The Iran conflict and the silent threat looming over the AI economy
Energy costs have always been one of the quietest yet most decisive factors in the global economy. When they go up, everyone feels it — from the consumer filling up their car to the massive corporation running servers 24 hours a day, 7 days a week.
Now, with the conflict in the Middle East escalating and the Strait of Hormuz front and center, this issue has roared back to the top of the global economic agenda. And this time around, one sector in particular could take a serious hit: the artificial intelligence industry. 🤖
Donald Trump’s most immediate concern in demanding that Iran reopen the Strait of Hormuz might be the price of gas at American pumps, but the ripple effects of a prolonged conflict go far beyond the fuel station. Systematically higher energy prices and fractured supply chains tend to squeeze industries and consumers worldwide. And for the United States, one of the most unexpected consequences could be the threat to the AI economy, which was already on shaky ground before any geopolitical crisis entered the picture.
Revenues of roughly 60 billion dollars in 2025 against capital expenditures approaching 400 billion dollars in the same period — that imbalance already said a lot about how fragile the model is. Now, with energy getting more expensive and supply chains under pressure, the math could get even harder to make work. 💡
The Strait of Hormuz and what it means for the world
Few people stop to think about how much a strip of water roughly 21 miles wide can influence the global economy. The Strait of Hormuz is the transit point for about 20% of all oil traded worldwide, along with a massive share of the liquefied natural gas that supplies Europe and Asia. When any kind of instability threatens this route, markets react immediately — and energy costs spike before a single barrel is actually blocked.
The Iran conflict brought this concern back with an intensity not seen in years. Many oil-importing economies, especially in the so-called global south, are already having to deal with the very real possibility of oil and fuel shortages. In Egypt, shops are facing forced curfews. Indonesia imposed mandatory work-from-home Fridays as an energy-saving measure. And the Philippines declared a national energy emergency. These are clear signs that the impact has already moved beyond speculation and become a concrete reality for millions of people.
As a major oil exporter, the United States can avoid the worst shortage scenarios. But as the rising cost of filling up at the pump already shows, not even the world’s largest economy can fully shield itself from the global surge in energy prices. Many analysts believe these elevated prices will persist for months, even if the strait is reopened within days. And that is exactly where the story intersects with artificial intelligence.
What a lot of people still haven’t quite connected is that AI is not some immaterial business. It depends on massive data centers that consume staggering amounts of electricity. It depends on chips manufactured through extremely energy-intensive processes. And it depends on global supply chains that are directly affected by the price of oil — from transporting components to manufacturing the semiconductors themselves. When the price per barrel goes up, the cost of all of this goes up with it, and the bill lands on Big Tech’s desk way faster than they would like. 🌐
The AI economy was already at its limit before the crisis
Before any external factor entered the equation, the AI economy was already showing warning signs that deserved serious attention. The Bank of England itself highlighted the potential link between energy costs and AI company stock prices in its regular report on risks to the UK financial system, published last week.
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The Bank’s financial policy committee got straight to the point: investors had already been raising questions about the sector before Trump even went to war. In the report’s own words, prior to the conflict, the growing need for debt financing and concerns about whether expected returns on very significant AI-related investments would materialize had already generated selling pressure in the markets.
And the report went further, stating that the conflict could heighten these concerns, particularly given the energy-intensive nature of the supply chain for key components and the operation of data centers. This was just one part of a broader warning about how the Iran war could exacerbate pre-existing vulnerabilities in the markets, given that the conflict tends to weigh on growth, increase inflation, and tighten financial conditions.
The numbers tell a pretty revealing story about this fragility. With revenues of 60 billion dollars and capital investments of 400 billion dollars, the ratio is roughly seven dollars spent for every dollar earned — something that would be unsustainable in any more mature sector of the economy. This dynamic happens because the AI boom is still largely in the infrastructure-building phase. Companies are betting big on the idea that returns will show up down the road, as models become more sophisticated and adoption grows wider.
Robert Staiger, chief economist at the World Trade Organization, also made the connection between AI and the conflict’s impact, stating that a prolonged period of high energy prices could squeeze investment in the sector. In his own words, the boom is very energy-intensive. To put the real-world consequences of a potential pullback into perspective, the WTO calculated in its latest global trade outlook report that 70% of investment growth in the United States during the first three quarters of last year was in AI-related goods of one kind or another. That single data point shows just how tied the American economy is to the success — or failure — of this sector. 📊
The financial engineering behind the boom and its hidden risks
Perhaps the most concerning aspect of this entire scenario is not the data centers themselves, but the complexity of the financial engineering propping up the mega-boom in AI investment. A detailed report published by the American law firm Quinn Emanuel last month laid out this structure in painstaking detail — and for anyone who followed the 2008 global financial crisis, the reading is unsettling at best.
What the report revealed is that the so-called hyperscalers — the big companies leading the AI race — and infrastructure providers like CoreWeave are borrowing unimaginably large sums as they race to build data centers. The lenders are frequently private firms, such as asset managers, which makes each company’s total liabilities harder to track for regulators or even their own investors.
This is where things get really concerning. Data center operators have been creating off-balance-sheet special purpose vehicles — structures that own the massive data centers and their future rental revenues, and that borrow against those assets. In some cases, these debts are then bundled, sliced up, and resold to pension funds and investment managers.
For anyone who remembers 2008, structures like these can create a false sense of security that risks are being distributed when, in reality, they are being accumulated. And they make it virtually impossible to figure out exactly who owes what to whom. Analysts at Quinn Emanuel estimate that roughly 120 billion dollars in data center debt has been moved off balance sheets over the past two years.
As they put it themselves, the deeply interconnected AI ecosystem means that stress at any single point can propagate across multiple counterparties and layers of financing. And guess what can be a trigger for that kind of stress? Higher energy costs for a prolonged period, combined with volatile interest rate expectations and weaker consumer demand — all likely consequences of the war in the Middle East. 😬
Sam Altman and the optimistic narrative that not everyone bought
Amid all these concerns, OpenAI CEO Sam Altman tried to downplay fears about AI’s environmental and energy impact back in February, in an attempt to calm nerves in the lead-up to what is expected to be a massive IPO for the company.
The comparison he used was, to say the least, curious. Altman said that people talk about how much energy it takes to train an AI model, but that it also takes a lot of energy to train a human. According to him, it takes about 20 years of life and all the food consumed during that time before someone becomes intelligent.
Regardless of the analogy’s merit, it doesn’t address the central point: AI models need to be constantly retrained, they run 24/7 serving millions of simultaneous requests, and they depend on physical infrastructure with rising operational costs. A trained human keeps running on food and sleep. A language model needs continuous megawatts just to exist.
Meanwhile, recent analyses by technology writer and critic Ed Zitron suggest that actual data center projects are significantly behind the promises companies have made to the market. There is a considerable gap between the grand expansion announcements and the reality of what is actually being built — adding yet another layer of uncertainty about the sustainability of the current model. 🧐
Supply chains under pressure: the link most people miss
There is one aspect of the geopolitical impact on AI that rarely makes the headlines but is equally concerning: semiconductor supply chains. The chips that run AI models — especially GPUs made by Nvidia and similar companies — depend on an extremely globalized production chain that is sensitive to any kind of logistical or economic disruption.
Taiwan manufactures a large share of these components, but the raw materials, production equipment, and distribution processes cross dozens of borders before a chip reaches its final destination. When the price of oil rises due to instabilities like the Iran conflict, ocean freight costs rise with it. This directly impacts the final price of electronic components, which were already at elevated levels due to the explosive demand of recent years.
AI companies that need to expand their processing capacity — and they constantly do, given the speed at which new models are being developed — face an equation where each new server installed costs more than the last. This erodes margins and puts extra pressure on a business model that was already operating at its limit.
The Bank of England specifically called attention to this point, highlighting the energy-intensive nature of the supply chain for key components. It is not just the running data center that consumes energy — it is the entire process of manufacturing, transporting, and installing the components that make it work. It is a chain of energy costs that compounds at every stage.
The role of private credit and the opacity that worries regulators
An additional risk factor worth highlighting is the role of the private credit sector in financing AI expansion. Many of the loans supporting data center construction come from asset managers and private funds, not traditional banks. This creates a significant transparency problem.
Regulators, including the Bank of England itself, have consistently warned about the opacity of this sector. When loans sit on the books of regulated banks, there is a supervisory framework that allows for risk monitoring. When they sit in private credit vehicles, that visibility drops dramatically. And when some of these debts are repackaged into asset-backed securities and sold to pension funds and other institutional investors, tracking who actually carries the risk becomes a nearly impossible task.
In some cases, tech companies have simply issued traditional debt securities. But there are far more complex arrangements in play — byzantine structures reminiscent of the financial instruments that helped inflate the bubble that burst in 2008. The difference is that this time the underlying asset is not residential mortgages but data centers and future rental revenue contracts for computational capacity. The logic is different, but the risk mechanics are uncomfortably similar.
What to expect going forward
The combination of an unstable geopolitical landscape in the Middle East with an AI economy that still has not found its balance between revenue and spending creates a moment that demands heightened attention across the sector. This is not doomsaying — it is reading the room.
The major market players, like Microsoft, Google, Amazon, and Meta, have enough financial reserves to absorb shocks in the short term. But even for these giants, a prolonged rise in energy costs combined with supply chain pressure would be a significant complicating factor in the coming quarters. Planned technology investments for the next few years could start being revisited. New data center projects could be delayed, energy supply contracts could be renegotiated, and the launch timeline for new models could be stretched out.
For smaller companies in the AI ecosystem — startups, scale-ups, and specialized suppliers — the outlook is more delicate. Many of them depend on infrastructure leased from major cloud providers, and any pass-through of energy costs on those platforms directly impacts their financial viability. Access to capital also tends to tighten during periods of heightened geopolitical uncertainty, making the environment for new investments considerably harder.
The fundamental question remains the same one that was already hanging over the sector before the crisis: will the AI industry ever generate enough revenue to justify stratospheric valuations? But now that question comes with an added dimension — because even modestly higher energy costs could trigger a reassessment that, given the financial engineering at play, could cascade through American markets and beyond.
What becomes clear from this analysis is that AI, despite all the narratives of digital revolution and unprecedented innovation, is deeply rooted in the physical economy of the real world — with all the vulnerabilities that entails. Oil, electricity, shipping lanes, electronic components: all of this is part of the foundation on which the artificial intelligence boom is being built. And as long as the Iran conflict keeps escalating, that foundation will keep shaking — enough to make any tech sector executive think twice before signing the next billion-dollar check. 😬
