In 1757, a private British trading company defeated the ruler of Bengal at the Battle of Plassey. The East India Company had originally arrived in India to conduct business, not to govern a civilization. It purchased goods, established trading posts, negotiated agreements, and built commercial relationships. Yet, over time, a company created to facilitate trade accumulated sufficient economic, military, and political power to govern vast territories and millions of people.

This historical episode illustrates a disturbing possibility: political power does not always begin with political ambitions. Sometimes it emerges from economic dependence.

The merchants did not initially need to abolish local institutions. By gaining control over crucial commercial and financial systems, they gradually acquired influence over the institutions themselves.

Today, humanity may be witnessing the emergence of a different kind of power. Instead of controlling the trade in cotton, spices, or oil, a small number of corporations and countries are beginning to control something potentially more consequential: artificial intelligence.

For most of history, empires sought to control what people produced, where they traded, and how they were governed. The emerging AI economy introduces a new possibility: controlling the technological systems through which people and institutions process information and make decisions.

What happens to national sovereignty when the intelligence supporting a country's economy, government, and military is developed and operated beyond its borders?

And what if the institutions gradually becoming dependent on this intelligence are themselves unaware of how much power they are surrendering?

In 2025, according to the World Bank, high-income countries accounted for 87% of the world's notable AI models, despite representing only 17% of its population. They also hosted 77% of global colocation data-center capacity as of June that year.

Stanford University's 2026 AI Index reported that industry produced more than 90% of notable frontier AI models in 2025.

These numbers describe something historically unusual.

Technologies that may increasingly influence public administration, financial markets, education, scientific research, and national security are being developed primarily by private organizations concentrated in a relatively small number of economies.

The United Nations Conference on Trade and Development projected in 2025 that the global AI market could reach $4.8 trillion by 2033. Yet it also warned that the economic and technological capabilities required to participate in this market are distributed very unequally.

This does not mean that the countries developing AI are necessarily exploiting those using it. Indeed, inexpensive access to powerful foreign AI systems could accelerate development in poorer countries.

But it raises a fundamental distinction between access and control.

A country might have access to the world's most advanced intelligence while possessing very little influence over how that intelligence is developed, priced, restricted, or withdrawn.

To understand why this matters, consider the difference between importing a machine and importing a decision-making capability.

A factory that purchases a foreign machine can generally continue operating that machine, provided it has the necessary parts, technical knowledge, and maintenance resources. But an institution that depends on continuously updated, remotely operated AI services may require an ongoing relationship with its foreign supplier.

That supplier may control software updates, technical capabilities, usage conditions, and access to the underlying infrastructure.

The machine becomes more than a product. It becomes a continuing relationship of dependence.

And as AI systems move from answering questions to executing complex tasks, this relationship could extend much deeper into the functioning of entire societies.

Imagine a country in 2035.

Its government uses AI agents to process public services and coordinate administrative functions. Its banks rely on AI systems to detect fraud and assess financial risks. Its hospitals use AI to support medical decisions. Its power grid employs AI to optimize electricity distribution. Its military uses AI for intelligence analysis, surveillance, logistics, and operational planning.

The country remains politically independent. It conducts elections, passes laws, collects taxes, and maintains armed forces.

Yet many of the technological systems supporting these activities belong to companies operating under foreign jurisdictions.

Now imagine that relations deteriorate between the country and the state in which its principal AI suppliers operate.

Perhaps new export restrictions are introduced. Perhaps a company is legally prohibited from serving certain customers. Perhaps the service becomes prohibitively expensive. Or perhaps a technical failure interrupts access to an essential platform.

Not every AI system can be remotely disabled, and sophisticated institutions can reduce these risks through local deployment and technical safeguards. Nevertheless, the vulnerability is real whenever essential functions depend on systems that cannot easily be replaced.

The question is no longer simply whether a country has the legal right to make independent decisions.

It is whether it possesses the technical capacity to implement those decisions independently.

Political scientists Henry Farrell and Abraham Newman have described a related phenomenon as weaponized interdependence. Their research demonstrates how countries controlling critical nodes of global economic networks can sometimes exploit that position for surveillance or coercion.

The argument is particularly relevant to AI because advanced AI depends on interconnected networks of semiconductors, cloud infrastructure, software, data, electricity, and technical expertise.

Control over one critical part of such a network may create influence far beyond its apparent economic importance.

A country may possess enormous financial resources and still lack the ability to manufacture advanced semiconductors. Another may own substantial computing infrastructure but remain dependent on foreign AI models. A third may develop sophisticated models while relying on externally manufactured chips.

Indeed, Stanford's 2026 AI Index identifies Taiwan's TSMC as the manufacturer of almost every leading AI chip.

Even the countries dominating AI development are not completely independent.

The emerging world is therefore not neatly divided between powerful AI empires and helpless AI colonies. It is a complex network of unequal dependencies, in which different actors control different critical resources.

The important question is who can exploit these dependencies when interests diverge.

The historical comparison with colonialism becomes more interesting when we examine how economic value is created.

During the Industrial Revolution, many colonies supplied raw materials to industrial powers. Cotton grown in India, for example, could be transported to British mills, transformed into finished products, and sold in markets around the world.

The greatest profits often accumulated where industrial processing, capital, and commercial networks were concentrated.

Artificial intelligence could create a comparable imbalance, although through very different mechanisms.

A country generates economic activity, language, cultural knowledge, public information, and data. Foreign technology companies develop sophisticated AI systems that may use some of these resources, together with enormous investments in computing, talent, and research. Businesses and governments in that country then purchase access to the resulting services.

The country may enjoy substantial economic benefits. Its workers become more productive. Its institutions deliver better services. Its businesses become more competitive.

But who captures the greatest share of the long-term value?

Who owns the underlying models and intellectual property? Who determines the commercial terms? Who accumulates the capital required to build the next generation of systems?

And who develops the technical knowledge necessary to compete?

These are empirical questions, not conclusions that can be answered simply by invoking colonial history. AI services are not identical to manufactured goods, data is not identical to cotton, and countries can benefit greatly from technologies they did not invent.

Yet the historical analogy highlights a potential problem: an economy can become increasingly productive without necessarily acquiring greater control over the foundations of that productivity.

This creates a paradox.

A nation may become wealthier through AI while simultaneously becoming more dependent on those who supply it.

Economic growth and strategic autonomy are not the same thing.

The same paradox applies to corporations.

A company might replace large parts of its administrative operations with AI agents and report remarkable gains in efficiency. But if the resulting operations become deeply dependent on a single external platform, the company may have improved its financial performance while weakening its resilience.

When its supplier changes the rules, how easily can it adapt?

Efficiency measures how well a system performs under expected conditions. Resilience measures whether it can continue functioning when those conditions change.

The two do not always move together.

Many governments have responded to these concerns by announcing sovereign AI initiatives.

They promise national data centers, domestic language models, local computing infrastructure, and public investments in AI development.

But what exactly makes artificial intelligence sovereign?

If a government owns a data center while depending on foreign chips, foreign software, foreign models, and foreign technical expertise, does it possess genuine technological independence?

Conversely, if a country uses foreign AI technology but can audit it, modify it, operate critical functions locally, and switch between competing suppliers, is it necessarily dependent in a politically dangerous sense?

There is no simple answer.

The idea that every nation must develop its own advanced foundation models overlooks both the enormous costs of frontier AI and the benefits of international specialization.

The International Energy Agency estimated that data centers consumed approximately 415 terawatt-hours of electricity worldwide in 2024. Its 2025 baseline projection suggests consumption could reach about 945 terawatt-hours by 2030, with AI among the principal drivers.

Building the infrastructure required for advanced AI is therefore not merely a software challenge. It also requires energy, capital, semiconductors, facilities, and specialized human expertise.

Few countries can economically reproduce the entire global AI supply chain.

But sovereignty has never required a country to manufacture everything it uses.

Countries import medicine without necessarily surrendering control of their healthcare systems. They purchase aircraft without automatically surrendering control of their airspace. They trade internationally without automatically becoming colonies.

What matters is the nature of the dependence.

Can essential services continue if a foreign supplier disappears? Can contracts and technical arrangements be changed? Can domestic institutions retain meaningful authority over critical decisions? Are there alternative providers? Is the country developing sufficient expertise to evaluate the systems it increasingly relies upon?

The danger is not dependence itself. Modern civilization is built on interdependence.

The danger arises when dependence becomes so concentrated, indispensable, and difficult to reverse that one party acquires the practical ability to impose conditions on another.

At that point, what began as an ordinary commercial relationship can acquire political significance.

And this is where AI imperialism becomes a possibility rather than merely a provocative metaphor.

There is one final complication.

Traditional accounts of imperialism usually involve recognizable imperial ambitions. Governments sought territories, resources, markets, military advantages, and political influence.

But the emergence of AI dependency does not require a coordinated imperial project.

Technology companies can pursue entirely legitimate commercial objectives: attracting customers, improving products, increasing market share, and earning profits.

Governments can pursue legitimate objectives too: protecting national security, enforcing domestic laws, and maintaining technological competitiveness.

Meanwhile, customers choose the most capable and affordable technologies available.

Each decision may appear individually reasonable.

Yet the combined outcome could be a global system in which essential intelligence infrastructure becomes concentrated among a small number of suppliers and jurisdictions.

Nobody needs to announce the creation of an empire for such a concentration of power to emerge.

This is perhaps the most troubling possibility.

A system designed to maximize commercial efficiency may gradually produce relationships of strategic dependence that neither its developers nor its customers originally intended.

And by the time those dependencies become politically significant, reversing them may be extraordinarily expensive.

The history of industrialization offers a relevant warning. Countries that failed to develop industrial capabilities were not necessarily doomed to permanent poverty or subordination, but the resulting differences in productivity and military power profoundly influenced the international order.

Artificial intelligence may create a similarly consequential divergence.

Some countries will become centers of AI research, engineering, infrastructure, and intellectual property. Others may specialize in deploying AI, adapting it to local needs, and building industries around it. Still others may struggle to obtain reliable access.

These outcomes are not fixed. Open-source models, smaller AI systems, education, competition, and international partnerships can redistribute capabilities.

But the choices made during this period could shape economic and political relationships for decades.

We are accustomed to thinking of sovereignty as control over territory.

A state is sovereign because it governs the people living within its borders. It enforces laws, levies taxes, and possesses legitimate political authority.

But modern sovereignty also depends on control over essential systems.

A country that cannot reliably access energy faces obvious constraints on its independence. A country whose financial system can be disrupted from abroad may discover limits to its economic autonomy.

As AI becomes more deeply embedded in national institutions, it may create another such dependency.

The central issue is not whether artificial intelligence will replace political leaders or whether foreign companies will secretly govern entire nations.

Those scenarios are speculative, and AI systems remain far from infallible.

The more immediate question is whether governments and organizations are accumulating technological dependencies faster than they are developing the institutional capacity to manage them.

The answer cannot be found simply by counting data centers, announcing AI strategies, or measuring how many government employees use AI tools.

It requires examining who controls critical systems, what happens when access is interrupted, and whether institutions retain credible alternatives.

History suggests that political independence is not secured once and for all. It must be sustained by economic resources, institutional capabilities, and the ability to act when circumstances change.

Artificial intelligence adds a new dimension to this old problem.

Throughout history, empires have sought to control land, labor, trade, capital, and information.

Now a new form of power is emerging: the ability to supply, shape, and potentially restrict the intelligence upon which other institutions depend.

Whether this becomes another instrument of domination or a foundation for shared prosperity remains an open question.

But we should not confuse the widespread availability of intelligence with the widespread distribution of power.

A society may have access to extraordinary technological capabilities without controlling the conditions under which it can use them.

A nation may remain formally independent while losing much of its practical freedom to choose.

And an enterprise may celebrate the intelligence of its systems without asking who ultimately controls those systems.

Perhaps the defining question of the coming decades will not be which countries possess the most advanced artificial intelligence.

It will be which countries retain the freedom to make their own decisions when their intelligence is supplied by someone else.

Sam Obeidat is an AI strategist, investor, and venture builder who works at the intersection of AI, business, and capital to turn complex opportunities into scalable ventures and measurable enterprise outcomes. As Founder, President, and Chief AI Officer of World AI X Ventures, he has led 60+ corporate and government AI strategies, helped develop more than a dozen enterprise AI solutions, coached 5,000+ executives worldwide, contributed to AI ventures valued at over $100M, and previously managed investment portfolios exceeding $300M—combining deep strategic insight with the ability to move from idea to execution, commercialization, and scale.

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