recently, the ai economy institute, a think tank under microsoft, released its q1 2026 ai adoption report. the report shows that in the first quarter of 2026, among the global working-age population, the adoption rate of generative ai rose from 16.3% to 17.8%, an increase of 1.5 percentage points. in economies with higher levels of ai adoption, user engagement intensity also increased accordingly. currently, 26 economies have ai adoption rates exceeding 30% among their working-age populations.
the number of economies where generative ai adoption exceeds the 30% threshold has grown from 18 in the previous quarter to 26. the united arab emirates, with a rate of 70.1%, has become the first country globally to surpass 70%, followed closely by singapore (63.4%), norway (48.6%), ireland (48.4%), and france (47.8%). south korea’s adoption rate increased by 6.4 percentage points from the previous quarter to 37.1%, marking the fastest growth worldwide and moving its ranking up from 18th to 16th place.
the report notes that among the 15 fastest-growing markets, 12 are located in asia. these gains are driven by long-term investments in digital infrastructure, national-level ai strategies, high consumer acceptance, improved performance of asian-language models, and the rapid integration of new technologies into daily life and economic activities.
on the technological front, enhanced support for local languages and expanded multimodal interaction capabilities are key drivers. multilingual benchmark tests like mmmlu demonstrate that improved non-english language performance enables ai tools to handle multilingual tasks more effectively, thereby increasing accessibility across everyday scenarios such as messaging, search, learning, and content creation.
there is a growing alignment between user demand and practical applications. mckinsey research indicates that ai adoption in southeast asia is outpacing the global average, with many organizations transitioning from pilot phases to large-scale deployment.
the influence of generative ai in software development is expanding significantly. coding-specific systems developed by companies such as anthropic and openai showcase the ability to tackle complex engineering tasks, with gpt‑5.3‑codex achieving top scores on swe‑bench pro. meanwhile, github copilot is evolving from a code‑recommendation tool into an ai-native development platform.
