The financing behind the AI infrastructure boom is growing up. And getting more discriminating.

Goldman Sachs analysts estimates that the broader AI ecosystem has issued nearly $500 billion of debt so far in 2026. Separately, AI-linked borrowers accounted for about 18% of U.S. investment-grade issuance, up from 7% in 2025 and just 1% in 2024. Hyperscalers represented only about 40% of Goldman’s broader AI-related total, showing how far the financing boom has spread beyond the largest technology companies.

Lenders have noticed. Before they write another big check for a data center, they increasingly want stronger guarantees, completed permits, committed tenants and a higher yield. Meanwhile, some exposure is moving off hyperscaler balance sheets and into special-purpose vehicles, private-credit funds, infrastructure investors and insurers, although guarantees can still leave hyperscalers carrying much of the ultimate risk.

The bearish take is that investors are losing faith in the economics of AI infrastructure. But I think this call is early.

What I see is a market that still believes AI demand will grow but is no longer willing to swallow construction, power, refinancing and obsolescence risk without getting paid (and protected) for it.

The real question is: Who absorbs the loss when a project shows up late, blows through its budget or is stuffed with hardware that ages before the debt does?

AI debt is too big to dismiss as a technology-sector footnote

In the first leg of this cycle, the biggest technology companies largely funded AI infrastructure from their own enormous cash flows and balance sheets.

This worked when the numbers were merely big, but now they are eye-watering.

Alphabet expects to invest between $195 billion and $205 billion in 2026. At this scale, the funding mix becomes an issue. Put simply, AI is now reshaping corporate credit and competing with governments, utilities and ordinary businesses for long-dated capital.

Amazon’s first sterling bond sale is a good example. The company raised £4.25 billion across four maturities after drawing more than £10 billion of orders. There was plenty of demand, clearly, covering the deal about 2.5 times versus roughly five times for Alphabet’s sterling sale in February. Demand runs deep, but it is not unlimited.

Still, Amazon can raise billions because it has a diversified business, investment-grade credit and a formidable cash engine. A speculative data-center developer does not get those terms simply by sprinkling “AI” over a pitch deck and suggesting a hyperscaler might eventually need the space.

Placing them all under the label “AI debt” disguises very different risks.

The first headache is delivery, not demand

There is still plenty of demand for compute. The International Energy Agency says global data-center electricity consumption rose 17% in 2025, while consumption by AI-focused data centers jumped by roughly 50%.

In its base case, as shown below, total data-center electricity demand roughly doubles (485 terawatt-hours in 2025) to about 950 terawatt-hours by 2030.

Source: IEA

But wanting compute and delivering a revenue-producing data center are two very different things. A project needs the right land, transmission equipment, grid access, cooling, chips and local approval. If just one piece goes missing, the whole schedule can slip. These projects are only as fast as their slowest bottleneck.

AI demand may be abundant while financeable sites remain scarce.

Google’s Finland bet: Is this what a financeable project looks like now?

Google’s newly announced Finnish expansion is a useful example. The company plans to invest at least €13 billion across digital infrastructure, clean energy and local partnerships in Finland over the next two years. Reuters reports that the program includes three new data centers in northern Finland.

The financing case is unusually strong. Google has signed a 22-year agreement to purchase up to half the output of Fortum’s Loviisa nuclear plant from 2030, while also supporting new wind capacity and a 94-megawatt battery system. A strong sponsor reduces tenant and refinancing risk, secured long-term power reduces a major operational uncertainty, and Finland’s colder climate lowers the cooling burden.

The project gives both bulls and bears something to work with.

The bull case: one of the world’s largest technology companies is committing €13 billion because it expects AI demand to stick around.

The bear case: getting a project like this over the line increasingly takes hyperscaler backing, multi-decade energy commitments and an unusually favorable location.

So, is this a credit bubble?

Not yet, at least not in the strict sense.

Current market evidence supports a repricing story more clearly than a funding-collapse story. Deals are still clearing, but spreads, new-issue concessions and order-book coverage are becoming less favorable to issuers.

The best bubble argument is that infrastructure spending is outrunning proven AI revenue, with part of the buildout funded through complex structures whose risks may be too lightly priced. Capital is being committed today against forecasts for future grid access, equipment values and customer demand.

The strongest rebuttal is that the largest direct borrowers remain investment-grade hyperscalers, while many project-financed facilities have anchor tenants or long-term leases. That reduces tenant risk, but it does not eliminate construction, power-delivery, refinancing or hardware-obsolescence risk.

I can hold both ideas at once.

But is this looking like an underwriting story? In the first phase, investors were rewarded for backing almost anything with a credible AI angle. The next phase will separate projects with secured power, strong counterparties and believable schedules from those running on rosy assumptions about all three.

What I’m watching for signs of trouble

My real alarm bell would be three things happening together: projects getting cancelled, utilization falling and guarantees being called. But this would detect the problem too late. Most likely, the earliest warning signs would be weaker bond-cover ratios, wider new-issue concessions and delays or lease changes after financing has closed.

For now, I see repricing risk, not capital heading for the exits.

Power, permits, tenant quality, hardware life and guarantees now play a much bigger role in deciding which projects get funded and who takes the hit if the timetable slips.

The next fault line may appear in financing documents before it reaches chip orders or headline capex guidance: wider spreads, tighter covenants, stronger guarantees and lease provisions revealing who remains on the hook if a project slips.

Sources

1. Goldman Sachs: How AI debt is reshaping the credit market
2. Google: Google deepens its commitment to Finland with a €13 billion investment in AI infrastructure
3. International Energy Agency: Energy demand from AI and data centers
4. International Energy Agency: Key Questions on Energy and AI
5. Reuters : AI construction crunch widens credit fault lines
6. Reuters: Amazon’s first sterling bond sale
7. Reuters: Five debt hotspots in the AI data-center boom
8. Yahoo Finance: Alphabet Will Spend as Much as $205 Billion This Year. The Depreciation Bill Starts Landing in 2027.