AI Race Puts Tech Giants Under Debt Pressure

The frenzied spending on artificial intelligence (AI) has revealed a new equation in the technology sector, where business models that are asset-light and generate high cash flows—such as search engines, e-commerce, and social media platforms—are no longer sufficient to drive the market. They are being replaced by massive investments requiring enormous capital, which only a handful of companies possess.

Companies such as Nvidia, Microsoft, Amazon, Alphabet, Meta, and Oracle have poured trillions of dollars into AI-related projects, with direct and indirect financial commitments potentially reaching $4.2 trillion through debt and long-term contracts. While debate usually centers on the viability of these investments and their future returns, a more pressing question has emerged: Can these companies continue to fund this costly race?

Data shows that the scale of these commitments is raising growing concerns, despite the huge profits these companies are generating. Part of the ambiguity stems from the fact that many AI-related obligations do not appear clearly on financial statements but are instead buried in notes and side disclosures that are difficult to track, according to an extensive Bloomberg analysis reviewed by Arabiyya Business.

Although this situation does not necessarily pose a long-term threat to the stock market, the weight of these companies within major indices and investment portfolios means that any potential stumble could have a wide-ranging impact on investors and the U.S. economy, which currently benefits from the wave of AI-related spending.

These companies have found themselves facing a complex equation: they cannot withdraw from the AI race for fear of losing their competitive position, yet not all of them can win. In the best-case scenario, some companies may end up in weaker financial positions after spending billions to maintain their market share.

Some companies face greater risks if AI erodes their core businesses and reduces profits while debt accumulates, potentially forcing them later to sell AI-related assets to competitors at depressed prices. Observers cite the concept of “Google Zero,” which anticipates a decline in website traffic from search engines as users increasingly rely on AI models to obtain answers directly.

This scenario has revived memories of the late-1990s internet infrastructure bubble, when telecom companies invested hundreds of billions in fiber-optic networks and wireless communications in anticipation of rising demand. However, slowing demand at the turn of the millennium led to the collapse of major companies such as WorldCom and Global Crossing under the weight of debt.

Although those investments later contributed to building the modern internet, the same scenario could repeat with AI if the supply of computing capacity exceeds actual demand in the coming years.

A significant portion of current revenue depends on advanced AI labs such as OpenAI and Anthropic, which pay huge sums for the computing power needed to develop large language models. However, these models face increasing competition from cheaper alternatives, particularly from Chinese companies offering open-weight models, which could squeeze profit margins and delay the realization of expected returns from current capital expenditure.

Collectively, the tech giants recorded operating profits nearing $1 trillion this year, yet that may not be enough. Their disclosed commitments reached approximately $1.6 trillion by June, nearly double their level two years prior, in addition to about $2.6 trillion in off-balance-sheet obligations, such as lease agreements, operating contracts, and guarantees.

Although these figures remain estimates subject to adjustment, deferral, or cancellation, they provide an indication of the scale of burdens accumulated by companies in their bid to dominate the artificial intelligence market.

A comparison of commitments to operating profits reveals significant variation among firms. Nvidia can cover its current commitments in about a year and a half, whereas Microsoft, Amazon, and Alphabet require between four and six years. Meta and Oracle, meanwhile, would need a full decade or more due to their lower operating profits relative to the rest of the group.

The bond market has also reflected these concerns. Meta’s and Oracle’s medium-term bonds are trading at wider premiums over U.S. government bonds than the average for companies with similar credit ratings, suggesting that investors perceive less room for these two firms to absorb additional debt compared to their competitors.

Estimates suggest that trillions of additional dollars will continue to flow into AI projects as long as current profitability levels hold. However, any economic slowdown or decline in demand for AI technologies could turn these commitments into a heavy burden for some companies.

In any case, the AI journey appears set to continue. Current large language models represent only the first stage of the technology, with attention now shifting toward more advanced applications, including robotics, autonomous vehicles, and sophisticated automation systems. The pressing question is no longer whether AI investments will pay off, but which companies will be able to withstand the pressure until those returns materialize.