Energy Colonialism, or the Coming Era of Digital Isolation

VZ Lens

From the VZ perspective, this topic matters only when translated into execution architecture. By 2030, AI data centers will consume as much electricity as Japan—945 TWh per year. Those who export energy today will lose their digital future tomorrow. Its business impact starts when this becomes a weekly operating discipline.

TL;DR

• The energy consumption of AI systems is growing exponentially, not linearly—by 2030, the world’s data centers could consume as much electricity as Japan’s total electricity consumption (~945 TWh/year).
• The EROI spiral (energy return on investment) and the AI demand curve intersect between 2040 and 2050 — this is not a technological issue, but a civilizational one.
• The Jevons effect is no joke: the cheaper machine learning becomes, the more people use it, and the more electricity it requires—efficiency alone is not the solution, but part of the problem.
• Those who export energy today for short-term profit will lose their own digital development trajectory tomorrow—this is the new logic of energy colonization.

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The Matrix’s Electricity Bill

Energy colonization is the process by which the AI infrastructure of developed countries drains the energy reserves of smaller countries, depriving them of the opportunity for their own technological development. The International Energy Agency (IEA) forecasts that by 2030, data centers will require ~945 TWh of electricity annually—equivalent to Japan’s total consumption.

Today, the world’s data centers consume as much electricity as a major industrial power. By 2030, they will consume Japan’s entire annual electricity usage—just so that machines can answer our questions.

This isn’t science fiction. It’s the IEA’s official forecast.

The future isn’t decided between lines of code, but at the power outlet: who gets the electricity, and who doesn’t. This is the new geopolitical dividing line—energy apartheid. It isn’t fought with tanks, nor enforced with sanctions. It simply comes down to this: whoever possesses energy and computing capacity today will possess knowledge and a competitive advantage tomorrow. Those who don’t—will be left watching.

Case connected for the last time and felt cyberspace pulsing around him like a dying heart. The light slowly faded. The data moved ever more slowly. Somewhere in the distance, he heard the clatter of generators, felt the hum of the cities’ air conditioning—every single byte he processed brought the energy collapse a little closer. The console cowboys dreamed that one day all data would be free. They were wrong. The data became free—but the electricity that keeps it alive fell into bondage.

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Digital metabolism—when machines consume as much as a medium-sized city

The energy consumption of artificial intelligence does not show linear growth—it is exponential. A single ChatGPT query requires roughly ten times more energy than a traditional web search. Training state-of-the-art models consumes energy comparable to that of an entire city district. And this is just the beginning.

The numbers are frightening, but they are important:

Indicator	Value	Source
Global data center electricity demand (2030, forecast)	~945 TWh/year	IEA
Growth in electricity demand of AI-optimized data centers by 2030	>4×	IEA, S&P Global
Number of hyperscale data centers (Q1 2025)	1,189	Synergy Research Group
Share of hyperscale data centers in global data center capacity	~44%	Synergy Research Group
Ireland — Share of data centers in electricity consumption (2023)	21%	CSO Ireland
Ireland — Same figure in 2015	5%	CSO Ireland
Daily ChatGPT queries	>2.5 billion	The Verge, TechCrunch
Annual ChatGPT interactions (estimated)	~912.5 billion	daily × 365
Cost reduction for GPT-3.5-level inference (Nov. 2022 – Oct. 2024)	~280×	Stanford HAI
Industry average PUE (2024)	~1.56	Uptime Institute

Ireland’s energy policy is already a preview of the future: there is a moratorium on new data centers in the Dublin area until 2028—not because they don’t want to grow, but because there is no more available capacity. This isn’t a 2040 scenario. It’s happening today in an EU member state.

The world’s data centers consume roughly ten times as much electricity as all of Hungary combined—and AI, as an energy-hungry technology, is accelerating this demand. The training requirements for deep learning models are growing at a rate that exceeds Moore’s Law.

Why Isn’t Efficiency the Solution? — The Jevons Trap

This is where William Stanley Jevons, the 19th-century English economist, enters the story. He articulated the contradiction regarding coal consumption that we now call the [Jevons paradox](https://hu.wikipedia.org/wiki/Jevons paradox): if the use of a resource becomes more efficient, it does not reduce but rather increases total consumption—because efficiency lowers the unit price, which in turn stimulates demand.

The cheaper it is to ask a question, the more people ask—and the more electricity is needed to generate the answers.

The cost of AI inference has fallen by about 280 times in recent years. This is precisely why it now handles billions of requests daily. Efficiency is not the ultimate solution, but part of the problem—unless accompanied by demand management and an energy framework.

Neuroinformatics offers a sobering comparison: the human brain performs an astonishing amount of processing using roughly 20 watts, while current AI systems require power consumption measured in megawatts for tasks of the same level—or even more limited ones. This simultaneously highlights the efficiency gap in today’s artificial architectures and signals the depth of the impending energy constraint.

The EROI spiral — when physics overcomes technology

EROI (Energy Return on Investment) expresses how much useful energy we can produce per unit of energy invested. In the 1930s, this ratio was often ~100:1 for oil production. Today it is ~10–20:1, and for renewable energy sources it is often only ~5–10:1, depending on the technology and location.

Era / Source	EROI
Oil production, 1930s	~100:1
Oil production, current	~10–20:1
Renewable energy sources (average)	~5–10:1
Minimum often cited for societal functioning	~11:1

This decline is not merely a technological failure—it is a thermodynamic inevitability. Easily accessible resources are being depleted, so we must invest more and more to extract the same amount of energy. While the energy appetite of AI systems is growing exponentially, the system-wide efficiency of available energy is steadily declining.

“Nature’s first law: there is no such thing as a free lunch. The second law: the bill always comes later.” — Herman Daly

Around 2040–2050, these two curves are likely to intersect: AI development and operation will demand energy that will become increasingly difficult to meet as EROI declines. This is not just an economic issue—it is a matter of civilization. History teaches us that when energy becomes scarce, conflicts over the last unit intensify.

The Anatomy of Neural Colonization

Traditional colonization was based on physical resources—gold, spices, raw materials. Digital colonization is based on energy and data. Energy colonization is even more sophisticated: it takes away not only resources but also future development opportunities.

One of the most painful lessons of the war in Ukraine is that the primary target of modern warfare is energy infrastructure. There is no need to defeat armies immediately—it is enough to leave civilians in the dark, without heat or internet. Energy colonization is the same, only in peacetime: through economic coercion, not bombs.

The process unfolds in four phases:

Phase One (2025–2030): Developed countries are building AI infrastructure on a massive scale. Energy demand is still manageable; the system appears to be able to handle the load.

Phase Two (2030–2040): AI systems become widespread, and energy consumption grows exponentially. Smaller countries begin exporting their energy to sustain growth—just as they export their skilled workers today.

Phase Three (2040–2050): Energy crisis. Smaller countries face a choice: sell their energy to developed nations, or face economic turmoil. Those who sell cannot maintain their own AI infrastructure. A familiar pattern: we export wheat, we import bread. We export electricity, we import technology.

Phase Four (2050+): Digital apartheid. The world splits into two parts: those who can use AI, and those who cannot. This is not merely a technological gap—it is also a cognitive and social divide. The dividing line of the 21st century will not be the Berlin Wall, but an energy divide.

What will happen to Hungary? — The Eastern European trap

Hungary’s energy situation clearly illustrates the mechanism of neural colonization. As the German automotive industry switches to AI-based production lines and builds massive data centers to support this, the question is legitimate: where will the energy come from?

Hungary’s electricity balance is becoming increasingly flexible: periodic exports are now a reality, but the situation remains chronically strained. What happens when Germany’s AI infrastructure doubles its electricity demand? When a real choice must be made: heating Hungarian households or ensuring the continuous operation of BMW’s AI systems in Munich?

This is not a theoretical question. Hyperscale data centers currently operate at 1,189 locations worldwide, and their numbers are growing rapidly. The big players are not only building more, but also larger ones. Each one wants energy—wherever it is cheaper, more stable, and can be secured more quickly.

The answer is both simple and frightening: countries that sell their energy today to keep their economies afloat will later be unable to finance their own technological development. This is not merely economic inequality—it is a new form of colonization, in which electrons and kilowatt-hours, rather than land, are used to maintain dominance. Wars today are no longer fought only with tanks—but with targeted attacks on networks, power plants, substations, and energy grids, as well as economic sanctions. Energy dependence is the quietest—and perhaps the most effective—weapon.

Psycho-technological dependence—when we outsource our thinking

From a psychological perspective, AI dependence creates a new type of dependency: not material, but cognitive. People are increasingly relying on AI tools for decision-making, problem-solving, and even creativity.

We already cool server rooms well; from now on, we must make thinking itself more energy-efficient.

This dependency breeds societal anxiety in communities that sense they might lose access to these tools due to energy restrictions. A kind of technological withdrawal syndrome emerges: the fear of falling behind in digital development.

Just as young people in countries with platform restrictions fear being cut off from social media, we too may fear potential restrictions on ChatGPT access. The difference: while the former is often an ideological decision, restrictions on AI access may also stem from energy constraints.

According to social psychology research, individual identity is becoming increasingly intertwined with the use of technological devices. Those who are not “connected” to the digital network may find themselves socially marginalized. Thus, the ultimate impact of energy colonization is not merely economic—outsourcing of competence, an illusion of control, and cognitive vulnerability emerge at the systemic level.

What happens when AI diagnoses, but not everyone?

In medicine, AI is already revolutionizing diagnostics. Radiological image processing, histopathological analyses, and genetic research are all being enhanced by AI-based systems. But these systems are highly energy-intensive.

The AI-based interpretation of an MRI scan—preprocessing, inference, storage, and transmission—often results in a larger energy footprint than the scan itself. The computational processes of precision medicine—where predictions are made based on a patient’s complete genetic, clinical, and environmental data—require computational capacity on the scale of a small research institute.

Countries that export their energy are gradually losing access to these technologies.

This is not merely a health inequality—it is a development gap. Populations with access to AI-based healthcare can expect longer lifespans, better quality of life, and—indirectly—higher cognitive performance. Those without it will fall behind. Just as in the era of colonization—when colonizers grew stronger and healthier while the colonized withered away—the same scenario may now be replaying itself in the form of energy colonization.

The neurological paradox—machines that “think better” than their creators

In neuroscience, it is becoming increasingly clear that the human brain is an extremely energy-efficient organ. At the same time, artificial neural networks—inspired by the brain’s functioning—consume orders of magnitude more energy than their biological counterparts.

This paradox highlights a fundamental problem: current AI technology is not an efficient imitation of human intelligence, but rather a crude, energy-wasting caricature of it. But since it works, and since it brings enormous economic benefits, we continue to develop it—until energy systems collapse under the burden.

From the perspective of cognitive science, this is a peculiar evolutionary trap: we have created an “intelligence” that is energetically unsustainable, yet economically advantageous. Now we must choose: either we develop AI in a radically different direction—toward biomimetic (mimicking living systems), quantum-based, and neuromorphic (directly mimicking the brain’s structure) architectures—or we accept that the energy crisis will set back technological progress.

The Geopolitical Chessboard — Energy as the New Atomic Bomb

According to the theory of international relations, power always depends on who controls the most important resources. In the 20th century, these were oil, uranium, and rare earth metals. In the 21st century: energy and data.

However, energy colonization differs from its earlier forms: it is not military conquest, but economic coercion. Smaller countries “voluntarily” sell their energy because, in the short term, this is the only way to sustain growth. This creates a vicious cycle: the more energy they sell, the less able they are to develop their own technological infrastructure.

Meanwhile, developed countries are becoming increasingly dependent on these imports. If a smaller country suddenly cuts off energy exports, it could lead to a partial or complete shutdown of AI infrastructure. This is a new type of geopolitical weapon—a sort of “energy EMP” (electromagnetic pulse) that can paralyze a developed country’s digital systems.

Is Green Energy Really Green? — The Illusion of Sustainability

At first glance, the development of renewable energy sources may offer a solution to the problem of energy colonization. The reality, however, is more complex. The construction of solar farms and wind turbines is highly material-intensive: they require massive amounts of copper, nickel, lithium, cobalt, and—for certain technologies—rare earth metals. The burden of mining and processing often falls on the very countries that later export the energy.

This is a perverse cycle: smaller countries extract the critical materials, developed countries build renewable systems from them, and use these to operate AI infrastructure—whose energy demand may ultimately exceed the total production of the smaller countries.

We have known since the advent of environmental psychology just how powerful the “green illusion” is: we tend to view renewables as limitless and clean, while their material and energy life cycles entail very real burdens. The energy demand of AI systems could grow to such an extent that supply will remain scarce even with very rapid expansion of renewables—especially where the grid, storage, and infrastructure cannot keep pace.

The Existential Dilemma — Freedom vs. Computing Capacity

From the perspective of existential philosophy, energy colonization raises a deeper question: what is the price of human freedom in the digital age?

The use of AI tools undoubtedly enhances human capabilities. But what happens when this use leads to energy dependence? When a country’s population’s ability to think and make decisions depends on whether there is enough electricity to power AI servers?

This is a peculiar existential trap: the more we rely on AI, the more we depend on the energy that AI consumes. And the stronger this dependence becomes, the less capable we are of thinking and deciding independently.

According to the classic conception of freedom, a person is free when they are capable of making independent choices. But what happens when evaluating possible choices already requires AI, while its operation demands energy we cannot afford?

Other thinkers on the same topic

Yuval Noah Harari — the master of historical parallels

Harari would likely place the topic of energy colonization within the context of major historical transformations. For him, this is not simply a technological problem, but the next evolutionary step—or dead end—for Homo sapiens.

In his view, energy colonization is a new form of “dataism”—where energy, rather than information, becomes the new deity. “In the 21st century, power does not depend on who owns the land or the factories, but on who can feed the algorithms. And the algorithms are hungry—for energy, data, and attention.”

Shoshana Zuboff — surveillance energy capitalism

For Zuboff, energy colonialism is the logical consequence and escalation of surveillance capitalism. “First they privatized our behavior; now they’re privatizing our energy.” In her analysis, AI’s hunger for energy is not accidental but systemic.

Zuboff highlights a new form of “instrumental power”: tech companies not only monitor and manipulate, but directly control our essential infrastructure. “Energy colonization is version 3.0 of surveillance capitalism—when surveillance becomes physical control.”

Jaron Lanier — the man who didn’t give up

Lanier is deeply skeptical of technological determinism. The AI energy crisis is a perfect example of how technology turns from a tool into a master. “When I was developing VR in the ’80s, we believed that technology would extend human creativity. But today, AI doesn’t need human creativity—it just needs energy. And that’s a dramatic shift.”

For him, neuromorphic computing is not just a technical solution, but a philosophical statement: “The efficiency of the human brain proves that there is a better way. But to achieve this, we must abandon the brute-force approach and return to elegant, human-scale design.”

Nick Bostrom — the existential risk calculator

Bostrom would likely analyze the topic of energy colonialism as a critical juncture on the path toward superintelligence. “The intersection of the EROI spiral and AI exponentiality could occur between 2040 and 2050. This is a narrow window: either we solve the energy efficiency problem, or AI development will slow down. There is no third option.”

But Bostrom also envisions a darker scenario: “If superintelligence decides that human civilization is energy-inefficient, then energy colonization may be merely a prelude to something far more radical. Machines will not share the planet with us if we cannot share energy with them.”

Innovation — is there a chance for change?

A critical window of opportunity will open between 2025 and 2035. This decade will determine whether we are capable of developing energy-sustainable AI technologies, or whether we will stand by and watch as the digital divide becomes irreversible.

Reordering research and development priorities is key. Today, the focus is overwhelmingly on improving model performance; energy efficiency is merely a side issue. This balance must be reversed: in the coming decade, at least half of R&D should focus on efficiency, data reduction, edge computing, and new architectures.

International agreements are needed to regulate AI energy consumption—not censorship, but an acknowledgment of physical limits. Just as we established safety and transparency standards during the nuclear age, we now need AI energy standards:

• Transparent energy metrics for all AI services
• Time-aligned, 24/7 carbon-free operation (24/7 CFE — Carbon-Free Energy)
• Waste heat utilization
• Public access quotas

The window of opportunity is now. If we optimize for efficiency and access protection in the coming years, the revolution will become a tool for freedom. If not, the digital divide will become the new dividing line.

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Key Takeaways

• Account for energy, not just data — An energy SLA is needed alongside an AI SLA. Every machine decision has an energy footprint.
• Avoid energy dependence — don’t trade future growth potential for short-term profits. This is the 21st-century bread-and-butter trap.
• Invest in leaner models — energy efficiency will be a strategic advantage, not a technical detail. Neuromorphic architectures and conditional execution are the way forward.
• Prepare for psycho-technological dependency — do not let your cognitive capacity be entirely tied to AI. Cognitive sovereignty is the new digital literacy.
• Get involved in regulation early — without AI energy standards, quotas, and transparent metrics, there will be no sustainable development.
• The EROI spiral does not stop — thermodynamics does not negotiate. The question is not whether energy scarcity will occur, but whether we are prepared for it.
• Digital apartheid is not a metaphor — but a possible scenario. The stakes are simple: will we build an AI ecosystem that creates more value with less energy, or will we accept the logic of energy apartheid?

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FAQ

How much more energy does an AI query consume than a traditional search?

A ChatGPT query requires roughly ten times as much energy as a traditional Google search. This may not seem like much on its own, but the volume is decisive: ChatGPT alone receives over 2.5 billion requests daily, which amounts to ~912.5 billion interactions annually. According to data from Stanford HAI, the cost of GPT-3.5-level inference fell by approximately 280 times between November 2022 and October 2024—and this is a textbook case of the Jevons effect: falling costs did not reduce demand but caused it to skyrocket.

What is EROI, and why is it important for the future of AI?

EROI (Energy Return on Investment) measures how much useful energy can be extracted per unit of energy invested. In the 1930s, the EROI of oil production was ~100:1; today it is ~10–20:1, and for renewables, ~5–10:1. The minimum often cited for societal functioning is around ~11:1. The exponentially growing energy demand of AI systems and the declining EROI could reach a tipping point between 2040 and 2050—which means there simply won’t be enough efficiently extractable energy to sustain AI development. This isn’t pessimism; it’s thermodynamics.

What can Hungary—or any smaller country—do to counter energy colonization?

There are three strategic directions. First: do not export energy for short-term profit if it comes at the expense of your own digital infrastructure. Second: invest in energy-efficient AI architectures—neuromorphic computing, edge computing, conditional execution—that deliver more value with less energy. Third: lobby for international AI energy standards that include public access quotas and 24/7 carbon-free operation requirements. The window of opportunity is open between 2025 and 2035—after that, the digital divide may become irreversible.

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Related Thoughts

• AI and the Mirror of Civilization — system-level feedback where technology magnifies structures
• Hungary’s Unique Path in AI — Eastern European position, opportunities, and pitfalls
• Zuboff and the Smart Machine — when surveillance becomes an economic model

Key Takeaways

• The energy consumption of AI systems is growing exponentially, and by 2030, data centers could consume an amount equivalent to Japan’s total annual electricity consumption (~945 TWh), creating a new geopolitical divide based on energy.
• According to the Jevons paradox, the drastic reduction in the costs of AI inference does not decrease but rather explosively increases total energy demand, because efficiency is part of the problem, not the solution.
• The essence of energy colonization is that those who export energy today will lose their own opportunities for digital development tomorrow; as Harari also points out regarding digital empires, new forms of exploitation are emerging.
• The downward spiral of EROI (Energy Return on Investment) and the curve of AI’s energy demand may intersect between 2040 and 2050, which represents not merely a technological but a civilizational limit.
• The example of Ireland shows that the crisis is not a problem of the distant future: there is already a moratorium on new data centers in the Dublin area because there is not enough available power capacity.
• According to a comparison in neuroinformatics, the human brain performs complex tasks using ~20 watts, while current AI systems operate on megawatts, highlighting the massive efficiency gap in today’s architectures and the severity of energy constraints.

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Zoltán Varga - LinkedIn
Neural • Knowledge Systems Architect | Enterprise RAG architect
PKM • AI Ecosystems | Neural Awareness • Consciousness & Leadership
The grid doesn’t dream — it just decides who gets to.

Strategic Synthesis

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