Microsoft's 2026 AI diffusion update finds growth alongside a wider gap
Aggregated telemetry estimates broader generative-AI use in Q2 while showing persistent differences between the Global North and South.
Key takeaways
Microsoft published its latest Global AI Diffusion update on September 21. Using aggregated and anonymized telemetry adjusted for operating-system share, device mix, internet penetration, and population, Microsoft estimates that generative-AI use reached 18.8% of the world's working-age population in June, about one percentage point higher than in the first quarter. The reported rates were 28.8% in the Global North and 16.2% in the Global South, widening the measured gap. Microsoft also observes a larger share of education and learning use in the Global South. The measure does not capture every new model or tool and is being revised, so it is useful for directional comparison rather than as a direct ranking of national capability, productivity, or social benefit.
Direct answer
Microsoft published its latest Global AI Diffusion update on September 21. Using aggregated and anonymized telemetry adjusted for operating-system share, device mix, internet penetration, and population, Microsoft estimates that generative-AI use reached 18.8% of the world's working-age population in June, about one percentage point higher than in the first quarter. The reported rates were 28.8% in the Global North and 16.2% in the Global South, widening the measured gap. Microsoft also observes a larger share of education and learning use in the Global South. The measure does not capture every new model or tool and is being revised, so it is useful for directional comparison rather than as a direct ranking of national capability, productivity, or social benefit.
Verified facts
Microsoft estimates that 18.8% of people aged 15 to 64 worldwide used a generative-AI product in June 2026, about one percentage point above Q1.
The reported usage rates are 28.8% in the Global North and 16.2% in the Global South, widening the measured gap.
The metric uses aggregated and anonymized telemetry adjusted for operating-system and device share, internet penetration, and population, and Microsoft plans to include more tools later.
What changed
- Estimated global use continues to rise
- Regional gaps do not automatically narrow with growth
- Education use shows a different regional pattern
- The metric will change as more tools are included
Impact for AI users
Organizations can use diffusion data to investigate access, skills, and language support, but one adoption rate cannot replace local research. Telemetry reflects product coverage, connectivity, devices, and statistical adjustment and does not measure use quality or outcomes. Decisions should add task success, depth of use, affordability, training completion, and surveys of non-users.
Operating checklist
- Define the denominator and source for local adoption metrics
- Slice use by region, language, and task
- Survey non-users and failed tasks
- Report access indicators separately from real outcomes
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FAQ
Does 18.8% imply an 18.8% productivity gain?
No. The metric estimates whether people used a generative-AI product; it does not directly measure frequency, quality, output, or productivity.
Can countries be ranked directly from this measure?
Only with the stated methodological limits and alongside product coverage, connectivity, population, and local evidence.
Summary
AI use is spreading, but reach is not an outcome metric. Sound decisions measure access, capability, task quality, and social benefit separately and disclose methodological limits.
This AI-assisted article is checked by ENHE AI automation for official sources, bilingual fields, media rights, page safety, and historical duplication before publication.
What this means for everyday users
Organizations can use diffusion data to investigate access, skills, and language support, but one adoption rate cannot replace local research. Telemetry reflects product coverage, connectivity, devices, and statistical adjustment and does not measure use quality or outcomes. Decisions should add task success, depth of use, affordability, training completion, and surveys of non-users.
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