The Geography of AI Power: Rethinking Global Inequality in the Age of Artificial Intelligence

Views expressed are personal and based on my individual perspectives and experience. These thoughts do not represent the positions or policies of my employer or any affiliated organizations.

During my time at Georgetown University in 2017, pursuing a graduate diploma in data science, a question kept nagging at me: How would the physical infrastructure of AI reshape global power? At the inaugural Unleash program in Denmark that same year, my team explored the implications of data center locations on global AI development. What seemed like a technical infrastructure question then has evolved into something far more profound today - a fundamental reshaping of global power dynamics.

The question isn't merely academic anymore. As AI systems become the backbone of modern society, their physical and computational geography is creating new forms of global inequality, more subtle but perhaps more pervasive than traditional economic divides.

Think about Estonia. This tiny Baltic nation, smaller than many cities, became a digital governance pioneer. But even Estonia, with all its digital prowess, can't independently develop large AI models. Why? The computing power required for a single large language model training run costs millions in hardware and energy - more than many national AI budgets.

The reality of computational geography is stark. The largest AI-capable data centers are clustered in specific regions - Northern Virginia, Silicon Valley, Singapore, Dublin. This isn't random. These locations combine stable power grids, political stability, and massive fiber optic connections. When a country wants to develop AI capabilities, they can't just wish this infrastructure into existence. The global map of computational power tells a story of concentration - a single US tech company often has more AI computing capacity than entire continents.

Consider this scenario: What if tomorrow, the handful of companies controlling AI infrastructure decided to triple their prices? We've seen similar dynamics before - when Oracle raised database licensing costs, many organizations found themselves trapped, having built their entire operations around Oracle's systems. But with AI, the dependency runs deeper. In 2023, when OpenAI briefly went offline, thousands of businesses worldwide discovered just how dependent they'd become on a single American company's AI infrastructure. Government agencies using AI for document processing, businesses using it for customer service - all ground to a halt.

The implications for global development are profound. When Saudi Arabia uses AI systems for urban planning in NEOM, who controls the insights generated from that data? When India's UPI payment system processes billions of transactions, who benefits from the patterns in that data? These aren't theoretical questions - they're about real power and real control over the future of nations.

Look at South Korea's response: They've launched a national project to develop their own large language models, arguing that language and culture are too important to outsource. But even this tech-savvy nation is finding the task enormously challenging. What does this tell us about the options available to smaller or less wealthy nations?

Morocco's recent announcement to become an AI hub for Africa presents an interesting counterpoint. They're investing in data centers, education, and partnerships with global tech firms. Could focused regional hubs offer a different future than one dominated by a few global powers? The strategy acknowledges a crucial reality: in the AI age, power might be better measured in computing capacity than traditional metrics of national strength.

The immediate challenges are concrete: How does a country develop AI talent when the best jobs are abroad? Singapore tackled this by creating a specialized visa for tech talent and investing heavily in AI research centers. But can this be replicated without Singapore's unique advantages in location, capital, and governance?

When AI systems are trained primarily on English language data and Western cultural contexts, they embed these perspectives into the tools used globally. Brazilian Portuguese or Thai speakers get a fundamentally different - often inferior - experience from these systems. This isn't just about language - it's about whose worldview gets encoded into the systems that increasingly shape global decision-making.

The policy choices facing nations are stark but real: Join the existing AI infrastructure and accept dependency, build expensive parallel systems like China, or find new models of regional cooperation. Each choice carries immediate economic and social consequences. The challenge isn't theoretical - it's measured in data centers, computing chips, and the daily decisions of governments and businesses worldwide.

Looking ahead, several critical questions demand attention:

How do we prevent AI capabilities from becoming another marker of global inequality? The gap between nations with and without advanced AI infrastructure could dwarf traditional development divides.

What happens when the basic tools of modern governance require computing power that most nations can't afford? This isn't about luxury technologies - it's about basic governmental functions in an AI-driven world.

Can regional cooperation offer a viable alternative to complete dependency on current AI powers? The African Continental Free Trade Area, for instance, could provide a framework for shared AI infrastructure development.

My experience at Unleash in Denmark showed me the power of international cooperation in tackling global challenges. Perhaps that's part of the answer - not just bilateral agreements between nations and tech companies, but new forms of international cooperation focused specifically on AI infrastructure development.

The questions I pondered in Georgetown's halls in 2017 have only grown more urgent. The geography of AI power isn't just about where data centers are located - it's about who shapes the future of human society. As we grapple with these challenges, we need to move beyond simple narratives of digital divides to understand and address the complex interplay of physical infrastructure, computational power, and global governance that will determine whether AI becomes a force for global equality or another driver of division.