AI infrastructure funding increasingly shifts from equity markets to structured debt as capex demands exceed publicly-available capital

The AI trade is acquiring a new kind of leverage: debt. UBP estimates Microsoft, Amazon, Alphabet, Meta, and Oracle could spend $1 trillion to $1.3 trillion on capital expenditure in 2027, pushing AI financing far beyond the stock market and into global credit markets.

The numbers explain why borrowing has become unavoidable. UBP estimates these five hyperscalers will spend roughly $820 billion on capex in 2026, compared with about $750 billion in operating cash flow. In 2027, projected spending rises to $1 trillion to $1.3 trillion, creating a financing gap that earnings alone cannot cover. Bonds are taking much of the load, but they are no longer the only source of capital. UBP points to project finance, securitization, leveraged loans, and chip-backed financing as additional channels supporting the AI buildout.

Goldman Sachs estimates investors have provided roughly $500 billion of financing to AI-linked groups this year, with the five hyperscalers accounting for about $200 billion of that figure and potentially issuing more than $1 trillion of additional debt over the next few years.

The ECB reports that hyperscalers issued more than $100 billion of bonds last year and now account for close to a tenth of new euro bond issuance by non-financial companies. Its analysis warns that continued borrowing could eventually force other companies to compete harder for investor capital. Some companies are already timing bond sales around hyperscaler issuance or shortening maturities to avoid direct competition for demand. Even sovereign debt managers are reportedly watching the timing of major technology deals.

This creates a feedback loop: AI requires more infrastructure, infrastructure requires more capital, and more borrowing increases the supply of bonds investors must absorb. Credit investors are increasingly financing the same companies, data centers, chips, and power infrastructure supporting the AI boom. UBP estimates the five hyperscalers' cumulative investment could reach $5.6 trillion through 2030, while riskier AI infrastructure companies such as CoreWeave are also tapping high-yield markets.

That creates a wider test for the AI investment thesis. The boom ultimately has to generate enough operating cash flow to support the infrastructure being financed today. If spending keeps accelerating while returns lag, the pressure would show up not only in stock valuations but also in bond spreads, borrowing costs, and future capex decisions. For investors, the next AI catalyst may come from the credit market rather than the Nasdaq: whether the world's biggest technology companies can turn extraordinary capital spending into the cash flow needed to finance the next phase of the boom.