AI Diffusion Report 2025 H1 — transcribed tables
These tables reproduce the existing in/out transcription of printed Microsoft report values. Notes retain source periods, units, approximations and known inconsistencies. Point values are not inferred from unlabeled charts. This is a transcription, not a new Microsoft dataset; verify critical figures against the linked original page.
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 4
Historical economic comparison in report prose
Time interval for these quoted growth rates is not specified in paragraph. Report also says both economies had about $2,000 per capita income in 1960 (2025 dollars), and South Korea doubled living standards every eleven years. The GDP chart’s exact annual series is not printed.
Read Historical economic comparison in report prose in the original PDF
| Economy | Annual economic growth (%) |
|---|---|
| South Korea | 6.2 |
| Philippines | 1.8 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 7
Population and prerequisites for AI access
Exact labels on source pyramid. “Datacenters” is an additional qualitative layer with no numeric count. Source references population 2024, electricity, internet 2024, and digital-literacy data.
Read Population and prerequisites for AI access in the original PDF
| Measure | Billion people |
|---|---|
| World population | 8.1 |
| With electricity | 7.4 |
| Access to internet | 5.5 |
| Basic digital skills | 4.2 |
| AI users | 1.2 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 8
Sub-Saharan Africa share of global population without electricity
Exact percentages in prose; report says over 750 million people globally lack electricity and 18 of the 20 countries with largest electricity-access deficits are in Sub-Saharan Africa.
Read Sub-Saharan Africa share of global population without electricity in the original PDF
| Period | Share (%) |
|---|---|
| 2010 | 50 |
| Report time | 85 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 10
Zambia: connectivity and AI adoption
Rounded narrative values. H1 appendix gives national diffusion 11.7%. Distinct denominators: not directly comparable counts.
Read Zambia: connectivity and AI adoption in the original PDF
| Population denominator | AI adoption (%) |
|---|---|
| National population | 12 |
| People with internet access | 34 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 12
English representation in web content and population
Rounded narrative shares (“half” and “just 5%”). Different denominators; these are not parts of a common whole.
Read English representation in web content and population in the original PDF
| Measure | Share (%) |
|---|---|
| Open web content in English | 50 |
| Native English speakers in world population | 5 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 12
Further language-access counts
Counts and bounds printed in the report. The detailed language shares in the treemap are not numerically labelled.
Read Further language-access counts in the original PDF
| Metric | Value as stated |
|---|---|
| Malawi population speaking English fluently | Fewer than 4% |
| Other spoken languages in web-content illustration | 7,000+ |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 13
Language barrier: numerical claims
Narrative bounds retained; no original detailed data table is supplied.
Read Language barrier: numerical claims in the original PDF
| Metric | Value as stated |
|---|---|
| Swahili speakers | Over 200 million |
| German/Swahili digital content disparity | Swahili has over 500 times less content |
| AI adoption in low-resource language countries | 20% lower, controlling for GDP/connectivity |
| LLM accuracy in English | Around 80% |
| LLM accuracy for some low-resource languages such as Yoruba | Below 55% |
| Yoruba speakers across Africa | More than 50 million |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 15
Open model ecosystem claim
Narrative claim attributed by report to VentureBeat reference 29.
Read Open model ecosystem claim in the original PDF
| Metric | Value as stated |
|---|---|
| LLaMA community variant models | Over 7,000 within months of debut |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 16
AI frontier index and months to frontier
Visually transcribed from original table. Frontier index is normalized; time lag is in months. Original prose rounds Israel lag to 11 months or less than a year.
Read AI frontier index and months to frontier in the original PDF
| Country | Best model | Frontier index | Months to frontier |
|---|---|---|---|
| United States | GPT-5 (high) | 1.0 | 0.0 |
| China | DeepSeek V3.1 Terminus (Reasoning) | 0.841 | 5.3 |
| South Korea | EXAONE 4.0 32B (Reasoning) | 0.824 | 5.9 |
| France | Magistral Medium 1.2 | 0.789 | 7.0 |
| United Kingdom | Gemma 3 27B Instruct | 0.768 | 7.7 |
| Canada | Command A | 0.767 | 7.8 |
| Israel | Jamba 1.7 Large | 0.651 | 11.6 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 17
Installed data center capacity, mid-2025
Visually transcribed original table. Report describes IEA estimates. Region labels preserved; Brazil and Central/South America both appear as printed. The prose claim “US and China 86%” is not supported by treating these rows as disjoint components.
Read Installed data center capacity, mid-2025 in the original PDF
| IEA region | Installed capacity (GW) |
|---|---|
| United States | 53.7 |
| China | 31.9 |
| European Union | 11.9 |
| Japan and South Korea | 6.9 |
| India | 3.5 |
| Other Asia Pacific | 3.1 |
| United Kingdom | 2.6 |
| Australia and New Zealand | 1.6 |
| Other North America | 1.5 |
| Africa | 1.5 |
| Central and South America | 1.4 |
| Eurasia | 1.2 |
| Middle East | 1.1 |
| Brazil | 0.6 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 18
Additional quantitative claims
Rounded report claims; broad-region exact figures appear in later H2 report. Capacity percentage conflicts with table if its regions are summed. Same claims recur on pages 2–3.
Read Additional quantitative claims in the original PDF
| Metric | Value as stated |
|---|---|
| AI users | More than 1.2 billion in under 36 months |
| People lacking basic AI-access capabilities | About four billion / nearly half of humanity |
| AI adoption in Global North | Approximately 23% |
| AI adoption in Global South | Approximately 13% |
| GDP per capita threshold with largest adoption disparities | Below $20,000 |
| US and China share of global compute | Roughly 86% |
| Countries hosting frontier-level models | Seven |
| Windows devices underlying telemetry | Over one billion |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 19
AI diffusion data source
Values visually verified against original H1 appendix and match H1 column in H2 appendix. Rows sorted by printed diffusion; source does not explicitly assign ranks and equal values need not have unique ranks.
Read AI diffusion data source in the original PDF
| Economy | H1 2025 AI diffusion (%) |
|---|---|
| United Arab Emirates | 59.4 |
| Singapore | 58.6 |
| Norway | 45.3 |
| Ireland | 41.7 |
| France | 40.9 |
| Spain | 39.7 |
| New Zealand | 37.6 |
| United Kingdom | 36.4 |
| Netherlands | 36.3 |
| Qatar | 35.7 |
| Australia | 34.5 |
| Israel | 33.9 |
| Belgium | 33.5 |
| Canada | 33.5 |
| Switzerland | 32.4 |
| Sweden | 31.2 |
| Austria | 29.1 |
| Hungary | 27.9 |
| Denmark | 26.6 |
| Germany | 26.5 |
| Poland | 26.4 |
| Taiwan | 26.4 |
| United States | 26.3 |
| Czechia | 26.0 |
| South Korea | 25.9 |
| Italy | 25.8 |
| Finland | 25.6 |
| Bulgaria | 25.4 |
| Jordan | 25.4 |
| Costa Rica | 25.1 |
| Lebanon | 24.8 |
| Slovenia | 24.6 |
| Saudi Arabia | 23.7 |
| Oman | 22.6 |
| Portugal | 22.4 |
| Jamaica | 22.2 |
| Slovakia | 22.1 |
| Dominican Republic | 22.0 |
| Croatia | 21.8 |
| Vietnam | 21.2 |
| Lithuania | 21.0 |
| Uruguay | 20.9 |
| Colombia | 20.4 |
| Panama | 20.3 |
| Serbia | 19.7 |
| Chile | 19.6 |
| South Africa | 19.3 |
| Malaysia | 18.3 |
| Bosnia and Herzegovina | 18.2 |
| Argentina | 17.8 |
| Kuwait | 17.7 |
| Greece | 17.7 |
| Georgia | 17.3 |
| Philippines | 17.1 |
| Ecuador | 17.0 |
| Japan | 16.7 |
| Mexico | 16.7 |
| Moldova | 16.6 |
| Albania | 15.8 |
| Brazil | 15.6 |
| China | 15.4 |
| Romania | 15.3 |
| El Salvador | 14.6 |
| India | 14.2 |
| Azerbaijan | 14.2 |
| Guatemala | 13.7 |
| Peru | 13.4 |
| Türkiye | 13.4 |
| Namibia | 13.0 |
| Botswana | 12.8 |
| Libya | 12.7 |
| Kazakhstan | 12.7 |
| Mongolia | 12.6 |
| Egypt | 12.5 |
Microsoft-AI-Diffusion-Report-2025-H1.pdf · page 20
AI diffusion data source
Values visually verified against original H1 appendix and match H1 column in H2 appendix. Rows sorted by printed diffusion; source does not explicitly assign ranks and equal values need not have unique ranks.
Read AI diffusion data source in the original PDF
| Economy | H1 2025 AI diffusion (%) |
|---|---|
| Honduras | 12.4 |
| Senegal | 12.4 |
| Gabon | 12.3 |
| Nepal | 12.3 |
| Tunisia | 12.3 |
| Indonesia | 11.7 |
| Zambia | 11.7 |
| Algeria | 11.3 |
| Bolivia | 10.9 |
| Côte d’Ivoire | 10.8 |
| Gambia | 10.6 |
| Morocco | 10.5 |
| Iraq | 10.3 |
| Paraguay | 10.1 |
| Nicaragua | 10.0 |
| Pakistan | 9.7 |
| Iran | 9.6 |
| Thailand | 9.1 |
| Ukraine | 9.1 |
| Angola | 8.9 |
| Madagascar | 8.9 |
| Malawi | 8.9 |
| Mozambique | 8.9 |
| Lesotho | 8.8 |
| Benin | 8.7 |
| Burkina Faso | 8.7 |
| Ghana | 8.7 |
| Guinea | 8.7 |
| Guinea-Bissau | 8.7 |
| Liberia | 8.7 |
| Mali | 8.7 |
| Mauritania | 8.7 |
| Niger | 8.7 |
| Nigeria | 8.7 |
| Sierra Leone | 8.7 |
| Togo | 8.7 |
| Myanmar | 8.4 |
| French Guiana | 8.3 |
| Guyana | 8.3 |
| Suriname | 8.3 |
| Venezuela | 8.3 |
| Kenya | 7.8 |
| Belarus | 7.6 |
| Kyrgyzstan | 7.6 |
| Russia | 7.6 |
| Papua New Guinea | 7.2 |
| Haiti | 7.1 |
| Cameroon | 7.0 |
| Central African Republic | 7.0 |
| Chad | 7.0 |
| Congo | 7.0 |
| Congo (DRC) | 7.0 |
| Zimbabwe | 6.9 |
| Syria | 6.7 |
| Bangladesh | 6.5 |
| Burundi | 6.4 |
| Eritrea | 6.4 |
| Ethiopia | 6.4 |
| Somalia | 6.4 |
| South Sudan | 6.4 |
| Sudan | 6.4 |
| Tanzania | 6.4 |
| Uganda | 6.4 |
| Armenia | 6.2 |
| Sri Lanka | 6.2 |
| Laos | 6.0 |
| Rwanda | 6.0 |
| Uzbekistan | 5.7 |
| Cuba | 5.7 |
| Afghanistan | 5.1 |
| Tajikistan | 5.1 |
| Turkmenistan | 5.1 |
| Cambodia | 4.6 |