Tech Giants' AI CapEx on Corporate Debt: A Riskier Race?

TradingKey - The narrative surrounding the AI bubble is growing more complex as tech giants like Meta and Google aggressively tap corporate bond markets to finance surging capital expenditures and AI infrastructure. Investors are increasingly concerned that massive corporate debt and novel off-balance sheet financing are steering the AI race into a debt-driven trap, warning that any misstep in AI monetization could severely impact capital markets.

To fund data centers and other AI infrastructure, major tech companies are seeking to draw substantial capital from financial markets. Beyond issuing large-scale corporate bonds, a new and controversial financing model — off-balance sheet financing — is gaining attention.

This involves tech giants raising tens of billions of dollars in debt through Special Purpose Vehicles (SPVs) and joint ventures, keeping these liabilities off their balance sheets. The aim is to mitigate the adverse impact of massive debt on their financial health.

However, off-balance sheet financing and other non-traditional funding tools have historically cast a shadow over investors, notably in events like the Enron bankruptcy.

Tech giants are adopting these more opaque financing routes, which largely shift risk to third parties, possibly influenced by Elon Musk's xAI SPV model. Furthermore, they may be learning from Oracle's experience with traditional debt issuance.

Oracle's financial outlook has been challenged by limited free cash flow and expanding capital expenditures. Moody's has already downgraded Oracle's debt rating, and Barclays has warned that Oracle could be relegated to junk bond status next year.

The first indication of tech giants' aggressive borrowing emerged with Oracle's $18 billion bond issuance in September. This trend was further confirmed when Meta raised $27 billion for its Hyperion data center in October, followed by another $30 billion. Alphabet, Google's parent company, also sold $25 billion in bonds earlier this month.

This year alone, U.S. AI companies have issued at least $200 billion in corporate bonds. Morgan Stanley suggests that debt-financed AI projects, such as Meta's $30 billion raise for a Louisiana data center, signal an increasing complexity in the narrative driving the tech stock bull market.

Lisa Shalett, an analyst at the firm, notes that a new reliance on private credit, increasingly intricate AI vendor relationships, and uncertainty in AI startup profitability are intensifying investor pressure for tangible returns.The previously straightforward investment narrative is suddenly becoming a lot more complex.

Shalett explained that with debt appearing off-balance sheet, these tech companies will face greater scrutiny as investors demand a return on their investments.

Bank of America also expressed concern over the shift in financing methods employed by major tech companies to support their AI ambitions, indicating that their capital expenditures are nearing the maximum level supportable by company cash flows.

Over the past few days, there have been signs that AI bubble concerns are spreading from equity markets to corporate debt. Bank of America data shows that the yield spread of a bond portfolio comprising hyperscalers like Google, Meta, Microsoft, and Oracle has widened against benchmark U.S. Treasury rates, climbing to its highest level since April.

Google, Amazon, Microsoft, and Meta collectively require $350 billion for AI infrastructure development this year, projected to further increase to $400 billion next year.

JPMorgan anticipates that AI infrastructure construction could cost $5 trillion, potentially requiring participation from every public capital market, private credit, alternative capital provider, and even governments—effectively "draining" every credit market.

Even with significant liquid cash reserves held by tech giants, drawing a wider range of market participants into an AI narrative already showing signs of a bubble naturally raises systemic risk concerns.

Naveen Sarma, an analyst at S&P Global, points out that these companies themselves cannot accurately predict the evolution of the AI industry's landscape five years from now. This uncertainty is a key reason they are not only issuing corporate bonds but also aiming to preserve financial flexibility, guarding against the possibility of certain data centers becoming redundant assets.

While we are still in the early stages of AI development, investors, having experienced three years of AI investment enthusiasm, remain worried about the widening gap between AI computing infrastructure spending and the revenue generated by AI.

For instance, OpenAI, which is projected to cost $1.4 trillion over the next eight years, currently has an annualized revenue of only about $20 billion and is still exploring its monetization model.

Morgan Stanley warns that if this AI unicorn faces a "black swan" event—such as ultimately failing to establish a viable monetization model to support its enormous committed computing power investments—the S&P 500 index could see a 10% to 20% correction.