The sharp decline in the NASDAQ sparks the AI debt crisis: trillion dollars of capital turned to US debt

According to Woofun AI, in mid-October 2026, a fierce battle over long-term capital pricing power broke out in the global capital market. Artificial intelligence infrastructure companies and sovereign governments became the two core players in this game. The apparent correction in technology stocks conceals deep structural conflicts: the NASDAQ index has accumulated a decline of about 4.5% over the course of a week, and the market value of the Philadelphia Semiconductor Index has evaporated by more than $1.6 trillion.

This phenomenon is not simply a fluctuation in market sentiment; it is a drastic rebalance between the highly leveraged AI industry chain and risk-free sovereign assets when capital re-evaluates the risk and return boundaries. As the bond yields of next-generation cloud service providers such as CoreWeave (CRWV.US) soared to more than 15%, and the cost of debt default insurance implied a five-year default probability of over 60%, US 10-year Treasury yields fell back to around 5.1% after a brief surge, marking a significant rise in risk aversion in the market.

This shift in capital flows has profoundly revealed direct competition between aggressive private sector expansion and public sector financing needs, and is reshaping the underlying logic of global capital markets.

The degree of differentiation in market performance far exceeded the moderate decline in the overall index, and the lethality of the segments showed extreme asymmetry. Although the overall decline in the Nasdaq 100 index was about 5%, the trend of capital migration from the highly leveraged AI industry chain to relatively safe assets was clearly reflected at the individual stock level. The stock price of Dell (DELL.US) experienced a sharp drop of about 20% in the short term; CoreWeave (CRWV.US), which is at the forefront of the AI computing power arms race, fell close to 25% in a single week, and even experienced sharp fluctuations of more than 15% on some trading days. In contrast, although big tech giants such as Microsoft (MSFT.US), Apple (AAPL.US), and Oracle (ORCL.US) declined slightly, they were not spared from the overall pressure of the market.

This fragmentation is not an accident of market noise, but a direct result of investors repricing the vulnerability of the highly leveraged AI industry chain. When the market realized that the debt chain of some AI infrastructure companies was at risk of breaking, capital quickly withdrew from high-risk targets and sought safe havens with higher certainty, leading to significant valuation compression and liquidity differentiation within the technology sector.

The core variable triggering this market turmoil is the extreme fragility of AI infrastructure companies' debt chains. Take CoreWeave (CRWV.US), an AI cloud service provider supported by Nvidia (NVDA.US), as an example. As of the end of June 2026, its total debt and lease obligations reached 52 billion US dollars, of which simple debt was about 35.6 billion US dollars. Even worse, the company's net interest expenses surged to $640 million in the second quarter, doubling the year-on-year increase. Against the backdrop of rising financing costs, the yield on CoreWeave (CRWV.US) bonds due in 2031 once approached 16%, and the bond price fell below 80 cents, officially entering the typical distressed bond range.

At the same time, some Oracle (ORCL.US) data center project loans have already been traded at a discount, and the debt scale of hyperscale cloud vendors such as Amazon (AMZN.US) and Alphabet (GOOGL.US) continues to expand. According to data compiled by Woofun AI, if the current investment pace is followed, AI-related infrastructure investment may reach trillions of dollars by around 2030, but the corresponding service revenue expectations are far below this scale. Some analyses even indicate that there may be trillions of dollars in potential depreciation risks. The market is beginning to question a core assumption: can the huge capital expenditure on AI infrastructure be converted into sufficient cash returns in the foreseeable future to cover the growing debt burden.

In this battle for capital, the US government is also facing tremendous pressure to refinance, forming a direct competitive relationship with AI companies. The size of the federal debt has exceeded 40 trillion US dollars, and large amounts of maturing debt need to be rolled every year. This has made trillions of dollars of capital held by pensions, insurance companies, foreign central banks, and sovereign wealth funds the focus of competition between the two sides. The amount of capital controlled by management agencies such as BlackRock (BLK.US) and Vanguard can easily be measured in trillions, and their allocation decisions directly affect market pricing. AI companies have a clear and aggressive financing logic. They claim to master transformative technology and require huge upfront capital to build data centers, purchase GPUs to train next-generation models, and ultimately provide services to end users. The main source of funding is debt. Investors lend funds in exchange for interest returns ranging from 5% to 15%. The debt balances of hyperscale cloud vendors such as Amazon (AMZN.US), ORCL.US (ORCL.US), and Alphabet (GOOGL.US) have risen significantly over the past three years, and Oracle (ORCL.US) debt is close to half of their market value.

However, as investors begin to worry that the AI sector may burst into a bubble, slow demand, and increase deflationary pressure, they tend to lock in the current high treasury yield ahead of time. The 10-year US bond yield declined after a short stay around 5.3%. It is a reflection of this safe-haven demand. Government bonds are viewed as close to risk-free assets because the US has the right to issue currency, so there is no possibility of default in theory.

The debt structure of the AI industry chain has a clear mismatch between term and return, further exacerbating market concerns. Data center leases are often as long as 10 to 15 years, and customer contract periods are shorter; companies borrow at a higher cost, but the actual return on capital may initially be much lower than the cost of financing. The analysis indicates that the borrowing costs of some new cloud service providers are close to 9%, while the return on capital may only be around 1%, causing continued negative cash flow pressure. Although hyperscale manufacturers have stronger balance sheets, they are unable to completely avoid the pressure. Their capital expenses were close to or surpassed operating cash flow in 2026, and their debt financing ratio rose from less than 10% in the past to over 30%.

Global AI-related debt issuance plummeted to $23 billion in September 2026, far below the peak of $113 billion in June, indicating a sharp tightening of the financing environment. Banks are beginning to be wary of concentration risks, and loans for some projects have already entered the pressure zone. The classic dilemma faced by investors has reappeared: with high returns comes high risk. Lending money to established tech giants and rapidly expanding new cloud service providers has a very different risk premium. When the market began to doubt that the final consumer's ability to pay was insufficient to cover the entire debt chain, risk appetite quickly reversed, and capital flows to deterministic government bonds. Even though the inflation data is still resilient, the 10-year US Treasury yield is still declining due to safe-haven purchases. The investor logic is simple: if the bursting of the AI bubble causes demand to slow down, economic growth slows, and inflation cools down, interest rates may fall in the future. The current long-term yield of 5% or more has locked in value.

This migration is not a complete sell-off of risky assets, but rather a redistribution of risk budgets. Part of the capital is being transferred from NASDAQ-related positions to treasury bonds, forming a phased situation where “AI is pressured and treasury bonds benefit.” The impending midterm elections have also heightened uncertainty, and the market is more inclined to reduce risk exposure before clarifying policy direction. The essence of this round of adjustments is the capital market's re-evaluation of the gap between 'technological optimism and capital reality'. The long-term potential of AI technology itself has not been denied, but the pace of debt-driven expansion, uncertainty in payback cycles, and direct competition with sovereign financing requirements are forcing investors to recalculate risk premiums. Actual barriers to data center construction — power supply, delays in approval, and rising equipment costs — further amplify concerns. Some projects have been delayed, and rising financing costs will directly reduce future profit margins.

At the same time, hyperscale manufacturers are still maintaining high capital expenditure plans, showing that the emphasis placed on competitive positions within the industry far exceeds short-term cash flow pressure. From a broader perspective, this battle for capital has revealed a structural characteristic of the modern financial system: when technological changes require huge upfront investments, competition between the private sector and the public sector for long-term savings will increase significantly. As a result, the status of treasury bonds as the benchmark risk-free asset has been re-emphasized, and highly leveraged growth assets must prove that they can generate sufficient real returns. Whether the market can stabilize depends on three key variables: first, whether the actual rate of implementation of AI service revenue can support debt repayment; second, whether financing costs continue to rise, or whether central bank policies shift to easing; and third, how the relative appeal between sovereign debt issuance and corporate debt issuance evolves.

If signs of economic slowdown become further evident, the safe-haven properties of treasury bonds will continue to strengthen; if AI application scenarios are quickly commercialized and bring considerable cash flow, the debt pressure on highly leveraged companies is expected to ease. Currently, the market is in a 'wait and see and repricing' phase. Any news of slowing demand or project delays may cause further fluctuations. Capital is never limitless, and when AI companies and governments reach out to compete for the same pool of capital at the same time, the pricing mechanism is bound to come into play. The result of investors voting with their feet was that risk asset valuations were reduced and safe asset returns were favored in stages. This is not simply the bursting of a technology bubble; it is a profound rebalancing of capital scarcity. Market trends over the next few months will depend on whether this rebalancing is completed in an orderly manner, rather than evolving into a broader credit crunch.