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AI infrastructure spending is quietly reshaping Big Tech balance sheets and risk profiles.
Oracle and Apple stand out as carrying higher debt relative to cash.
Big tech borrowing reflects urgency to compete, not immediate financial distress.
‘Big tech’ once conjured images of asset-light software giants swimming in cash, companies whose financial reserves dwarfed the entire economic output of small countries. That picture has quietly shifted.
Today, the sector's swelling debt loads tell a more complicated story. These companies have started to resemble capital-hungry utilities and telecommunications giants far more than the nimble software firms they once were.
This transformation is driven by the relentless AI infrastructure boom. To keep pace with 'hyperscaling,' companies are no longer just spending their pocket change; they are tapping bond markets for hundreds of billions to build data centers, secure specialized power grids, and purchase the GPUs required to power the next generation of artificial intelligence.
When examining which big tech companies have the most debt, two names consistently stand out: Oracle and Apple. While their reasons for borrowing differ, they represent the most leveraged players in the hyperscaler race.
Oracle has become the 'poster child' for this debt-fueled pivot. By February 2026, Oracle's long-term debt had ballooned to roughly $100 billion, largely to fund its massive cloud infrastructure expansion. Apple, while still incredibly profitable, is the only other major player that carries more debt than it has liquid cash on its balance sheet.
The following table breaks down the current solvency of the key players:
Investors assess these numbers by studying the Debt-to-Equity (D/E) and Debt-to-Capital (D/C) ratios. The ratios show how much money a company needs to borrow to finance its growth compared to its own funds. The high Oracle ratio, which exceeds 500%, indicates that the company relies on lenders for its operations, creating financial risks if its artificial intelligence investments fail to generate immediate returns.
If these companies are so profitable, why do big tech companies borrow so much? The answer lies in the sheer scale and speed of the AI arms race:
The Scale of Hyperscaling: Developing one AI-capable data center requires an investment of multiple billions. Alphabet's 2026 capital expenditures projection is $185 billion, which exceeds what the company can fund with its internal cash resources.
Urgency vs. Liquidity: In AI, being second is often losing. Borrowing allows companies to deploy capital immediately to secure GPUs and land for data centers before competitors do.
Attractive Bond Markets: Despite high interest rates, Big Tech firms maintain stellar credit ratings. Investors are 'starved' for high-quality corporate bonds, allowing companies like Alphabet to issue rare 100-year "century bonds" at favorable rates.
Rapid Obsolescence: AI hardware ages at an incredible rate. To stay at the frontier, companies must reinvest in new chips every 12 to 18 months, creating a cycle of constant, high-cost capital needs.
The current borrowing increase represents a fundamental transformation of market conditions instead of being an accounting anomaly. The operational structure of Big Tech companies now operates similarly to traditional industrial companies. This means that instead of high-margin software returns, we may see margins pressured by interest payments and depreciation.
The excessive borrowing by the company will create financial difficulties if the actual AI returns do not meet the anticipated results. The 'debt tsunami' from tech giants, which includes Oracle’s $25 billion bond offering, creates such an overwhelming financial burden that it prevents smaller companies from obtaining capital because it consumes all available funds in the credit markets.
For most, this debt is strategic. Microsoft, Meta, and Alphabet maintain their strong financial positions because they possess more cash than their total debt obligations. The company uses its various revenue sources, which include advertisements and business software, to create a financial protection system.
However, Oracle remains the outlier. With a credit rating in the BBB range and a negative outlook, it is the most exposed if the AI market cools. For the rest of the sector, the debt is an expensive but necessary bet on the future of computing.
Big tech debt signals a new era of ‘capital intensity’. The days of tech companies operating as unleveraged, high-liquidity entities are fading. As we move forward, a company’s ability to manage its leverage will be just as important as its revenue growth. For investors, the 'AI winner' won't just be the one with the best model but the one who can afford to build the infrastructure to run it without breaking the bank.
What is Big Tech debt and why is it rising in 2026?
Big Tech debt refers to money borrowed to fund AI data centers, chips, and cloud infrastructure, as internal cash alone can no longer cover the scale of spending.
Which Big Tech companies have the most debt right now?
Oracle and Apple stand out because their long-term debt is higher than their cash, unlike most peers with larger cash buffers.
Why do Big Tech companies borrow so much instead of using cash?
Borrowing helps protect cash reserves for operations and flexibility, while bond markets offer relatively cheap funding for long-term projects like AI infrastructure and data centers.
Is rising Big Tech debt a serious financial risk today?
For most companies, cash flow and strong credit ratings make the risk manageable, but heavy borrowing becomes dangerous if AI investments fail to generate expected returns.
How does Big Tech borrowing affect smaller companies and markets?
Large tech bond issuances can raise borrowing costs for smaller firms, as investors shift capital toward safer, well-known issuers instead of riskier businesses.
