment-defies-economics-butmakes-perfect-strategic-sense/. Lippoldt, Douglas. 2024. AI Innovation Concentration and the Governance Challenge. CIGI Paper No. 292. Waterloo, ON: Centre for International Governance Innovation. www.cigionline.org/publications/ai-innovationconcentration-and-the-governance-challenge/. ———. 2025. Developing Countries’ Business Participation in the AI Economy. CIGI Paper No. 323. Waterloo, ON: Centre for International Governance Innovation. www.cigionline.org/publications/developing-countriesbusiness-participation-in-the-ai-economy/. Mannuru, Nishith Reddy, Sakib Shahriar, Zoë A. Teel, Ting Wang, Brady D. Lund, Solomon Tijani, Chalermchai Oak Pohboon, Daniel Agbaji, Joy Alhassan, JaKLyn Galley et al. 2023. “Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development.” Information Development 41 (3): 1036–54. https://doi.org/10.1177/02666669231200628.

Moens, Barbara and Melissa Heikkilä. 2025. “How the EU botched its attempt to regulate AI.” Financial Times, November 19. www.ft.com/content/6585fb32-8a864ffb-a940-06b17e06345a. Moradi Haghighi, Faezeh and Michael Augustín. 2026. “The Inﬂuence of the Belt and Road Initiative on China’s Advancement in Global Value Chains and Developing Economies: A Systematic Review.” The Chinese Economy 59 (2): 87–114. https://doi.org/10.1080/10971475.2025.2526259. Moreau, Flavien. 2024. “An AI-Powered Boost to Anguilla’s Revenues.” IMF News, May 15. www.imf.org/en/news/ articles/2024/05/15/cf-an-ai-powered-boost-to-anguillasrevenues. Motohashi, Kazuyuki. 2024. “Japanese university reforms offer valuable lessons.” China Daily, May 27. www.chinadaily.com.cn/a/202405/27/ WS6653c7b6a31082fc043c931d.html. Muñoz Sanchez, Alonso. 2026. “Canada’s Principled and Pragmatic AI Opportunity.” Open Canada, February 23. https://opencanada.org/canadasprincipled-and-pragmatic-ai-opportunity/. National Science and Technology Council. 2016. “The National Artificial Intelligence Research and Development Strategic Plan.” Networking and Information Technology Research and Development Subcommittee, Executive Office of the President. October. https://cra.org/ccc/wp-content/uploads/ sites/2/2016/11/NSTC_national_ai_rd_strategic_plan.pdf.

Maslej, Nestor, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika et al. 2024. Artificial Intelligence Index Report 2024. AI Index Steering Committee, Institute for Human-Centered Artificial Intelligence, Stanford University. April. https://hai.stanford.edu/ai-index/2024-ai-index-report.

North, Douglass C. 1997. “The Contribution of the New Institutional Economics to an Understanding of the Transition Problem.” World Institute for Development Economics Research Annual Lecture. www.wider.unu.edu/publication/contribution-newinstitutional-economics-understanding-transition-problem-0.

Mayer, Jörg. 2021. “Development strategies for middleincome countries in a digital world — Insights from modern trade economics.” The World Economy 44 (9): 2515–46. September. https://ideas.repec.org/a/ bla/worlde/v44y2021i9p2515-2546.html.

Rahnama, Hossein. 2025. The CANADA AI Framework: A Blueprint for Global Leadership. CIGI Paper No. 337. October. Waterloo, ON: Centre for International Governance Innovation. www.cigionline.org/publications/the-canadaai-framework-a-blueprint-for-global-leadership/.

METI. 2026. “GENIAC: Unleashing Japan’s Generative AI Development Capabilities.” www.meti.go.jp/english/ policy/mono_info_service/geniac/index.html.

Robinson, Dan. 2023. “Cloudflare dishes up the stats on internet traffic in 2023.” The Register, December 13. www.theregister.com/2023/12/13/ cloudflare_internet_traffic_2023/.

Ministry of Internal Affairs and Communications of Japan. 2026. “ASEAN-Japan Digital Work Plan 2026.” January. www.soumu.go.jp/main_content/001050577.pdf.

Schweitzer, Frank and Ian Saccomanno. 2024. “The Rise of Artificial Intelligence, Big Data, and the Next Generation of International Rules Governing Cross-Border Data Flows and Digital Trade.” White & Case LLP Insight, March 14. www.whitecase.com/insight-our-thinking/rise-artificialintelligence-big-data-next-generation-international-rules.

Middle-Income Economies: Navigating Digital Dependencies in AI

Setser, Brad W. 2019. “Finding Ireland in the U.S. Balance of Payments Data…” Council on Foreign Relations, July 16. www.cfr.org/articles/finding-ireland-us-balance-paymentsdata. Singh, Shalabh Kumar and Shubhashis Sengupta. 2025. “Sovereign AI: Rethinking Autonomy in the Age of Global Interdependence.” arXiv, November 21. https://arxiv.org/pdf/2511.15734. Tanner, Brooke, Cameron F. Kerry, Andrew W. Wyckoff, Nicoleta Kyosovska, Andrea Renda and Elham Tabassi. 2026. Is AI Sovereignty Possible? Balancing Autonomy and Interdependence. Brookings Institution and the Center for European Policy Studies. February. www.brookings.edu/wp-content/ uploads/2026/02/20260217_AI_sovereignty_final.pdf. The Hub Staff. 2026. “What Canada must get right in its AI strategy: DeepDive.” The Hub, February 17. https://thehub.ca/2026/02/17/what-canadaneeds-to-get-right-in-its-ai-strategy-deepdive/. Trump, Donald J. 2019. “Maintaining American Leadership in Artificial Intelligence.” Executive Order 13859. Federal Register 84 (31): 3967. February 14. www.federalregister.gov/ documents/2019/02/14/2019-02544/maintainingamerican-leadership-in-artificial-intelligence. ———. 2026. “Promoting Advanced Artificial Intelligence Innovation and Security.” Executive Order 14409. Federal Register 91 (108): 34565–67. June 5. www.federalregister.gov/ documents/2026/06/05/2026-11415/promotingadvanced-artificial-intelligence-innovation-and-security. United Nations. 2026. “The UN Global Dialogue on AI Governance Explained.” UN Web TV, July 1. https://webtv.un.org/en/asset/k1v/k1vqmc2dm7. United Nations Conference on Trade and Development. 2023. Handbook on Measuring Digital Trade. Second Edition. UNCTAD/DTL/ECDE/2023/8. https://unctad.org/publication/handbook-measuringdigital-trade. U.S. Department of Justice and the Federal Trade Commission. 2010. “Horizontal Merger Guidelines.” August 19. https://www.justice.gov/atr/file/810276/dl?inline. van der Marel, Erik. 2026. “From Factories to Ideas: The New Map of Global Power.” European Centre for International Political Economy Policy Brief No. 04/2026. March. https://ecipe.org/publications/from-factories-to-ideas/.

CIGI Papers No. 361 — September 2026 • Douglas Lippoldt

Wadhwa, Vivek, AnnaLee Saxenian, Muthukumaraswamy D. P. K. and M. H. Bala Subrahmanya. 2026. “The Indian Returnee Paradox: Why the Diaspora Is No Longer Driving Startup Success.” Observer Research Foundation Occasional Paper No. 519. February. www.orfonline.org/ public/uploads/posts/pdf/20260209065719.pdf. WCO. 2022. “HS Nomenclature 2022 edition.” www.wcoomd.org/en/topics/nomenclature/ instrument-and-tools/hs-nomenclature-2022edition/hs-nomenclature-2022-edition.aspx. World Bank Group. 2020. World Development Report 2020: Trading for Development in the Age of Global Value Chains. https://doi.org/10.1596/978-1-4648-1457. WTO. 1998. “The Geneva Ministerial Declaration on global electronic commerce.” WT/MIN(98)/DEC/2. https://www.wto.org/english/tratop_E/ ecom_e/mindec1_e.htm. Xinhua. 2025. “China tops global AI model count with over 1,500 large models released.”July 28. http://english.scio.gov.cn/ chinavoices/2025-07/28/content_117999955.html. Yin, Kate, Gil Zhang, Sherman Deng and Huihui Li. 2026. “AI Regulatory Landscape, and Development Trends in China.” In Data Protection Laws and Regulations 2026. International Comparative Legal Guides, July 20. https://iclg.com/ practice-areas/data-protection-laws-and-regulations/02ai-regulatory-landscape-and-development-trends-in-china. Yoshinaga, Kyoko. 2025. “Japan’s Hybrid Approach to AI Governance: Balancing Soft Law and Hard Law.” National Bureau of Asian Research Brief. November. www.nbr.org/publication/japans-hybrid-approach-toai-governance-balancing-soft-law-and-hard-law/.

Human Analysis Standard For AI Use in Policy Research

67 Erb Street West Waterloo, ON, Canada N2L 6C2 cigionline.org