AI Boom Turns Into a Debt Race for Global Investors

Artificial intelligence has spent the past few years as one of the world’s biggest technology and stock-market stories. Now it is becoming something else: a debt story. Behind the excitement over models, chips and applications sits an enormous physical infrastructure buildout requiring data centers, electricity, networking equipment, cooling systems and specialized hardware. The capital required is so large that even some of the world’s most profitable technology companies are increasingly turning to debt and alternative financing to fund the next stage of the AI race.

That shift matters because borrowing changes the economics of the AI boom. Investors are no longer assessing only whether AI companies can generate spectacular revenue growth. They must increasingly ask whether those companies can generate enough cash to justify the infrastructure being financed today.

AI Debt Financing Is Turning Technology Into a Credit-Market Story

The scale of investment is difficult to ignore. The five major US hyperscalers — Amazon, Microsoft, Alphabet, Meta, and Oracle — are expected to spend around $600 billion on infrastructure in 2026, with a substantial portion related to AI.

That spending is creating a funding gap.

Historically, major technology companies could finance much of their investment from operating cash flow. AI infrastructure is different because the upfront requirements are enormous. Data centers can require billions of dollars before generating meaningful returns, while power connections, chips, and networking infrastructure add further costs.

The Bank for International Settlements says the scale of anticipated AI investment is forcing companies to shift financing away from operating cash flows and increasingly toward debt, with private credit playing a growing role.

The transformation is already visible in bond markets. Hyperscaler corporate bond issuance exceeded $100 billion in 2025, according to the BIS, while companies are also using special-purpose vehicles and other structures to finance infrastructure outside traditional corporate balance sheets.

That creates what the BIS describes as a form of “shadow borrowing” — obligations that may economically resemble debt but are not always immediately visible on a company’s main balance sheet.

AI Debt Financing Could Change the Risk Equation for Investors

The biggest question is not whether companies can borrow.

They can.

The question is whether the returns from AI infrastructure will arrive quickly enough to justify the borrowing.

That is where the credit market becomes particularly important.

If AI revenues continue expanding rapidly, debt can help companies accelerate investment while preserving shareholder ownership. But if demand grows more slowly than expected, companies could find themselves carrying expensive infrastructure with insufficient cash flows to support the associated obligations.

Credit investors are already watching the risks.

The Financial Times reported that credit-default swap prices for several major technology companies, including Oracle, Alphabet, Amazon, Meta, Broadcom, and Nvidia, had risen amid concerns about massive AI capital expenditure. Oracle’s CDS costs increased sharply after announcing a $70 billion data-center investment, while S&P Global subsequently downgraded the company’s credit rating.

This does not mean an AI debt crisis is inevitable.

The BIS currently assesses the broader macroeconomic and financial-stability risks from the AI boom as moderate, while stressing that sustainability depends heavily on AI companies meeting ambitious earnings expectations.

But the financial architecture is changing.

Private credit funds, banks, bond investors, insurers and infrastructure financiers are increasingly becoming part of the AI ecosystem. Some financing structures use dedicated vehicles that borrow against data-center assets, leases or long-term contracts rather than relying entirely on the technology company’s balance sheet.

That creates both opportunity and vulnerability.

For investors, AI exposure can increasingly appear in places that do not immediately look like AI investments. It can sit inside corporate bonds, private-credit portfolios, infrastructure debt, utilities, and property-related financing.

For the global economy, the implications could be even larger.

AI infrastructure requires electricity, construction, semiconductors, real estate and telecommunications networks. As borrowing finances more of that investment, changes in interest rates and credit conditions can directly influence the speed of AI development.

If financing remains cheap and investor confidence remains strong, the AI infrastructure boom could continue accelerating.

If credit conditions tighten, however, projects may be delayed, financing costs could rise and companies could become more selective about which data centers and AI projects actually receive capital.

That makes the next phase of the AI revolution fundamentally different.

The first phase was about who could build the best models.

The second became a race for chips and computing capacity.

The next phase may be determined by who can finance the infrastructure — and generate enough returns to pay for it.

AI is therefore no longer just a technology story.

It is increasingly a capital-markets story.

And ultimately, it may become a test of whether the financial system can fund the enormous infrastructure ambitions being built around artificial intelligence without taking on risks that become visible only when the boom slows.