The Middle East conflict is creating a structural shock to the global AI industry, pushing already elevated tech assets toward the brink of systemic risk.
Energy shocks have already substantively transmitted to AI infrastructure. Shipping disruptions in the Strait of Hormuz have interrupted nearly one-third of global crude oil and one-fifth of natural gas exports. Brent crude prices rose 40% in a single month, liquefied natural gas prices surged simultaneously in European, American, and Asian markets, and spot prices for helium doubled. The costs of critical links such as chip manufacturing and data center operations are facing a reassessment.
Meanwhile, the rift between the AI investment boom and macroeconomic fragility is deepening. According to The Atlantic, by the end of 2025, almost all economic growth in the U.S. will be driven by AI investment, but this singular growth engine is now facing the risk of multiple simultaneous failures. Year-to-date, core tech stocks including Alphabet, Meta Platforms, Microsoft, Amazon, NVIDIA, and Oracle have fallen cumulatively by 8% to 27%, dragging down overall stock market performance.
Investor Paul Kedrosky points out that the fundamental difference between the current AI crisis and the 2008 commercial real estate risk is that &#34;all the fragile nodes are interwoven.&#34; Under the combined effects of energy shocks, debt accumulation, and supply chain disruptions, the AI industry is facing its first systemic stress test since becoming an economic pillar.
<h2>Highly Concentrated Supply Chains, Energy Shocks Strike at the Core</h2>
The highly concentrated supply chain layout of the global AI industry is being exposed to systemic shocks due to the blockade of the Strait of Hormuz.
Currently, the most advanced memory and training chips are primarily supplied by three Asian companies, and these economies rely on imports from the Persian Gulf for the vast majority of their crude oil and a significant amount of liquefied natural gas. Key raw materials essential for chip manufacturing, such as helium, sulfur, and bromine, are also highly dependent on imports from this region. The de facto closure of the Strait of Hormuz has triggered multiple transmission mechanisms:
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Shortage of critical materials: Tight helium supply has begun to threaten the stable production of AI chips, with significant upward pressure on prices;
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Rising operational costs: Surging energy prices place greater operational pressure on large-scale data centers that are already struggling to achieve profitability;
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Impeded capacity expansion: New data center projects face the risk of stagnation under the dual squeeze of costs and supply chains.
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Sam Winter-Levy, a researcher in technology and national security at the Carnegie Endowment for International Peace, noted that the Strait of Hormuz &#34;is critical to almost every aspect of the global economy, and the AI supply chain is not immune to this.&#34;
Simultaneously, the conflict is eroding the support base of the AI industry from two directions: physical security and capital supply. As the Middle East conflict persists, Amazon's data centers in the UAE and Bahrain have come under attack, and the security situation in Gulf nations—key nodes in U.S. AI strategy—has deteriorated sharply.
The Trump administration had previously positioned Saudi Arabia, the UAE, Qatar, and Oman as core partners in the AI field and actively sought their financial support. However, the war is both weakening the economic resilience of these oil-producing nations and threatening their capacity for sustained investment in U.S. AI companies.
<h2>High Debt, Financial Risks Accumulating Rapidly</h2>
The financial risks of the AI industry stem not only from external shocks but also from concerns over its inherent business logic.
Hyperscale data center operators such as Microsoft, Alphabet, Meta Platforms, and Amazon have invested a combined total of nearly $700 billion in the AI sector in a single year. To raise these funds, data center providers have significantly increased leverage, including entering into structured financing arrangements with private equity firms such as Blackstone, BlackRock, and Blue Owl Capital. In 2025, the scale of debt issuance by hyperscale operators reached $121 billion, four times the average level of the past several years, and is expected to grow significantly in the future.
Brad Lipton, former senior advisor at the U.S. Consumer Financial Protection Bureau and current Director of Corporate Power and Financial Regulation at the Roosevelt Institute, pointed out: &#34;The current situation is reminiscent of certain precursors to the 2008 financial crisis. Various market entities are highly interconnected—banks lend to private credit institutions, which then channel funds to other sectors, thereby amplifying systemic risk.&#34;
The business model of the AI industry is simultaneously facing endogenous deflationary pressure. The largest cost item for data centers, advanced AI chips, is depreciating rapidly due to accelerated iterations, meaning the value of data centers as the underlying assets for debt is not solid.
At the same time, AI services are billed by the token, and unit costs continue to decline as model capabilities improve. Kedrosky describes this as a &#34;death spiral to zero&#34;: token prices are plummeting, simultaneously eroding the total value that data centers can generate.
<h2>Clear Risk Transmission, the Crisis is Not Hyperbole</h2>
Private equity firms are facing a double squeeze: on one hand, the software companies they acquired are facing valuation pressure due to AI disruption; on the other hand, recent bets on data center investments are also in trouble.
Institutions such as Blackstone and Blue Owl have invested heavily in data center construction with the prerequisite of rental income from tech companies for debt repayment, but as hyperscale operators' cash flows tighten, the viability of this business model is being questioned. The funding for these private equity firms primarily comes from pensions, endowments, and insurance institutions, thus extending the risk transmission chain to the entire financial system.
According to an analysis by The Atlantic, minor issues in just a few links could simultaneously trigger a systemic crisis. For example:
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<li>Cost-side shocks: Persistently high oil and gas prices drive up the costs of chip manufacturing and data center operations.</li>
<li>Industry funding chain breakdown: Hyperscale data center operators, already tight on funds, become unable to pay rent; private credit institutions, also stretched thin, are hit hard as AI-related bonds become &#34;dead debt.&#34;</li>
<li>Financial market transmission: Tech company valuations fall, dragging down public market performance; private equity firms are forced to sell assets, putting immense pressure on institutional investors and banks.</li>
<li>Real economy imbalance: Due to years of excessive capital concentration in data centers, other economic sectors have lacked investment, leaving the overall economy already weak.</li>
<li>Stagflation risks emerge: Ultimately, unemployment rates climb as interest rates rise simultaneously.</li>
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&#34;A bubble bursting is inevitable; it is a law of the economic system,&#34; Lipton said. &#34;What should not happen is that a bubble burst destroys the entire financial system. But the concern is that the impact of AI investment is not limited to this; it could spread to the entire economic sphere.&#34;
Of course, a full-scale crisis is not the only possible outcome. Data center spending may cool down in a sufficiently gradual manner to avoid a hard landing; supporters argue that generative AI products will eventually become profitable, with revenues for both Anthropic and OpenAI doubling annually. However, on the current trajectory, this goal is still years away, and the risk of slowing growth or even stagnation is equally real.

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Middle East conflicts are pushing up energy and helium prices, causing costs for chip manufacturing and data center operations to soar. AI giants are burdened with high debt, and private credit chains are highly interconnected. The AI business model faces a dual paradox: accelerating chip iterations lead to rapid depreciation, while declining token prices compress revenue margins. As energy shocks, supply chain pressures, and business model dilemmas coincide, tech stock valuations are under pressure. Year-to-date, core tech stocks including Alphabet, Meta Platforms, Microsoft, Amazon, NVIDIA, and Oracle have fallen cumulatively by 8% to 27%

- The ongoing Middle East conflict is creating structural shocks to the global AI industry, pushing technology assets towards systemic risk.  
- Energy disruptions have impacted AI infrastructure, leading to rising costs and supply chain vulnerabilities, particularly in advanced chips and data centers.  
- Increased financial risks within the AI sector, driven by high debt levels and external pressures, raise concerns of a potential systemic crisis affecting the broader economy.

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