The AI Debt Bubble 2025: Is "Shadow Debt" the Next 2008 Crisis?

By Tony Grayson Tech Executive (ex-SVP Oracle, AWS, Meta) & Former Nuclear Submarine Commander

The Bank of England recently warned that the financing behind the AI boom looks 'eerily familiar' to the structures that preceded the 2008 crash.

According to JP Morgan, debt tied to AI companies now accounts for 14% of its investment-grade index—surpassing U.S. banks as the dominant sector.

While hyperscalers drove AI infrastructure spending to nearly $200 billion in 2024, regulators have escalated their warning. The Bank of England's latest Financial Stability Report called public equity valuations "materially stretched" and explicitly flagged rising financial stability risks if the spending cycle turns. The financing boom looks eerily like the setup for a 2008-style AI market correction.

Understanding the Risk: Financial Engineering 2.0

To understand the financial engineering driving this boom, it helps to revisit the last crisis. The mechanism isn't new; the asset class has just changed from housing to compute.

Watch: The Warning Signs This discussion breaks down the exact risks building in the AI financing market right now.

The Jenga Analogy: This clip from The Big Short visually illustrates how low-quality, concentrated assets (the bottom blocks) create structural fragility and can cause the entire financial to collapse—a direct parallel to today's SASB debt risk.

As Dan McNamara from Polpo Capital warns:

"If there's a problem with AI data centers, like if their current chips are obsolete in five years, you could have big losses in these deals. When things go bad with SASB, they go really bad."

The concentration of hyperscaler debt is accelerating:

• Blackstone's Move: Recently closed a $3.46 billion CMBS offering backed by QTS data centers—larger than the entire data center CMBS market for all of 2024.

• 2025 Surge: In the first half of 2025 alone, eighteen ABS and CMBS deals totaling $13.4 billion closed.

AI Infrastructure Spending vs. Revenue: The Math Doesn't Work

The fundamental issue driving the AI bubble narrative is the gap between CapEx and revenue.

Here is the uncomfortable truth behind the valuation:

1. Revenue Reality: Only 3% of consumers currently pay for AI services, generating roughly $12 billion annually.

2. Spending Reality: Hyperscalers require $800 billion in private credit over the next two years to sustain current infrastructure spending.

As I detailed in my analysis of the Oracle-OpenAI deal, the unit economics simply do not close at these debt levels without a massive shift in adoption.

We are seeing a disconnect between "valuation" and "value." As I wrote in Contextual Intelligence, leaders must look beyond the hype cycle to understand the ground truth. Morgan Stanley is betting the industry can securitize its way to profitability, but central banks warn of "stretched valuations" in a sector where "the future is highly uncertain."

Two Paths for AI Financing: Diversification vs. The Gamble

Goldman Sachs laid out the choice clearly. Wall Street is currently betting on Path 1—using 2008-style financial engineering to fund the boom.

Feature	Path 1: The Current Bet (High Risk)	Path 2: The Sustainable Model
Tenant Structure	Single hyperscaler (SASB)	Co-location (Thousands of tenants)
Lease Terms	Long-term, debt-heavy	Flexible, shorter-term
Concentration	High AI Concentration Risk	Diversified Tech Risk
Hardware Lock-in	Locked into current chips	Agnostic to chip architecture

The Hardware Cliff: Financing Temporary Tech with Permanent Debt

If H200s and GB200s are already replacing NVIDIA's H100 chips, what happens to billions in securities backed by facilities optimized for last generation's hardware?

This is the NVIDIA Vendor Financing Trap: Hyperscalers are building permanent infrastructure for temporary technology, and financing it with off-balance sheet debt designed to hide risk.

The market has seen this movie before. In 2008, it was mortgage-backed securities. Today, the assets are real, the data centers exist, but the financial engineering is eerily familiar. The question isn't whether AI is real; it is whether the AI debt bubble can survive its own weight.

In my post on Fearlessness and Failure, I discussed the difference between calculated risk and reckless gambling. Right now, the market is gambling.

This time, we recognize the plot... but we are still funding the sequel.

FAQ:

What is the AI Debt Bubble?

The AI Debt Bubble refers to the massive accumulation of off-balance-sheet debt used to fund artificial intelligence infrastructure. Analysts warn that high interest rates, coupled with a gap between infrastructure spending ($200B+) and actual AI revenue (~$12B), could lead to a market correction similar to the 2008 financial crisis.

How do Data Center SPVs work?

A Data Center SPV (Special Purpose Vehicle) is a separate legal entity created by a parent company (like a hyperscaler) to isolate financial risk. The parent company transfers assets (like a data center) into the SPV, allowing them to raise debt against that specific asset without it appearing as direct liability on their main balance sheet.

What is SASB financing in data centers?

SASB stands for "Single-Asset-Single-Borrower." In the context of AI, it means a loan is backed by just one data center with one tenant (e.g., Microsoft or Meta). This is considered high-risk because if that single technology becomes obsolete or the tenant leaves, the entire loan can fail.

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Tony Grayson is a recognized Top 10 Data Center Influencer, a successful entrepreneur, and the President & General Manager of Northstar Enterprise + Defense.

A former U.S. Navy Submarine Commander and recipient of the prestigious VADM Stockdale Award, Tony is a leading authority on the convergence of nuclear energy, AI infrastructure, and national defense. His career is defined by building at scale: he led global infrastructure strategy as a Senior Vice President for AWS, Meta, and Oracle before founding and selling a top-10 modular data center company.

Today, he leads strategy and execution for critical defense programs and AI infrastructure, building AI factories and cloud regions that survive contact with reality.