=== PAGE 1 ===
May, 2026
Global AI Diffusion
Q1 2026 Trends and Insights 


=== PAGE 2 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Executive Summary
The global adoption of artificial intelligence continued to 
rise in the first quarter of 2026. During the quarter, AI usage 
increased by 1.5 percentage points from 16.3% to 17.8% 
of the world’s working age population. Intensity of use 
among economies with the highest rates of AI diffusion also 
increased, with 26 economies now exceeding 30% of the 
working age population using AI.
At the top of Microsoft’s National AI Leaderboard, the UAE 
continued to lead global AI diffusion at 70.1%. The United 
States finally started to move up the national rankings, albeit 
only from 24th to 21st based on a 31.3% usage rate by the 
working age population. 
Notable developments in the quarter included accelerating 
AI adoption in Asia driven in part by improving AI capabilities 
in Asian languages. South Korea, Thailand, and Japan saw 
the greatest movement. More broadly, the quarter brought 
continued widening of the AI gap between the Global North 
and South, with usage now at 27.5% in the North and 15.4% 
in the South. These trends are discussed below, including a 
deeper dive on the positive impact of enhanced multilingual 
AI capabilities in Japan.
AI user share
10%
20%
30%
Insufficient data
70.1%
Norway
48.6%
Ireland
48.4%
France
47.8%
UAE
Singapore
63.4%
AI diffusion by economy, March 2026
2

=== PAGE 3 ===
Global AI Diffusion Q1 2026 Trends and Insights 
To track all these trends, we continue to measure AI diffusion 
as the share of people worldwide between ages of 15 
and 64 who have used a generative AI product during the 
reported period. This measure is derived from aggregated 
and anonymized Microsoft telemetry and adjusted to 
reflect differences in OS and device-market share, internet 
penetration, and country population. Additional details on 
the methodology are available in our AI Diffusion technical 
paper. [1]
No single metric is perfect, and this one is no exception. 
Through the Microsoft AI Economy Institute, we continue 
to refine how we measure AI diffusion globally, including 
how adoption varies across countries in ways that 
best advance priorities such as scientific discovery and 
productivity gains. For this report, we rely on the strongest 
cross-country measure available today, and we expect to 
complement it over time with additional indicators as they 
emerge and mature.
Sectorally, the quarter saw strengthened AI coding 
capabilities leading to a dramatic increase in production 
of software code. This was reflected in production by 
Anthropic’s Claude Code, the OpenAI’s Codex, and 
Microsoft’s GitHub Copilot. Git pushes – through which 
software developers put coding changes online – increased 
78% year over year globally. 
Interestingly, the quarter brought added evidence that, 
at least for now, AI coding capabilities may be increasing 
demand for the employment of software developers. 
As discussed in more detail below, when developer 
productivity increases, the cost of building software 
declines. If demand for software is elastic, organizations 
can respond by building more software across a wider 
range of use cases, including across broader economic 
sectors. It is still too early to know the full labor-market 
impact of AI-assisted coding, and this may change over 
time. But the available data shows that in 2025, total U.S. 
software developer employment reached approximately 
2.2 million, rising 8.5% year over year and marking a record 
high for the profession. Early data for the first quarter of 
2026 shows that software developer employment in March 
2026 was about 4% higher than in March 2025.
3

=== PAGE 4 ===
Global AI Diffusion Q1 2026 Trends and Insights 
H2 2025 AI diffusion
Q1 2026 AI diffusion
1
United Arab Emirates
64.0%
2
Singapore
60.9%
3
Norway
46.4%
4
Ireland
44.6%
5
France
44.0%
6
Spain
41.8%
7
New Zealand
40.5%
8
United Kingdom
38.9%
9
Netherlands
38.9%
10
Qatar
38.3%
11
Australia
36.9%
12
Israel
36.1%
13
Belgium
36.0%
14
Canada
35.0%
15
Switzerland
34.8%
16
Sweden
33.3%
17
Austria
31.4%
18
South Korea
30.7%
19
Hungary
29.8%
20
Denmark
28.7%
21
Germany
28.6%
22
Poland
28.5%
23
T aiwan
28.4%
24
United States
28.3%
25
Italy
27.8%
1
United Arab Emirates
70.1%
6.1%
2
Singapore
63.4%
2.5%
3
Norway
48.6%
2.2%
4
Ireland
48.4%
3.8%
5
France
47.8%
3.8%
6
Spain
44.2%
2.4%
7
New Zealand
43.0%
2.5%
8
United Kingdom
42.2%
3.3%
9
Netherlands
42.1%
3.2%
10
Qatar
41.8%
3.5%
11
Australia
39.5%
2.6%
12
Belgium
39.0%
3.0%
13
Israel
38.1%
2.0%
14
Switzerland
37.8%
3.0%
15
Canada
37.3%
2.3%
16
South Korea
37.1%
6.4%
17
Sweden
36.1%
2.8%
18
Austria
34.1%
2.7%
19
Hungary
32.2%
2.4%
20
T aiwan
31.8%
3.4%
21
United States
31.3%
3.0%
22
Denmark
31.2%
2.5%
23
Germany
31.1%
2.5%
24
Poland
31.0%
2.5%
25
Italy
30.2%
2.4%
Al diffusion over time by economy
4

=== PAGE 5 ===
Sources: World Bank Total Population 2024 [4]; Share of the population with access to electricity, Our World in Data [5]; 
Individuals using the Internet 2024, ITU [6]; Internet users with basic information and data literacy skills, ITU [7]. 
Global AI Diffusion Q1 2026 Trends and Insights 
AI adoption in the Global North outpaces the Global South
At the same time, the gap between the Global North and 
Global South continues to widen, with adoption in the Global 
North growing more than twice as fast as in the Global South. 
In the first quarter of 2026, 27.5 percent of the population in 
the Global North used generative AI, up from 24.7 percent in 
the second half of 2025, a gain of 2.8 percentage points. In 
the Global South, usage reached 15.4 percent, up from 14.1 
percent, a gain of 1.3 percentage points.
This widening divide reflects the systemic challenges facing 
the Global South, where limited access to reliable electricity, 
internet connectivity, and digital skills continues to constrain 
adoption. Until these foundational gaps are addressed, the 
benefits of generative AI will remain unevenly distributed, 
risking a deepening of existing global inequalities.
Region
H1 2025  
AI Diffusion
H2 2025  
AI Diffusion
Q1 2026  
AI Diffusion Change
Global North 22.9% 24.7% 27.5% +2.8
Global South 13.1% 14.1% 15.4% +1.3
World 15.1% 16.3% 17.8% +1.5
Gap between Global North  
and Global South
9.8% 10.6% 12.1%
Global North
Global South
AI User Share
% Digital Skills
% Digital Skills
% Internet Access
% Internet Access
% Electricity Access
% Electricity Access
Population
Population
27.5%
15.4%
70.1%
48.2%
90.1%
65.7%
98.1%
88.9%
100.0%
100.0%
5

=== PAGE 6 ===
Global AI Diffusion Q1 2026 Trends and Insights 
A New Growth Wave in Asia
In addition to rising AI diffusion among the world’s top 
economies, Asia is experiencing a broader growth wave. 
Twelve of the fifteen fastest-growing economies since 
June 2025 are in Asia, and each has at least 25% more AI 
users than in June 2025. Growth has been led by South 
Korea (+43%), Thailand (+36%), and Japan (+34%), with 
Fastest growing economies since June 2025
similarly strong gains in Mongolia, Iran, Laos, and Turkey 
(all 30%+). The pattern spans both advanced and emerging 
economies, including Kazakhstan, Kyrgyzstan, Uzbekistan, 
Vietnam, and Cambodia.
% increase in AI user share, Q1 2026 vs. H1 2025
* Italicized economies are outside Asia.
0%
10%
20%
30%
40%
South Korea
Thailand
Japan
Mongolia
Iran
Laos
Turkey
Belarus
El Salvador
Kazakhstan
Kyrgyzstan
Uzbekistan
Vietnam
Cambodia
Russia
+43.2%
+36.4%
+34.1%
+32.3%
+31.4%
+31.2%
+30.3%
+27.6%
+25.7%
+25.6%
+25.5%
+25.5%
+24.9%
+24.8%
+24.6%
6

=== PAGE 7 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Local Language and Multimodal 
Capability
One of the key drivers of the surge appears to be the 
stronger support for local languages and multimodal 
interaction that has expanded the relevance of AI across 
diverse user groups. Improvements in non-English 
language performance, as measured by multilingual 
benchmarks such as MMMLU [8] which assess the 
same knowledge tasks across 14 languages (Arabic, 
Bengali, German, Spanish, French, Hindi, Indonesian, 
Italian, Japanese, Korean, Portuguese, Swahili, Yoruba, 
and Chinese), have made AI tools increasingly capable 
of handling multilingual tasks, making them more 
accessible for everyday use cases such as messaging, 
search, learning, and content creation. Combined with 
widespread smartphone adoption and high levels of 
digital engagement, [9] these factors have enabled 
faster diffusion across both advanced and emerging 
economies in the region.
How LLMs perform: English compared to 14 languages
Source: MMMLU Leaderboard.
English (MMLU)
14 languages (MMMLU)
80%
85%
90%
95%
Aug ’24
Nov ’24
Feb ’25
May ’25
Aug ’25
Nov ’25
Feb ’26
May ’26
7

=== PAGE 8 ===
World
Japan
10%
15%
20%
25%
Dec ’24
Mar ’25
Jun ’25
Sep ’25
Dec ’25
Mar ’26
GPT-4.5 release
4o image generation
OpenAI o3 release
GPT-5 release
GPT-5.2 release
Global AI Diffusion Q1 2026 Trends and Insights 
Strong Demand and Rapid Scaling
High user demand and a rapid shift from experimentation to 
real-world usage are reinforcing this growth. Research from 
McKinsey [10] shows that AI adoption in Southeast Asia is 
growing faster than the global average, with a significant 
share of organizations moving beyond pilot use cases to 
scaled deployment. At the same time, the Stanford HAI AI 
Index [11] reports strong positive sentiment toward AI in 
markets such as Thailand and Turkey, indicating sustained 
openness and demand. These factors enable adoption to 
translate quickly into widespread, repeated use.
Japan Accelerates 
Japan’s position in the global AI diffusion ranking 
improvedfrom 56th in H1 2025 to 48th in Q1 2026. 
Over the past quarter, adoption in Japan increased 3.4 
percentage points, which is more than three times faster 
than the global average. This acceleration in diffusion is 
consistent with improvements in model capabilities and the 
emergence of a more diversified AI ecosystem, alongside 
the implementation of national policies coming into effect.
Performance on Japanese professional exams has improved 
markedly across successive model generations, increasing 
from approximately 50.8% accuracy in earlier models 
to over 90% in recent systems. This represents a step 
change in the ability of AI systems to handle complex, 
domain-specific tasks in Japanese. [12][13][14][15] These 
improvements are also reflected in standardized Japanese 
language benchmarks. On the Massive Multitask Language 
Understanding (MMLU) benchmark, Japanese accuracy 
increased from approximately 50 percent on GPT-3.5 
Turbo to around 80 percent on GPT-4o, reducing the gap 
with English performance from 20 percentage points to 9. 
On the more challenging MMLU-Pro benchmark, GPT-5 
reached 87 percent in Japanese, exceeding its 85 percent 
Al user share (3-month average, %)
Source: Microsoft AI for Good Lab, OpenAI.
8

=== PAGE 9 ===
Global AI Diffusion Q1 2026 Trends and Insights 
English score. [16][17][18][19] As highlighted in the 
2025 AI Diffusion Report, [20] AI adoption in South Korea 
increased about five percentage points in the second 
half of the year after ChatGPT usage surged after GPT-5, 
substantially better in Korean, was released. 
Better Japanese capability is helping drive AI diffusion in Japan
Source: Benchmarks results compiled from [16] - [19].
The improvements in Japanese-language AI models 
are also showing up in how developers work. In Japan, 
developers uploaded 129% more code changes to GitHub 
than a year earlier, compared with 78% growth globally. 
English
Japanese
MMLU
70%
50%
89%
80%
GPT-3.5 Turbo
Mar 2023
GPT-4o
Aug 2024
MMLU-Pro
75%
69%
87%
85%
GPT-4o
Aug 2024
GPT-5
Aug 2025
25%
50%
75%
100%
0%
50%
100%
150%
Japan
Global
129%
78%
Year-over-year growth in Git pushes
Source: GitHub.
9

=== PAGE 10 ===
Global AI Diffusion Q1 2026 Trends and Insights 
New AI Coding models are driving a step-change in GitHub code production
In 2025, Anthropic and OpenAI delivered significant 
advancements in AI-powered coding tools, with both 
companies releasing models purpose-built for tackling 
complex software projects from start to finish.
Anthropic launched Claude Opus 4.5 on November 
24, 2025, describing it as state of the art on real-world 
software engineering tasks. OpenAI launched three 
successive Codex releases in rapid succession: GPT-5.1-
Codex-Max (November 19), GPT-5.2-Codex (December 18), 
and GPT-5.3-Codex (February 2026). 
These tools are designed to support increasingly complex 
software engineering tasks, showing strong performance 
on standardized benchmarks and a growing ability to 
handle real-world development workflows. [2][21] For 
example, GPT-5.3-Codex achieved state-of-the-art results 
on SWE-Bench Pro and performed strongly on agentic 
evaluations, including approximately 77% accuracy on 
Terminal-Bench and around 65% on OSWorld. [22]
At the same time, GitHub Copilot evolved from a code 
suggestion tool into a broader AI coding platform. It 
introduced support for multiple models, enabled coding 
agents that can complete tasks and generate pull requests, 
expanded into the command line, and integrated with 
collaboration and project management tools. This 
positioned Copilot as an active participant across the 
software development lifecycle rather than a tool used only 
within the editor.
Count of Git pushes globally
Source: GitHub.
Count of Git pushes globally
0
100M
200M
300M
2020
2021
2022
2023
2024
2025
2026
Q1 2026
380M Git pushes
+78% YoY
Q1 2025
213M Git pushes
Agentic
workflow
adoption
accelerates
10

=== PAGE 11 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Beyond benchmarks, these tools are changing how 
software gets built and who can build it. They can take 
on multi step development tasks with little human input, 
handling everything from writing and debugging code 
to testing and refining user interfaces. They are also 
increasingly able to coordinate other agents to manage 
complex development workflows, making it possible for 
individuals and teams to accomplish far more than before.
As a result, there has been an expected, but dramatic, 
increase in code uploads (“Git pushes”) in GitHub. Git 
pushes increased 78% year over year globally. 
In the same time period, the number of new Git 
repositories increased 45% compared with Q1 2025. 
This trend aligns with the emergence of AI-assisted 
development practices in which code contributions can 
scale without a proportional increase in human effort. 
Developers and non-developers are “vibe coding,” 
expressing their ideas in natural language and then 
refining the result through repeated reviews, edits, and 
code uploads. [23]
This shift is already showing up clearly in AI-assisted 
workflows: merged GitHub pull requests, which measure 
how often code or updates are downloaded from a 
shared repository, have grown more than 28x since 
June 2025 for interactions associated with AI coding 
agents. While this captures only a fraction of total AI-
augmented activity, it provides a useful proxy for the 
rapid expansion of AI-driven coding workflows.
New repositories created
Source: GitHub.
New repositories created
5M
10M
15M
20M
2021
2022
2023
2024
2025
2026
Q1 2026
21.3M new repositories
+45% YoY
Q1 2025
14.7M new repositories
Agentic
workflow
adoption
accelerates
11

=== PAGE 12 ===
Global AI Diffusion Q1 2026 Trends and Insights 
This trend will continue to make software development 
more dynamic. It is reducing the time to market for new 
applications and new features, and making it easier to 
iterate based on performance and user feedback. The rate 
at which new ideas can be translated into shippable code 
has never been faster. 
That improvement in productivity could, of course, have 
effects on the labor market.
When developer productivity increases, the cost of building 
software declines. If demand for software is elastic, 
organizations can respond by building more software 
across a wider range of use cases. Economists describe this 
dynamic through productivity and reinstatement effects: 
technology can increase labor demand when it expands 
output and creates new tasks. In software, this mechanism 
is especially plausible because AI coding tools are already 
increasing developer output, while official labor projections 
continue to show strong growth in software-related roles.
It is still too early to know the full labor-market impact 
of AI-assisted coding, but the available data is consistent 
with this theory. In 2025, total software developer 
employment reached approximately 2.2 million, rising 
8.5% year over year and marking a record high for the 
profession. Early BLS data also shows that software 
developer employment in March 2026 was about 4% 
higher than in March 2025. [24]
Count of GitHub pull requests associated with Al agents
Source: GitHub.
Count of Git pulls associated with AI agents
0
0.5M
1M
1.5M
2M
May '25
Aug '25
Nov '25
Feb '26
Mar 2026
2.3M agentic pull requests
28× in 10 months
May 2025
83K agentic pull requests
Agentic workflow
adoption accelerates
12

=== PAGE 13 ===
Global AI Diffusion Q1 2026 Trends and Insights 
AI adoption continued to accelerate in Q1 2026, but the 
benefits are spreading unevenly. The Global North is 
pulling further ahead of the Global South, underscoring 
the need to address foundational gaps in electricity, 
connectivity, digital skills, and local-language access. 
At the same time, Asia is emerging as a major growth 
engine, with Japan, South Korea, and several emerging 
economies showing rapid gains as AI tools become 
more useful in local languages and daily workflows. The 
clearest sign of AI’s near-term economic impact is in 
software development, where new coding models and 
agentic tools are dramatically increasing code production, 
repository creation, and AI-assisted development activity. 
Together, these trends suggest that AI diffusion is entering 
a new phase: broader, faster, and more practical, but also 
one that requires deliberate action to ensure its benefits 
are shared globally.
Conclusion
13

=== PAGE 14 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Citation and data availability
This report is based on the following technical paper:
Misra, A., Wang, J., McCullers, S., White, K., & Lavista Ferres, 
J. (2025). Measuring AI Diffusion: A Population-Normalized 
Metric for Tracking Global AI Usage.  
arXiv. https://doi.org/10.48550/arXiv.2511.02781
Also available at: https://aka.ms/AI_Diffusion_Technical_Report 
The underlying data used in this report is publicly available:
AI Diffusion dataset (Q1 2026 update):
https://raw.githubusercontent.com/microsoft/ai-diffusion-
report/main/data/AI_Diffusion_Q12026_Update.csv
When using this data, please cite the technical paper above 
and include a link to the dataset to ensure reproducibility 
and version transparency.
14

=== PAGE 15 ===
Global AI Diffusion Q1 2026 Trends and Insights 
[1] A. Misra, J. Wang, S. McCullers, K. White, and J. 
L. Ferres, “Measuring AI Diffusion: A Population-
Normalized Metric for Tracking Global AI Usage,” 
Nov. 04, 2025, arXiv: arXiv:2511.02781. doi: 10.48550/
arXiv.2511.02781. https://arxiv.org/abs/2511.02781
[2] Anthropic. (2025). Introducing Claude Opus 4.5. 
https://www.anthropic.com/news/claude-opus-4-5
[3] OpenAI. “Building more with GPT-5.1-Codex-Max.” 
https://openai.com/index/gpt-5-1-codex-max/
[4] World Bank, “Population, total,” World Development 
Indicators, World Bank Open Data, May 30, 2024. 
https://data.worldbank.org/indicator/SP .POP .TOTL
[5] “Share of the population with access to electricity,” 
Our World in Data. Accessed: Sept. 12, 2025. 
https://ourworldindata.org/grapher/share-of-the-
population-with-access-to-electricity
[6] “Facts and Figures 2024 - Internet use.” Accessed: 
Sept. 12, 2025. https://www.itu.int/itu-d/reports/
statistics/2024/11/10/ff24-internet-use
[7] International Telecommunication Union (ITU), “SDG-
related ICT indicators.” Accessed: Oct. 29, 2025. 
https://www.itu.int/en/ITU-D/Statistics/Pages/SDGs-
ITU-ICT-indicators.aspx 
[8] LLM Stats. (2026). MMMLU Benchmark Leaderboard. 
https://llm-stats.com/benchmarks/mmmlu
[9] Shaw, A. (2025). Digital 2025 April Statshot by We 
Are Social: Southeast Asians among the world’s 
highest short-form video consumers. Campaign Brief 
Asia. https://campaignbriefasia.com/2025/04/24/
digital-2025-april-statshot-by-we-are-social-
southeast-asians-among-the-worlds-highest-short-
form-video-consumers/
[10] McKinsey & Company. (2026). AI in Southeast Asia: 
An era of opportunity. https://www.mckinsey.com/
featured-insights/future-of-asia/ai-in-southeast-
asia-an-era-of-opportunity
[11] Stanford Institute for Human-Centered Artificial 
Intelligence (HAI). (2025). AI Index Report 2025. 
Stanford University. https://hai.stanford.edu/ai-
index/2025-ai-index-report
[12] Umehara, K., Ota, J., Nishii, T., Kishimoto, R., & Ishida, 
T. (2025). Benchmarking GPT-5 performance and 
repeatability on the Japanese National Examination 
for Radiological Technologists over the past decade 
(2016–2025). European Journal of Radiology 
Artificial Intelligence. https://doi.org/10.1016/j.
ejrai.2025.100064
[13] Miyazaki, Y., Hata, M., Omori, H., Hirashima, A., 
Nakagawa, Y., Eto, M., Takahashi, S., & Ikeda, M. 
(2024). Performance of ChatGPT-4o on the Japanese 
Medical Licensing Examination: Evaluation of accuracy 
in text-only and image-based questions. JMIR Medical 
Education, 10, e63129. https://doi.org/10.2196/63129
[14] Liu, M., Okuhara, T., Dai, Z., Huang, W., Gu, L., Okada, 
H., Furukawa, E., & Kiuchi, T. (2025). Evaluating the 
effectiveness of advanced large language models 
in medical knowledge: A comparative study using 
Japanese national medical examination. International 
Journal of Medical Informatics, 193, 105673. https://
doi.org/10.1016/j.ijmedinf.2024.105673
[15] Takagi, S., Watari, T., Erabi, A., & Sakaguchi, K. (2023). 
Performance of GPT-3.5 and GPT-4 on the Japanese 
Medical Licensing Examination: Comparison study. 
JMIR Medical Education, 9, e48002. https://doi.
org/10.2196/48002
Appendix
15

=== PAGE 16 ===
Global AI Diffusion Q1 2026 Trends and Insights 
[16] Hugging Face, “Llama-3.3-Swallow-70B-Instruct-v0.4.” 
https://huggingface.co/tokyotech-llm/Llama-3.3-
Swallow-70B-Instruct-v0.4
[17] LLM Stats, “MMLU Leaderboard,” 2026. https://llm-
stats.com/benchmarks/mmlu
[18] OpenAI, “Hello GPT-4o,” 2024.  https://openai.com/
index/hello-gpt-4o/
[19] Swallow Project, “Swallow LLM Leaderboard.”  https://
swallow-llm.github.io/leaderboard/about.en.html
[20] Microsoft. (2026). Microsoft AI Diffusion Report 2025 
H2. Microsoft Research. https://www.microsoft.
com/en-us/research/wp-content/uploads/2026/01/
Microsoft-AI-Diffusion-Report-2025-H2.pdf
[21] OpenAI. (2025). Codex for (almost) everything. https://
openai.com/index/codex-for-almost-everything/
[22] OpenAI. “Introducing GPT 5.3 Codex.” February 5, 
2026. https://openai.com/index/introducing-gpt-5-
3-codex/
[23] GitHub Blog. (2025). Octoverse: A new developer joins 
GitHub every second as AI leads TypeScript to #1. 
The GitHub Blog. Updated February 28, 2026. https://
github.blog/news-insights/octoverse/octoverse-a-
new-developer-joins-github-every-second-as-ai-
leads-typescript-to-1/
[24] U.S. Bureau of Labor Statistics, Current Population 
Survey (CPS). https://www.bls.gov/cps/
16

=== PAGE 17 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Economy H1 2025 H2 2025 Q1 2026 Q1 Change
United Arab Emirates 59.4% 64.0% 70.1% 6.1%
Singapore 58.6% 60.9% 63.4% 2.5%
Norway 45.3% 46.4% 48.6% 2.2%
Ireland 41.7% 44.6% 48.4% 3.8%
France 40.9% 44.0% 47.8% 3.8%
Spain 39.7% 41.8% 44.2% 2.4%
New Zealand 37.6% 40.5% 43.0% 2.5%
United Kingdom 36.4% 38.9% 42.2% 3.3%
Netherlands 36.3% 38.9% 42.1% 3.2%
Qatar 35.7% 38.3% 41.8% 3.5%
Australia 34.5% 36.9% 39.5% 2.6%
Belgium 33.5% 36.0% 39.0% 3.0%
Israel 33.9% 36.1% 38.1% 2.0%
Switzerland 32.4% 34.8% 37.8% 3.0%
Canada 33.5% 35.0% 37.3% 2.3%
South Korea 25.9% 30.7% 37.1% 6.4%
Sweden 31.2% 33.3% 36.1% 2.8%
Austria 29.1% 31.4% 34.1% 2.7%
Hungary 27.9% 29.8% 32.2% 2.4%
Taiwan 26.4% 28.4% 31.8% 3.4%
United States 26.3% 28.3% 31.3% 3.0%
Denmark 26.6% 28.7% 31.2% 2.5%
Germany 26.5% 28.6% 31.1% 2.5%
Poland 26.4% 28.5% 31.0% 2.5%
Italy 25.8% 27.8% 30.2% 2.4%
Czech Republic 26.0% 27.8% 30.1% 2.3%
Jordan 25.4% 27.0% 29.7% 2.7%
Bulgaria 25.4% 27.3% 29.7% 2.4%
Finland 25.6% 27.3% 29.5% 2.2%
Saudi Arabia 23.7% 26.2% 29.4% 3.2%
Slovenia 24.6% 26.5% 29.0% 2.5%
Costa Rica 25.1% 26.5% 28.5% 2.0%
Lebanon 24.8% 25.7% 27.3% 1.6%
Vietnam 21.2% 23.5% 26.5% 3.0%
Oman 22.6% 24.2% 26.5% 2.3%
Portugal 22.4% 24.2% 26.4% 2.2%
Croatia 21.8% 23.7% 26.1% 2.4%
Slovak Republic 22.1% 23.8% 26.1% 2.3%
Dominican Republic 22.0% 22.7% 24.8% 2.1%
Uruguay 20.9% 22.5% 24.6% 2.1%
AI Diffusion Data Source
17

=== PAGE 18 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Economy H1 2025 H2 2025 Q1 2026 Q1 Change
Colombia 20.4% 22.0% 24.5% 2.5%
Lithuania 21.0% 22.4% 24.3% 1.9%
Serbia 19.7% 21.5% 24.1% 2.6%
Jamaica 22.2% 22.1% 24.0% 1.9%
Panama 20.3% 21.5% 23.3% 1.8%
South Africa 19.3% 21.1% 23.1% 2.0%
Chile 19.6% 20.8% 22.7% 1.9%
Japan 16.7% 19.1% 22.5% 3.4%
Bosnia And Herzegovina 18.2% 19.5% 22.1% 2.6%
Argentina 17.8% 19.6% 21.9% 2.3%
Malaysia 18.3% 19.7% 21.8% 2.1%
Kuwait 17.7% 19.1% 21.1% 2.0%
Greece 17.7% 19.1% 20.8% 1.7%
Georgia 17.3% 18.2% 20.5% 2.3%
Mexico 16.7% 17.8% 20.1% 2.3%
Philippines 17.1% 18.3% 20.1% 1.8%
Ecuador 17.0% 17.7% 19.5% 1.8%
Brazil 15.6% 17.1% 19.1% 2.0%
Albania 15.8% 16.5% 18.5% 2.0%
Moldova 16.6% 17.0% 18.5% 1.5%
El Salvador 14.6% 16.2% 18.3% 2.1%
Azerbaijan 14.2% 15.5% 17.7% 2.2%
India 14.2% 15.7% 17.6% 1.9%
Romania 15.3% 16.2% 17.5% 1.3%
Turkey 13.4% 14.6% 17.4% 2.8%
Mongolia 12.6% 14.3% 16.7% 2.4%
Guatemala 13.7% 14.8% 16.4% 1.6%
Peru 13.4% 14.7% 16.4% 1.7%
China 15.4% 16.3% 16.4% 0.1%
Kazakhstan 12.7% 13.7% 15.9% 2.2%
Namibia 13.0% 13.8% 15.1% 1.3%
Gabon 12.3% 13.4% 15.0% 1.6%
Libya 12.7% 13.7% 15.0% 1.3%
Egypt 12.5% 13.4% 14.8% 1.4%
Botswana 12.8% 13.7% 14.8% 1.1%
Nepal 12.3% 13.0% 14.2% 1.2%
Indonesia 11.7% 12.7% 14.1% 1.4%
Honduras 12.4% 13.1% 14.0% 0.9%
Senegal 12.4% 12.9% 13.9% 1.0%
Tunisia 12.3% 12.7% 13.5% 0.8%
18

=== PAGE 19 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Economy H1 2025 H2 2025 Q1 2026 Q1 Change
Algeria 11.3% 12.0% 13.2% 1.2%
Zambia 11.7% 12.3% 13.1% 0.8%
Cote D'Ivoire 10.8% 11.7% 13.1% 1.4%
Bolivia 10.9% 11.6% 12.7% 1.1%
Iran 9.6% 10.7% 12.6% 1.9%
Iraq 10.3% 11.2% 12.5% 1.3%
Thailand 9.1% 10.7% 12.4% 1.7%
Paraguay 10.1% 11.0% 12.2% 1.2%
Nicaragua 10.0% 10.7% 11.8% 1.1%
Morocco 10.5% 10.9% 11.7% 0.8%
Gambia 10.6% 10.9% 11.4% 0.5%
Pakistan 9.7% 10.3% 11.4% 1.1%
Angola 8.9% 9.7% 10.9% 1.2%
Madagascar 8.9% 9.7% 10.9% 1.2%
Malawi 8.9% 9.7% 10.9% 1.2%
Mozambique 8.9% 9.7% 10.9% 1.2%
French Guiana 8.3% 9.0% 10.3% 1.3%
Guyana 8.3% 9.0% 10.3% 1.3%
Suriname 8.3% 9.0% 10.3% 1.3%
Venezuela 8.3% 9.0% 10.3% 1.3%
Benin 8.7% 9.3% 10.1% 0.8%
Burkina Faso 8.7% 9.3% 10.1% 0.8%
Ghana 8.7% 9.3% 10.1% 0.8%
Guinea 8.7% 9.3% 10.1% 0.8%
Guinea-Bissau 8.7% 9.3% 10.1% 0.8%
Liberia 8.7% 9.3% 10.1% 0.8%
Mali 8.7% 9.3% 10.1% 0.8%
Mauritania 8.7% 9.3% 10.1% 0.8%
Niger 8.7% 9.3% 10.1% 0.8%
Nigeria 8.7% 9.3% 10.1% 0.8%
Sierra Leone 8.7% 9.3% 10.1% 0.8%
Togo 8.7% 9.3% 10.1% 0.8%
Myanmar 8.4% 9.1% 10.0% 0.9%
Lesotho 8.8% 9.1% 9.8% 0.7%
Belarus 7.6% 8.4% 9.6% 1.2%
Kyrgyzstan 7.6% 8.2% 9.5% 1.3%
Russia 7.6% 8.0% 9.5% 1.5%
Ukraine 9.1% 9.0% 9.4% 0.4%
Kenya 7.8% 8.1% 8.7% 0.6%
Cameroon 7.0% 7.8% 8.7% 0.9%
19

=== PAGE 20 ===
Global AI Diffusion Q1 2026 Trends and Insights 
Economy H1 2025 H2 2025 Q1 2026 Q1 Change
Central African Republic 7.0% 7.8% 8.7% 0.9%
Chad 7.0% 7.8% 8.7% 0.9%
Congo 7.0% 7.8% 8.7% 0.9%
Democratic Republic Of The Congo 7.0% 7.8% 8.7% 0.9%
Zimbabwe 6.9% 7.6% 8.5% 0.9%
Haiti 7.1% 7.6% 8.5% 0.9%
Laos 6.0% 6.7% 7.8% 1.1%
Bangladesh 6.5% 7.1% 7.8% 0.7%
Papua New Guinea 7.2% 7.3% 7.7% 0.4%
Burundi 6.4% 6.8% 7.6% 0.8%
Eritrea 6.4% 6.8% 7.6% 0.8%
Ethiopia 6.4% 6.8% 7.6% 0.8%
Somalia 6.4% 6.8% 7.6% 0.8%
South Sudan 6.4% 6.8% 7.6% 0.8%
Sudan 6.4% 6.8% 7.6% 0.8%
Tanzania 6.4% 6.8% 7.6% 0.8%
Uganda 6.4% 6.8% 7.6% 0.8%
Syria 6.7% 7.1% 7.5% 0.4%
Armenia 6.2% 6.6% 7.4% 0.8%
Sri Lanka 6.2% 6.6% 7.3% 0.7%
Rwanda 6.0% 6.3% 7.2% 0.9%
Uzbekistan 5.7% 6.3% 7.2% 0.9%
Cuba 5.7% 6.1% 6.7% 0.6%
Afghanistan 5.1% 5.6% 6.1% 0.5%
Tajikistan 5.1% 5.6% 6.1% 0.5%
Turkmenistan 5.1% 5.6% 6.1% 0.5%
Cambodia 4.6% 5.1% 5.7% 0.6%
20

=== PAGE 21 ===
©2022 Microsoft Corporation. All rights reserved.