By Michael Schymura Microsoft · Independent analysis / 18 min read
01 / 04
Your persistent specimenDEU
Germany
32.3%
Level June 2026 · ages 15–64
Microsoft estimatesJune 2026
Try it here. Same country, another denominator.
Optical artwork. Never a data encoding.Reduced motion active
Published global adoption
18.8%
June 2026 · estimated share of ages 15–64
Economies with rising estimates
145 / 147
March → June 2026 · one frozen CSV vintage
North–South absolute gap
12.1 → 12.6 pp
Published aggregates · proportional change is rounding-sensitive
The evidence instrument / Microsoft country estimates
Move the lens. Keep the world.
One frozen dataset. Four ways to describe it. Follow an economy, change the question, then decide what the evidence can support.
Following DEU
Germany
June 2026
32.3%% of people aged 15-64
Full-sample rank23.5 / 147Average positions for ties
The calculation32.3 out of every 100 people aged 15–64
Watch the whole distribution reorganize. Every mark remains the same economy; only the measure changes.
Exhibit 02 / Does the comparison survive a different denominator?
Adoption level
% of people aged 15-64 · June 2026
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
147 economies, one mark each. Horizontal position is the selected measure; vertical separation only prevents overlap. The lower companion isolates your comparisons on the same scale. Ranks use the full eligible sample, never just the peers.
Source & boundary Microsoft frozen country table. All displayed changes use the selected compatible endpoints from one data vintage. Undefined zero-user or zero-nonuser denominators remain unavailable.
Adoption level. % of people aged 15-64 · June 2026
Economy
Adoption level
Unit
Global rank
United Arab Emirates
73.3000
% of people aged 15-64
1.0000
Singapore
64.3000
% of people aged 15-64
2.0000
Ireland
49.9000
% of people aged 15-64
3.0000
France
49.6000
% of people aged 15-64
4.0000
Norway
49.4000
% of people aged 15-64
5.0000
Spain
45.1000
% of people aged 15-64
6.0000
New Zealand
44.1000
% of people aged 15-64
7.0000
United Kingdom
43.7000
% of people aged 15-64
8.0000
Netherlands
43.5000
% of people aged 15-64
9.5000
Qatar
43.5000
% of people aged 15-64
9.5000
Australia
40.7000
% of people aged 15-64
11.0000
South Korea
40.6000
% of people aged 15-64
12.0000
Belgium
40.3000
% of people aged 15-64
13.0000
Switzerland
39.1000
% of people aged 15-64
14.0000
Israel
38.7000
% of people aged 15-64
15.0000
Canada
38.5000
% of people aged 15-64
16.0000
Sweden
37.3000
% of people aged 15-64
17.0000
Austria
35.4000
% of people aged 15-64
18.0000
Taiwan
33.8000
% of people aged 15-64
19.0000
Hungary
33.3000
% of people aged 15-64
20.0000
United States
33.0000
% of people aged 15-64
21.0000
Denmark
32.4000
% of people aged 15-64
22.0000
Germany
32.3000
% of people aged 15-64
23.5000
Poland
32.3000
% of people aged 15-64
23.5000
Italy
31.4000
% of people aged 15-64
25.5000
Saudi Arabia
31.4000
% of people aged 15-64
25.5000
Czech Republic
31.2000
% of people aged 15-64
27.5000
Jordan
31.2000
% of people aged 15-64
27.5000
Bulgaria
30.7000
% of people aged 15-64
29.0000
Finland
30.6000
% of people aged 15-64
30.0000
Slovenia
30.1000
% of people aged 15-64
31.0000
Costa Rica
29.7000
% of people aged 15-64
32.0000
Vietnam
27.9000
% of people aged 15-64
33.0000
Lebanon
27.7000
% of people aged 15-64
34.0000
Oman
27.6000
% of people aged 15-64
35.0000
Portugal
27.4000
% of people aged 15-64
36.0000
Croatia
27.2000
% of people aged 15-64
37.0000
Slovakia
27.1000
% of people aged 15-64
38.0000
Colombia
25.9000
% of people aged 15-64
39.0000
Dominican Republic
25.8000
% of people aged 15-64
40.0000
Uruguay
25.7000
% of people aged 15-64
41.0000
Serbia
25.3000
% of people aged 15-64
42.0000
Lithuania
25.1000
% of people aged 15-64
43.0000
Jamaica
25.0000
% of people aged 15-64
44.0000
Japan
24.7000
% of people aged 15-64
45.0000
Panama
24.2000
% of people aged 15-64
46.0000
Chile
24.0000
% of people aged 15-64
47.0000
South Africa
23.9000
% of people aged 15-64
48.0000
Argentina
23.4000
% of people aged 15-64
49.0000
Bosnia And Herzegovina
23.2000
% of people aged 15-64
50.0000
Kuwait
22.6000
% of people aged 15-64
51.5000
Malaysia
22.6000
% of people aged 15-64
51.5000
Georgia
21.6000
% of people aged 15-64
53.0000
Greece
21.5000
% of people aged 15-64
54.0000
Mexico
21.4000
% of people aged 15-64
55.0000
Philippines
20.7000
% of people aged 15-64
56.0000
Ecuador
20.3000
% of people aged 15-64
57.0000
Brazil
20.2000
% of people aged 15-64
58.0000
El Salvador
20.1000
% of people aged 15-64
59.0000
Albania
19.2000
% of people aged 15-64
60.0000
Moldova
19.1000
% of people aged 15-64
61.0000
Türkiye
18.9000
% of people aged 15-64
62.0000
Azerbaijan
18.7000
% of people aged 15-64
63.0000
India
18.5000
% of people aged 15-64
64.0000
Romania
18.0000
% of people aged 15-64
65.0000
Mongolia
17.9000
% of people aged 15-64
66.0000
China
17.5000
% of people aged 15-64
67.0000
Guatemala
17.3000
% of people aged 15-64
68.5000
Peru
17.3000
% of people aged 15-64
68.5000
Kazakhstan
16.9000
% of people aged 15-64
70.0000
Gabon
15.7000
% of people aged 15-64
71.0000
Egypt
15.5000
% of people aged 15-64
72.5000
Libya
15.5000
% of people aged 15-64
72.5000
Namibia
15.4000
% of people aged 15-64
74.0000
Botswana
15.0000
% of people aged 15-64
75.0000
Indonesia
14.8000
% of people aged 15-64
76.0000
Nepal
14.6000
% of people aged 15-64
77.0000
Honduras
14.5000
% of people aged 15-64
78.0000
Senegal
14.4000
% of people aged 15-64
79.0000
Tunisia
13.9000
% of people aged 15-64
80.0000
Algeria
13.8000
% of people aged 15-64
81.0000
Cote D'Ivoire
13.7000
% of people aged 15-64
82.0000
Thailand
13.4000
% of people aged 15-64
83.0000
Bolivia
13.3000
% of people aged 15-64
84.5000
Zambia
13.3000
% of people aged 15-64
84.5000
Iraq
13.0000
% of people aged 15-64
86.0000
Iran
12.8000
% of people aged 15-64
87.0000
Paraguay
12.6000
% of people aged 15-64
88.0000
Nicaragua
12.5000
% of people aged 15-64
89.0000
Morocco
12.0000
% of people aged 15-64
90.0000
Pakistan
11.9000
% of people aged 15-64
91.0000
Gambia
11.5000
% of people aged 15-64
92.0000
Angola
11.4000
% of people aged 15-64
94.5000
Madagascar
11.4000
% of people aged 15-64
94.5000
Mozambique
11.4000
% of people aged 15-64
94.5000
Malawi
11.4000
% of people aged 15-64
94.5000
French Guiana
11.3000
% of people aged 15-64
98.5000
Guyana
11.3000
% of people aged 15-64
98.5000
Suriname
11.3000
% of people aged 15-64
98.5000
Venezuela
11.3000
% of people aged 15-64
98.5000
Benin
10.4000
% of people aged 15-64
107.0000
Burkina Faso
10.4000
% of people aged 15-64
107.0000
Ghana
10.4000
% of people aged 15-64
107.0000
Guinea
10.4000
% of people aged 15-64
107.0000
Guinea-Bissau
10.4000
% of people aged 15-64
107.0000
Liberia
10.4000
% of people aged 15-64
107.0000
Mali
10.4000
% of people aged 15-64
107.0000
Myanmar
10.4000
% of people aged 15-64
107.0000
Mauritania
10.4000
% of people aged 15-64
107.0000
Niger
10.4000
% of people aged 15-64
107.0000
Nigeria
10.4000
% of people aged 15-64
107.0000
Sierra Leone
10.4000
% of people aged 15-64
107.0000
Togo
10.4000
% of people aged 15-64
107.0000
Belarus
10.3000
% of people aged 15-64
114.0000
Kyrgyzstan
10.0000
% of people aged 15-64
115.0000
Lesotho
9.9000
% of people aged 15-64
116.5000
Russia
9.9000
% of people aged 15-64
116.5000
Ukraine
9.5000
% of people aged 15-64
118.0000
Central African Republic
9.1000
% of people aged 15-64
121.0000
Cameroon
9.1000
% of people aged 15-64
121.0000
Congo (DRC)
9.1000
% of people aged 15-64
121.0000
Congo
9.1000
% of people aged 15-64
121.0000
Chad
9.1000
% of people aged 15-64
121.0000
Haiti
8.9000
% of people aged 15-64
125.0000
Kenya
8.9000
% of people aged 15-64
125.0000
Zimbabwe
8.9000
% of people aged 15-64
125.0000
Laos
8.4000
% of people aged 15-64
127.0000
Burundi
8.0000
% of people aged 15-64
132.0000
Bangladesh
8.0000
% of people aged 15-64
132.0000
Eritrea
8.0000
% of people aged 15-64
132.0000
Ethiopia
8.0000
% of people aged 15-64
132.0000
Sudan
8.0000
% of people aged 15-64
132.0000
Somalia
8.0000
% of people aged 15-64
132.0000
South Sudan
8.0000
% of people aged 15-64
132.0000
Tanzania
8.0000
% of people aged 15-64
132.0000
Uganda
8.0000
% of people aged 15-64
132.0000
Armenia
7.7000
% of people aged 15-64
137.0000
Sri Lanka
7.6000
% of people aged 15-64
139.5000
Papua New Guinea
7.6000
% of people aged 15-64
139.5000
Rwanda
7.6000
% of people aged 15-64
139.5000
Uzbekistan
7.6000
% of people aged 15-64
139.5000
Syria
7.4000
% of people aged 15-64
142.0000
Cuba
6.8000
% of people aged 15-64
143.0000
Afghanistan
6.3000
% of people aged 15-64
145.0000
Tajikistan
6.3000
% of people aged 15-64
145.0000
Turkmenistan
6.3000
% of people aged 15-64
145.0000
Cambodia
6.1000
% of people aged 15-64
147.0000
Exhibit 03 / Where does the same economy rank under each lens?
Four measures. Different orders.
March to June 2026 · columns are measures, not time
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
Every thread is one of the same 147 eligible economies; the selected economy is coral and its peers indigo. Bends cross measurement definitions, not time. All columns use descending average ranks for ties.
Source & boundary Same Microsoft rows and transformations. Descending global ranks use one all-metrics-eligible sample for the selected interval; ties receive their average rank within 1e−9. Rank differences can be smaller than the precision of the published estimates. Magnitudes remain visible in the companion plot and exact table.
Four measures. Different orders.. March to June 2026 · columns are measures, not time
Economy
Level: rank (value)
Point gain: rank (value)
Relative growth: rank (value)
Nonuser-relative gain: rank (value)
United Arab Emirates
1.0 (73.3000)
2.0 (3.2000)
62.0 (4.5649)
1.0 (10.7023)
Singapore
2.0 (64.3000)
60.5 (0.9000)
141.0 (1.4196)
11.0 (2.4590)
Norway
5.0 (49.4000)
66.0 (0.8000)
135.0 (1.6461)
39.0 (1.5564)
Ireland
3.0 (49.9000)
12.5 (1.5000)
111.0 (3.0992)
6.0 (2.9070)
Spain
6.0 (45.1000)
60.5 (0.9000)
133.0 (2.0362)
35.0 (1.6129)
New Zealand
7.0 (44.1000)
39.5 (1.1000)
131.0 (2.5581)
19.0 (1.9298)
United Kingdom
8.0 (43.7000)
12.5 (1.5000)
94.0 (3.5545)
9.0 (2.5952)
Qatar
9.5 (43.5000)
8.5 (1.7000)
74.0 (4.0670)
5.0 (2.9210)
Australia
11.0 (40.7000)
29.0 (1.2000)
112.0 (3.0380)
17.0 (1.9835)
Belgium
13.0 (40.3000)
21.5 (1.3000)
100.5 (3.3333)
15.0 (2.1311)
Israel
15.0 (38.7000)
79.0 (0.6000)
137.0 (1.5748)
69.0 (0.9693)
Switzerland
14.0 (39.1000)
21.5 (1.3000)
96.0 (3.4392)
16.0 (2.0900)
Canada
16.0 (38.5000)
29.0 (1.2000)
110.0 (3.2172)
21.0 (1.9139)
South Korea
12.0 (40.6000)
1.0 (3.5000)
7.0 (9.4340)
2.0 (5.5644)
Sweden
17.0 (37.3000)
29.0 (1.2000)
103.0 (3.3241)
25.0 (1.8779)
Austria
18.0 (35.4000)
21.5 (1.3000)
84.0 (3.8123)
18.0 (1.9727)
Hungary
20.0 (33.3000)
39.5 (1.1000)
97.0 (3.4161)
34.0 (1.6224)
Taiwan
19.0 (33.8000)
4.5 (2.0000)
19.5 (6.2893)
4.0 (2.9326)
Denmark
22.0 (32.4000)
29.0 (1.2000)
82.0 (3.8462)
28.0 (1.7442)
Italy
25.5 (31.4000)
29.0 (1.2000)
79.0 (3.9735)
30.0 (1.7192)
Czech Republic
27.5 (31.2000)
39.5 (1.1000)
91.0 (3.6545)
37.0 (1.5737)
Bulgaria
29.0 (30.7000)
51.5 (1.0000)
98.0 (3.3670)
45.0 (1.4225)
Jordan
27.5 (31.2000)
12.5 (1.5000)
43.0 (5.0505)
14.0 (2.1337)
Finland
30.0 (30.6000)
39.5 (1.1000)
89.0 (3.7288)
38.0 (1.5603)
Saudi Arabia
25.5 (31.4000)
4.5 (2.0000)
16.0 (6.8027)
8.0 (2.8329)
Slovenia
31.0 (30.1000)
39.5 (1.1000)
85.0 (3.7931)
40.0 (1.5493)
Costa Rica
32.0 (29.7000)
29.0 (1.2000)
67.5 (4.2105)
32.0 (1.6783)
Lebanon
34.0 (27.7000)
105.5 (0.4000)
140.0 (1.4652)
94.0 (0.5502)
Oman
35.0 (27.6000)
39.5 (1.1000)
71.0 (4.1509)
41.0 (1.4966)
Vietnam
33.0 (27.9000)
17.0 (1.4000)
32.0 (5.2830)
22.0 (1.9048)
Portugal
36.0 (27.4000)
51.5 (1.0000)
86.0 (3.7879)
49.0 (1.3587)
Slovakia
38.0 (27.1000)
51.5 (1.0000)
83.0 (3.8314)
50.0 (1.3532)
Croatia
37.0 (27.2000)
39.5 (1.1000)
66.0 (4.2146)
42.0 (1.4885)
Dominican Republic
40.0 (25.8000)
51.5 (1.0000)
76.0 (4.0323)
51.0 (1.3298)
Uruguay
41.0 (25.7000)
39.5 (1.1000)
64.0 (4.4715)
43.0 (1.4589)
Colombia
39.0 (25.9000)
17.0 (1.4000)
24.0 (5.7143)
26.0 (1.8543)
Lithuania
43.0 (25.1000)
66.0 (0.8000)
104.0 (3.2922)
65.0 (1.0568)
Serbia
42.0 (25.3000)
29.0 (1.2000)
44.0 (4.9793)
36.0 (1.5810)
Jamaica
44.0 (25.0000)
51.5 (1.0000)
70.0 (4.1667)
52.5 (1.3158)
Panama
46.0 (24.2000)
60.5 (0.9000)
80.0 (3.8627)
56.0 (1.1734)
South Africa
48.0 (23.9000)
66.0 (0.8000)
95.0 (3.4632)
66.0 (1.0403)
Chile
47.0 (24.0000)
21.5 (1.3000)
23.0 (5.7269)
31.0 (1.6818)
Japan
45.0 (24.7000)
3.0 (2.2000)
2.0 (9.7778)
7.0 (2.8387)
Bosnia And Herzegovina
50.0 (23.2000)
39.5 (1.1000)
45.0 (4.9774)
46.0 (1.4121)
Argentina
49.0 (23.4000)
12.5 (1.5000)
15.0 (6.8493)
20.0 (1.9206)
Malaysia
51.5 (22.6000)
66.0 (0.8000)
90.0 (3.6697)
67.0 (1.0230)
Kuwait
51.5 (22.6000)
12.5 (1.5000)
13.0 (7.1090)
23.0 (1.9011)
Greece
54.0 (21.5000)
72.0 (0.7000)
99.0 (3.3654)
70.0 (0.8838)
Georgia
53.0 (21.6000)
39.5 (1.1000)
31.0 (5.3659)
47.0 (1.3836)
Philippines
56.0 (20.7000)
79.0 (0.6000)
113.0 (2.9851)
77.0 (0.7509)
Mexico
55.0 (21.4000)
21.5 (1.3000)
18.0 (6.4677)
33.0 (1.6270)
Ecuador
57.0 (20.3000)
66.0 (0.8000)
73.0 (4.1026)
68.0 (0.9938)
Brazil
58.0 (20.2000)
39.5 (1.1000)
22.0 (5.7592)
48.0 (1.3597)
Moldova
61.0 (19.1000)
79.0 (0.6000)
109.0 (3.2432)
78.0 (0.7362)
Albania
60.0 (19.2000)
72.0 (0.7000)
87.0 (3.7838)
71.0 (0.8589)
El Salvador
59.0 (20.1000)
6.5 (1.8000)
1.0 (9.8361)
13.0 (2.2032)
Azerbaijan
63.0 (18.7000)
51.5 (1.0000)
25.0 (5.6497)
54.0 (1.2151)
India
64.0 (18.5000)
60.5 (0.9000)
42.0 (5.1136)
62.0 (1.0922)
Romania
65.0 (18.0000)
88.0 (0.5000)
127.0 (2.8571)
83.0 (0.6061)
Türkiye
62.0 (18.9000)
12.5 (1.5000)
8.0 (8.6207)
27.0 (1.8160)
Mongolia
66.0 (17.9000)
29.0 (1.2000)
12.0 (7.1856)
44.0 (1.4406)
Guatemala
68.5 (17.3000)
60.5 (0.9000)
28.5 (5.4878)
63.5 (1.0766)
Peru
68.5 (17.3000)
60.5 (0.9000)
28.5 (5.4878)
63.5 (1.0766)
Kazakhstan
70.0 (16.9000)
51.5 (1.0000)
19.5 (6.2893)
55.0 (1.1891)
Namibia
74.0 (15.4000)
125.5 (0.3000)
134.0 (1.9868)
118.0 (0.3534)
Libya
72.5 (15.5000)
88.0 (0.5000)
100.5 (3.3333)
84.0 (0.5882)
Gabon
71.0 (15.7000)
72.0 (0.7000)
51.0 (4.6667)
72.0 (0.8235)
Botswana
75.0 (15.0000)
137.5 (0.2000)
142.0 (1.3514)
134.0 (0.2347)
Egypt
72.5 (15.5000)
72.0 (0.7000)
47.0 (4.7297)
73.0 (0.8216)
Nepal
77.0 (14.6000)
105.5 (0.4000)
128.0 (2.8169)
95.0 (0.4662)
Indonesia
76.0 (14.8000)
72.0 (0.7000)
46.0 (4.9645)
74.0 (0.8149)
Honduras
78.0 (14.5000)
88.0 (0.5000)
93.0 (3.5714)
85.0 (0.5814)
Senegal
79.0 (14.4000)
88.0 (0.5000)
92.0 (3.5971)
86.0 (0.5807)
Tunisia
80.0 (13.9000)
105.5 (0.4000)
126.0 (2.9630)
96.0 (0.4624)
Algeria
81.0 (13.8000)
79.0 (0.6000)
63.0 (4.5455)
79.0 (0.6912)
Zambia
84.5 (13.3000)
137.5 (0.2000)
138.0 (1.5267)
135.0 (0.2301)
Cote D'Ivoire
82.0 (13.7000)
79.0 (0.6000)
61.0 (4.5802)
80.0 (0.6904)
Bolivia
84.5 (13.3000)
79.0 (0.6000)
48.0 (4.7244)
81.0 (0.6873)
Iran
87.0 (12.8000)
137.5 (0.2000)
136.0 (1.5873)
136.0 (0.2288)
Iraq
86.0 (13.0000)
88.0 (0.5000)
77.5 (4.0000)
87.0 (0.5714)
Thailand
83.0 (13.4000)
51.5 (1.0000)
9.0 (8.0645)
57.0 (1.1416)
Paraguay
88.0 (12.6000)
105.5 (0.4000)
106.5 (3.2787)
97.0 (0.4556)
Nicaragua
89.0 (12.5000)
72.0 (0.7000)
21.0 (5.9322)
75.0 (0.7937)
Morocco
90.0 (12.0000)
125.5 (0.3000)
129.5 (2.5641)
119.0 (0.3398)
Gambia
92.0 (11.5000)
143.5 (0.1000)
145.0 (0.8772)
142.0 (0.1129)
Pakistan
91.0 (11.9000)
88.0 (0.5000)
65.0 (4.3860)
88.0 (0.5643)
Angola
94.5 (11.4000)
88.0 (0.5000)
58.5 (4.5872)
90.5 (0.5612)
Madagascar
94.5 (11.4000)
88.0 (0.5000)
58.5 (4.5872)
90.5 (0.5612)
Malawi
94.5 (11.4000)
88.0 (0.5000)
58.5 (4.5872)
90.5 (0.5612)
Mozambique
94.5 (11.4000)
88.0 (0.5000)
58.5 (4.5872)
90.5 (0.5612)
French Guiana
98.5 (11.3000)
51.5 (1.0000)
4.5 (9.7087)
59.5 (1.1148)
Guyana
98.5 (11.3000)
51.5 (1.0000)
4.5 (9.7087)
59.5 (1.1148)
Suriname
98.5 (11.3000)
51.5 (1.0000)
4.5 (9.7087)
59.5 (1.1148)
Venezuela
98.5 (11.3000)
51.5 (1.0000)
4.5 (9.7087)
59.5 (1.1148)
Benin
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Burkina Faso
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Ghana
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Guinea
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Guinea-Bissau
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Liberia
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Mali
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Mauritania
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Niger
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Nigeria
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Sierra Leone
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Togo
107.0 (10.4000)
125.5 (0.3000)
119.5 (2.9703)
125.5 (0.3337)
Myanmar
107.0 (10.4000)
105.5 (0.4000)
77.5 (4.0000)
98.0 (0.4444)
Lesotho
116.5 (9.9000)
143.5 (0.1000)
144.0 (1.0204)
143.0 (0.1109)
Belarus
114.0 (10.3000)
72.0 (0.7000)
11.0 (7.2917)
76.0 (0.7743)
Kyrgyzstan
115.0 (10.0000)
88.0 (0.5000)
37.0 (5.2632)
93.0 (0.5525)
Russia
116.5 (9.9000)
105.5 (0.4000)
67.5 (4.2105)
99.0 (0.4420)
Ukraine
118.0 (9.5000)
143.5 (0.1000)
143.0 (1.0638)
144.0 (0.1104)
Kenya
125.0 (8.9000)
137.5 (0.2000)
132.0 (2.2989)
137.0 (0.2191)
Cameroon
121.0 (9.1000)
105.5 (0.4000)
54.0 (4.5977)
102.0 (0.4381)
Central African Republic
121.0 (9.1000)
105.5 (0.4000)
54.0 (4.5977)
102.0 (0.4381)
Chad
121.0 (9.1000)
105.5 (0.4000)
54.0 (4.5977)
102.0 (0.4381)
Congo
121.0 (9.1000)
105.5 (0.4000)
54.0 (4.5977)
102.0 (0.4381)
Congo (DRC)
121.0 (9.1000)
105.5 (0.4000)
54.0 (4.5977)
102.0 (0.4381)
Haiti
125.0 (8.9000)
105.5 (0.4000)
49.5 (4.7059)
105.5 (0.4372)
Zimbabwe
125.0 (8.9000)
105.5 (0.4000)
49.5 (4.7059)
105.5 (0.4372)
Bangladesh
132.0 (8.0000)
137.5 (0.2000)
129.5 (2.5641)
138.0 (0.2169)
Laos
127.0 (8.4000)
79.0 (0.6000)
10.0 (7.6923)
82.0 (0.6508)
Papua New Guinea
139.5 (7.6000)
146.5 (-0.1000)
146.0 (-1.2987)
147.0 (-0.1083)
Burundi
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Eritrea
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Ethiopia
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Somalia
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
South Sudan
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Sudan
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Tanzania
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Uganda
132.0 (8.0000)
105.5 (0.4000)
37.0 (5.2632)
110.5 (0.4329)
Syria
142.0 (7.4000)
146.5 (-0.1000)
147.0 (-1.3333)
146.0 (-0.1081)
Armenia
137.0 (7.7000)
125.5 (0.3000)
75.0 (4.0541)
132.0 (0.3240)
Sri Lanka
139.5 (7.6000)
125.5 (0.3000)
72.0 (4.1096)
133.0 (0.3236)
Uzbekistan
139.5 (7.6000)
105.5 (0.4000)
26.5 (5.5556)
115.5 (0.4310)
Rwanda
139.5 (7.6000)
105.5 (0.4000)
26.5 (5.5556)
115.5 (0.4310)
Cuba
143.0 (6.8000)
143.5 (0.1000)
139.0 (1.4925)
145.0 (0.1072)
Afghanistan
145.0 (6.3000)
137.5 (0.2000)
106.5 (3.2787)
140.0 (0.2130)
Tajikistan
145.0 (6.3000)
137.5 (0.2000)
106.5 (3.2787)
140.0 (0.2130)
Turkmenistan
145.0 (6.3000)
137.5 (0.2000)
106.5 (3.2787)
140.0 (0.2130)
Cambodia
147.0 (6.1000)
105.5 (0.4000)
14.0 (7.0175)
117.0 (0.4242)
France
4.0 (49.6000)
6.5 (1.8000)
88.0 (3.7657)
3.0 (3.4483)
Netherlands
9.5 (43.5000)
17.0 (1.4000)
102.0 (3.3254)
12.0 (2.4180)
United States
21.0 (33.0000)
8.5 (1.7000)
30.0 (5.4313)
10.0 (2.4745)
Poland
23.5 (32.3000)
21.5 (1.3000)
69.0 (4.1935)
24.0 (1.8841)
China
67.0 (17.5000)
39.5 (1.1000)
17.0 (6.7073)
52.5 (1.3158)
Germany
23.5 (32.3000)
29.0 (1.2000)
81.0 (3.8585)
29.0 (1.7417)
Exhibit 01 / Which populations does the adoption map represent?
A world measured in people.
Circle area: population aged 15–64 in 2025. Color: June 2026 adoption.
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
China and India dominate the population view. A large territory is not a large share of the people represented.
Source & boundary Microsoft Q2 2026 update; World Bank population aged 15–64 (2025); Natural Earth public-domain geography. The Dorling layout covers 144 economies and displaces positions to avoid overlap. Singapore lacks a source anchor; Taiwan and French Guiana lack compatible population data. Circle areas, not radii, encode population.
A world measured in people.. Circle area: population aged 15–64 in 2025. Color: June 2026 adoption.
Economy
June 2026 (%)
Population 15–64 (2025)
Cartogram coverage
United Arab Emirates
73.3000
9,467,296.0000
Included
Singapore
64.3000
4,528,368.0000
Unavailable
Norway
49.4000
3,646,833.0000
Included
Ireland
49.9000
3,603,008.0000
Included
France
49.6000
42,079,685.0000
Included
Spain
45.1000
32,445,472.0000
Included
New Zealand
44.1000
3,435,132.0000
Included
United Kingdom
43.7000
44,007,878.0000
Included
Netherlands
43.5000
11,611,748.0000
Included
Qatar
43.5000
2,475,822.0000
Included
Australia
40.7000
17,736,013.0000
Included
Belgium
40.3000
7,563,359.0000
Included
Israel
38.7000
6,088,301.0000
Included
Switzerland
39.1000
5,881,391.0000
Included
Canada
38.5000
26,973,329.0000
Included
South Korea
40.6000
35,902,741.0000
Included
Sweden
37.3000
6,610,247.0000
Included
Austria
35.4000
5,963,025.0000
Included
Hungary
33.3000
6,135,748.0000
Included
Taiwan
33.8000
Unavailable
Unavailable
United States
33.0000
220,498,172.0000
Included
Denmark
32.4000
3,804,949.0000
Included
Germany
32.3000
52,107,640.0000
Included
Poland
32.3000
23,605,260.0000
Included
Italy
31.4000
37,261,028.0000
Included
Czech Republic
31.2000
6,938,669.0000
Included
Bulgaria
30.7000
4,076,441.0000
Included
Jordan
31.2000
7,494,228.0000
Included
Finland
30.6000
3,470,751.0000
Included
Saudi Arabia
31.4000
27,109,801.0000
Included
Slovenia
30.1000
1,349,054.0000
Included
Costa Rica
29.7000
3,552,415.0000
Included
Lebanon
27.7000
3,741,835.0000
Included
Oman
27.6000
4,010,720.0000
Included
Vietnam
27.9000
68,730,842.0000
Included
Portugal
27.4000
6,736,123.0000
Included
Slovakia
27.1000
3,540,288.0000
Included
Croatia
27.2000
2,429,912.0000
Included
Dominican Republic
25.8000
7,558,944.0000
Included
Uruguay
25.7000
2,227,837.0000
Included
Colombia
25.9000
37,294,820.0000
Included
Lithuania
25.1000
1,871,141.0000
Included
Serbia
25.3000
4,105,768.0000
Included
Jamaica
25.0000
2,074,923.0000
Included
Panama
24.2000
3,008,610.0000
Included
South Africa
23.9000
43,634,244.0000
Included
Chile
24.0000
13,676,187.0000
Included
Japan
24.7000
72,499,552.0000
Included
Bosnia And Herzegovina
23.2000
2,018,817.0000
Included
Argentina
23.4000
30,422,322.0000
Included
Malaysia
22.6000
25,421,096.0000
Included
Kuwait
22.6000
3,836,386.0000
Included
Greece
21.5000
6,534,624.0000
Included
Georgia
21.6000
2,500,022.0000
Included
Philippines
20.7000
78,407,418.0000
Included
Mexico
21.4000
88,915,724.0000
Included
Ecuador
20.3000
12,346,520.0000
Included
Brazil
20.2000
147,123,684.0000
Included
Moldova
19.1000
1,501,484.0000
Included
Albania
19.2000
1,545,359.0000
Included
El Salvador
20.1000
4,284,402.0000
Included
Azerbaijan
18.7000
7,135,101.0000
Included
India
18.5000
1,001,696,269.0000
Included
Romania
18.0000
12,231,235.0000
Included
Türkiye
18.9000
58,746,219.0000
Included
Mongolia
17.9000
2,248,767.0000
Included
China
17.5000
980,126,399.0000
Included
Guatemala
17.3000
11,962,255.0000
Included
Peru
17.3000
23,138,487.0000
Included
Kazakhstan
16.9000
12,893,926.0000
Included
Namibia
15.4000
1,841,967.0000
Included
Libya
15.5000
5,068,925.0000
Included
Gabon
15.7000
1,546,207.0000
Included
Botswana
15.0000
1,638,744.0000
Included
Egypt
15.5000
74,774,809.0000
Included
Nepal
14.6000
19,347,692.0000
Included
Indonesia
14.8000
194,890,399.0000
Included
Honduras
14.5000
7,175,291.0000
Included
Senegal
14.4000
11,096,220.0000
Included
Tunisia
13.9000
8,211,984.0000
Included
Algeria
13.8000
30,011,643.0000
Included
Zambia
13.3000
12,497,248.0000
Included
Cote D'Ivoire
13.7000
18,609,973.0000
Included
Bolivia
13.3000
8,155,505.0000
Included
Iran
12.8000
64,135,210.0000
Included
Iraq
13.0000
28,458,766.0000
Included
Thailand
13.4000
49,824,787.0000
Included
Paraguay
12.6000
4,552,204.0000
Included
Nicaragua
12.5000
4,624,236.0000
Included
Morocco
12.0000
25,498,901.0000
Included
Gambia
11.5000
1,611,183.0000
Included
Pakistan
11.9000
151,585,194.0000
Included
Angola
11.4000
20,693,794.0000
Included
Madagascar
11.4000
18,855,173.0000
Included
Malawi
11.4000
12,726,492.0000
Included
Mozambique
11.4000
18,919,550.0000
Included
French Guiana
11.3000
Unavailable
Unavailable
Guyana
11.3000
535,750.0000
Included
Suriname
11.3000
425,317.0000
Included
Venezuela
11.3000
18,554,732.0000
Included
Benin
10.4000
8,234,974.0000
Included
Burkina Faso
10.4000
13,516,880.0000
Included
Ghana
10.4000
21,312,986.0000
Included
Guinea
10.4000
8,452,616.0000
Included
Guinea-Bissau
10.4000
1,314,563.0000
Included
Liberia
10.4000
3,300,298.0000
Included
Mali
10.4000
13,063,829.0000
Included
Mauritania
10.4000
2,889,731.0000
Included
Niger
10.4000
14,281,544.0000
Included
Nigeria
10.4000
133,991,180.0000
Included
Sierra Leone
10.4000
5,209,467.0000
Included
Togo
10.4000
4,819,719.0000
Included
Myanmar
10.4000
37,517,374.0000
Included
Lesotho
9.9000
1,465,674.0000
Included
Belarus
10.3000
5,980,999.0000
Included
Kyrgyzstan
10.0000
4,561,968.0000
Included
Russia
9.9000
93,543,032.0000
Included
Ukraine
9.5000
26,246,998.0000
Included
Kenya
8.9000
34,934,452.0000
Included
Cameroon
9.1000
16,751,853.0000
Included
Central African Republic
9.1000
2,708,090.0000
Included
Chad
9.1000
10,905,005.0000
Included
Congo
9.1000
3,695,842.0000
Included
Congo (DRC)
9.1000
57,506,566.0000
Included
Haiti
8.9000
7,670,052.0000
Included
Zimbabwe
8.9000
9,518,709.0000
Included
Bangladesh
8.0000
115,401,404.0000
Included
Laos
8.4000
5,145,215.0000
Included
Papua New Guinea
7.6000
6,809,239.0000
Included
Burundi
8.0000
7,694,426.0000
Included
Eritrea
8.0000
2,097,521.0000
Included
Ethiopia
8.0000
78,471,564.0000
Included
Somalia
8.0000
9,990,238.0000
Included
South Sudan
8.0000
7,183,876.0000
Included
Sudan
8.0000
29,137,268.0000
Included
Tanzania
8.0000
38,530,560.0000
Included
Uganda
8.0000
28,087,155.0000
Included
Syria
7.4000
17,093,970.0000
Included
Armenia
7.7000
2,054,803.0000
Included
Sri Lanka
7.6000
14,320,772.0000
Included
Uzbekistan
7.6000
23,210,348.0000
Included
Rwanda
7.6000
8,589,557.0000
Included
Cuba
6.8000
7,430,870.0000
Included
Afghanistan
6.3000
24,120,179.0000
Included
Tajikistan
6.3000
6,479,060.0000
Included
Turkmenistan
6.3000
4,874,057.0000
Included
Cambodia
6.1000
11,445,126.0000
Included
Germany: 32.3% estimated adoption in June 2026. Rank 23.5 of 147 eligible economies, with average ranks for ties.
Primary research / Microsoft
Start with the study. Then test the claim.
Microsoft's global AI diffusion research makes a difficult question measurable: how widely is generative AI being used across economies? Its published country estimates are the empirical starting point for this article.
Disclosure. I work at Microsoft. This is my analysis of publicly available research, not an official Microsoft publication.
One frozen CSV vintage supplies every country comparison here. Source estimates stay separate from this article's calculated gains, ranks and descriptive models.
The technical account of telemetry, participation and device-access adjustments. The method paper is version 1, November 2025; the displayed outcomes are the later report vintage.
The question behind the numbers
Reach ≠ catch-up
Between March and June 2026, the published AI adoption estimate rose in 145 of 147 economies. That is a substantial expansion in measured use. It is also an incomplete answer to the question that matters: are places that started behind gaining ground?
The answer depends on the comparison. Higher-adoption economies generally added more percentage points. Relative growth tells a much less decisive story. And the broad country ordering looks different when a European household survey replaces the telemetry-based estimate. These are reasons to examine diffusion more carefully, rather than to give up measuring it.
This investigation follows one economy through that argument. Germany is the default, but another country can remain in view as the measurement changes. The claim on trial is simple: growing adoption means the world is catching up.
01
Where AI has reached
A wider reach. An uneven world.
Before asking who is catching up, decide what the map should make visible: territory, people, or the share using AI.
Microsoft's June 2026 estimates range from 6.1% in Cambodia to 73.3% in the United Arab Emirates. Germany stands at 32.3%, France at 49.6%, the United States at 33.0% and Canada at 38.5%. These are estimated shares of the working-age population using the tracked products. They do not measure how much work those products perform.
A conventional world map gives large territories visual prominence, whether or not many people live there. The population cartogram changes that emphasis. Circle area represents the 2025 population aged 15–64; color separately represents estimated adoption. China and India become central to the number of people involved without being made to look like adoption leaders.
There is a second reason to resist the precision of a league table. Thirty-six economy rows share an identical four-period path with at least one other row. The technical paper describes regional pooling when country traffic is insufficient. We retain the published rows, flag repeated paths and examine what changes when those rows leave the model. Identical estimates should not be read as independently observed identical behavior.
02
Put catch-up on trial
The denominator changes the question.
Keep the economies. Change the lens. Follow what happens to the values, their ranks and the claim they can support.
The convenient claim
“More users means the world is catching up.”
145 economies report a higher adoption estimate. That supports a claim of wider measured reach.
The objection
Expansion says little about the distance between places.
A divide can widen in points and narrow in ratios. The choice of denominator is part of the claim.
A percentage-point gain asks how much more of the population uses the technology. Relative growth divides that gain by the initial user share. The nonuser-relative measure divides it by the initial nonuser share. Each is legitimate, but each asks a different question.
Germany's move from 31.1% to 32.3% becomes a 1.2-point gain, 3.9% relative growth, or a net gain equal to 1.7% of the initial nonuser share. The last figure is not an individual adoption probability. Aggregate snapshots cannot tell us who started using AI, who stopped, or whether the same people remained active.
For the published Global North and South aggregates, the absolute gap widens from 12.1 to 12.6 points. The North-to-South ratio edges down from 1.786 to 1.778. Both follow from the printed figures. However, values published to one decimal place leave enough rounding latitude to reverse that tiny ratio movement. The proportional narrowing is too small to carry a strong convergence claim.
Exhibit 04 / Are lower starting shares associated with larger subsequent gains?
Catching up with whom?
March to June 2026 · 147 economies · dashed line: unweighted descriptive OLS
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
In this March to June 2026 comparison, the unweighted fitted relationship is positive. This is a descriptive association, not a causal catch-up test.
Source & boundary Microsoft estimates; unweighted descriptive OLS fitted to the displayed country rows. The active growth lens follows the instrument; Adoption level defaults this plot to point gains. Shared published paths are not independent country observations. Initial-level/change coupling, source error and ceilings preclude a causal reading.
Catching up with whom?. March to June 2026 · 147 economies · dashed line: unweighted descriptive OLS
Economy
Initial adoption (%)
Percentage-point gain
Repeated published path
United Arab Emirates
70.1000
3.2000
No
Singapore
63.4000
0.9000
No
Norway
48.6000
0.8000
No
Ireland
48.4000
1.5000
No
France
47.8000
1.8000
No
Spain
44.2000
0.9000
No
New Zealand
43.0000
1.1000
No
United Kingdom
42.2000
1.5000
No
Netherlands
42.1000
1.4000
No
Qatar
41.8000
1.7000
No
Australia
39.5000
1.2000
No
Belgium
39.0000
1.3000
No
Israel
38.1000
0.6000
No
Switzerland
37.8000
1.3000
No
Canada
37.3000
1.2000
No
South Korea
37.1000
3.5000
No
Sweden
36.1000
1.2000
No
Austria
34.1000
1.3000
No
Hungary
32.2000
1.1000
No
Taiwan
31.8000
2.0000
No
United States
31.3000
1.7000
No
Denmark
31.2000
1.2000
No
Poland
31.0000
1.3000
No
Italy
30.2000
1.2000
No
Czech Republic
30.1000
1.1000
No
Bulgaria
29.7000
1.0000
No
Jordan
29.7000
1.5000
No
Finland
29.5000
1.1000
No
Saudi Arabia
29.4000
2.0000
No
Slovenia
29.0000
1.1000
No
Costa Rica
28.5000
1.2000
No
Lebanon
27.3000
0.4000
No
Oman
26.5000
1.1000
No
Vietnam
26.5000
1.4000
No
Portugal
26.4000
1.0000
No
Slovakia
26.1000
1.0000
No
Croatia
26.1000
1.1000
No
Dominican Republic
24.8000
1.0000
No
Uruguay
24.6000
1.1000
No
Colombia
24.5000
1.4000
No
Lithuania
24.3000
0.8000
No
Serbia
24.1000
1.2000
No
Jamaica
24.0000
1.0000
No
Panama
23.3000
0.9000
No
South Africa
23.1000
0.8000
No
Chile
22.7000
1.3000
No
Japan
22.5000
2.2000
No
Bosnia And Herzegovina
22.1000
1.1000
No
Argentina
21.9000
1.5000
No
Malaysia
21.8000
0.8000
No
Kuwait
21.1000
1.5000
No
Greece
20.8000
0.7000
No
Georgia
20.5000
1.1000
No
Philippines
20.1000
0.6000
No
Mexico
20.1000
1.3000
No
Ecuador
19.5000
0.8000
No
Brazil
19.1000
1.1000
No
Moldova
18.5000
0.6000
No
Albania
18.5000
0.7000
No
El Salvador
18.3000
1.8000
No
Azerbaijan
17.7000
1.0000
No
India
17.6000
0.9000
No
Romania
17.5000
0.5000
No
Türkiye
17.4000
1.5000
No
Mongolia
16.7000
1.2000
No
China
16.4000
1.1000
No
Guatemala
16.4000
0.9000
No
Peru
16.4000
0.9000
No
Kazakhstan
15.9000
1.0000
No
Namibia
15.1000
0.3000
No
Libya
15.0000
0.5000
No
Gabon
15.0000
0.7000
No
Botswana
14.8000
0.2000
No
Egypt
14.8000
0.7000
No
Nepal
14.2000
0.4000
No
Indonesia
14.1000
0.7000
No
Honduras
14.0000
0.5000
No
Senegal
13.9000
0.5000
No
Tunisia
13.5000
0.4000
No
Algeria
13.2000
0.6000
No
Zambia
13.1000
0.2000
No
Cote D'Ivoire
13.1000
0.6000
No
Bolivia
12.7000
0.6000
No
Iran
12.6000
0.2000
No
Iraq
12.5000
0.5000
No
Thailand
12.4000
1.0000
No
Paraguay
12.2000
0.4000
No
Nicaragua
11.8000
0.7000
No
Morocco
11.7000
0.3000
No
Gambia
11.4000
0.1000
No
Pakistan
11.4000
0.5000
No
Angola
10.9000
0.5000
Yes
Madagascar
10.9000
0.5000
Yes
Malawi
10.9000
0.5000
Yes
Mozambique
10.9000
0.5000
Yes
French Guiana
10.3000
1.0000
Yes
Guyana
10.3000
1.0000
Yes
Suriname
10.3000
1.0000
Yes
Venezuela
10.3000
1.0000
Yes
Benin
10.1000
0.3000
Yes
Burkina Faso
10.1000
0.3000
Yes
Ghana
10.1000
0.3000
Yes
Guinea
10.1000
0.3000
Yes
Guinea-Bissau
10.1000
0.3000
Yes
Liberia
10.1000
0.3000
Yes
Mali
10.1000
0.3000
Yes
Mauritania
10.1000
0.3000
Yes
Niger
10.1000
0.3000
Yes
Nigeria
10.1000
0.3000
Yes
Sierra Leone
10.1000
0.3000
Yes
Togo
10.1000
0.3000
Yes
Myanmar
10.0000
0.4000
No
Lesotho
9.8000
0.1000
No
Belarus
9.6000
0.7000
No
Kyrgyzstan
9.5000
0.5000
No
Russia
9.5000
0.4000
No
Ukraine
9.4000
0.1000
No
Kenya
8.7000
0.2000
No
Cameroon
8.7000
0.4000
Yes
Central African Republic
8.7000
0.4000
Yes
Chad
8.7000
0.4000
Yes
Congo
8.7000
0.4000
Yes
Congo (DRC)
8.7000
0.4000
Yes
Haiti
8.5000
0.4000
No
Zimbabwe
8.5000
0.4000
No
Bangladesh
7.8000
0.2000
No
Laos
7.8000
0.6000
No
Papua New Guinea
7.7000
-0.1000
No
Burundi
7.6000
0.4000
Yes
Eritrea
7.6000
0.4000
Yes
Ethiopia
7.6000
0.4000
Yes
Somalia
7.6000
0.4000
Yes
South Sudan
7.6000
0.4000
Yes
Sudan
7.6000
0.4000
Yes
Tanzania
7.6000
0.4000
Yes
Uganda
7.6000
0.4000
Yes
Syria
7.5000
-0.1000
No
Armenia
7.4000
0.3000
No
Sri Lanka
7.3000
0.3000
No
Uzbekistan
7.2000
0.4000
No
Rwanda
7.2000
0.4000
No
Cuba
6.7000
0.1000
No
Afghanistan
6.1000
0.2000
Yes
Tajikistan
6.1000
0.2000
Yes
Turkmenistan
6.1000
0.2000
Yes
Cambodia
5.7000
0.4000
No
Germany
31.1000
1.2000
No
Unweighted dispersion12.16 → 12.57 pp
The standard deviation rises.
Relative dispersion0.637 → 0.633
The coefficient of variation edges down.
The same March–June observations
Absolute and proportional comparisons can point in different directions. Neither is a substitute for stating the question.
Across the 147 economy rows, higher initial adoption is strongly associated with larger point gains: the rank correlation is 0.81. For relative growth it is only −0.07, offering little evidence of systematic low-base catch-up. Excluding repeated paths leaves the qualitative reading intact. Population weighting changes the relative-growth slope from slightly negative to slightly positive.
Neither relationship identifies a diffusion mechanism. Initial adoption enters the change calculation and its denominators, creating mathematical coupling. Measurement error can manufacture apparent catch-up, while ceilings limit further gains near 100%. With only four published snapshots, fitting an S-curve would turn a short record into an unjustified forecast.
03
What wealth explains
A gradient is not a verdict.
Income helps describe the pattern. The interpretation gets less comfortable when the model's assumptions move.
Income can make adoption easier through affordable devices, reliable connectivity, education and jobs in which a digital assistant has an immediate use. Those channels are plausible mechanisms. A cross-country regression cannot separate them from institutions, language, industrial structure or the construction of the adoption measure itself.
The baseline model uses log GDP per person at purchasing-power parity and internet use, both from 2024, to describe June 2026 adoption across 136 economies with complete data. Fractional logit keeps fitted means between zero and 100%. A linear alternative is transparent but produces a slightly negative prediction at the lower end, illustrating the advantage of a bounded specification.
Exhibit 05 / How much of the cross-country pattern is associated with income?
The income gradient is real. Its explanation is incomplete.
136 complete cases · curve holds internet use at 82.0%
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
The shaded 95% interval is conditional model-mean uncertainty. It excludes uncertainty in the adoption estimates and covariates.
Source & boundary Microsoft June 2026 outcome; World Bank GDP per capita at PPP and internet use, both 2024. Fractional-logit QMLE, 136 complete cases. The band uses covariance clustered by identical four-period source paths. Conditional model uncertainty excludes source-measure uncertainty; it is not an adoption error bar.
The income gradient is real. Its explanation is incomplete.. 136 complete cases · curve holds internet use at 82.0%
Economy
GDP per person PPP (2024)
Internet use % (2024)
AI adoption % (June 2026)
Fitted %
Deviation (pp)
United Arab Emirates
79,343.7793
100.0000
73.3000
35.4627
37.8373
Singapore
153,161.1754
94.3776
64.3000
45.3179
18.9821
Norway
105,657.0339
99.0000
49.4000
39.5812
9.8188
Ireland
140,779.2034
97.1877
49.9000
43.9050
5.9950
France
62,524.5434
88.6538
49.6000
32.7680
16.8320
Spain
58,063.7351
95.7575
45.1000
31.4553
13.6447
New Zealand
55,793.9990
93.5193
44.1000
31.0397
13.0603
United Kingdom
62,839.4679
95.4721
43.7000
32.5112
11.1888
Netherlands
85,218.3689
97.0068
43.5000
36.6099
6.8901
Qatar
126,712.5829
98.0854
43.5000
42.2923
1.2077
Australia
72,117.8827
96.1314
40.7000
34.3372
6.3628
Belgium
74,224.5511
95.7758
40.3000
34.7493
5.5507
Israel
57,277.8631
88.1824
38.7000
31.6302
7.0698
Switzerland
97,467.7447
97.3444
39.1000
38.5022
0.5978
Canada
65,482.9455
94.3533
38.5000
33.1152
5.3848
South Korea
61,051.3493
97.8956
40.6000
32.0142
8.5858
Sweden
71,221.9379
95.5293
37.3000
34.1958
3.1042
Austria
75,774.4137
94.9197
35.4000
35.0761
0.3239
Hungary
48,540.1615
93.7790
33.3000
29.2501
4.0499
United States
86,169.6642
94.6938
33.0000
36.8825
-3.8825
Denmark
80,937.8001
99.7692
32.4000
35.7506
-3.3506
Poland
51,262.5106
88.5850
32.3000
30.1753
2.1247
Italy
62,292.9012
89.2207
31.4000
32.6915
-1.2915
Czech Republic
57,625.0962
87.6878
31.2000
31.7327
-0.5327
Bulgaria
41,969.5955
82.4390
30.7000
27.9497
2.7503
Jordan
11,916.3623
95.6224
31.2000
14.9463
16.2537
Finland
65,137.1817
93.7241
30.6000
33.0743
-2.4743
Saudi Arabia
71,936.2511
100.0000
31.4000
34.1147
-2.7147
Slovenia
57,570.5904
90.7636
30.1000
31.5765
-1.4765
Costa Rica
31,552.1490
87.1702
29.7000
24.4130
5.2870
Lebanon
11,917.4503
80.6340
27.7000
15.3633
12.3367
Oman
41,800.7841
95.2517
27.6000
27.3471
0.2529
Vietnam
16,387.9088
84.1500
27.9000
17.9337
9.9663
Portugal
52,512.5651
88.4870
27.4000
30.4885
-3.0885
Slovakia
47,846.6169
89.8251
27.1000
29.2463
-2.1463
Croatia
49,550.8073
83.6324
27.2000
29.9673
-2.7673
Dominican Republic
27,581.6607
91.0022
25.8000
22.7938
3.0062
Uruguay
36,596.7847
91.9911
25.7000
25.9100
-0.2100
Colombia
22,439.4001
79.3454
25.9000
21.0833
4.8167
Lithuania
55,699.3734
89.1612
25.1000
31.2195
-6.1195
Serbia
32,832.2745
87.6907
25.3000
24.8398
0.4602
Jamaica
12,908.9862
90.1087
25.0000
15.7308
9.2692
Panama
41,429.5093
72.7715
24.2000
28.2129
-4.0129
South Africa
15,478.6457
78.3576
23.9000
17.6108
6.2892
Chile
36,070.8929
95.5902
24.0000
25.5935
-1.5935
Japan
54,156.2688
85.5415
24.7000
31.0220
-6.3220
Bosnia And Herzegovina
25,148.0128
86.1009
23.2000
22.0044
1.1956
Argentina
30,475.3877
89.6672
23.4000
23.9283
-0.5283
Malaysia
38,835.6013
98.0206
22.6000
26.3535
-3.7535
Kuwait
53,119.3784
99.7473
22.6000
30.1218
-7.5218
Greece
44,170.9557
86.2697
21.5000
28.4095
-6.9095
Georgia
27,486.8762
83.7986
21.6000
23.0318
-1.4318
Philippines
11,813.8059
67.2630
20.7000
15.6726
5.0274
Mexico
25,820.3528
83.1223
21.4000
22.3918
-0.9918
Ecuador
15,848.3400
77.1727
20.3000
17.8570
2.4430
Brazil
22,370.9182
84.4635
20.2000
20.8693
-0.6693
Moldova
18,661.6606
77.3688
19.1000
19.3502
-0.2502
Albania
26,701.7649
85.8639
19.2000
22.6436
-3.4436
El Salvador
13,258.1805
66.4777
20.1000
16.6427
3.4573
Azerbaijan
25,146.3589
90.4398
18.7000
21.8433
-3.1433
India
10,718.5934
64.9435
18.5000
14.9723
3.5277
Romania
49,076.7137
91.2905
18.0000
29.4999
-11.4999
Türkiye
45,122.9317
87.3079
18.9000
28.6274
-9.7274
Mongolia
19,172.5296
85.1235
17.9000
19.3443
-1.4443
China
27,139.5182
92.0000
17.5000
22.5841
-5.0841
Guatemala
14,393.0528
60.2214
17.3000
17.5397
-0.2397
Peru
17,881.8686
81.9571
17.3000
18.7977
-1.4977
Kazakhstan
40,950.3167
93.3917
16.9000
27.1796
-10.2796
Namibia
11,697.9202
64.8746
15.4000
15.6618
-0.2618
Libya
14,324.5819
81.9545
15.5000
16.8305
-1.3305
Gabon
21,541.4113
68.7209
15.7000
21.0532
-5.3532
Botswana
20,612.4785
57.4595
15.0000
21.0134
-6.0134
Egypt
19,121.8749
74.6484
15.5000
19.6745
-4.1745
Nepal
5,745.6453
46.3219
14.6000
11.1589
3.4411
Indonesia
16,472.4826
72.7808
14.8000
18.3448
-3.5448
Honduras
7,502.5690
58.6227
14.5000
12.5712
1.9288
Senegal
5,097.2432
60.1178
14.4000
10.1833
4.2167
Tunisia
14,542.0966
76.5000
13.9000
17.1253
-3.2253
Algeria
17,646.3326
77.4178
13.8000
18.8247
-5.0247
Zambia
4,221.0140
17.1010
13.3000
9.9890
3.3110
Cote D'Ivoire
7,680.3698
41.4322
13.7000
13.1464
0.5536
Bolivia
12,896.3370
79.7047
13.3000
16.0230
-2.7230
Iran
19,902.7485
85.3404
12.8000
19.6929
-6.8929
Iraq
14,485.3249
81.4745
13.0000
16.9400
-3.9400
Thailand
24,909.2266
90.8672
13.4000
21.7297
-8.3297
Paraguay
18,631.6436
81.5781
12.6000
19.1935
-6.5935
Nicaragua
8,720.8692
61.3955
12.5000
13.5384
-1.0384
Morocco
10,430.2490
91.2000
12.0000
14.0634
-2.0634
Gambia
3,480.6600
49.4943
11.5000
8.4296
3.0704
Pakistan
6,263.1869
57.2530
11.9000
11.4464
0.4536
Angola
9,963.5902
40.7048
11.4000
15.0754
-3.6754
Madagascar
1,888.6640
18.6779
11.4000
6.3598
5.0402
Malawi
1,860.3153
18.9616
11.4000
6.3018
5.0982
Mozambique
1,708.3036
20.5273
11.4000
5.9843
5.4157
Guyana
80,271.4254
83.0008
11.3000
36.4707
-25.1707
Benin
4,441.0044
33.9838
10.4000
9.9381
0.4619
Burkina Faso
2,901.0421
28.2527
10.4000
7.9444
2.4556
Ghana
8,051.1938
72.1757
10.4000
12.7205
-2.3205
Guinea
4,571.9309
33.3047
10.4000
10.1102
0.2898
Guinea-Bissau
3,093.4653
29.7774
10.4000
8.2091
2.1909
Liberia
1,873.7613
32.2336
10.4000
6.1598
4.2402
Mali
3,321.5761
36.8015
10.4000
8.4223
1.9777
Mauritania
7,379.4542
45.7794
10.4000
12.7672
-2.3672
Niger
2,044.0490
15.5576
10.4000
6.6933
3.7067
Nigeria
9,099.8694
41.2114
10.4000
14.3715
-3.9715
Sierra Leone
3,527.4212
25.1050
10.4000
8.9107
1.4893
Togo
3,814.8353
39.4832
10.4000
9.0450
1.3550
Lesotho
3,070.7958
51.7966
9.9000
7.8255
2.0745
Belarus
33,166.3828
94.2618
10.3000
24.6896
-14.3896
Kyrgyzstan
8,208.0308
92.0286
10.0000
12.3782
-2.3782
Russia
47,686.0664
94.3650
9.9000
29.0018
-19.1018
Ukraine
18,636.1367
82.4749
9.5000
19.1658
-9.6658
Kenya
6,649.6792
34.9762
8.9000
12.3305
-3.4305
Cameroon
5,597.1032
46.2588
9.1000
11.0038
-1.9038
Central African Republic
1,264.9367
13.7825
9.1000
5.1080
3.9920
Chad
2,766.9813
12.6269
9.1000
7.9816
1.1184
Congo
7,003.6623
47.2516
9.1000
12.3839
-3.2839
Congo (DRC)
1,823.2771
19.6735
9.1000
6.2212
2.8788
Haiti
3,168.8791
47.8614
8.9000
8.0266
0.8734
Zimbabwe
5,932.8702
41.6428
8.9000
11.4553
-2.5553
Bangladesh
9,660.7796
53.4217
8.0000
14.4936
-6.4936
Laos
9,790.0254
65.5927
8.4000
14.2691
-5.8691
Papua New Guinea
4,886.5319
18.8116
7.6000
10.7803
-3.1803
Burundi
1,193.0353
8.5960
8.0000
4.9916
3.0084
Ethiopia
3,292.3093
21.9413
8.0000
8.6308
-0.6308
Somalia
1,604.2912
27.9402
8.0000
5.6871
2.3129
Tanzania
4,226.9263
31.1621
8.0000
9.7266
-1.7266
Uganda
3,278.0209
8.9498
8.0000
8.8334
-0.8334
Armenia
22,856.3251
81.3393
7.7000
21.1976
-13.4976
Sri Lanka
15,701.0907
54.5900
7.6000
18.4984
-10.8984
Uzbekistan
12,518.4323
89.5055
7.6000
15.5025
-7.9025
Rwanda
3,784.9629
31.7033
7.6000
9.1446
-1.5446
Afghanistan
2,236.1991
16.0950
6.3000
7.0340
-0.7340
Tajikistan
5,413.8311
55.7934
6.3000
10.6107
-4.3107
Cambodia
7,981.5082
68.4753
6.1000
12.7507
-6.6507
Germany
73,957.0012
93.5000
32.3000
34.8112
-2.5112
Exhibit 06 / How does the model benchmark change with its assumptions?
A deviation is not a national report card.
Germany · deviations in percentage points; lower companion, when available: baseline model mean in %
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
The model, weights and sample define the benchmark. These deviations do not identify policy quality or national merit. The baseline interval concerns the conditional model mean, not uncertainty in the published adoption estimate.
Source & boundary Frozen baseline, weighted, income-only, unique-path, influence-exclusion and OLS coefficients; no refitting on interaction. Samples differ; missing inputs and held-out predictions are labeled. The companion is the baseline conditional 95% model-mean interval, clustered by source path. It excludes source-measure uncertainty.
A deviation is not a national report card.. Germany · deviations in percentage points; lower companion, when available: baseline model mean in %
Specification
Fitted sample n
Prediction (%)
Observed (%)
Deviation (pp)
Estimation status
Baseline mean lower 95% (%)
Baseline mean upper 95% (%)
baseline
136.0000
34.8112
32.3000
-2.5112
Included in fitted sample
31.5632
38.2069
population-weighted
136.0000
32.0447
32.3000
0.2553
Included in fitted sample
Not estimated here
Not estimated here
income-only
136.0000
34.5901
32.3000
-2.2901
Included in fitted sample
Not estimated here
Not estimated here
unique-paths-only
107.0000
36.3920
32.3000
-4.0920
Included in fitted sample
Not estimated here
Not estimated here
without-UAE-Singapore
134.0000
33.1435
32.3000
-0.8435
Included in fitted sample
Not estimated here
Not estimated here
OLS-same-covariates
136.0000
33.4471
32.3000
-1.1471
Included in fitted sample
Not estimated here
Not estimated here
Income has a positive conditional association in the baseline. The internet coefficient is imprecise and negative after conditioning on income. That does not show that access discourages AI use. Income and connectivity overlap, omitted variables remain, and the published AI measure already adjusts for device access and mobile usage. An infrastructure regression can partly recover assumptions used to construct its dependent variable.
Germany's baseline prediction is 34.8%, compared with the published 32.3%. Its deviation is −2.5 points. Under population weighting, the prediction becomes 32.0% and the deviation turns slightly positive. Removing the UAE and Singapore reduces the negative deviation to −0.8 points. A claim of structural German underperformance is therefore too strong for this evidence.
France, the UAE and Singapore sit well above the baseline fit; China and Russia are among the cases requiring especially careful measurement interpretation. A model deviation is a difference from specified covariates and weights. It does not grade policy, national ingenuity or institutional quality.
04
Where Germany belongs
Below the benchmark. A similar recent gain.
Levels, changes and population scale answer three different questions about the same country.
Germany / June 202632.3%
Covered EU / weighted36.1%
Benchmark coverage22 / 2798.7% of the EU working-age denominator
This editorial benchmark stays anchored to Germany. The figures continue to carry your selected economy.
Exhibit 07 / How do levels and changes compare on the same measure?
Germany in its European neighborhood.
Lower panel: March to June 2026 · open = earlier; filled = June 2026 · % of ages 15–64
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
Germany is below the covered-EU June benchmark of 36.1%, with a similar March–June gain. The upper trajectory spaces the three source endpoints by elapsed months; dashed connectors do not supply missing observations.
Source & boundary Microsoft H1 2025, Q1 2026 and Q2 2026 from the same frozen table. Those endpoints are associated with June 2025, March 2026 and June 2026. H2 is not assigned an invented month or treated as an equal-duration interval. EU comparison uses World Bank 2025 working-age weights; the US and Canada are external comparisons.
Germany in its European neighborhood.. Lower panel: March to June 2026 · open = earlier; filled = June 2026 · % of ages 15–64
Economy
June 2025 (%)
March 2026 (%)
June 2026 (%)
Selected-period gain (pp)
France
40.9000
47.8000
49.6000
1.8000
Norway
45.3000
48.6000
49.4000
0.8000
Netherlands
36.3000
42.1000
43.5000
1.4000
Canada
33.5000
37.3000
38.5000
1.2000
Sweden
31.2000
36.1000
37.3000
1.2000
Austria
29.1000
34.1000
35.4000
1.3000
United States
26.3000
31.3000
33.0000
1.7000
Denmark
26.6000
31.2000
32.4000
1.2000
Germany
26.5000
31.1000
32.3000
1.2000
Poland
26.4000
31.0000
32.3000
1.3000
Finland
25.6000
29.5000
30.6000
1.1000
The Microsoft file covers 22 of the EU's 27 members. Weighting those economies by their 2025 populations aged 15–64 gives a June estimate of 36.1%, compared with 34.9% in March. They account for 98.7% of the EU working-age population in the same World Bank extract. Cyprus, Estonia, Latvia, Luxembourg and Malta are absent, so this is a covered-EU benchmark rather than a complete EU27 estimate.
Germany is 3.8 points below that benchmark in June, yet its quarterly gain of 1.2 points is close to the benchmark's 1.22 points. It is below France and the Netherlands, almost level with Denmark and Poland, and above Finland in this measurement. The word 'Nordic' is not a sufficient explanation for these estimates.
Germany's population matters for a different question. Applying its 1.2-point change to the fixed 2025 working-age denominator implies roughly 625,000 additional user equivalents. This is a derived scale estimate, not a count of observed unique people. Keeping the denominator fixed isolates the rate contribution; it deliberately attributes no change to population.
From percentages to scale
The denominator is also people.
Exhibit 08 / Which economies contribute most to the fixed-population increase?
Small rate changes can represent millions.
March to June 2026 · millions of derived user equivalents · fixed 2025 population
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
48.1 million across 145 matched economies; China and India contribute 41%. The population contribution is zero by construction, not because population stopped changing.
Source & boundary Derived rate-change equivalents: selected-period change × fixed World Bank 2025 population aged 15–64. This is not an observed count of unique people. Top positive contributors, all declines and the selected economy are shown; the exact table includes all 145 matched economies. China is sensitive to the announced expansion of tracked tools.
Small rate changes can represent millions.. March to June 2026 · millions of derived user equivalents · fixed 2025 population
Economy
Population 15–64 (2025)
Rate change (pp)
Additional user equivalents (millions)
China
980,126,399.0000
1.1000
10.7814
India
1,001,696,269.0000
0.9000
9.0153
United States
220,498,172.0000
1.7000
3.7485
Brazil
147,123,684.0000
1.1000
1.6184
Japan
72,499,552.0000
2.2000
1.5950
Indonesia
194,890,399.0000
0.7000
1.3642
South Korea
35,902,741.0000
3.5000
1.2566
Mexico
88,915,724.0000
1.3000
1.1559
Vietnam
68,730,842.0000
1.4000
0.9622
Türkiye
58,746,219.0000
1.5000
0.8812
Pakistan
151,585,194.0000
0.5000
0.7579
France
42,079,685.0000
1.8000
0.7574
United Kingdom
44,007,878.0000
1.5000
0.6601
Germany
52,107,640.0000
1.2000
0.6253
Saudi Arabia
27,109,801.0000
2.0000
0.5422
Egypt
74,774,809.0000
0.7000
0.5234
Colombia
37,294,820.0000
1.4000
0.5221
Thailand
49,824,787.0000
1.0000
0.4982
Philippines
78,407,418.0000
0.6000
0.4704
Argentina
30,422,322.0000
1.5000
0.4563
Italy
37,261,028.0000
1.2000
0.4471
Nigeria
133,991,180.0000
0.3000
0.4020
Russia
93,543,032.0000
0.4000
0.3742
South Africa
43,634,244.0000
0.8000
0.3491
Canada
26,973,329.0000
1.2000
0.3237
Ethiopia
78,471,564.0000
0.4000
0.3139
Poland
23,605,260.0000
1.3000
0.3069
United Arab Emirates
9,467,296.0000
3.2000
0.3030
Spain
32,445,472.0000
0.9000
0.2920
Bangladesh
115,401,404.0000
0.2000
0.2308
Congo (DRC)
57,506,566.0000
0.4000
0.2300
Australia
17,736,013.0000
1.2000
0.2128
Peru
23,138,487.0000
0.9000
0.2082
Malaysia
25,421,096.0000
0.8000
0.2034
Venezuela
18,554,732.0000
1.0000
0.1855
Algeria
30,011,643.0000
0.6000
0.1801
Chile
13,676,187.0000
1.3000
0.1778
Netherlands
11,611,748.0000
1.4000
0.1626
Tanzania
38,530,560.0000
0.4000
0.1541
Myanmar
37,517,374.0000
0.4000
0.1501
Iraq
28,458,766.0000
0.5000
0.1423
Kazakhstan
12,893,926.0000
1.0000
0.1289
Iran
64,135,210.0000
0.2000
0.1283
Sudan
29,137,268.0000
0.4000
0.1165
Jordan
7,494,228.0000
1.5000
0.1124
Uganda
28,087,155.0000
0.4000
0.1123
Cote D'Ivoire
18,609,973.0000
0.6000
0.1117
Guatemala
11,962,255.0000
0.9000
0.1077
Angola
20,693,794.0000
0.5000
0.1035
Ecuador
12,346,520.0000
0.8000
0.0988
Belgium
7,563,359.0000
1.3000
0.0983
Mozambique
18,919,550.0000
0.5000
0.0946
Madagascar
18,855,173.0000
0.5000
0.0943
Uzbekistan
23,210,348.0000
0.4000
0.0928
Sweden
6,610,247.0000
1.2000
0.0793
Austria
5,963,025.0000
1.3000
0.0775
Nepal
19,347,692.0000
0.4000
0.0774
El Salvador
4,284,402.0000
1.8000
0.0771
Morocco
25,498,901.0000
0.3000
0.0765
Switzerland
5,881,391.0000
1.3000
0.0765
Czech Republic
6,938,669.0000
1.1000
0.0763
Dominican Republic
7,558,944.0000
1.0000
0.0756
Azerbaijan
7,135,101.0000
1.0000
0.0714
Kenya
34,934,452.0000
0.2000
0.0699
Hungary
6,135,748.0000
1.1000
0.0675
Portugal
6,736,123.0000
1.0000
0.0674
Cameroon
16,751,853.0000
0.4000
0.0670
Ghana
21,312,986.0000
0.3000
0.0639
Malawi
12,726,492.0000
0.5000
0.0636
Romania
12,231,235.0000
0.5000
0.0612
Kuwait
3,836,386.0000
1.5000
0.0575
Senegal
11,096,220.0000
0.5000
0.0555
Ireland
3,603,008.0000
1.5000
0.0540
Serbia
4,105,768.0000
1.2000
0.0493
Bolivia
8,155,505.0000
0.6000
0.0489
Afghanistan
24,120,179.0000
0.2000
0.0482
Cambodia
11,445,126.0000
0.4000
0.0458
Greece
6,534,624.0000
0.7000
0.0457
Denmark
3,804,949.0000
1.2000
0.0457
Oman
4,010,720.0000
1.1000
0.0441
Chad
10,905,005.0000
0.4000
0.0436
Sri Lanka
14,320,772.0000
0.3000
0.0430
Niger
14,281,544.0000
0.3000
0.0428
Costa Rica
3,552,415.0000
1.2000
0.0426
Qatar
2,475,822.0000
1.7000
0.0421
Belarus
5,980,999.0000
0.7000
0.0419
Bulgaria
4,076,441.0000
1.0000
0.0408
Singapore
4,528,368.0000
0.9000
0.0408
Burkina Faso
13,516,880.0000
0.3000
0.0406
Somalia
9,990,238.0000
0.4000
0.0400
Mali
13,063,829.0000
0.3000
0.0392
Finland
3,470,751.0000
1.1000
0.0382
Zimbabwe
9,518,709.0000
0.4000
0.0381
New Zealand
3,435,132.0000
1.1000
0.0378
Israel
6,088,301.0000
0.6000
0.0365
Honduras
7,175,291.0000
0.5000
0.0359
Slovakia
3,540,288.0000
1.0000
0.0354
Rwanda
8,589,557.0000
0.4000
0.0344
Tunisia
8,211,984.0000
0.4000
0.0328
Nicaragua
4,624,236.0000
0.7000
0.0324
Laos
5,145,215.0000
0.6000
0.0309
Burundi
7,694,426.0000
0.4000
0.0308
Haiti
7,670,052.0000
0.4000
0.0307
Norway
3,646,833.0000
0.8000
0.0292
South Sudan
7,183,876.0000
0.4000
0.0287
Georgia
2,500,022.0000
1.1000
0.0275
Panama
3,008,610.0000
0.9000
0.0271
Mongolia
2,248,767.0000
1.2000
0.0270
Croatia
2,429,912.0000
1.1000
0.0267
Ukraine
26,246,998.0000
0.1000
0.0262
Guinea
8,452,616.0000
0.3000
0.0254
Libya
5,068,925.0000
0.5000
0.0253
Zambia
12,497,248.0000
0.2000
0.0250
Benin
8,234,974.0000
0.3000
0.0247
Uruguay
2,227,837.0000
1.1000
0.0245
Kyrgyzstan
4,561,968.0000
0.5000
0.0228
Bosnia And Herzegovina
2,018,817.0000
1.1000
0.0222
Jamaica
2,074,923.0000
1.0000
0.0207
Paraguay
4,552,204.0000
0.4000
0.0182
Sierra Leone
5,209,467.0000
0.3000
0.0156
Lithuania
1,871,141.0000
0.8000
0.0150
Lebanon
3,741,835.0000
0.4000
0.0150
Slovenia
1,349,054.0000
1.1000
0.0148
Congo
3,695,842.0000
0.4000
0.0148
Togo
4,819,719.0000
0.3000
0.0145
Tajikistan
6,479,060.0000
0.2000
0.0130
Central African Republic
2,708,090.0000
0.4000
0.0108
Gabon
1,546,207.0000
0.7000
0.0108
Albania
1,545,359.0000
0.7000
0.0108
Liberia
3,300,298.0000
0.3000
0.0099
Turkmenistan
4,874,057.0000
0.2000
0.0097
Moldova
1,501,484.0000
0.6000
0.0090
Mauritania
2,889,731.0000
0.3000
0.0087
Eritrea
2,097,521.0000
0.4000
0.0084
Cuba
7,430,870.0000
0.1000
0.0074
Armenia
2,054,803.0000
0.3000
0.0062
Namibia
1,841,967.0000
0.3000
0.0055
Guyana
535,750.0000
1.0000
0.0054
Suriname
425,317.0000
1.0000
0.0043
Guinea-Bissau
1,314,563.0000
0.3000
0.0039
Botswana
1,638,744.0000
0.2000
0.0033
Gambia
1,611,183.0000
0.1000
0.0016
Lesotho
1,465,674.0000
0.1000
0.0015
Papua New Guinea
6,809,239.0000
-0.1000
-0.0068
Syria
17,093,970.0000
-0.1000
-0.0171
China contributes about 10.8 million additional user equivalents and India about 9.0 million between March and June. Together they account for roughly 41% of the 48.1 million increase across the 145 economies with a usable population denominator. The United States adds about 3.7 million. These calculations describe the scale implied by the published rates.
Taiwan and French Guiana are excluded from that calculation because the selected World Bank extract lacks compatible denominators. The matched sample's weighted June rate is 18.7%, close to but distinct from Microsoft's published world figure of 18.8%. Coverage, denominator vintage and rounding prevent exact reconciliation. The matched sample is not relabeled as the world.
China needs a qualification beside every count. Microsoft announced that its next report would expand the tracked tool set and expects a larger revision there. The current figure can underrepresent use outside that set. A later increase must be separated, where possible, into improved coverage and changed behavior before being described as acceleration.
05
Call another witness
The source changes the picture.
Country matching is not measurement harmonization. The disagreement belongs in the evidence, not in a footnote.
Witness A / Microsoft
Telemetry-derived estimate
Ages 15–64 · tracked tools · H1 2025
≠
Witness B / Eurostat
Household survey
Ages 16–74 · previous-three-month use · 2025
Follow the specimen / Germany
Microsoft · H1 202526.5%
Telemetry-derived adoption People aged 15–64
≠
Eurostat · 2025 survey32.25%
Use in the previous three months People aged 16–74
The published estimates differ by 5.75 percentage points (Eurostat minus Microsoft). Different populations and reference windows; this is not a validation score.
Eurostat's 2025 household survey asks whether individuals aged 16–74 used generative AI tools in the previous three months. Microsoft normalizes its telemetry estimate to ages 15–64. Survey fieldwork is usually in the second quarter, so the closest available Microsoft comparison here is H1 2025, associated with June. It is still not an exact match of ages, months or tool definitions.
Across 28 matched European economies, the country rank correlation is 0.65. That suggests a shared broad pattern alongside substantial disagreement. Denmark is 48.4% in the survey versus 26.6% in Microsoft's H1 column; Finland is 46.3% versus 25.6%. Germany is 32.25% versus 26.5%, while France is 37.46% versus 40.9%. The exceptions are too consequential to hide behind the correlation.
Exhibit 09 / Where do survey and telemetry estimates disagree?
Another source. Another picture.
28 paired European economies · Eurostat 2025 / Microsoft H1 2025
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
The pairwise mean is only a plotting coordinate, not a blended index. Zero means equal printed values, not identical estimands. No limits of agreement are inferred.
Source & boundary Eurostat isoc_ai_iaiu, I_IUAI, PC_IND, IND_TOTAL, 2025; Microsoft H1 2025. The same 28 individual economies enter each pair. EU and euro-area aggregates are excluded. This comparison remains in 2025 regardless of the instrument's selected change interval. No harmonized estimand, blended adoption index or agreement interval is claimed.
Another source. Another picture.. 28 paired European economies · Eurostat 2025 / Microsoft H1 2025
Economy
Microsoft H1 2025, ages 15–64 (%)
Eurostat 2025, ages 16–74 (%)
Pairwise mean (%)
Survey minus telemetry (pp)
Norway
45.3000
56.3200
50.8100
11.0200
Ireland
41.7000
44.9300
43.3150
3.2300
France
40.9000
37.4600
39.1800
-3.4400
Spain
39.7000
37.8800
38.7900
-1.8200
Netherlands
36.3000
44.7000
40.5000
8.4000
Belgium
33.5000
42.0100
37.7550
8.5100
Switzerland
32.4000
47.0200
39.7100
14.6200
Sweden
31.2000
42.0100
36.6050
10.8100
Austria
29.1000
39.4200
34.2600
10.3200
Hungary
27.9000
29.5600
28.7300
1.6600
Denmark
26.6000
48.4400
37.5200
21.8400
Poland
26.4000
22.6800
24.5400
-3.7200
Italy
25.8000
19.8600
22.8300
-5.9400
Czech Republic
26.0000
35.3500
30.6750
9.3500
Bulgaria
25.4000
22.5000
23.9500
-2.9000
Finland
25.6000
46.2700
35.9350
20.6700
Slovenia
24.6000
37.5600
31.0800
12.9600
Portugal
22.4000
38.7000
30.5500
16.3000
Slovakia
22.1000
30.7900
26.4450
8.6900
Croatia
21.8000
27.5200
24.6600
5.7200
Lithuania
21.0000
36.8900
28.9450
15.8900
Serbia
19.7000
18.6400
19.1700
-1.0600
Bosnia And Herzegovina
18.2000
20.2600
19.2300
2.0600
Greece
17.7000
44.0900
30.8950
26.3900
Albania
15.8000
27.2800
21.5400
11.4800
Romania
15.3000
17.7600
16.5300
2.4600
Türkiye
13.4000
17.1900
15.2950
3.7900
Germany
26.5000
32.2500
29.3750
5.7500
The almost identical German numbers of 32.25% in Eurostat and 32.3% in Microsoft's latest release are particularly tempting. They refer to different years as well as different populations and reference windows. Treating them as independent confirmation of one precise current adoption rate would be a mistake.
The survey also reveals differences that a country average conceals. Age and purpose matter. The age comparison keeps the denominator as all individuals within each age group. Work and private-use shares must not be added together because respondents can report both. Education breakdowns remain in the research data.
Exhibit 10 / How do age and purpose divide reported use within a country?
The country average leaves people out of view.
Germany · Eurostat 2025 · % of all individuals within each age group
Inspect a mark by pointer or touch. With the chart focused, use arrow keys; Enter follows an economy.
Any, work and private use have overlapping respondents. They cannot be added together. The survey covers ages 16–74, not Microsoft’s 15–64 denominator.
Source & boundary Eurostat isoc_ai_iaiu, 2025; I_IUAI (all purposes), I_IUAIWP (work) and I_IUAIPR (private use), PC_IND. All people within each age group form the denominator. Purpose categories overlap. Source flags are retained in the exact table. EU27 context is explicitly labeled when a selected economy has no survey breakdown.
The country average leaves people out of view.. Germany · Eurostat 2025 · % of all individuals within each age group
Age
All purposes (%)
Work (%)
Private (%)
Flags
16–24
52.6500
20.4800
43.1800
None
25–34
53.1700
28.0300
46.2000
None
35–44
41.7100
22.9500
35.8700
None
45–54
27.7700
14.3400
22.6600
None
55–64
17.1600
8.4100
13.9600
None
65–74
7.5000
1.9600
6.5400
None
Survey recall, response patterns and sampling differ from the selection and scaling problems in telemetry. A discrepancy cannot by itself tell us which source is closer to the desired target. A useful next step is a harmonized comparison using the same age bands, reference months and tool definition, with country-level uncertainty and coverage information.
06
What survives scrutiny
More reach is established. Catch-up needs a definition.
Take the evidence with its conditions attached.
The convenient claim does not survive in its general form. Adoption increased almost everywhere in this release, but countries with higher initial use generally added more percentage points. Relative growth supplies a weaker and weighting-sensitive account of catch-up, while the slight North–South ratio narrowing is not robust to the precision of the published inputs.
For Germany, the supported conclusion is narrower: adoption sits below the covered-EU benchmark in Microsoft's measure, with a similar recent gain. A claim of structural national underperformance depends on model weights and is challenged by the independent survey comparison.
For policymakers and employers, the next measurement question is whether access becomes sustained, useful practice across ages, education levels and types of work. User shares help establish reach. Establishing economic benefit requires separate evidence on tasks, quality, costs and outcomes. A country can move up an adoption ranking without moving up a productivity ranking.
Take the finding with youDEU
Germany, through this lens.
Germany: 32.3% estimated adoption in June 2026. Rank 23.5 of 147 eligible economies, with average ranks for ties.
Comparison economies
France, Netherlands, Poland, United States, China
Evidence boundary
Estimated reach, not intensity, individual transitions or economic impact. Model deviations are not causal grades.
The link uses the current page address and preserves the country, peers, measurement, period, view and theme.
Open the record
How the evidence was built.
One frozen vintage, reproducible transformations and visible limits. This is an independent empirical investigation, not a causal evaluation or an official Microsoft publication.
Definitions, periods and construction
Microsoft estimates AI users as a share of the population aged 15–64. March and June 2026 are the primary compatible endpoints. H1 2025 is associated with June 2025; its comparison with June 2026 spans 12 months. H2 remains a reporting label and is never assigned a fabricated date. Underlying internal aggregation details are not fully public.
The estimates adjust telemetry participation, device access and mobile use. Expansion of the tracked tool set can produce a coverage break. Source estimates have no published country-specific uncertainty intervals in this package.
Cleaning, missingness and dependence
The CSV contains 147 unique economy rows. A known Türkiye encoding byte is repaired only after the original bytes are preserved. Regional aggregates stay separate. Thirty-six rows share complete four-observation paths; there are 117 distinct paths. Equality flags potential dependence rather than proving a complete pooling mechanism.
The frozen CSV and report appendix differ in Q1 for Algeria, Gambia, Ukraine and Cambodia. All changes here consistently use the CSV vintage. Missing data are never replaced by zero.
Four measures and global tied ranks
For percentage shares a₀ and a₁: point gain = a₁ − a₀; relative growth = 100(a₁ − a₀)/a₀; nonuser-relative net gain = 100(a₁ − a₀)/(100 − a₀). Relative growth is undefined at a₀ = 0 and the nonuser-relative measure at a₀ = 100. Negative changes remain negative.
All rank columns use the same all-metrics-eligible sample. Descending ties receive their average rank at a 1e−9 numerical tolerance. Adding or removing peers never recomputes the ranking universe.
Weighting, regions and rounding
Unweighted country means answer a different question from working-age-population-weighted means. The supplied North–South aggregates are source-published classifications; membership and original weights are not supplied, so a bottom-up reconstruction is not claimed.
The absolute gap rises from 12.1 to 12.6 points. The tiny ratio decline is not robust to ±0.05-point rounding ranges for each printed input. These are arithmetic precision ranges, not confidence intervals.
Fractional-logit model and sensitivities
The conditional mean is logistic(b₀ + b₁ log(GDP PPP / 10,000) + b₂ internet share), estimated on 136 complete cases. GDP and internet use refer to 2024. The shaded model-mean interval clusters the covariance by repeated published paths and excludes all source-measure uncertainty.
Alternatives change weighting, covariates, repeated-path inclusion, influential economies and the functional form. The residual exhibit evaluates frozen coefficients; it does not simulate policy or estimate new causal effects. The research files retain complete-case exclusions and leave-one-out diagnostics.
Population accounting and EU coverage
User equivalents use the same 2025 working-age population at both 2026 endpoints: ΔU = P₂₀₂₅ × Δa. The population contribution is therefore zero by construction. Compatible 2026 endpoint populations are unavailable.
With valid contemporaneous denominators, the exact symmetric decomposition would be mean(P) × Δa + mean(a) × ΔP. This package does not assign a 2024–2025 population change to March–June 2026. The covered-EU benchmark includes 22 members and 98.6638% of the EU working-age denominator.
Microsoft · March 2026 endpoint, with comparisons to June 2025 · Publisher report linked for verification; not redistributed
Research cutoff: 23 September 2026. Frozen Microsoft, World Bank, Eurostat and Natural Earth evidence. Editable analysis, methods, claim ledger and exact values accompany the source package. No live service, generated observation or forecast is required to read the story.