The “Strengthening AI Foundations” report from 2025 offers a data-rich snapshot of the global AI landscape — with special focus on how low- and middle-income countries (LMICs) can harness AI for development. Open Knowledge Repository
The report argues that meaningful AI-driven development depends on four foundational elements, dubbed the “4 Cs”: Connectivity (digital infrastructure, reliable energy, broadband), Compute (access to AI-ready hardware, data centers or cloud), Context (locally relevant data, data governance), and Competency (skills, AI literacy, local capacity). World Bank
While frontier AI (large proprietary models, expensive compute) remains concentrated in a few high-income countries, the report highlights a “promising trend”: many developing countries are already using “small AI” — affordable, lightweight, easy-to-deploy AI tools that run on mobiles or modest hardware. These are being applied in agriculture, health, education, small business — enabling early gains even under resource constraints. World Bank
However, large inequalities persist: a handful of high-income countries dominate access to the largest models, venture funding, data-center infrastructure, and AI talent — creating real risks of an “AI divide.” World Bank
Think About It This Way
AI isn’t a magic bullet — it’s more like a powerful new toolset. But just as a carpenter needs good wood, tools, and a workshop, AI too requires infrastructure (internet, power), compute capacity, reliable data, and human know-how. If a country skips any of those, the toolset becomes useless or worse: a source of inequality. The “small AI” approach shows it’s possible to build real value even without cutting-edge frontier AI — as long as the foundations are laid.
Implications (What This Means in Practice)
- AI-driven development is conditional on basic infrastructure
Countries without widespread connectivity, affordable power, or data-center/compute capacity will struggle to benefit — emphasising infrastructure as development priority, not luxury. - “Small AI” is a pragmatic entry point for resource-constrained economies
Governments and development partners should support lightweight, locally relevant AI tools (e.g. in agriculture, health, education) rather than chasing frontier models from day one. - Data governance and local context must be central
Without good data — relevant, high-quality, governed — AI risks replicating biases or producing irrelevant outputs. Local data policy, privacy, and capacity building are vital. - Equity in AI requires building human capital, not just importing tools
To avoid becoming passive consumers of AI, LMICs must invest in skills, AI literacy and local talent — so they can adapt, maintain, and innovate AI solutions locally. - Risk of widening global inequality if AI foundations are unequal
If AI R&D, compute power, and investment stay concentrated in high-income countries, then benefits (jobs, startup growth, productivity) may disproportionately accrue there — widening the development gap. - Policy and strategic planning matter — not just tech adoption
Building national AI strategies, regulatory frameworks, and institutions that support competition and inclusion becomes a core development task, not an afterthought.
Further Reading
| Report / Study (Year – Institution) | What It Covers / Why Useful | Link |
|---|---|---|
| Digital Progress and Trends Report 2025: Strengthening AI Foundations (2025 – World Bank) | Comprehensive global snapshot of AI readiness, adoption, “4 Cs” framework | [Download PDF] Open Knowledge Repository+1 |
| Devising a Strategic Approach to Artificial Intelligence: A Handbook for Policy Makers (2025 – World Bank) | Practical guide for governments to design national AI strategies tailored to their context Open Knowledge Repository | |
| Who on Earth Is Using Generative AI? Global Trends and Shifts in 2025 (2025 – World Bank Policy Research Working Paper) | Empirical analysis of generative AI adoption across countries — shows early diffusion and disparities World Bank+1 | |
| Harnessing Artificial Intelligence for Development: A New Policy and Regulatory Framework (2021 – World Bank) | Exploration of AI potential across development sectors + policy and governance recommendations World Bank+1 | |
| AI, the new wingman of development (2024 – World Bank) | Sector-level review (agriculture, health, education, etc.) of where AI can support development goals The World Bank Documents |
Explore With VoD
Copy and paste any of the below (our your own) prompts into the VoDGPT to explore further:
- What would a “small-AI first” national plan look like for a resource-constrained country? Draft a 3–5 pillar outline.
- Examine your own country (or one you choose): map where the biggest gaps are among the “4 Cs” — connectivity, compute, context, competency. What are possible low-cost steps to strengthen each?
- Investigate potential risks: what kind of data governance and regulation would be needed to avoid AI reinforcing inequality or bias in LMICs?
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