The Credit Mispricing Behind the AI Computing Power Boom: How Infrastructure Financing Models Collide With Depreciating Assets

The tech headlines paint a rosy picture for early 2026: AI infrastructure investments continue accelerating, data center construction across North America surges, and crypto miners are celebrating their successful pivot to stable AI computing power services. But behind the scenes, credit analysts at major financial institutions are experiencing a very different sentiment. In conference rooms across Wall Street, the focus isn’t on model performance or GPU specifications—it’s on spreadsheets showing a structural nightmare: the market is financing 18-month-lifespan assets using 10-year mortgage models. This mismatch isn’t theoretical. Recent reports from Reuters and Bloomberg expose what’s really happening: AI infrastructure has become a debt-intensive sector, and the financial architecture built beneath the AI boom contains the seeds of a significant credit crisis.

The core problem isn’t technology failure—it’s a profound misalignment between rapidly depreciating computing power assets, overlevered collateral, and inflexible infrastructure debt. When these three forces converge, a hidden chain of default transmission activates, and the illusion of safety shatters.

The Deflationary Trap: When Moore’s Law Meets Fixed Debt

At the foundation of every bond or debt investment sits a fundamental assumption: the Debt Service Coverage Ratio (DSCR). For the past 18 months, market participants have bet that AI computing power rental income would behave like commercial real estate—stable, predictable, possibly even inflation-resistant. The data, however, tells a different story entirely.

According to tracking by SemiAnalysis and Epoch AI released in Q4 2025, the cost of running AI inference workloads has collapsed 20-40% year-over-year. This isn’t a modest correction; it’s the inexorable march of Moore’s Law meeting the accelerating adoption of model quantization, distillation techniques, and application-specific integrated circuits (ASICs). Each efficiency breakthrough makes yesterday’s expensive GPU deployment systematically less valuable for generating rental income.

This creates the first critical duration mismatch: investors purchased GPUs at peak 2024 valuations, locking in CapEx costs while simultaneously locking in a rental yield curve destined to decline through 2025 and beyond. The math is straightforward: if you owe debt service payments on hardware purchased at $10,000 per GPU but the computing power those GPUs generate drops 30% in annual rental value, the margin between revenue and obligations evaporates. From an equity investor’s perspective, this is “technological progress.” From a creditor’s perspective, this is “collateral devaluation”—the foundation of default risk.

The paradox deepens when you consider the computing power business model itself: unlike real estate that may appreciate or remain stable, the fundamental asset—computing power capacity—is inherently deflationary by design. Each new GPU generation performs more calculations per dollar, reducing the rental income per unit of deployed infrastructure. This means debt issued today against computing power revenue is being repaid from an asset class with structurally declining cash flows.

The Financing Reversal: Venture Capital Risk Masquerading as Infrastructure Safety

Faced with thinning returns on the asset side, rational market participants should tighten lending standards and demand higher risk premiums. Instead, the opposite has occurred. Total debt financing for AI data centers and related computing power infrastructure is projected to surge 112% to approximately $25 billion in 2025 alone, according to The Economic Times and Reuters reporting.

This explosion in debt isn’t being driven by conservative infrastructure lenders. Rather, it’s dominated by Neo-Cloud vendors like CoreWeave and Crusoe Energy, along with cryptocurrency miners undergoing their supposed “transformation,” utilizing asset-backed lending (ABL) and project finance structures—models designed for stable, low-risk assets like toll roads or hydroelectric plants.

This represents a fundamental category error in risk classification:

The old model (pre-2024): AI was a venture capital game. You invested in a company, built technology, hoped for success. Failure meant equity loss; creditors weren’t involved.

The new model (2025-present): AI has become an infrastructure play. Debt now finances the deployment of computing power. Failure means defaulting on bonds and structured obligations. Risk of loss extends to creditors and fixed-income investors.

The market, however, is pricing this as if nothing fundamental has changed. Lenders are applying infrastructure-grade risk models (utility-grade leverage, lower spreads, longer maturities) to venture-grade assets (high depreciation, technological obsolescence, binary success/failure profiles). This is a systematic credit mispricing with significant consequences.

The Miner Deleveraging Illusion: Playing Double-Leverage

The most precarious position is occupied by cryptocurrency miners who have pivoted toward AI computing power. Media narratives celebrate this transition as “risk mitigation”—miners finally escaping the volatility of crypto mining to provide stable infrastructure services. But examining actual balance sheets tells a darker story.

Data from VanEck and TheMinerMag reveals that the net debt ratios of leading listed mining companies in 2025 remain largely unchanged from the 2021 peak. Some aggressive players have even increased debt by as much as 500%. How did miners achieve this apparent deleveraging without actually reducing leverage?

The mechanism is deceptively simple:

• Left side of the balance sheet (assets): Miners continue holding volatile cryptocurrency positions (BTC/ETH) or book future computing power rental revenue as implicit collateral.

• Right side of the balance sheet (liabilities): They issue convertible notes, high-yield bonds, and other instruments denominated in US dollars to fund purchases of H100/H200 GPUs and associated infrastructure.

This isn’t deleveraging—it’s rollover risk combined with correlation concentration. Miners are essentially playing a “double-leverage” game: they’re using the volatility of cryptocurrency assets as collateral to gamble on GPU rental cash flows. In benign market environments, this amplifies returns. But once macroeconomic tightening occurs, both components fail simultaneously. Crypto prices drop while GPU rental rates decline in parallel (fewer projects funding AI research, lower overall investment velocity). In credit modeling, this scenario is called correlation convergence—a nightmare for structured products and a disaster for unsecured creditors.

The assumption that computing power revenue would serve as a stabilizing force for miner balance sheets hasn’t materialized. Instead, miners have layered additional debt atop existing volatility, creating a structure that amplifies downside risk while providing limited upside cushion.

The Vanishing Liquidity: When Collateral Becomes Theoretical

What keeps credit managers awake is not the default itself, but what happens afterward. In the 2008 subprime mortgage crisis, creditors could auction foreclosed properties to recover capital. But if a major computing power operator defaults and creditors repossess 10,000 H100 graphics cards, what happens next? Who purchases them, and at what price?

This secondary market doesn’t exist at meaningful scale—a fact hidden beneath the veneer of published collateral valuations. The illusion of safety rests on three critical weaknesses:

Physical infrastructure dependency: High-performance GPUs aren’t plug-and-play devices. They require purpose-built liquid cooling racks, specific power infrastructure (30-50kW per rack), and specialized networking configurations. A repossessed GPU outside its native data center infrastructure faces significant friction in finding alternative deployment locations.

Non-linear depreciation from technological obsolescence: With NVIDIA releasing Blackwell architecture in late 2024 and planning for Rubin in subsequent years, older GPU generations don’t depreciate linearly. Instead, they face cliff-drop depreciation as newer, more efficient chips become available. An H100 that was worth $40,000 months ago may command $8,000-12,000 in a distressed sale, a 70-80% haircut.

Absence of a liquidity provider: Most critically, there exists no “lender of last resort” mechanism in the used computing power hardware market willing to absorb billions in selling pressure. Unlike equity markets or government bonds where central banks and financial intermediaries stabilize prices during stress, specialized GPU secondary markets lack such stabilizers. When distressed selling begins, price discovery becomes catastrophic.

This represents what might be termed a “collateral illusion”—the LTV (loan-to-value) ratios on paper appear prudent, often 50-70% based on published hardware valuations. But these ratios assume orderly liquidation in functioning secondary markets. The actual market for used, specialized GPUs facing obsolescence risk is far thinner and messier, rendering theoretical collateral values largely fictional when stress arrives.

Credit Cycles Peak Before Technology Cycles: The Real Risk Timeline

To be clear, this analysis does not deny AI’s technological potential or computing power’s fundamental importance to future infrastructure. The technology will continue advancing, and demand for AI computing capacity will remain robust. What’s being challenged is the financial architecture underpinning the industry—specifically, how computing power financing has been misprice.

Deflationary assets driven by Moore’s Law are being priced as inflation-hedging infrastructure. Miners who haven’t meaningfully deleveraged are being financed as though they’re utilities with stable balance sheets. Computing power with 18-24 month technological relevance is being financed with 10-year debt structures. These aren’t marginal risks; they’re fundamental pricing errors embedded into billions of dollars of outstanding debt.

Historical analysis shows a consistent pattern: credit cycles peak and implode before technology cycles mature. The great railroad boom of the 1880s saw massive credit excesses that peaked before rail networks reached full utility. The dot-com era saw excessive tech debt financing in 1999-2000, years before internet adoption matured. The subprime crisis peaked in 2007-2008 before home prices stabilized.

For macro strategists and credit traders, the primary analytical task before mid-2026 isn’t predicting which AI model will achieve breakthrough capability—it’s re-examining the true credit spreads and default probabilities embedded in those “AI Infrastructure + Crypto Miner” combinations. The market may have mispriced the financial risk substantially. And when that repricing occurs, it will affect not just equity investors but the fixed-income markets that now carry the bulk of this leverage.

The computing power boom is real. What’s questionable is whether the credit markets supporting it have accurately priced the risk of that reality.