2026-01-24 03:31:52
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Iran’s international trade has long been shaped by geopolitical pressures and economic sanctions, limiting its reach to a narrow group of countries willing—or able—to do business. The latest data from the World Trade Organization’s Trade Data Monitor gives a snapshot of which nations are still major trading partners for Iran in 2024.
The visualization above, created by Aneesh Anand, ranks Iran’s top export destinations by goods value. Here’s the export data:
| Rank | Export Destination | Value of goods exported (USD billions, 2024) | Region |
|---|---|---|---|
| 1 |
 China |
14.57 | Asia |
| 2 |
 Iraq |
11.69 | Middle East |
| 3 |
 UAE |
7.16 | Middle East |
| 4 |
 Turkey |
6.09 | Middle East |
| 5 |
 Afghanistan |
2.29 | Asia |
| 6 |
 Pakistan |
2.27 | Asia |
| 7 |
 India |
1.96 | Asia |
| 8 |
 Oman |
1.56 | Middle East |
| 9 |
 Russia |
1.05 | Europe |
| 10 |
 Azerbaijan |
0.72 | Asia |
| -- |
 Rest of the world |
6.58 | Rest |
| -- |
 Global Total |
56.0 | World |
China alone accounts for more than $14.5 billion of Iran’s $56 billion in total exports—about 26% of the total. Iraq and the UAE follow closely, with Türkiye and Afghanistan rounding out the top five. Meanwhile, exports to Europe remain extremely limited, with Russia being the only European nation in the top 10.
Iran’s export market is highly regional. Eight of its top 10 destinations are in Asia or the Middle East, reflecting both geographic proximity and limited global access due to sanctions. This includes smaller but geopolitically significant trade flows to Pakistan, India, and Azerbaijan.
Iran’s heavy economic reliance on oil shapes its export patterns and trade relationships. As we covered in Iran’s Oil Exports, China receives the lion’s share of Iran’s energy exports, despite U.S. sanctions and diplomatic tensions.
In January 2026, the U.S. announced 25% tariffs on countries that continue significant trade with Iran. These new measures target key players like Iraq, the UAE, and Türkiye, countries that collectively import nearly half of Iran’s goods. The sanctions aim to further isolate Iran economically, but they could also strain U.S. relations with regional allies.
Meanwhile, Iran’s economy is under growing domestic pressure. The country faces a toxic mix of inflation, currency devaluation, and limited investment, making exports one of the few lifelines for hard currency.
See how Iran’s oil exports are flowing to key buyers like China despite sanctions, only on the Voronoi app.
2026-01-24 01:35:38
Economic health, trade dynamics, and financial stability (among other factors) remain critical determinants of currency performance.
This graphic, created in partnership with OANDA, illustrates the 2025 performance of the most-traded currencies by region, offering an overall health check on some of the world’s most influential currencies.
The most-traded currency across North America, and the world, is the U.S. dollar (USD). According to the BIS, the USD has an average daily trading volume of $8.56 trillion.
In Europe, the euro (EUR) takes the top spot, with an average daily trading volume of $2.77 trillion.
Other leading regional currencies include the Japanese yen (East Asia & Pacific), Mexican peso (Latin America & Caribbean), Indian rupee (South Asia), UAE dirham (Middle East & North Africa), and South African rand (Sub-Saharan Africa).
After a challenging prior year, most major currencies rebounded in 2025, posting gains against the U.S. dollar.
The strongest performers were the Mexican peso, which surged 15.3%, and the South African rand, up 13.5%. The euro also staged a notable comeback, climbing 13.0% as easing inflation pressures and improving growth expectations supported the currency.
| Region | Currency | 2025 (% change) |
|---|---|---|
| North America | U.S. dollar (USD) | -9.1% |
| Middle East & North Africa | UAE dirham (AED) | 0.0% |
| South Asia | Indian rupee (INR) | -4.8% |
| Sub-Saharan Africa | South African rand (ZAR) | 13.5% |
| Europe & Central Asia | Euro (EUR) | 13.0% |
| East Asia & Pacific | Japanese yen (JPY) | 0.1% |
| Latin America & Caribbean | Mexican peso (MXN) | 15.3% |
Meanwhile, the Japanese yen finished the year essentially flat, gaining 0.1%, reflecting continued monetary policy divergence with the U.S.
In contrast to broad global gains, the U.S. dollar weakened sharply in 2025, falling 9.1% as slowing economic momentum and shifting interest rate expectations weighed on the currency.
The Indian rupee also declined, slipping 4.8% against the USD amid persistent structural challenges and capital flow pressures. The UAE dirham, which remains pegged to the U.S. dollar, finished the year flat, mirroring USD performance.
The reversal in currency performance highlights how quickly conditions can change in global FX markets. As capital rotated away from the U.S. dollar in 2025, several previously underperforming currencies staged strong recoveries.
For investors looking ahead, opportunities may emerge in tracking relative economic momentum, central bank policy shifts, and commodity-linked currencies. This is particularly the case in regions that showed resilience or renewed strength over the year.
OANDA can help you trade smarter with a wide range of global currencies, including the USD.
Note: Past performance is not indicative of future results.
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2026-01-23 23:21:07
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In 2025, an estimated five billion passengers took to the skies, with airline revenues projected to surpass $1 trillion.
Overall, the Asia-Pacific region was a leading driver of growth, with passenger travel up 10% over the year, largely driven by China and India. In 2026, passenger volume in the region is forecast to increase by another 7%.
This graphic shows the busiest domestic flights by region in 2025, based on data from OAG.
Here are the busiest domestic flight routes by scheduled seat capacity in each region:
| Region | Busiest Domestic Airline Route 2025 | Airport Codes | Number of Seats |
|---|---|---|---|
| Asia-Pacific | Jeju - Seoul Gimpo | CJU – GMP | 14.4M |
| Middle East | Jeddah - Riyadh | JED – RUH | 9.8M |
| Latin America | Bogotá - Medellin | BOG – MDE | 6.2M |
| Africa | Cape Town - Johannesburg | CPT – JNB | 5.5M |
| North America | Vancouver - Toronto | YVR – YYZ | 3.7M |
| Europe | Barcelona - Palma | BCN – PMI | 3.0M |
In 2025, scheduled seat capacity between Jeju and Seoul totaled 14.4 million, and over much of the past decade, it has been the busiest route globally.
Around 200 flights operate daily between Jeju International and Seoul Gimpo, the highest frequency of any route worldwide. Often dubbed the “Hawaii of South Korea,” Jeju continues to grow as a major tourist destination.
In the Middle East, Jeddah to Riyadh saw 9.8 million scheduled seats, rising 13% over the year. Jeddah houses Saudi Arabia’s busiest port, while the capital is a key hub for economic activity.
As we can see, Bogotá to Medellin in Colombia ranks highest in Latin America, with 6.2 million scheduled seats. Meanwhile, Cape Town to Johannesburg saw 5.5 million seats, the busiest in Africa.
When it comes to the busiest domestic flights overall in 2025, the Asia-Pacific region holds nine of the top 10 flights:
| Airline Route | Region | Number of Seats | Change vs 2024 |
|---|---|---|---|
| Jeju International - Seoul Gimpo | Asia-Pacific | 14.4M | 1% |
| Sapporo New Chitose - Tokyo Haneda | Asia-Pacific | 12.1M | 1% |
| Fukuoka - Tokyo Haneda | Asia-Pacific | 11.5M | 1% |
| Hanoi - Ho Chi Minh City | Asia-Pacific | 11.1M | 4% |
| Jeddah - Riyadh | Middle East | 9.8M | 13% |
| Melbourne - Sydney | Asia-Pacific | 9.0M | -3% |
| Tokyo Haneda - Okinawa Naha | Asia-Pacific | 8.1M | 0% |
| Mumbai - Delhi | Asia-Pacific | 7.6M | -4% |
| Beijing - Shanghai Hongqiao | Asia-Pacific | 7.5M | -3% |
| Shanghai Hongqiao - Shenzhen | Asia-Pacific | 7.1M | 5% |
High flight frequency and close proximity between major cities have helped drive the region’s airline industry. At the same time, rapid economic growth in countries such as India and Vietnam is fueling increased air traffic as incomes rise.
To learn more about this topic, check out this graphic on passport power around the world.
2026-01-23 21:12:48
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Who are the richest Americans of all time when wealth is adjusted for inflation?
While modern tech leaders dominate today’s billionaire rankings, America’s industrial past produced fortunes that still rival—or exceed—those seen today.
This graphic ranks American billionaires throughout history by their peak net worth, adjusted to 2025 dollars. The data for this visualization comes from Business Insider, drawing on Forbes’ 2007 list of history’s wealthiest individuals. Historical fortunes were calculated at peak wealth and adjusted to 2025 dollars using the U.S. Bureau of Labor Statistics inflation calculator. Net worth figures for living individuals reflect Forbes estimates as of December 29, 2025.
Elon Musk ranks first on the list, with an estimated net worth of $744.6 billion. His fortune reflects the explosive growth of Tesla and SpaceX.
| Rank | Name | Lifespan | Net Worth (2025 dollars) | Source of Wealth |
|---|---|---|---|---|
| 1 | Elon Musk | 1971– | $744.6B | CEO of Tesla and SpaceX |
| 2 | John D. Rockefeller | 1839–1937 | $499.0B | Founder of Standard Oil |
| 3 | Andrew Carnegie | 1835–1919 | $459.6B | Founder of Carnegie Steel |
| 4 | Cornelius Vanderbilt | 1794–1877 | $275.3B | Shipping & Railroads |
| 5 | Larry Page | 1973– | $257.6B | Co-founder of Google |
| 6 | Larry Ellison | 1944– | $249.4B | Co-founder of Oracle |
| 7 | Jeff Bezos | 1964– | $244.0B | Founder of Amazon |
| 8 | Sergey Brin | 1973– | $237.7B | Co-founder of Google |
| 9 | Mark Zuckerberg | 1984– | $227.4B | Co-founder of Facebook |
| 10 | John Jacob Astor | 1763–1848 | $180.0B | Fur Trade & Real Estate |
| 11 | Jensen Huang | 1963– | $165.4B | CEO of Nvidia |
| 12 | Stephen Girard | 1750–1831 | $156.3B | Banking & Trade |
| 13 | Steve Ballmer | 1956– | $148.3B | Former CEO of Microsoft |
| 14 | Warren Buffett | 1930– | $147.4B | Chairman of Berkshire Hathaway |
| 15 | Michael Dell | 1965– | $141.2B | CEO of Dell Technologies |
| 16 | Richard B. Mellon | 1870–1933 | $134.5B | Heir to the Mellon banking dynasty |
| 17 | Alexander Turney Stewart | 1803–1876 | $132.1B | Founder of A.T. Stewart & Co. |
| 18 | Rob Walton | 1944– | $131.2B | Walmart Heir |
| 19 | Jim Walton | 1948– | $128.5B | Walmart Heir |
| 20 | Alice Walton | 1949– | $119.7B | Walmart Heir |
| 21 | Frederick Weyerhaeuser | 1834–1914 | $118.0B | Timber, Founder of Weyerhaeuser |
| 22 | Michael Bloomberg | 1942– | $109.4B | Founder of Bloomberg LP |
| 23 | Bill Gates | 1955– | $104.0B | Co-founder of Microsoft |
| 24 | Marshall Field | 1834–1906 | $98.2B | Founder of Marshall Field & Company |
| 25 | Sam Walton | 1918–1992 | $95.8B | Founder of Walmart |
| 26 | Jay Gould | 1836–1892 | $95.1B | Railroad |
| 27 | Henry Ford | 1863–1947 | $88.8B | Founder of Ford Motor Company |
| 28 | Andrew W. Mellon | 1855–1937 | $82.5B | Heir to the Mellon banking dynasty |
Despite the rise of technology billionaires, industrial-age magnates remain firmly entrenched near the top. John D. Rockefeller’s Standard Oil empire translates to nearly $500 billion today, while Andrew Carnegie’s steel fortune exceeds $450 billion when adjusted for inflation.
These fortunes were built during periods of rapid industrialization, when monopolistic advantages and limited regulation allowed wealth to compound at extraordinary rates.
Beyond founders and innovators, the ranking also highlights multigenerational wealth.
Members of the Walton family—Rob, Jim, and Alice Walton—each appear individually, reflecting Walmart’s enduring scale. Banking and industrial dynasties such as the Mellons and figures like Stephen Girard and John Jacob Astor illustrate how early control over finance, trade, and land helped shape America’s first great fortunes.
If you enjoyed today’s post, check out What the Top 1% Richest Americans Pay in Taxes Across the U.S. on Voronoi, the new app from Visual Capitalist.
2026-01-23 02:05:36
Data centers have quietly become some of the most important infrastructure in the U.S. economy. As artificial intelligence (AI) workloads explode and cloud services proliferate, builders are racing to add capacity at record speed.
This graphic, in partnership with BHP, shows U.S. data center construction spending from 2014 to 2025 using data from the U.S. Census Bureau.
Here is a table showing monthly U.S. data center construction since 2014, seasonally adjusted at an annual rate.
| Month | Billions of Current U.S. Dollars (Seasonally Adjusted Annual Rate) |
|---|---|
| Jul-25 | 41.19 |
| Jun-25 | 41.10 |
| May-25 | 40.07 |
| Apr-25 | 39.60 |
| Mar-25 | 36.88 |
| Feb-25 | 37.13 |
| Jan-25 | 35.77 |
| Dec-24 | 34.98 |
| Nov-24 | 35.62 |
| Oct-24 | 35.86 |
| Sep-24 | 32.96 |
| Aug-24 | 32.94 |
| Jul-24 | 31.74 |
| Jun-24 | 31.24 |
| May-24 | 29.98 |
| Apr-24 | 28.34 |
| Mar-24 | 27.78 |
| Feb-24 | 26.29 |
| Jan-24 | 24.93 |
| Dec-23 | 24.45 |
| Nov-23 | 23.72 |
| Oct-23 | 23.84 |
| Sep-23 | 21.13 |
| Aug-23 | 20.25 |
| Jul-23 | 19.55 |
| Jun-23 | 18.62 |
| May-23 | 18.11 |
| Apr-23 | 18.16 |
| Mar-23 | 18.10 |
| Feb-23 | 17.87 |
| Jan-23 | 15.85 |
| Dec-22 | 14.35 |
| Nov-22 | 13.77 |
| Oct-22 | 12.88 |
| Sep-22 | 14.47 |
| Aug-22 | 13.02 |
| Jul-22 | 12.71 |
| Jun-22 | 12.15 |
| May-22 | 12.08 |
| Apr-22 | 12.26 |
| Mar-22 | 11.36 |
| Feb-22 | 10.53 |
| Jan-22 | 11.08 |
| Dec-21 | 11.39 |
| Nov-21 | 10.51 |
| Oct-21 | 9.74 |
| Sep-21 | 10.77 |
| Aug-21 | 10.43 |
| Jul-21 | 9.73 |
| Jun-21 | 9.30 |
| May-21 | 9.31 |
| Apr-21 | 9.44 |
| Mar-21 | 10.32 |
| Feb-21 | 9.04 |
| Jan-21 | 9.31 |
| Dec-20 | 9.23 |
| Nov-20 | 9.18 |
| Oct-20 | 9.34 |
| Sep-20 | 10.05 |
| Aug-20 | 9.07 |
| Jul-20 | 8.73 |
| Jun-20 | 8.97 |
| May-20 | 8.70 |
| Apr-20 | 8.95 |
| Mar-20 | 9.49 |
| Feb-20 | 9.68 |
| Jan-20 | 9.46 |
| Dec-19 | 7.94 |
| Nov-19 | 9.57 |
| Oct-19 | 9.16 |
| Sep-19 | 8.08 |
| Aug-19 | 8.82 |
| Jul-19 | 8.82 |
| Jun-19 | 8.88 |
| May-19 | 8.74 |
| Apr-19 | 7.86 |
| Mar-19 | 7.67 |
| Feb-19 | 8.03 |
| Jan-19 | 8.12 |
| Dec-18 | 8.05 |
| Nov-18 | 7.10 |
| Oct-18 | 7.86 |
| Sep-18 | 6.29 |
| Aug-18 | 7.00 |
| Jul-18 | 7.45 |
| Jun-18 | 7.06 |
| May-18 | 6.99 |
| Apr-18 | 6.56 |
| Mar-18 | 6.47 |
| Feb-18 | 6.24 |
| Jan-18 | 6.08 |
| Dec-17 | 5.74 |
| Nov-17 | 5.46 |
| Oct-17 | 4.96 |
| Sep-17 | 4.69 |
| Aug-17 | 4.39 |
| Jul-17 | 4.67 |
| Jun-17 | 4.58 |
| May-17 | 5.03 |
| Apr-17 | 4.23 |
| Mar-17 | 3.99 |
| Feb-17 | 4.22 |
| Jan-17 | 3.99 |
| Dec-16 | 4.46 |
| Nov-16 | 3.96 |
| Oct-16 | 3.70 |
| Sep-16 | 4.22 |
| Aug-16 | 4.58 |
| Jul-16 | 4.25 |
| Jun-16 | 4.56 |
| May-16 | 4.15 |
| Apr-16 | 4.42 |
| Mar-16 | 4.12 |
| Feb-16 | 3.80 |
| Jan-16 | 3.16 |
| Dec-15 | 3.09 |
| Nov-15 | 2.98 |
| Oct-15 | 2.75 |
| Sep-15 | 2.80 |
| Aug-15 | 2.84 |
| Jul-15 | 2.68 |
| Jun-15 | 3.93 |
| May-15 | 3.34 |
| Apr-15 | 2.31 |
| Mar-15 | 2.14 |
| Feb-15 | 1.93 |
| Jan-15 | 1.92 |
| Dec-14 | 1.66 |
| Nov-14 | 1.95 |
| Oct-14 | 1.83 |
| Sep-14 | 1.92 |
| Aug-14 | 1.96 |
| Jul-14 | 1.79 |
| Jun-14 | 1.75 |
| May-14 | 1.81 |
| Apr-14 | 1.80 |
| Mar-14 | 1.68 |
| Feb-14 | 1.74 |
| Jan-14 | 1.64 |
In early 2014, U.S. data center construction ran at an annualized rate of roughly $1.6 billion. By July 2025, that number reached about $41.0 billion, more than 25 times the 2014 level.
Consequently, data centers now compete directly with offices, warehouses, and industrial facilities for land, power, and transmission capacity.
A typical data center uses roughly 27 metric tons of copper per megawatt of capacity for power, cabling, and cooling. Because each new megawatt adds more copper-intensive equipment, incremental AI capacity has a disproportionate impact on metal demand.
As operators add AI servers and dense racks, each data center concentrates more copper in cables, transformers, and cooling infrastructure.
BHP projects that the amount of copper used in data centers globally will increase by around 6x from 2025 to 2050, rising from about 0.5 million tonnes a year to around 3 million tonnes.
That uplift is roughly equivalent to the combined annual output of the world’s four largest copper mines today. As this layer of “digital” demand stacks on top of the broader energy transition, data centers could become one of the fastest-growing sources of structural copper demand.
Looking ahead, the U.S. grid will neeed to keep pace with this construction surge as data centers use more electricity.
Data centers’ share of global electricity demand could rise from about 2% today to roughly 9% by 2050, with some markets already seeing data centers account for around one-fifth of national power use.
Overall, surging U.S. data center investment signals rapid AI infrastructure growth. It also demonstrates the central roles of copper and electricity in enabling the next era of digital services.
Projected electricity usage growth and economic development are driving global copper demand—see how energy use is rising from 2024 to 2035.
Projected electricity usage growth in major economies is driving global copper demand—see how energy use is rising from 2023 to 2035.
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Since 1960, potash demand has outpaced both population growth and crop production.
Copper discoveries are becoming increasingly rare and often found deeper underground.
Copper demand globally is estimated to rise by 70% from 2021 to 2050. What are the main sources of this increase in demand?
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2026-01-22 21:11:04
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In the second half of 2025, 16.1% of the global working-age population used AI, indicating substantial room for further adoption.
At the same time, usage varies widely across countries. Adoption rates average 24.7% in the Global North, while they are 14.1% in the Global South. Key countries stand as clear regional outliers including the UAE and Singapore.
This graphic shows AI adoption by country, based on data from the Global AI Adoption in 2025 report from Microsoft.
For the analysis, Microsoft estimated AI usage across 147 countries worldwide.
Below, we show the share of each country’s working-age population that used AI at least once over H2 2025. Additionally, we show the percentage point (p.p.) change compared to H1 2025, representing the absolute difference between the two periods.
| Rank | Country | Share of Working-Age Population Using AI H2 2025 (%) |
Change vs H1 2025 (p.p.) |
|---|---|---|---|
| 1 |
UAE |
64.0 | 4.6 |
| 2 |
 Singapore |
60.9 | 2.3 |
| 3 |
 Norway |
46.4 | 1.1 |
| 4 |
 Ireland |
44.6 | 2.9 |
| 5 |
 France |
44.0 | 3.1 |
| 6 |
 Spain |
41.8 | 2.1 |
| 7 |
 New Zealand |
40.5 | 2.9 |
| 8 |
 Netherlands |
38.9 | 2.6 |
| 9 |
 UK |
38.9 | 2.5 |
| 10 |
 Qatar |
38.3 | 2.6 |
| 11 |
 Australia |
36.9 | 2.4 |
| 12 |
 Israel |
36.1 | 2.2 |
| 13 |
 Belgium |
36.0 | 2.5 |
| 14 |
 Canada |
35.0 | 1.5 |
| 15 |
 Switzerland |
34.8 | 2.4 |
| 16 |
 Sweden |
33.3 | 2.1 |
| 17 |
 Austria |
31.4 | 2.3 |
| 18 |
 South Korea |
30.7 | 4.8 |
| 19 |
 Hungary |
29.8 | 1.9 |
| 20 |
 Denmark |
28.7 | 2.1 |
| 21 |
 Germany |
28.6 | 2.1 |
| 22 |
 Poland |
28.5 | 2.1 |
| 23 |
 Taiwan |
28.4 | 2 |
| 24 |
 U.S. |
28.3 | 2 |
| 25 |
 Italy |
27.8 | 1.8 |
| 26 |
 Czechia |
27.8 | 2 |
| 27 |
 Bulgaria |
27.3 | 1.9 |
| 28 |
 Finland |
27.3 | 1.7 |
| 29 |
 Jordan |
27.0 | 1.6 |
| 30 |
 Slovenia |
26.5 | 1.4 |
| 31 |
 Costa Rica |
26.5 | 1.9 |
| 32 |
 Saudi Arabia |
26.2 | 2.5 |
| 33 |
 Lebanon |
25.7 | 0.9 |
| 34 |
 Portugal |
24.2 | 1.6 |
| 35 |
Oman |
24.2 | 1.8 |
| 36 |
 Slovakia |
23.8 | 1.7 |
| 37 |
 Croatia |
23.7 | 1.9 |
| 38 |
 Vietnam |
23.5 | 2.3 |
| 39 |
 Dominican Republic |
22.7 | 0.7 |
| 40 |
 Uruguay |
22.5 | 1.6 |
| 41 |
 Lithuania |
22.4 | 1.4 |
| 42 |
 Jamaica |
22.1 | -0.1 |
| 43 |
 Colombia |
22.0 | 1.6 |
| 44 |
 Serbia |
21.5 | 1.2 |
| 45 |
 Panama |
21.5 | 1.8 |
| 46 |
 South Africa |
21.1 | 1.8 |
| 47 |
 Chile |
20.8 | 1.2 |
| 48 |
 Malaysia |
19.7 | 1.4 |
| 49 |
 Argentina |
19.6 | 1.8 |
| 50 |
 Bosnia andHerzegovina |
19.5 | 1.3 |
| 51 |
 Japan |
19.1 | 1.4 |
| 52 |
 Kuwait |
19.1 | 1.4 |
| 53 |
 Greece |
19.1 | 2.4 |
| 54 |
 Philippines |
18.3 | 1.2 |
| 55 |
 Georgia |
18.2 | 0.9 |
| 56 |
 Mexico |
17.8 | 1.1 |
| 57 |
 Ecuador |
17.7 | 0.7 |
| 58 |
 Brazil |
17.1 | 1.5 |
| 59 |
 Moldova |
17.0 | 0.4 |
| 60 |
 Albania |
16.5 | 0.7 |
| 61 |
China |
16.3 | 0.9 |
| 62 |
 El Salvador |
16.2 | 0.9 |
| 63 |
 Romania |
16.2 | 1.6 |
| 64 |
India |
15.7 | 1.5 |
| 65 |
Azerbaijan |
15.5 | 1.3 |
| 66 |
 Guatemala |
14.8 | 1.1 |
| 67 |
 Peru |
14.7 | 1.3 |
| 68 |
Türkiye |
14.6 | 1.2 |
| 69 |
 Mongolia |
14.3 | 1.7 |
| 70 |
 Namibia |
13.8 | 0.8 |
| 71 |
 Libya |
13.7 | 1 |
| 72 |
 Kazakhstan |
13.7 | 1 |
| 73 |
 Botswana |
13.7 | 0.9 |
| 74 |
 Gabon |
13.4 | 1.1 |
| 75 |
 Egypt |
13.4 | 0.9 |
| 76 |
 Honduras |
13.1 | 0.7 |
| 77 |
 Nepal |
13.0 | 0.7 |
| 78 |
 Senegal |
12.9 | 0.5 |
| 79 |
 Indonesia |
12.7 | 1 |
| 80 |
 Tunisia |
12.7 | 0.4 |
| 81 |
 Zambia |
12.3 | 0.6 |
| 82 |
 Algeria |
12 | 0.7 |
| 83 |
 Cote D'Ivoire |
11.7 | 0.9 |
| 84 |
 Bolivia |
11.6 | 0.7 |
| 85 |
Iraq |
11.2 | 0.9 |
| 86 |
 Paraguay |
11.0 | 0.9 |
| 87 |
 Morocco |
10.9 | 0.4 |
| 88 |
 Gambia |
10.9 | 0.3 |
| 89 |
 Thailand |
10.7 | 1.6 |
| 90 |
 Iran |
10.7 | 0.7 |
| 91 |
 Nicaragua |
10.7 | 1.1 |
| 92 |
Pakistan |
10.3 | 0.6 |
| 93 |
 Angola |
9.7 | 0.8 |
| 94 |
 Madagascar |
9.7 | 0.8 |
| 95 |
 Malawi |
9.7 | 0.8 |
| 96 |
 Mozambique |
9.7 | 0.8 |
| 97 |
 Benin |
9.3 | 0.6 |
| 98 |
 Burkina Faso |
9.3 | 0.6 |
| 99 |
 Ghana |
9.3 | 0.6 |
| 100 |
 Guinea |
9.3 | 0.6 |
| 101 |
 Guinea-Bissau |
9.3 | 0.6 |
| 102 |
 Liberia |
9.3 | 0.6 |
| 103 |
 Mali |
9.3 | 0.6 |
| 104 |
 Mauritania |
9.3 | 0.6 |
| 105 |
 Niger |
9.3 | 0.6 |
| 106 |
 Nigeria |
9.3 | 0.6 |
| 107 |
 Sierra Leone |
9.3 | 0.6 |
| 108 |
 Togo |
9.3 | 0.6 |
| 109 |
 Myanmar |
9.1 | 0.3 |
| 110 |
 Lesotho |
9.1 | 0.7 |
| 111 |
 French Guiana |
9.0 | -0.1 |
| 112 |
 Guyana |
9.0 | 0.7 |
| 113 |
 Suriname |
9.0 | 0.7 |
| 114 |
 Venezuela |
9.0 | 0.7 |
| 115 |
 Ukraine |
9.0 | 0.7 |
| 116 |
 Belarus |
8.4 | 0.8 |
| 117 |
 Kyrgyzstan |
8.2 | 0.6 |
| 118 |
 Kenya |
8.1 | 0.3 |
| 119 |
Russia |
8.0 | 0.4 |
| 120 |
 Cameroon |
7.8 | 0.8 |
| 121 |
 Central AfricanRepublic |
7.8 | 0.8 |
| 122 |
 Chad |
7.8 | 0.8 |
| 123 |
 Congo |
7.8 | 0.8 |
| 124 |
 DRC Congo |
7.8 | 0.8 |
| 125 |
 Zimbabwe |
7.6 | 0.5 |
| 126 |
 Haiti |
7.6 | 0.7 |
| 127 |
 Papua New Guinea |
7.3 | 0.1 |
| 128 |
 Bangladesh |
7.1 | 0.4 |
| 129 |
 Syria |
7.1 | 0.6 |
| 130 |
 Burundi |
6.8 | 0.4 |
| 131 |
 Eritrea |
6.8 | 0.4 |
| 132 |
 Ethiopia |
6.8 | 0.4 |
| 133 |
 Somalia |
6.8 | 0.4 |
| 134 |
 South Sudan |
6.8 | 0.4 |
| 135 |
 Sudan |
6.8 | 0.4 |
| 136 |
 Tanzania |
6.8 | 0.4 |
| 137 |
 Uganda |
6.8 | 0.4 |
| 138 |
 Laos |
6.7 | 0.7 |
| 139 |
 Armenia |
6.6 | 0.4 |
| 140 |
 Sri Lanka |
6.6 | 0.4 |
| 141 |
 Uzbekistan |
6.3 | 0.6 |
| 142 |
 Rwanda |
6.3 | 0.3 |
| 143 |
 Cuba |
6.1 | 0.4 |
| 144 |
Afghanistan |
5.6 | 0.5 |
| 145 |
 Tajikistan |
5.6 | 0.5 |
| 146 |
 Turkmenistan |
5.6 | 0.5 |
| 147 |
 Cambodia |
5.1 | 0.5 |
| -- |
 World Average |
16.3 | 1.2 |
With 64.0% of the population using generative AI tools, the UAE not only ranks first globally, but stands as one of the fastest-growing countries in adoption.
Even before ChatGPT launched, AI technology was being used across public services in the UAE. This was supported by early governance frameworks that were established in 2017, as part of its national AI strategy targeting nine key sectors.
Ranking in second is Singapore, where 60.9% of the population uses AI. Like the UAE, Singapore invested early in AI infrastructure and research and development.
In Europe, Norway ranks first along with taking third place globally with a 46.4% adoption rate. It is followed by Ireland (44.6%), and France (44.0%), two countries with robust tech ecosystems.
Meanwhile, adoption rates in the U.S. stood at 28.3%, or 24th overall. Interestingly, although the U.S. develops world-class AI research and is home to some of the world’s largest AI-related firms, trust in AI technology is fairly low.
According to the Edelman Trust Barometer, just 32.0% of the U.S. population trusts AI. In comparison, the figure jumps to 67.0% in the UAE.
At the tail end of the adoption spectrum is Cambodia, where it stands at just 5.1%. While progress is underway, limited investment and infrastructure remain key barriers to wider adoption.
To learn more about this topic, check out this graphic on AI compute hubs by country.
