OpenAI orders are for the future, but debt is massive! Oracle (ORCL.US) default risk hits historic high, CDS surpasses 2008 peak

According to The Edge Finance APP, the latest statistical data shows that the default risk of database software and cloud computing giant Oracle (ORCL.US) has surged to a historical high, surpassing the peak levels recorded during the 2008 global financial crisis. The company's five-year Credit Default Swap (CDS) spread—a key indicator measuring the cost of insuring against the risk of default on its debt—has soared to an all-time high, indicating that investors are increasingly concerned about Oracle’s real credit default risk.

A Credit Default Swap (CDS) is a financial contract that provides protection against the risk of a borrower defaulting on debt. A higher CDS spread indicates that investors demand a higher risk premium to insure the related debt against default, reflecting a perceived rise in credit risk by the market.

Oracle’s CDS Surpasses the Peak Achieved in 2008

In less than a year, Oracle has shifted from a global leader in AI cloud computing to a company with default risk at record highs, exceeding the “junk-rated cliff” reached during the 2008 financial crisis. On July 9, 2026, S&P Global—a leading international credit rating agency—downgraded Oracle to BBB-, just one notch above the most pessimistic “junk” rating.

As Oracle's CDS sharply rises, the company is ramping up its spend on AI infrastructure, and the still-unprofitable OpenAI has become one of its largest cloud computing order clients. This includes Oracle investing billions of dollars in cloud computing capacity and AI data centers to meet the surging AI compute demand from clients like OpenAI.

Oracle is increasingly reliant on debt financing and single AI application clients to support the expansion of its AI cloud infrastructure business. As capital expenditures in AI keep rising, investor concerns about its balance sheet are intensifying. Contracts related to OpenAI bring Oracle huge forward compute demand, but Oracle must commit capital up front for data center land agreements, chips, power, and financing costs.

Oracle is advancing long-term AI compute demand from major clients like OpenAI into data center construction, capital expenditures, and AI financing burdens, while whether these orders materialize still depends on the client’s financing capability, AI revenue growth, and contract performance. Oracle’s Remaining Performance Obligations (RPO) for fiscal year 2026 reached $638 billion, but high capital expenditures, about $129.5 billion of debt, and long-term lease commitments have led to a re-pricing of its credit risk.

These concerns have put persistent selling pressure on its share price. Oracle's stock price has fallen 27% so far this year, with a nearly 44% drop over the past twelve months—mainly because investors are reassessing the company’s aggressive AI investment strategy and its continuously rising credit leverage levels.

From AI Cloud Computing Leader to the Eye of a Credit Default Storm

Oracle has not fallen into credit crisis due to a complete failure in its AI compute-related cloud business. Rather, its AI order growth has far outpaced its own capital capacity.

In fiscal year 2026, Oracle's cloud infrastructure business—Oracle Cloud Infrastructure (OCI)—saw revenue rise 77%, cloud business revenue increase 39%, and RPO soar 363% to $638 billion, confirming its status as a key provider of global AI training and inference infrastructure. However, to deliver on these future revenues, the company’s annual capital expenditure jumped from $21.2 billion to $55.7 billion.

Even with record operating cash flow of $32 billion, free cash flow still turned negative to $23.7 billion. In other words, Oracle has experienced an “explosion in AI cloud infrastructure demand orders” but has yet to achieve a sustainable cash return from these massive contracts. Its commercial victory in AI cloud computing has thus evolved into a balance sheet stress test.

The real panic in the credit market lies in the simultaneous stacking of leverage, term, and counterparty risk: by the end of fiscal year 2026, Oracle had about $129.5 billion in debt, equivalent to 4.3x EBITDA, and signed approximately $260 billion in long-term data center lease commitments. These leases typically last 15–19 years, while some customer compute contracts run up to only about five years, creating a classic asset-liability maturity mismatch. More crucially, OpenAI accounts for about half of Oracle’s $638 billion RPO, resulting in Oracle essentially using its investment-grade credit to build long-term fixed assets ahead of time for an AI lab that is still burning cash.

As a result, S&P downgraded Oracle to BBB-, just one step above junk grade on July 9, 2026; as of early August, its five-year CDS had risen to about 215 basis points, and bond yields reached levels common in high-yield debt, around 7%–8%. This does not mean Oracle is about to default, but rather that the credit market is now pricing in unprecedented tail risks such as “data center delays, OpenAI underperformance, rapid chip depreciation, or rising refinancing costs.”

Oracle’s key difference from Microsoft, Alphabet, and Amazon is not weaker AI demand but that its leverage was already substantially higher, free cash flow much weaker, and client concentration much greater as it entered a new building cycle. Free cash flow conversion rate, client prepayment ratios, mismatches in lease and contract durations, adjusted debt/EBITDA, CDS, and credit ratings are becoming the core metrics as investors scrutinize who the real winners of the “AI super bull market” will be.

Oracle has thus become the first credit test case of the entire AI debt cycle. According to Castle Securities, by 2028, AI chip procurement alone could generate over $500 billion in new debt. This kind of “compute securitization” can lock in AI compute cluster procurement, optical communication/interconnections, data center CPUs, high-performance Ethernet switch infrastructure, data center HBM/DRAM/NAND memory components, and power chain orders ahead of time, but it also transforms commercial risks that originally depended on end AI revenues into nested credit risks among bonds, SPVs, long-term leases, and supplier guarantees.