Global AI Usage Climbs, But Not Evenly
Global AI usage is moving beyond early experimentation, with Microsoft’s latest Global AI Diffusion Report showing that 17.8% of the world’s working-age population used generative AI in the first quarter of 2026, up from 16.3%. Twenty-six economies now report more than 30% of their working-age residents using AI tools, signalling that advanced markets are entering a phase of routine, day-to-day deployment rather than isolated pilots. Leaders include the UAE with a diffusion rate above 70%, followed by high-income economies such as Singapore, Norway, Ireland, and France. The United States has also inched up the rankings. This rising global AI usage, however, masks sharp regional disparities. While some countries rapidly integrate AI into coding, content creation, and professional work, others are only beginning to gain basic exposure. The headline story is no longer whether AI is spreading, but where—and how fast—its benefits are being captured.
The Widening AI Adoption Gap Between North and South
Behind the aggregate rise in global AI usage lies a striking AI adoption gap between wealthier and developing regions. Microsoft’s data shows that in the Global North, 27.5% of the working-age population used generative AI in early 2026, up from 24.7% in the second half of 2025. In the Global South, usage reached only 15.4%, edging up from 14.1%. This pushed the difference between the two blocs from 10.6 to 12.1 percentage points in less than a year. Developed economies are not only signing up more users; they are also deepening intensity of use and experimenting across sectors such as software development, education, and professional services. By contrast, many developing markets are stuck in a slower adoption lane, often limited to basic experimentation. As a result, the global AI diffusion map is becoming more polarized, with a cluster of high-usage economies racing ahead while vast parts of the world remain at low to moderate adoption levels.
Why Developing Markets Struggle With AI Adoption
The growing AI adoption gap is not merely a matter of tool availability; it reflects deeper structural constraints that disproportionately affect developing markets. Microsoft links the divide to deficits in reliable electricity, high-speed internet connectivity, and foundational digital skills. In many countries, inconsistent power supply and limited broadband coverage make sustained use of cloud-based generative AI tools difficult or costly. An AI skills shortage compounds the problem. Employers may experiment with pilots, but lack workers who can integrate AI into workflows or build localized applications. Additionally, language support remains uneven despite recent progress, and many advanced tools are optimized for major global languages. These obstacles restrict generative AI access for large segments of the population, especially in rural and low-income communities. Without targeted interventions, these structural barriers risk locking developing economies into a peripheral role in the emerging AI ecosystem, even as global AI usage continues to climb.
Economic Risks of a Persistent AI Adoption Divide
A persistent AI adoption gap carries serious economic implications for developing markets. As advanced economies integrate AI into software development, services, and knowledge work, they can lower production costs, accelerate innovation, and expand digital exports. For example, AI coding tools have helped drive a sharp rise in software activity, with Git pushes and new repositories surging, and developer employment continuing to grow in leading markets. If these productivity gains remain concentrated in richer countries, emerging economies risk losing competitiveness in sectors where they once had cost advantages, from outsourcing to manufacturing support services. The gap could deepen existing income disparities, as high-AI economies compound their lead through faster innovation cycles and higher-value jobs. For developing markets, delayed adoption may translate into weaker bargaining power in global supply chains and reduced ability to shape AI standards, governance, and products that reflect their own social and economic priorities.
Closing the Gap: Building Skills, Infrastructure, and Localised AI
Narrowing the AI adoption gap requires more than simply rolling out tools; it demands coordinated investment in education, infrastructure, and locally relevant solutions. Expanding reliable electricity and affordable broadband is foundational, enabling consistent access to cloud-based AI services. At the same time, education systems and workforce programmes must address the AI skills shortage by integrating digital literacy, data competence, and prompt-based problem solving into curricula and vocational training. For developing markets, a key opportunity lies in fostering AI solutions tailored to local languages, regulatory contexts, and sector priorities such as agriculture, health, and small-business productivity. Partnerships between governments, global technology firms, and local startups can lower costs and broaden generative AI access, for example through subsidised tools or community-based digital hubs. Without such targeted efforts, the current trajectory suggests that global AI usage will continue to rise while the structural AI adoption gap—and its economic consequences—grows wider.