The Subprime Cycle in AI Computing: When Miner Leverage Masks a Gathering Financial Storm

A subprime crisis is quietly building within the AI infrastructure sector, one that credit analysts are watching far more carefully than technologists realize. The danger lies not in AI’s technological potential—which remains genuine—but in a fundamental mismatch between how the market is financing computing power and what computing power actually is as an asset. While tech investors cheer data center construction and GPU shipments, bond traders are staring at balance sheets and asking uncomfortable questions about an industry that’s applying real estate financing models to hardware that depreciates like smartphones.

The Brutal Math Behind Moore’s Law: Computing Power as a Deflationary Asset

The foundation of any infrastructure lending model rests on the Debt Service Coverage Ratio (DSCR)—the idea that an asset will generate stable cash flows to service debt. For decades, this has worked for highways, power plants, and fiber optic networks. But AI computing power fundamentally breaks this assumption.

According to Q4 2025 tracking data from SemiAnalysis and Epoch AI, the cost of running AI inference has plummeted 20–40% year-over-year. This isn’t a temporary dip; it reflects structural improvements in model compression techniques, specialized chip architectures (ASICS), and algorithmic efficiency. When you can do the same computation for 30% less cost, the rental income that’s supposed to pay off GPU debt evaporates. A data center operator who bought H100 chips at peak 2024 prices for $25 million is now competing with operators buying next-generation H200s while the resale value of those H100s cratered.

To a creditor, this is a collateral nightmare. The asset securing the loan depreciates not gradually, but according to an accelerating technology calendar. The operator is sitting on equipment financed at yesterday’s performance metrics but priced in tomorrow’s obsolescence cycle. This is why credit traders are losing sleep: you’re using a 30-year mortgage framework on 18-month shelf-life hardware.

The Financing Shift: When Venture Risk Masquerades as Infrastructure Safety

This is where the subprime elements truly crystallize. Historically, AI companies raised venture capital—you fail, investors write it off as equity loss. But something shifted in 2024-2025. According to Reuters and Bloomberg’s late 2025 investigations, total debt financing for AI data centers surged 112% to approximately $25 billion in annual commitment. This wasn’t organic growth; it was a conscious repricing of risk.

Companies like CoreWeave and Crusoe pivoted aggressively toward asset-backed lending (ABL) and project finance—the exact financing structures designed for utilities. The market essentially asked: “What if we apply infrastructure lending models to technology assets?” The answer, it turns out, was a category error of historic proportions.

Infrastructure lending assumes:

• Stable, predictable cash flows
• Long economic lifespan (20-30 years)
• Minimal technological displacement risk
• Liquid secondary markets for collateral

AI computing power offers none of these. Yet lenders packaged venture-scale risk inside infrastructure-grade debt structures. This is the core of the emerging subprime crisis in computing power financing—not that lending happened, but that it happened under fundamentally mismatched assumptions.

The Miner’s Trap: Fake Deleveraging, Real Leverage Accumulation

Crypto miners transitioning to AI compute services presented themselves as de-risking. Media narratives celebrated the “pivot”—mining companies were supposedly moving from highly volatile crypto markets to “stable” infrastructure returns. It’s a compelling story. And it’s mostly fiction.

Data from mining company disclosures shows the net debt ratios of major listed miners in 2025 remain comparable to 2021 peak levels. Some aggressive players actually increased debt by as much as 500%. How? They executed a financial sleight-of-hand:

Asset side: Hold volatile BTC/ETH positions + book future compute revenue as implicit collateral
Liability side: Issue convertible notes and high-yield bonds denominated in USD to purchase H100/H200s

This isn’t deleveraging; it’s leverage multiplication. Miners are now exposed to correlated downside across two risk axes simultaneously—if crypto prices plummet AND GPU rental rates compress (which they will, given Moore’s Law), both sides of the balance sheet collapse at once. In structured finance, this correlation convergence is what triggers cascading defaults.

The miners calling themselves “infrastructure operators” are actually running a double-leverage play: using unrelated crypto volatility as collateral to bet on compute economics that are structurally weakening. It’s not risk mitigation. It’s risk compounding dressed up as business evolution.

The Liquidity Illusion: Why Collateral on Paper Isn’t Collateral in Reality

Here’s what truly wakes credit analysts in the middle of the night: the absence of a functioning secondary market for GPU collateral.

If a major miner or compute operator defaults, lenders can repossess 10,000 H100 graphics cards. Then what? These aren’t commodities that can be listed on an exchange. They require:

• Physical infrastructure: Specialized liquid cooling racks, 30-50kW per rack power density, custom cabling systems
• Rapid obsolescence: The announcement of NVIDIA’s next-generation Blackwell and Rubin architectures means last-generation cards lose non-linear value almost overnight
• Absence of a buyer of last resort: When systematic distress selling occurs, there’s no market-maker, no central bank equivalent, no buyer willing to absorb billions in selling pressure for depreciating tech hardware

The stated LTV (Loan-to-Value) ratios on these deals may look prudent on spreadsheets. But the secondary repo market that would validate those numbers in a liquidation scenario simply doesn’t exist. This is a $25 billion wall of collateral backed by phantom liquidity.

Credit pricing assumes orderly markdown in distressed scenarios. The GPU market offers fire sales to no one, in a market that’s simultaneously shrinking and bifurcating by hardware generation.

The Real Risk: When Credit Cycles Outpace Technology Cycles

This is not a denial of AI’s future. The technology will mature. Compute needs will grow. But the financial market is mispricing the timing of when credit stress hits versus when technology adoption curves accelerate.

Historically, credit cycles peak earlier than technology adoption cycles. The subprime mortgage crisis didn’t kill housing demand—it killed the financing structures that preceded genuine housing need. Similarly, the subprime crisis in computing power financing could materialize before AI’s actual compute requirements justify the infrastructure buildout.

What started as a tech boom financed with infrastructure logic and miners seeking refuge is shaping up as a credit event that will test whether computing power assets can find liquidity when defaults actually occur. The answer, based on current market structure, is almost certainly no.

For traders and macro strategists, the next 12 months won’t be defined by which large language model outperforms. They’ll be defined by which overleveraged miner becomes the canary in the coal mine, triggering collateral cascades that no secondary market is equipped to absorb.