The global economy has become dependent on the AI industry. Trillions of dollars are being invested into the technology and the infrastructure it relies on; in the final months of 2025, functionally all economic growth in the United States came from AI investments. This would be risky even in ideal conditions. And we are very far from ideal conditions.Much of the AI supply chain—chips, data centers, combustion turbines, and so on—relies on key materials that are produced in or transported through just a few places on Earth, with little overlap. In particular, the industry is highly dependent on the Middle East, which has been destabilized by the war in Iran. A global energy shock seems all but certain to come soon—the kind where even the best-case scenario is a disaster. The war could grind the AI build-out to a halt. This would be devastating for the tech firms that have issued historic amounts of debt to race against their highly leveraged competitors, and it would be devastating for the private lenders and banks that have been buying up that debt in the hope of ever bigger returns.For the better part of the past year, Wall Street analysts and tech-industry observers have fretted publicly about an AI bubble. The fear is that too much money is coming in too fast and that generative-AI companies still have not offered anything close to a viable business model. If growth were to stall or the technology were to be seen as failing to deliver on its promises, the bubble might burst, triggering a chain reaction across the financial system. Everyone—big banks, private-equity firms, people who have no idea what’s mixed into their 401(k)—would be hit by the AI crash.Until recently, that kind of crash felt hypothetical; today, it feels plausible and, to some, almost inevitable. “What’s unusual about this, unlike commercial real estate during the global financial crisis,” Paul Kedrosky, an investor and financial consultant, told us, “is all of these interlocking points of fragility.”[Read: Here’s how the AI crash happens]Perhaps the clearest examples are advanced memory and training chips, which are among the most important—and are by far the most expensive—components of training any AI model. Currently, most of them are produced by two companies in South Korea and one in Taiwan. These countries, in turn, get a large majority of their crude oil and much of their liquefied natural gas—which help fuel semiconductor manufacturing—from the Persian Gulf. The chip companies also require helium, sulfur, and bromine—three key inputs to silicon wafers—largely sourced from the region. In addition, Saudi Arabia, Qatar, the United Arab Emirates, and other regional petrostates have become key investors in the American AI firms that purchase most of those chips.Because of the war in Iran, the Strait of Hormuz is functionally closed to most shipping vessels, stranding one-fifth of the world’s exports of natural gas, one-third of the world’s exports of crude oil, and significant quantities of the planet’s exportable fertilizer, helium, and sulphur. Meanwhile, Iran and Israel have begun bombing much of the fossil-fuel infrastructure in the region, which could take many years to replace. In only a month of war, the price of Brent crude—a global oil benchmark—has jumped by 40 percent and could more than double, liquefied-natural-gas prices are soaring in Europe and Asia, and helium spot prices have already doubled. The strait is “critical to basically every aspect of the global economy,” Sam Winter-Levy, a technology and national-security researcher at the Carnegie Endowment for International Peace, told us. “The AI supply chain is not insulated.”The situation could quickly deteriorate from here. A helium crunch could trigger a shortage of AI chips or cause chip prices to rise. AI companies need ever more advanced chips to fill their data centers—at higher prices, the massive server farms, already hurting from elevated energy costs caused by the war, would have almost no hope of becoming profitable. Without these chips, new data centers would not be built or would sit empty. Astronomical tech valuations, and in turn the entire stock market, could collapse.One industry’s precarious position isn’t usually everyone’s problem. Unfortunately, AI is different. The biggest data-center players, known as hyperscalers, are among the biggest corporations in the history of capitalism; they include Microsoft, Google, Meta, and Amazon. But even they will be pressed by collectively spending nearly $700 billion on AI in a single year. In order to get the money for these unprecedented projects, data-center providers are beginning to take on colossal amounts of debt. Some of this is done through creative deals with private-equity firms including Blackstone, BlackRock, and Blue Owl Capital—which themselves operate as sort of shadow banks that, since the most recent financial crisis, have arguably become as powerful and as influential as Bear Stearns and Lehman Brothers were prior to 2008. Endowments, pensions, insurance funds, and other major institutions all trust private equity to invest their money.For a while, it seemed like every time Google or Microsoft announced more data-center investments, their stock prices rose. Now the opposite occurs: The hyperscalers are spending far more, but investors have started to notice that they are not generating anything near the revenue they need to. The data-center boom’s top players—Google, Meta, Microsoft, Amazon, Nvidia, and Oracle—have all lost 8 to 27 percent of their value since the start of the year, making them a huge drag on the overall stock market. And the $121 billion of debt that hyperscalers issued in 2025, four times more than what they averaged for years prior, is expected to grow dramatically.All of the major players in this investment ecosystem are vulnerable. Private-equity firms are being squeezed on both ends by generative AI: During the coronavirus pandemic, they bought up software companies, which are now plummeting in value because AI is expected to eat their lunch. Meanwhile, private equity’s new investment strategy, data centers, is also falling apart because of AI. Blackstone, Blue Owl, and the like are sinking huge sums into data-center construction with the assumption that lease payments from tech companies will pay for their debt. In order to pay for their investments, private-equity companies raised money from major financial institutions—but now the viability of those lease payments is coming into question as the hyperscalers’ cash flow is strained. “There’s a reason to think we’re seeing some of the same 2008 dynamics now,” Brad Lipton, a former senior adviser at the Consumer Financial Protection Bureau and now the director of corporate power and financial regulation at the Roosevelt Institute, told us. “Everyone’s getting tied up together. Banks are lending money to private credit, which in turn lends it elsewhere. That amps up the risk.”[Annie Lowrey: How to guess if your job will exist in five years]The way the money moves is concerning, but so is the AI industry’s underlying business model. At every layer, the technology appears to decrease the value of its assets. The advanced AI chips that make up the majority of the cost of a data center? Their value rapidly decreases as they are superseded by the next generation of chips, meaning that the ultimate backstop for all of the data-center debt—selling the data center itself—is not actually a backstop. The way that AI companies make money when people use their products is also deflationary. OpenAI, Anthropic, and others charge users for using “tokens,” the components of words processed by their bots. This means that tokens are an industrial commodity akin to, say, crude oil or steel. But unlike other commodities, the cost of each token is rapidly decreasing owing to advancements in AI’s capabilities. Kedrosky called this “a death spiral to zero.” As the value of a token plummets, the value of what data centers can produce also falls.The war in Iran affects data-center finances as well. Should energy prices continue to skyrocket, so will the cost of this already very expensive computing equipment, because it needs tremendous amounts of energy to manufacture and operate. And the war has exposed physical risks to these buildings. Janet Egan, a senior fellow at the Center for a New American Security, described data centers to us as “large, juicy targets.” It is impossible to hide these facilities, which can cover 1 million square feet. Earlier this month, Iran bombed Amazon data centers in the UAE and Bahrain. American hyperscalers had been planning to build far more data centers in the region, because the Trump administration and the AI industry have sought funding from Saudi Arabia, the UAE, Qatar, and Oman. Now there’s a two-way strain on those relationships. The physical security of the data centers is more precarious, and the conflict is damaging the economic health of the petrostates, thereby jeopardizing a major source of further investment in American AI firms. The Trump administration “staked a lot on the Gulf as their close AI partner, and now the war that they’ve launched poses a huge threat to the viability of the Gulf as that AI partner,” Winter-Levy said.Plus, “what’s to prevent Iran or a proxy group, or another maligned actor, from tomorrow launching an armed drone against a data center in Northern Virginia?” Chip Usher, the senior director for intelligence at the Special Competitive Studies Project, a national-security and AI think tank, told us. “It could happen. Our defenses are not adequate.” State-sponsored cyberattacks of the variety Iran is known for could also knock a data center offline. You can build all manner of defenses—reinforced concrete, drone-interception systems—but doing so adds cost and time to already costly and slow construction.Just a few things going a bit wrong could compound, all at once, into a cataclysm. To wit: Qatari and Saudi money dries up. Sustained high oil and natural-gas prices drive up the costs of manufacturing chips and running data centers. Already cash-strapped hyperscalers struggle to make lease payments on their data centers, while similarly strained private lenders suffer as all of the AI bonds become deadweight. Tech valuations fall, taking public markets with them; private-equity firms have to sell and torch their assets, putting intense stress on the institutional investors and banks. The rest of the economy, drained of investment because everything was poured into data centers for years, is already weak. Unemployment goes up, as do interest rates. “Bubbles pop. That’s the system,” Lipton said. “What isn’t supposed to happen is that it takes down the whole financial system. But the concern here is that AI investment isn’t confined and may spread to the whole economy.”Even if Iran and the Strait of Hormuz don’t directly trigger an AI-driven financial crisis, the odds are decent that another vector could. (Remember tariffs?) Energy prices could stay elevated for years, because the targeted fossil-fuel facilities in the Persian Gulf will take a long time to restore. As the U.S. directs huge amounts of attention and military resources toward Iran, it’s easy to imagine China launching an invasion of Taiwan—a scenario that terrifies Silicon Valley, because it would halt the production of chips needed to train frontier models. That’s not even considering the single Dutch company that makes the high-tech lithography machines used to print virtually all AI chips, or the German company that makes the mirrors used in those machines. “There are too many ways for it to fail for it not to fail,” Kedrosky said of the AI industry’s web of risk. “All you can say for sure is this is a fragile and overdetermined system that must break, so it will.”There are, of course, possibilities other than a full-blown, AI-driven financial crisis. Data-center spending could cool gradually enough that a crash is avoided. The revenues of Anthropic and OpenAI have been multiplying every year, which proponents argue means that generative-AI products are on track to eventually become profitable. But on the current trajectory, that would still take years, and there are good reasons to think that this growth will slow or halt. Notably, the main draw of AI tools is “efficiency”: Rather than growing their overall output and the opportunities available to people, executives are hoping that AI will allow them to make cuts to their business operations. The medium-term success of generative AI would likely involve millions of people being put out of work. The range of options seems to be somewhere from mildly bad to historically so.Should the system break, much of the blame would lie squarely with the technology companies. The stakes of this build-out, from the beginning, have been framed in civilizational terms—a geopolitical race alongside an existential one. The winners will control the future and reap the rewards. At every step of the way, AI firms have appeared to prioritize speed above the physical security of data centers, supply-chain redundancy, energy efficiency and independence, political stability, even financial returns. And in that quest for unbridled growth, the AI industry has wrested ungodly amounts of capital from investors all looking for the next big thing, ensnaring the entire economy.Simultaneously, these firms have courted and even bent the knee to a presidential administration that has encouraged their “let it rip” ethos, only to watch as that same administration has plunged the industry into this emerging polycrisis. The AI industry was not made for the turbulence its leaders have helped usher in. The situation has grown so ungainly and untenable that, if Silicon Valley is merely forced to slow down, the viability of all this spending will likely be called into question in ways that could be devastating for many. In finance, being early is the same as being wrong. AI firms want the world to think they’re right on time. The world may have other plans.