Tech Giants Borrow Record Sums for AI Race, Raising Balance Sheet Risks

The technology industry has entered a borrowing spree unlike anything seen before. Through the first week of December 2025, global tech companies had issued $428.3 billion in bonds—a record that reflects something deeper than simple opportunism. These are not desperate firms scraping together capital. These are the world's largest, most profitable technology companies, many of them sitting on enormous cash reserves, and they are choosing to borrow anyway.

The numbers tell the story of an industry in the grip of competitive urgency. American firms alone accounted for $341.8 billion of that total, with European and Asian tech companies contributing $49.1 billion and $33 billion respectively. The scale is staggering, but the reason behind it is straightforward: artificial intelligence infrastructure is expensive, and the race to build it is unforgiving. Companies that fall behind risk losing their position in a technology that may define the next decade of computing.

For decades, large technology firms operated on a different financial model than most industries. They generated enormous cash flows from their existing operations and used those internal resources to fund new ventures and expansions. Borrowing was optional, a tool for specific circumstances. That calculus has shifted. With interest rates relatively low and investor appetite for tech debt remaining strong, companies have discovered that borrowing allows them to accelerate their AI spending without waiting for earnings to accumulate. Michelle Connell, president at Portia Capital Management, frames this as a structural change in how the industry must operate. The problem is that artificial intelligence chips and infrastructure become obsolete quickly. Companies cannot afford to wait. They must reinvest continuously, and debt has become the mechanism to do so at speed.

But this acceleration is beginning to show up in the financial metrics that investors and analysts watch closely. A Reuters analysis of more than 1,000 technology firms with market capitalizations of at least $1 billion reveals that their median debt-to-EBITDA ratio—a standard measure of how much debt a company carries relative to its earnings—has nearly doubled to 0.4 by the end of September. That ratio was half that level during the 2020 debt surge, when companies borrowed heavily during the pandemic. While the current levels remain below what financial analysts typically consider alarming, the trajectory is concerning. Debt is growing faster than earnings, which means the companies are taking on obligations that their current profitability may not easily support.

Operating cash flow tells a similar story. The median ratio of operating cash flow to total debt fell to 12.3 percent in the second quarter of 2025, the lowest point in five years, before recovering slightly later in the year. This metric matters because it shows how quickly a company could theoretically pay down its debt using the cash it generates from its core business. When that ratio falls, it signals that companies are becoming more dependent on continued strong performance to service their obligations.

The credit markets have begun to price in this risk. Oracle's five-year credit default swap spreads—a measure of how much investors demand to protect themselves against the company defaulting on its debt—have nearly doubled to 142.48 basis points over just two months. Microsoft's spreads have climbed to approximately 35 basis points from around 20.5 at the end of September. These are not dramatic moves, but they represent a visible shift in investor sentiment. The market is asking whether these companies can deliver the returns they are promising from their AI investments.

Scott Bickley, an advisory fellow at Info-Tech Research Group, sees the current moment as unsustainable. He describes the phenomenon as an overheated marketplace where companies feel compelled to borrow and spend aggressively to maintain their stock prices and competitive positions. The narrative has become binary: go big or go home. But Bickley argues this cannot continue indefinitely. The question now is what happens if the AI investments fail to generate the returns companies are banking on. The largest tech firms remain profitable and maintain substantial cash buffers, which provides a cushion. But the margin for error has narrowed. The industry has bet heavily that artificial intelligence will justify the spending. If that bet does not pay off, the balance sheets that seemed so strong could face real pressure.