AI wealth should be shared because it’s built on global knowledge.
Nearly half of respondents (47%) say the primary reason to share AI-generated wealth globally is that AI is built on shared human knowledge, and its benefits should therefore be shared. A further 33% frame it as compensation for job displacement (Chart 43).
The dominance of the shared knowledge argument is notable, as it frames wealth-sharing as a matter of principle rather than remedy, something people are owed because of what AI is built on, not because of what it might take away. The compensation framing, by contrast, is conditional on harm. That nearly half the sample reaches for the stronger, unconditional argument suggests broad public appetite for treating AI wealth distribution as a right rather than a safety net.
Why should people share in AI wealth?
"Which best explains why people should receive a share of the wealth created by AI?" (n=1,028)
Built on shared knowledge, so benefits should be shared (47%)
Compensation for job losses from automation (33%)
Payment for personal data used to train AI (13%)
Maintain economic stability amid disruption (8%)
The "shared knowledge" argument is also the plurality choice in almost every region, income group, and demographic slice, with one clear exception: the Middle East and North Africa, where job displacement compensation leads at 50% (Chart 43b). This may reflect the particular intensity of employment anxiety in the region. MENA respondents show the strongest preference of any region for guaranteed jobs over guaranteed income (61% vs. 28%; Chart 20b), and youth unemployment across the Middle East and North Africa has historically ranked among the highest in the world. In a context where secure employment is already scarce, the prospect of AI displacing jobs further may feel less like a theoretical risk and more like an acceleration of an existing crisis.
Why should people share in AI wealth?
Percentage selecting each justification — by region
Built on shared knowledge, so benefits should be shared
Overall
1,015
47%
Western Europe
136
38%
Sub-Saharan Africa
142
55%
South Asia
219
48%
Southeast Asia
81
47%
Middle East & N. Africa
74
28%
Eastern Europe
48
42%
East Asia
96
54%
Latin America
83
49%
North America
124
49%
Oceania
12
42%
0
10
20
30
40
50
60
Compensation for job losses from automation
Overall
1,015
33%
Western Europe
136
43%
Sub-Saharan Africa
142
28%
South Asia
219
30%
Southeast Asia
81
31%
Middle East & N. Africa
74
50%
Eastern Europe
48
35%
East Asia
96
23%
Latin America
83
31%
North America
124
30%
Oceania
12
33%
0
10
20
30
40
50
60
Payment for personal data used to train AI
Overall
1,015
13%
Western Europe
136
9%
Sub-Saharan Africa
142
11%
South Asia
219
16%
Southeast Asia
81
15%
Middle East & N. Africa
74
16%
Eastern Europe
48
12%
East Asia
96
14%
Latin America
83
13%
North America
124
11%
Oceania
12
8%
0
10
20
30
40
50
60
Maintain economic stability amid disruption
Overall
1,015
8%
Western Europe
136
11%
Sub-Saharan Africa
142
6%
South Asia
219
6%
Southeast Asia
81
7%
Middle East & N. Africa
74
5%
Eastern Europe
48
10%
East Asia
96
9%
Latin America
83
6%
North America
124
10%
Oceania
12
17%
0
10
20
30
40
50
60