The AI Divide: Why 40% of Countries, Including Bangladesh, Nigeria, Kenya, and Vietnam, Are Being Left Behind

August 3, 2025 Author: Talha Al Islam The global artificial intelligence (AI) market is projected to reach $4.8 trillion by 2033, rivaling the GDP of major economies like Germany. However, this transformative technology is deepening a global AI divide, with approximately 40% of countries—particularly in the Global South—struggling to adopt AI due to barriers in infrastructure, skills, investment, and governance. Nations like Bangladesh, Nigeria, Kenya, and Vietnam, despite their economic potential and digital ambitions, face significant challenges in keeping pace with AI-leading countries such as the United States, China, and Singapore.

These four nations serve as critical case studies, each representing a different regional context but sharing a common struggle: Bangladesh, a densely populated South Asian nation with a massive manufacturing base; Nigeria, Africa's largest economy and demographic giant; Kenya, the "Silicon Savannah" of East Africa; and Vietnam, a rapidly growing Southeast Asian manufacturing hub. Their collective journey illustrates the broader challenges faced by the Global South in the age of AI. This blog, in a reporting tone, examines the factors driving the AI divide, highlights recent developments in these four countries, and explores pathways to bridge the gap, optimized for SEO to rank highly on Google.

Understanding the AI Divide

The AI divide refers to the growing disparity between nations with the resources to leverage AI and those without. According to a 2024 UNCTAD report, AI could impact 40% of global jobs, with developing countries facing higher risks of job displacement due to reliance on labor-intensive industries. The World Economic Forum (WEF) notes that disparities in AI readiness exacerbate global inequality, with 32% of the world’s population (2.6 billion people) offline in 2024, per the International Telecommunication Union (ITU). This digital divide underpins the AI divide, limiting access to the infrastructure, skills, and data needed for AI adoption.

Key Drivers of the AI Divide

Critical infrastructure gaps threaten to lock billions out of the AI revolution

1. Infrastructure Gaps: AI requires high-speed internet, data centers, and reliable energy. Only 27% of low-income countries have internet access, compared to 93% in high-income nations.

2. Skills Shortages: Developing nations often lack advanced STEM education and AI training programs, with talent concentrated in the Global North.

3. Financial Constraints: The high cost of AI infrastructure, such as GPUs and cloud computing, is prohibitive for resource-constrained countries.

4. Data and Governance Challenges: AI systems rely on quality data and robust regulations, which many developing nations struggle to provide due to limited data ecosystems and policy frameworks.

Bangladesh: Navigating the AI Divide

Bangladesh, with a population of 170 million and a burgeoning IT sector, is making strides toward AI adoption but faces significant hurdles.

Recent Developments

• Policy Framework: The National AI Policy 2024, launched by the ICT Division, aims to generate $5 billion in revenue by integrating AI into healthcare, agriculture, and education. The Smart Bangladesh Strategy targets a technology-driven economy by 2041.
  

• Sectoral Applications:

  • Healthcare: AI platforms like SuSastho.AI and Praava Health enhance telemedicine, addressing the low doctor-to-patient ratio (5.26 per 10,000). AIeh-MD predicts diabetes with 93% accuracyusing gut microbiome data.

  • Agriculture: AI-driven precision farming, including drones and sensors, boosts yields by 20-30%and reduces water usage by 30% for the 40% of the workforce in agriculture.

  • Education: AI tools are piloted to address teacher shortages, with platforms personalizing learning for students.
    

• IT Growth: Bangladesh produces 10,000 IT graduates annually from 95 universities and 200 polytechnic colleges, supporting a growing AI workforce of 300,000 IT professionals.

Challenges

Rural Bangladesh

• Digital Divide: Only 48% of rural populations have internet access, limiting AI scalability.

• Skills Gap: Limited advanced AI training and research centers hinder expertise development.

• Job Displacement: The World Bank estimates 47% of jobs, particularly in the ready-made garment (RMG) sector (4.4 million workers, 84% of exports), are at risk from automation.

• Data Scarcity: Bangla, spoken by 230 million, has fewer than 10 million data points for natural language processing, risking linguistic exclusion.

"The risk is that AI could exacerbate inequality between and within countries. If we don't act, we could see a world where AI benefits are concentrated in a few countries and a few companies." — António Guterres, UN Secretary-General (2023)

Economic Impact

Failure to bridge the AI divide could reduce Bangladesh’s GDP growth by 5% annually, while strategic adoption could add $25 billion by 2030, per McKinsey.

Nigeria: AI Ambitions Amid Structural Barriers

Nigeria, Africa’s largest economy with 223 million people, is leveraging AI to address developmental challenges but faces significant constraints.

Recent Developments

• Policy and Institutions: The National Centre for AI and Robotics (NCAIR), under the National Information Technology Development Agency, promotes AI R&D. Nigeria’s National AI Strategy(drafted in 2023) focuses on agriculture, healthcare, and governance.

• Sectoral Applications:

  • Healthcare: RxAll uses AI to detect counterfeit drugs, addressing a critical issue in Nigeria’s pharmaceutical supply chain. Zipline drones deliver 75% of blood supplies outside major cities, improving healthcare access.

  • Agriculture: AI-powered precision farming tools enhance crop yields for Nigeria’s 70% agricultural workforce, with startups like FarmCrowdy using AI for farm management.

  • Governance: The World Bank supports AI-driven civil works monitoring in Nigeria, using sentiment analysis and tag clouds to improve citizen engagement.

• Digital Skills: Partnerships with Microsoft have trained 1 million Nigerians in digital skills since 2020, boosting AI capacity.

Challenges

• Infrastructure: Only 35% of Nigerians have internet access, and unreliable electricity hampers data center development.

• Brain Drain: Many AI engineers migrate to the US and Europe for better opportunities, exacerbating the talent gap.

• Job Risks: 40% of jobs in Nigeria are in high-exposure occupations, with 20% facing automation risks due to low complementarity with AI.

• Regulatory Gaps: Nigeria lacks comprehensive AI governance, increasing risks of bias and data privacy issues.

Economic Impact

The Global AI Index ranks Nigeria as a “nascent” AI nation, but AI could contribute $1.5 billion to Africa’s GDP by 2030 if infrastructure and skills gaps are addressed.

Kenya: Pioneering AI in East Africa

Mount Kilimanjaro, Africa's highest peak at 19,341 feet, is losing its iconic glaciers due to climate change

Kenya, with 54 million people and a vibrant tech ecosystem dubbed the “Silicon Savannah,” is a regional AI leader but still faces divide-related challenges.

Recent Developments

• Policy Framework: Kenya’s Distributed Ledgers Technology and AI Task Force (2018) and Digital Economy Blueprint integrate AI into public services and education. Coding was introduced into K-12 curricula in 2022.

• Sectoral Applications:

  • Healthcare: M-Shule, an ed-tech platform, uses AI to deliver personalized education to remote areas. Zipline drones enhance medical supply chains.

  • Financial Services: M-Pesa, Kenya’s mobile money platform, leverages AI for fraud detection and financial inclusion, serving 51 million users.

  • Innovation Hubs: iHub in Nairobi supports AI startups, fostering local innovation.

• Infrastructure: Kenya generates 40% of its electricity from geothermal power, supporting AI data centers, with Microsoft building a facility in collaboration with G42.

Challenges

• Digital Access: Only 48% of rural Kenyans have internet access, limiting AI scalability.

• Talent Development: Despite curriculum reforms, advanced AI skills training is limited.

• Geopolitical Barriers: Kenya was excluded from US semiconductor access lists in 2024, pushing reliance on Chinese AI chips.

• Job Exposure: 40% of jobs are in high-exposure occupations, with 20% at risk of automation.

Economic Impact

Kenya’s AI adoption could add $1 billion to its GDP by 2030, but infrastructure and policy gaps must be addressed to avoid falling further behind.

Vietnam: Balancing Growth and AI Challenges

Vietnam, with 104 million people and a fast-growing tech sector, is an emerging AI player in Southeast Asia but struggles with structural limitations.

Recent Developments

• Policy Framework: Vietnam’s National Strategy for AI Development to 2030 aims to position the country among ASEAN’s top AI nations. The strategy focuses on education, healthcare, and smart cities.
  

• Sectoral Applications:

  • Education: AI platforms like VinAI develop personalized learning tools, addressing teacher shortages in rural areas.

  • Healthcare: AI-driven diagnostics improve access in underserved regions, with startups like Med247 using AI for telemedicine.

  • Manufacturing: Vietnam’s $280 billion export sector employs AI for supply chain optimization, with companies like FPT Software leading AI adoption.
    

• Talent Development: Vietnam produces 57,000 IT graduates annually, with VinAI and FPT University training AI specialists.

• Innovation: PhoGPT, an open-source large language model for Vietnamese, addresses linguistic inclusion in AI systems.

Challenges

• Infrastructure: Only 70% of the population has internet access, with rural areas lagging.

• Skills Gap: Advanced AI research is limited, with most talent focused on applied AI rather than foundational development.

• Data Limitations: Vietnamese-language datasets are underdeveloped, hindering AI model training.

• Job Risks: 40% of jobs are exposed to AI, with manufacturing and textiles facing automation threats.

Economic Impact

Vietnam’s AI adoption could contribute $100 billion to its GDP by 2030, but infrastructure and talent development are critical to realizing this potential.

At a Glance: Comparing Key Metrics

Metric	Bangladesh	Nigeria	Kenya	Vietnam
Population	170 Million	223 Million	54 Million	104 Million
Internet Access	~50% (Rural: 48%)	35%	~50% (Rural: 48%)	70%
Key AI Policy	National AI Policy 2024	National AI Strategy (Draft)	Digital Economy Blueprint	AI Strategy to 2030
Top AI Startups	SuSastho.AI, Praava	RxAll, FarmCrowdy	M-Shule, iHub	VinAI, Med247, FPT
Primary Job Risk	Garment Sector (47%)	High-Exposure (40%)	High-Exposure (40%)	Manufacturing (40%)

The Geopolitical Dimension: Navigating a Two-Superpower World

The AI divide is not merely a technological or economic gap; it is deeply intertwined with geopolitics. Developing nations are navigating a complex landscape dominated by the United States and China, whose competition shapes access to technology, investment, and standards.

• Technological Alignments: Countries are often forced to choose between Western and Chinese technology stacks. As seen with Kenya's exclusion from US semiconductor access, geopolitical tensions can directly impede a nation's ability to source critical AI hardware. This pushes some nations toward Chinese suppliers like Huawei for 5G and AI infrastructure, creating divergent technological spheres.
  

• Competing Governance Models: The US and Europe advocate for a human-centric, democratic approach to AI governance (e.g., the EU AI Act), while China promotes a state-led model emphasizing surveillance and social stability. Developing nations must decide which framework—or hybrid model—best suits their domestic context, a choice with long-term implications for civil liberties and economic partnerships.
  

• The "Digital Silk Road": China's Belt and Road Initiative includes a significant digital component, offering developing nations AI-powered infrastructure for smart cities and surveillance. While attractive for closing infrastructure gaps, it raises concerns about data sovereignty and geopolitical dependence.
  

For countries like Bangladesh, Nigeria, Kenya, and Vietnam, successfully navigating this great power competition is as crucial as developing domestic talent.

Why 40% of Countries Are Falling Behind

The AI divide affects 40% of countries, including Bangladesh, Nigeria, Kenya, and Vietnam, due to systemic barriers:

• Investment Disparities: High-income nations like the US and China account for 40% of global AI R&D, with 100 firms dominating investment.
  

• Infrastructure Gaps: Developing countries lack the data centers, high-speed internet, and energy required for AI. For example, Africa hosts only 1% of global data centers.
  

• Talent Concentration: AI expertise is concentrated in the Global North, with brain drain affecting countries like Nigeria and Vietnam.
  

• Governance Exclusion: Developing nations have limited influence in global AI policy discussions, risking exclusion from ethical AI standards.
  

A 2024 OECD report notes that urban areas in developed countries have 32% exposure to generative AI, compared to 21% in rural areas, highlighting regional disparities within and between nations.

Opportunities to Bridge the AI Divide

Recent reports outline strategies to help Bangladesh, Nigeria, Kenya, Vietnam, and similar nations close the AI divide:

1. Infrastructure Investment:

   • Build local data centers and leverage renewable energy, as Kenya does with geothermal power.

   • Develop regional AI cloud services, like India’s Reliance Jio Cloud, to reduce reliance on Western providers.

2. Skills Development:

   • Integrate AI into education curricula, as seen in Kenya’s K-12 coding programs.

   • Partner with tech giants like Microsoft and Google, as Nigeria and Bangladesh have done, to train millions in digital skills.

3. Local AI Ecosystems:

   • Establish AI innovation hubs, such as Kenya’s iHub or Vietnam’s VinAI.

   • Support startups like RxAll (Nigeria) and Speaklar (Bangladesh) to drive local innovation.

4. Ethical and Inclusive AI:

   • Develop regulations inspired by the EU AI Act to address biases, as seen in Bangladesh’s National AI Policy.

   • Promote open-source models like PhoGPT (Vietnam) to ensure linguistic inclusion.

5. International Cooperation:

   • Leverage platforms like the WEF’s National AI Strategy Peer Network for knowledge-sharing.

   • Support UNCTAD’s call for global facilities to provide equitable AI access.

Conclusion

The AI divide, affecting 40% of the world, is more than a technological gap—it is a barrier to inclusive development that threatens to leave nations like Bangladesh, Nigeria, Kenya, and Vietnam behind. While these countries demonstrate remarkable resilience and innovation through national policies and homegrown startups, they are battling systemic headwinds in infrastructure, skills, and investment. Closing this divide requires more than domestic policy; it demands a shift in global priorities. As the AI market surges toward $4.8 trillion, ensuring that its benefits are shared equitably is not just a matter of fairness, but a prerequisite for stable and sustainable global progress. The central challenge, as emphasized by UNCTAD’s Rebeca Grynspan, is to transform the AI divide into a global bridge for shared prosperity.

Stay informed with AI News Hub for updates on the global AI divide and these nations’ AI journeys!

FAQs on AI Divide

What is the AI divide?

The AI divide is the gap between countries with resources to adopt AI and those without, affecting 40% of nations like Bangladesh, Nigeria, Kenya, and Vietnam.

How are Bangladesh, Nigeria, Kenya, and Vietnam addressing the AI divide?

1. They are implementing national AI strategies, fostering startups, and partnering with tech giants to enhance infrastructure and skills.

What risks does the AI divide pose for these countries?

Job displacement (up to 47% of jobs), reduced GDP growth, and linguistic exclusion in AI systems are major risks.

How can the AI divide be bridged?

Investments in infrastructure, skills training, local AI ecosystems, ethical regulations, and international cooperation are key.