Chapter 1
AI innovation, adaptation, and adoption are uneven across countries.
AI innovation is still concentrated in high-income countries, though some low- and middle-income countries are beginning to catch up. The rapid pace and concentration of breakthroughs make effective adaptation and localization challenging, but open-source technologies open doors for broader participation, enabling developing countries to tailor AI to local contexts without reinventing foundational technologies. AI adoption has grown quickly, with individuals in middle-income countries driving much of the global uptake, but its adoption by businesses and governments is still in its nascent stages.
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Chapter 2
Connectivity is the gateway to AI participation.
Internet access continues to expand, and satellites are opening new possibilities for closing remaining gaps. Despite this progress, huge disparities in affordability, speed, and data usage remain between richer and poorer countries. A cohesive policy approach is essential to expand affordable connectivity, including reliable electricity, and lay the groundwork for AI-ready economies.
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Chapter 3
The transformative power of AI is fundamentally reliant on compute.
Compute is the new electricity in the AI era—essential but unevenly distributed. Supply is concentrated among a few firms. While many countries access compute through importing cloud services, this trade remains imbalanced. Governments face a strategic decision: whether to build domestic compute capacity (e.g. data centers) or secure affordable access to international cloud infrastructure.
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Chapter 4
Context determines the capabilities of AI.
AI model capabilities hinge on the quantity, quality and diversity of the data that trains them, and the growing data industry shows strong investor appetite for these assets. While English dominates training data today, emerging formats like video create new opportunities for low- and middle-income countries to participate. Developing countries are navigating AI adoption of open-source and proprietary models. The key is ensuring models can be adapted to local economic, cultural, and institutional contexts—turning global data resources into locally relevant AI solutions.
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Chapter 5
Competency is a pre-requisite for AI participation and jobs.
Digital skills are now a baseline across many occupations, and AI is reshaping labor markets globally. Skills gaps are uneven, but AI-related jobs are growing faster in middle-income countries than high-income countries, and generative AI skills are expanding beyond ICT roles. Many low- and middle-income countries still face challenges in building and retaining talent, including supply shortage—due to the lack of quality, industry-aligned education and training programs —and brain drain, key barriers to fully participating in the AI-driven economy.
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Chapter 6
Conclusion.
As AI becomes part of the fabric of everyday lives across the world, millions cannot be left on the sidelines to watch as innovation transforms society. Investing in the 4Cs—connectivity, compute, context, and competency—represents a strategic, largely no-regret approach for LICs and MICs although specific priorities must be tailored to each nation’s unique circumstances.
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