1. Introduction
Artificial Intelligence (AI) is a transformative technology reshaping industries, economies, and governance worldwide. While AI has the potential to drive innovation, enhance productivity, and improve public services, it is also exacerbating existing global inequalities. The “digital divide”—historically defined by disparities in internet access and digital literacy—is now expanding into an “AI divide,” where high-income nations dominate AI development, research, and deployment, leaving developing economies struggling to keep pace.
AI leadership carries substantial economic and political stakes. Countries that control AI infrastructure, talent, and data ecosystems gain strategic advantages, from economic competitiveness to geopolitical influence. However, many developing countries lack the financial resources, institutional frameworks, and skilled workforce to fully leverage AI’s potential. Without targeted interventions, the AI power divide may deepen, reinforcing economic dependencies and limiting opportunities for emerging economies.
This report examines the AI power divide, exploring the factors that contribute to the global AI imbalance, analyzing case studies from emerging economies, and offering policy recommendations to help developing countries bridge the gap.
2. The AI Power Divide: Who Holds the Advantage?
2.1 AI Development and Investment Gaps
One of the most significant contributors to the AI power divide is the disparity in research and development (R&D) spending. High-income countries—particularly the United States, China, and the European Union—account for the vast majority of global AI investment. According to Stanford’s AI Index Report (2023), the U.S. and China alone invested over $90 billion in AI R&D in 2022, while developing nations collectively spent a fraction of this amount.
The private sector plays a crucial role in AI funding, with tech giants such as Google DeepMind, OpenAI, and Baidu driving cutting-edge innovation. Venture capital (VC) investment further amplifies the gap, as AI startups in Silicon Valley and Beijing attract billions in funding, whereas startups in Africa, Latin America, and South Asia struggle to secure comparable backing.
Additionally, the AI talent pipeline is disproportionately concentrated in high-income nations. Brain drain—where skilled professionals from developing countries migrate to AI hubs in the U.S., Europe, and China—exacerbates the divide. Countries such as India and Nigeria train AI engineers, but many relocate due to better financial incentives and research opportunities abroad.
2.2 AI Infrastructure and Data Challenges
Developing AI capabilities requires robust infrastructure, including cloud computing, high-performance computing (HPC), and access to large datasets. However, developing economies face significant hurdles:
- Limited Cloud and Computing Access: AI training relies on expensive computational power. Leading AI labs use cutting-edge GPUs and TPUs, but these resources are largely controlled by Western tech firms.
- Data Localization and Privacy Concerns: Many developing countries lack clear data governance frameworks. While data-rich, they often rely on foreign platforms (Google, Meta, Amazon) for AI development, raising concerns over data sovereignty.
- Big Tech’s Role in Infrastructure Monopoly: AI services—such as cloud-based machine learning platforms—are controlled by a few global corporations, making developing countries dependent on foreign technology providers.
2.3 Geopolitical and Economic Dependencies
The AI power divide is not just economic—it has geopolitical implications. Many developing countries rely on foreign AI models, such as OpenAI’s ChatGPT or Google’s Bard, rather than building their own. This dependency creates vulnerabilities, as seen in:
- AI-driven Neocolonialism: AI applications often reinforce Western-centric biases, limiting their adaptability to local contexts in Africa, Latin America, and Asia.
- Export Controls and Trade Restrictions: The U.S. and its allies have imposed semiconductor export restrictions on China, demonstrating how AI technology can be weaponized in geopolitical conflicts. Similar restrictions could limit developing countries’ access to critical AI hardware and software.
- Control Over AI Standards: AI ethics and regulatory frameworks are being shaped by the EU and the U.S., leaving developing countries with little influence over global AI governance.
3. Case Studies: AI Strategies in Emerging Economies
India’s AI Ambitions
India has positioned itself as a rising player in the AI ecosystem, leveraging its vast talent pool, government initiatives, and a growing digital infrastructure. The country sees AI as a transformative force to drive economic growth and improve public services.
Government Initiatives
- National AI Strategy (AI for All): In 2018, India’s NITI Aayog (policy think tank) released the National Strategy for AI, which prioritizes five key sectors: healthcare, agriculture, education, smart cities, and mobility.
- India AI Mission: The Indian government has announced plans to invest in AI research and development through the India AI Mission, aiming to enhance AI innovation within the country.
- Digital India & AI Research Institutes: India has been setting up AI research hubs, including the Centre for Artificial Intelligence and Robotics (CAIR) and collaborations with global tech firms.
AI for Public Services
- Healthcare: AI is being deployed for diagnostics, disease prediction, and telemedicine. Startups like Qure.ai use AI for medical imaging, making healthcare more accessible in rural areas.
- Agriculture: AI-powered tools, such as IBM’s AI-driven Watson Decision Platform for Agriculture, help Indian farmers predict weather patterns, optimize irrigation, and detect crop diseases.
- Smart Cities: India has launched AI-driven smart city projects, including AI-based traffic management systems to ease congestion in metropolitan areas.
Challenges in AI Development
- Infrastructure and Computing Power: India lacks large-scale AI computing infrastructure, making it reliant on cloud services from global tech giants like Google and AWS.
- Data Governance and Privacy: The country is working on the Personal Data Protection Bill, but concerns remain regarding data privacy and localization policies.
- Brain Drain and Talent Retention: While India produces a large number of AI engineers, many migrate to the US and Europe, leading to a shortage of high-level AI expertise domestically.
AI in Africa: The Potential and the Gaps
Africa faces both significant challenges and opportunities in AI development. While the continent has limited AI infrastructure, a growing number of AI startups, research initiatives, and international collaborations signal its potential.
Current AI Landscape
- Limited Infrastructure: Africa lacks supercomputing facilities necessary for AI research, making it dependent on cloud computing services from foreign providers.
- Data Scarcity: Many African countries lack comprehensive and high-quality datasets, particularly in local languages, which hinders AI model training.
International Collaborations and AI Governance
- UNESCO AI Initiatives: UNESCO has been working with African governments to develop ethical AI policies and frameworks, ensuring AI benefits are distributed equitably.
- AfriLabs and AI Hubs: Initiatives like AfriLabs and Google’s AI research center in Accra, Ghana, aim to build AI capacity in the region.
Use Cases of AI in Africa
- Healthcare: AI-driven mobile applications are being used to diagnose diseases like malaria and tuberculosis in remote areas. For example, DeepMind has partnered with African health institutions to develop AI-driven diagnostics.
- Agriculture: AI-based precision farming techniques help farmers optimize crop yields. Startups like Aerobotics use AI-powered drone technology to assess soil health and monitor crops.
- Financial Inclusion: AI is being used for credit scoring and fraud detection in mobile banking, benefiting the large unbanked population in Africa.
Challenges Facing AI Development
- Digital Colonialism Concerns: The reliance on Western AI models and cloud computing infrastructure raises concerns about data sovereignty and digital dependency.
- Lack of Skilled Workforce: Many African countries lack AI specialists, and universities have yet to fully integrate AI into their curricula.
- Regulatory Uncertainty: Few African nations have clear AI policies, leading to regulatory gaps that could either hinder innovation or result in unchecked AI use.
Latin America’s AI Ecosystem
Latin America has been making strides in AI development, with countries like Brazil, Mexico, Argentina, and Chile leading the way. The region is focusing on AI for economic growth, social innovation, and addressing inequality.
AI Adoption and Government Strategies
- Brazil: The Brazilian government launched its National AI Strategy (Estrategia Brasileira de Inteligencia Artificial) in 2021, aiming to develop AI in priority sectors like healthcare, agriculture, and public safety.
- Mexico: Mexico has established the AI Coalition, bringing together industry, government, and academia to build a robust AI ecosystem.
- Argentina and Chile: These countries have been investing in AI research and digital transformation to enhance productivity and innovation.
AI for Economic and Social Development
- Healthcare: AI is used in medical diagnostics, patient management, and drug discovery. For example, Brazil has AI-driven cancer detection programs that improve early diagnosis.
- Public Services: AI-powered chatbots are being deployed for government services to enhance citizen engagement and reduce bureaucracy.
- Crime Prevention: AI is being used in predictive policing and surveillance to combat organized crime and violence.
Challenges in AI Development
- Funding and Investment Gaps: Compared to North America and Europe, Latin America lacks sufficient venture capital investment in AI startups.
- Infrastructure and Connectivity: Many rural areas still suffer from poor internet connectivity, limiting AI adoption.
- Regulatory and Ethical Concerns: The absence of strong AI regulations could lead to ethical risks, including biased AI models and privacy violations.
4. Addressing the AI Power Divide
The AI divide between developed and developing nations is becoming increasingly pronounced, with wealthier countries leading in AI innovation, infrastructure, and regulation while many developing countries struggle to keep up. This disparity can lead to economic dependency, limited technological sovereignty, and exclusion from key global AI discussions. Bridging this divide requires targeted interventions at multiple levels, including international cooperation, investment in open-source AI, strengthening AI education and research, and fostering local AI ecosystems.
4.1 International Organizations’ Role in Reducing the AI Divide
International organizations such as the United Nations (UN), Organisation for Economic Co-operation and Development (OECD), and the World Bank play a crucial role in ensuring AI development is inclusive and beneficial to all nations. Their efforts should focus on:
AI Capacity-Building Initiatives
- Funding AI Research and Innovation Centers: Developing countries often lack the financial resources to establish AI research institutions. International organizations can fund AI labs, provide grants for AI startups, and sponsor research projects to encourage local innovation.
- Technical Training Programs: Many countries lack the necessary AI expertise. Organizations like UNESCO and the ITU (International Telecommunication Union) can offer AI training programs and certification courses for professionals in developing nations.
- Digital Infrastructure Development: The World Bank can assist in financing AI-related infrastructure, such as data centers, cloud computing services, and 5G networks, which are crucial for AI development.
AI Policy Development and Ethical Standards
- Helping Countries Develop AI Policies: Many developing countries lack comprehensive AI strategies. The UN’s AI for Good initiative and the OECD AI Principles can guide nations in drafting regulations that balance innovation with ethical concerns.
- Ensuring Fair Data Governance: AI is data-driven, but many developing nations lack robust data privacy laws. International organizations can support data governance frameworks that protect users while allowing responsible AI use.
- Facilitating AI Knowledge Sharing: Creating AI diplomacy platforms where countries exchange best practices and technological know-how can accelerate AI adoption in the Global South.
Multilateral AI Collaborations
- Encouraging South-South AI Cooperation: Many developing countries face similar AI challenges. Facilitating regional AI alliances, such as the African Union AI Task Force or ASEAN AI Coalition, can strengthen AI capabilities through collective learning and joint research.
- Bridging the AI Regulation Gap: AI regulations in the Global North may not fit developing contexts. The UN could create adaptable AI regulatory templates that allow developing countries to align with global standards without stifling innovation.
4.2 Open-Source AI: A Tool for Democratizing Access
The dominance of Big Tech companies like Google, Microsoft, OpenAI, and Amazon in AI development creates a dependency issue where developing countries rely on external AI systems, often at high costs. Open-source AI offers an alternative by making AI models, tools, and datasets freely available, allowing countries to develop their own AI applications without costly licensing fees.
Benefits of Open-Source AI for Developing Nations
- Lowering Barriers to AI Development: Open-source AI frameworks such as TensorFlow (by Google), PyTorch (by Meta), and Hugging Face’s open-source models allow developers to build AI applications without needing proprietary software.
- Reducing Dependence on Big Tech: Developing countries can use open-source AI models instead of relying on foreign AI providers, thus enhancing digital sovereignty.
- Fostering Local Innovation: Open AI platforms enable researchers and startups in developing nations to create region-specific AI tools, such as AI models trained in local languages or designed for specific social challenges.
Key Open-Source AI Initiatives Supporting Developing Countries
- AI4D Africa Initiative: Supports African researchers in developing AI solutions for agriculture, healthcare, and education using open-source tools.
- Latin America AI Commons: Encourages open collaboration between universities, governments, and private companies to create shared AI models for the region.
- Indian AI Stack: India has been developing an open AI ecosystem through public datasets and AI models tailored to local needs.
Challenges in Open-Source AI Adoption
- Limited Computing Infrastructure: Many developing nations lack access to high-performance computing (HPC) facilities required to train AI models.
- Need for Skilled Developers: Open-source AI requires technical expertise that is still scarce in many regions. AI education must be strengthened (see next section).
- Data Sovereignty Concerns: Even with open-source AI, developing countries may still need to rely on cloud platforms from Big Tech, raising concerns about data privacy and sovereignty.
4.3 AI Education & Research: Building Domestic AI Talent
One of the biggest barriers to AI development in developing nations is the lack of trained professionals. AI research and education must be expanded to build local expertise.
Integrating AI into National Education Systems
- AI Courses in Universities: Governments should make AI a mandatory subject in computer science and engineering degrees. Partnerships with leading AI institutions (such as Stanford AI Lab or DeepMind) can provide training resources.
- AI in Vocational Training: AI education should not be limited to elite universities. AI-focused vocational training and online courses (such as Coursera, Udacity, and edX AI programs) can make AI skills accessible to a broader population.
- Primary and Secondary AI Education: Countries like China and the UAE have introduced AI education in high schools. Developing nations can follow this model to prepare students for AI careers early.
Promoting AI Research & Innovation in Developing Countries
- Government-Funded AI Research Grants: Governments should fund local AI projects that address regional needs, such as AI for climate change adaptation, disease prediction, and urban planning.
- Encouraging AI Startups at Universities: AI incubators at universities can help students transform research ideas into real-world AI applications.
- Strengthening Global AI Research Collaboration: Universities in developing countries should collaborate with top AI research institutions globally to access resources and expertise.
4.4 Reducing Dependency on Foreign AI Providers
Developing nations often rely on US and Chinese AI firms for cloud computing, AI software, and digital infrastructure. This creates risks of data exploitation, digital colonialism, and national security concerns. To strengthen their AI autonomy, these nations need to invest in domestic AI capacity-building.
Fostering Local AI Startups and Industries
- AI Innovation Hubs: Governments can establish tech hubs and accelerators to support AI startups. Examples include Kenya’s iHub and Brazil’s Cubo AI Hub.
- Public Funding for AI Entrepreneurs: AI-focused grants and venture capital incentives can stimulate local AI industry growth.
- Local AI Cloud Services: Developing regional AI cloud providers (like India’s Reliance Jio Cloud) can reduce reliance on foreign cloud giants like AWS, Google Cloud, and Microsoft Azure.
Developing AI Infrastructure Locally
- AI-Specific Supercomputers: Countries like India and South Africa are investing in high-performance computing (HPC) to support AI research.
- Open National Data Repositories: Governments should create public datasets that researchers and companies can use to build AI models tailored to local needs.
- Smart Public-Private Partnerships: AI collaborations between governments, universities, and private companies can pool resources to develop domestic AI solutions.
5. Policy Recommendations
Governments in developing economies should:
- Develop National AI Strategies: Align AI policies with local economic priorities and governance frameworks.
- Encourage International Partnerships: Engage in AI research collaborations with global institutions.
- Strengthen AI Governance: Establish regulatory frameworks ensuring ethical AI use while fostering innovation.
- Retain AI Talent: Introduce incentives to keep AI professionals within the country, including funding for AI research and local job opportunities.
- Invest in AI Infrastructure: Expand access to cloud computing and data centers through public-private partnerships.
6. Conclusion
The AI power divide threatens to widen global inequalities, reinforcing existing economic dependencies. While high-income nations continue to lead AI development, emerging economies have opportunities to bridge the gap through strategic investments in infrastructure, education, and governance. International cooperation, open-source AI initiatives, and localized AI strategies can enable developing countries to harness AI’s potential for sustainable and inclusive growth. By prioritizing AI capacity-building and innovation, developing nations can secure a more equitable position in the global AI landscape.
