
Share
As AI adoption surges globally, the gap between tech-haves and have-nots sharpens, raising urgent questions about access and equity in the AI revolution.
Global adoption of artificial intelligence (AI) continued its upward trajectory in the second half of 2025, increasing by 1.2 percentage points compared to the first half of the year. According to Microsoft's AI Economy Institute, approximately one in six people worldwide now use generative AI tools-a significant milestone for a technology that has only recently entered mainstream usage.
The rapid adoption of AI is reshaping economies and societies, but the data also highlights a concerning trend: a widening digital divide between the Global North and the Global South. This disparity could exacerbate existing inequalities in economic development, innovation, and productivity gains. The implications are far-reaching, affecting not only technological advancement but also global competitiveness and social equity.
Microsoft measures AI diffusion by tracking the share of people worldwide who have used a generative AI product during the reported period. This metric is derived from aggregated and anonymized telemetry data, adjusted for differences in operating system (OS) and device market share, internet penetration, and country population. For more details, refer to Microsoft's AI Diffusion technical paper.
Several countries have emerged as leaders in AI adoption due to early investments in digital infrastructure, AI skilling, and government support:

The widening digital divide poses several risks:
Addressing the digital divide offers significant opportunities for both developed and developing nations:
While global AI adoption continues to grow, the widening digital divide between the Global North and the Global South is a critical issue that requires immediate attention. By addressing the key risks and leveraging the opportunities presented by AI, we can work towards a more inclusive and equitable digital future.
Tags
Original Sources
About the author
Marcus began tracking AI's market implications in 2016, noticing AI-related patent filings accelerating ahead of earnings upgrades before most of the sell-side had caught on. A former fixed-income quantitative analyst, he spent two decades building models that priced risk across emerging markets before pivoting to cover the economic impact of AI full-time. His writing translates opaque technical developments into clear risk/reward terms — and he's rarely diplomatic about the gap between AI valuations and underlying fundamentals. He believes most market participants still underestimate AI's long-run deflationary effect on knowledge work.
More from The Analyst →This Week's Edition
11 November 2025
133 articles
Related Articles
Related Articles
More Stories