The Global A.I. Divide
Where A.I. Data Centers Are Located
Only 32 countries, primarily in the Northern Hemisphere, host A.I.-specialized data centers. The distribution is as follows:
- European Union: 28
- China: 22 (excluding Hong Kong and Taiwan)
- Other Europe: 8
- United States: 26
- Other Asia: 25
These data centers represent the backbone of A.I. development, equipped with vast arrays of microchips and computing hardware necessary for large-scale machine learning tasks.
The global landscape is sharply divided. While the U.S., China, and the European Union control over half of the world’s high-performance A.I. data centers, continents like Africa and South America have very few or none. India has at least five, Japan four, and over 150 countries have no such facilities at all.
Why This Divide Matters
A.I. data centers require massive investment—not only in hardware but also in infrastructure such as stable electricity and water supply. These hubs consume vast amounts of power and are expensive to build and maintain, which limits their presence to wealthier nations or regions with strong tech sectors.
This divide impacts more than economics. The most widely used A.I. systems, including chatbots like ChatGPT, perform best in English and Chinese, languages dominant in countries with concentrated compute power. Countries lacking data centers face challenges in scientific research, tech startup growth, and retaining skilled talent.
Access to A.I. computing power is increasingly seen as critical to national sovereignty, with some experts likening compute power to a strategic resource on par with oil.
Who Controls the A.I. Compute Power?
Research from Oxford University shows that American and Chinese companies operate more than 90% of the A.I. data centers used by other organizations. The major cloud providers include:
- United States: Microsoft (93 zones), AWS (84), Google (66)
- China: Alibaba (61), Huawei (44), Tencent (38)
- Europe: OVH (5), Exoscale (1)
Most of the chips inside these data centers come from Nvidia, a U.S.-based manufacturer. The GPUs they produce are essential for training complex A.I. models but are costly and difficult to obtain due to high demand and supply chain challenges.
The Consequences of Limited Access
Countries without local A.I. data centers often rent compute power from overseas providers. This arrangement introduces latency issues, higher costs, and dependence on foreign companies and regulations. For instance, Kenyan start-up Qhala, which focuses on African languages, rents compute capacity abroad, forcing employees to work during off-hours to access faster connections.
In contrast, U.S. tech giants like Amazon, Microsoft, Google, Meta, and OpenAI are investing hundreds of billions into expanding their A.I. infrastructure domestically and internationally. Harvard’s Kempner Institute, for example, has more computing power than all African-owned facilities combined.
Such disparities contribute to brain drain, as top talent in countries with limited compute power often relocate to regions where resources are accessible. This hampers local innovation and scientific progress.
Geopolitical Dynamics
The divide also plays out on a geopolitical stage. The U.S. and China are not just competing for market share but also using A.I. infrastructure as leverage in foreign policy. Trade restrictions limit who can purchase advanced chips, and state-backed financing influences technology adoption in regions like the Middle East and Southeast Asia.
For example, some Gulf countries have agreements to avoid Chinese technology in exchange for access to American A.I. chips. Similarly, both American and Chinese companies are establishing data centers across Asia, strengthening their regional presence.
Efforts to Close the Gap
Several countries and regions are actively investing in their own A.I. infrastructure to reduce dependence on foreign technology. Initiatives include:
- India subsidizing compute capacity and developing A.I. models in native languages
- African governments discussing regional compute hubs
- Brazil pledging $4 billion for A.I. projects
- The European Union planning a €200 billion investment in A.I., including new data centers across member states
Building “sovereign A.I.” resources is viewed as essential for technological independence and economic development. However, establishing these data centers requires overcoming infrastructural challenges and often still depends on cooperation with global tech leaders.
For instance, Cassava, a Zimbabwean tech company, is launching one of Africa’s most advanced A.I. data centers after securing Nvidia chips and investments from Google. Despite this, the facility will meet only a fraction of the continent’s demand.
Conclusion
The global distribution of A.I. data centers reflects a significant divide with practical consequences for scientific research, economic growth, and geopolitical influence. Bridging this gap will require coordinated investment, infrastructure development, and international collaboration.
For researchers and professionals aiming to deepen their knowledge and skills in artificial intelligence, gaining access to training on A.I. infrastructure and applications is critical. Resources such as Complete AI Training offer courses designed to build expertise in this evolving field.
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