Big Tech’s AI ambitions drive massive debt-funded investment

Over the past few years, America’s largest tech companies have been competing fiercely to develop advanced AI systems while also providing computing power to a booming startup ecosystem—a race that has come at enormous cost. To achieve these goals, they have fundamentally transformed their financing strategies. Long reliant on strong revenues and rising stock prices, Alphabet’s Google, Meta Platforms, and other tech giants are now taking on massive amounts of debt to build the technology needed to power chatbots.

In March this year, Amazon issued bonds in Europe for the first time, raising €14.5 billion (approximately $17 billion), setting a record for the largest corporate bond issuance in euro history. The retail giant also issued $37 billion in bonds in the U.S. bond market, making it the fourth-largest corporate bond issuance in U.S. history. According to Bloomberg, Meta, the parent company of Facebook, issued $25 billion in investment-grade bonds on April 30 to fund its artificial intelligence initiatives.

The four major U.S. tech companies stated that this year alone, they will need to spend a total of approximately $650 billion on data centers, networking equipment, and other AI infrastructure to achieve their AI goals.

An analysis of how dependence on lending has transformed the technology industry and fueled the rise of artificial intelligence.

For years, tech companies that emerged during the internet boom grew by reinvesting massive profits back into their businesses. They also issued bonds, but this played a smaller role in raising and using capital. However, starting in late 2025, major tech companies began issuing hundreds of billions of dollars in bonds, competing to increase investments in artificial intelligence capabilities. Meanwhile, emerging companies like OpenAI and Anthropic have each raised billions of dollars from venture capital firms.

Most of the funding these tech companies raise—whether through debt or equity financing—is directed toward AI-related equipment, services, and real estate. Alphabet alone has stated that approximately 40% of its technology infrastructure spending goes toward data centers and networking equipment, while 60% is allocated to servers. Oracle is a prime example of data center spending: this database giant has been raising funds through corporate debt and project-specific loans to build data centers across the country.

However, this is not just a real estate issue. These companies also need to equip their facilities with expensive chips for training and running AI models. Typically, companies establish special-purpose vehicles (SPVs)—essentially independent entities formed for specific financial objectives, including the purchase of technology equipment. Through SPVs, debt can be kept off the company’s balance sheet, protecting the company from potential downgrades in credit ratings. Since late 2025, Elon Musk’s xAI has been working to raise up to $20 billion through off-balance-sheet entities that purchase chips and lease them back to xAI.

Two additional expenses have intensified the race for first place: electricity costs and AI talent. Facing challenges in meeting its data center demands, Alphabet recently acquired a clean energy developer to power its facilities. Meta has also been spending millions of dollars to hire skilled engineers.

Major tech companies are under significant pressure to build data centers to support AI capabilities. Meta, Alphabet, and other tech giants can leverage their existing cash reserves to construct data centers. Their advertising businesses generate ample cash, enabling them to borrow easily and reinvest a portion of their revenue into AI. For example, Google’s revenue in the fourth quarter of 2025—excluding partner earnings—exceeded $97 billion.

Borrowing remains attractive, especially as Wall Street firms are eager to extend loans. Special-purpose entities (SPEs) further enhance this appeal by allowing companies to remove debt from their balance sheets.

For AI startups, which typically generate far lower revenues than large corporations, taking on significant debt is not always a viable option. Instead, private companies like OpenAI and xAI have raised billions of dollars by selling equity stakes and used these funds to meet their AI needs. However, this approach has limited scope, as equity holders’ ownership stakes are continuously diluted. In 2025, xAI borrowed $5 billion in corporate debt, which it has since repaid. OpenAI and Anthropic have not yet entered the debt capital markets and are currently exploring alternative financing options.

At the end of last year, the lending surge related to artificial intelligence caused panic among investors, as major tech companies raised nearly $100 billion in just a few weeks to expand cloud and data center capacity.

This wave of financing follows Meta’s approximately $30 billion funding round for building a data center in Louisiana. The transaction highlights the massive capital requirements for AI infrastructure and the growing diversification of corporate financing methods. The financing was executed through a special-purpose entity owned by Meta, but the lenders will be repaid via long-term lease agreements with the tech giant. This structure demonstrates that data center operators can issue traditional bonds and raise substantial debt from lenders without significantly increasing their balance sheet liabilities or jeopardizing their credit ratings.

To highlight its appetite for capital, Alphabet issued a rare 100-year bond in early 2026—a transaction unseen among technology companies since the late 1990s—meeting the demands of long-term investors such as insurance companies and pension funds.

Meta returned to the bond market, issuing $25 billion in investment-grade bonds. This bond offering came the day after Meta announced its annual capital expenditure forecast exceeded January’s projection.

This wave of lending in the field of artificial intelligence is notable for its speed, scale, and the types of borrowers involved. Historically, surges in corporate debt have often been linked to speculative bubbles, such as the leveraged buyout boom of the 1980s, when high-risk bonds were issued to fund aggressive corporate acquisitions. In contrast, the recent wave of bonds has been issued by some of the world’s most cash-rich and highest-rated companies.

Despite high interest rates, billions of dollars have been raised in a short period to continue advancing AI infrastructure, reflecting the urgent competition in generative AI. Some market participants have drawn parallels to early infrastructure booms, such as the fiber-optic network build-out during the dot-com bubble, when telecom companies took on massive debt to lay networks.

But there are key differences between the two. Today’s largest issuers are more profitable and have more diversified businesses than many telecom operators at the end of the 1990s.

Despite recent increases in debt financing, debt is expected to remain a relatively small portion of total artificial intelligence spending by large tech companies. Analysts estimate that approximately 80% to 90% of their planned capital expenditures will be funded through operating cash flow. Additionally, despite recent increases in borrowing, the total debt levels of major data center operators are expected to remain low relative to their annual revenues.

However, such a large-scale financing effort can have significant impacts. Higher borrowing levels may alter the company’s financial position, affecting its credit rating and ability to secure low-cost loans. Beyond the technology sector, substantial debt issuance could reshape the credit market by absorbing investor demand that might otherwise flow to other industries. This not only raises borrowing costs for other companies but also increases lenders’ exposure to industries, such as artificial intelligence, where the long-term returns on investment remain unproven.

Morgan Stanley forecasts that in 2026, investment-grade corporate bond issuance could exceed $2 trillion, setting a new record, partly driven by artificial intelligence investments. JPMorgan analysts estimated last year that the high-grade bond market may need to absorb approximately $1.5 trillion in AI data center bond issuance over the next five years. They noted that by 2030, such debt could account for more than 20% of the investment-grade bond market.

If the AI boom fails to meet expectations, major tech companies that have made massive investments in data centers, chips, and power to support AI may end up facing excess capacity and rapidly obsolete equipment. This situation mirrors the dot-com bubble, when telecommunications companies built network capacity far exceeding actual customer demand.

Lower-than-expected profits will strain the company’s cash flow, potentially forcing it to cut investments or increase borrowing, ultimately weakening its financial position.

In addition, there are broader market risks. Investors have poured significant funds into tech bonds and stocks, betting on AI-driven growth. If this optimism fades, stock prices could decline, and lenders could suffer heavy losses.