Computing Power Financing: Why Wall Street Sees an "Subprime" Crisis Brewing

The media narrative in 2025 painted an optimistic picture: AI investments surging, data center construction accelerating across North America, and crypto miners successfully transitioning into stable computing power service providers. Yet behind closed doors, Wall Street’s credit departments were experiencing a very different emotion—growing alarm. The core issue wasn’t about GPU performance or AI model capabilities. Rather, credit analysts were staring at spreadsheets in disbelief: the market was essentially applying 10-year mortgage models to products with 18-month lifespans. The hidden problem is that highly depreciated computing power assets, volatile miner collateral, and rigid infrastructure debt have created a dangerous financial mismatch that could trigger a chain of defaults.

The Deflation Trap: When Moore’s Law Destroys Collateral Value

The fundamental building block of credit analysis is the Debt Service Coverage Ratio (DSCR)—the cash flow generated by an asset relative to its debt obligations. Market participants had been betting that computing power rental income would be as stable as residential rent or as inflation-resistant as oil. The data painted a starkly different picture.

According to Q4 2025 tracking data from SemiAnalysis and Epoch AI, the cost of unit AI inference declined 20-40% year-over-year. This wasn’t a temporary glitch but a structural trend driven by model quantization, distillation techniques, and the increasing efficiency of application-specific integrated circuits (ASICs). The implication was straightforward: computing power rental rates possess an inherent deflationary characteristic.

For equity investors, this technological progress is cause for celebration. For creditors, it represents collateral devaluation in real time. Here lies the first duration mismatch: buyers purchased GPUs at 2024 peak prices to generate rental income streams that are mathematically destined to decline after 2025. The asset backing the debt is appreciating in computational capability while depreciating in financial return—a structurally unstable foundation.

The Financing Pivot: Infrastructure Labels on Venture-Grade Risks

Logically, when asset returns are thinning, liability structures should become more conservative. Reality diverged sharply from logic. According to The Economic Times and Reuters reporting, total debt financing for AI data centers surged by 112% to reach $25 billion by 2025. This expansion was led by cloud infrastructure vendors like CoreWeave and Crusoe, as well as crypto miners undergoing strategic repositioning, many deploying asset-backed lending (ABL) and project finance mechanisms.

The structural danger lies in category mismatch. The market had successfully repackaged venture-grade assets—high-risk, high-depreciation computing power—into utility-grade financing models traditionally reserved for toll roads, hydroelectric plants, and telecommunications infrastructure. This wasn’t a minor accounting distinction. It fundamentally shifted failure outcomes: when tech VC investments fail, equity investors absorb losses; when infrastructure debt fails, obligors default on creditor claims.

The Double Leverage Trap: Miners’ “Transformation” Paradox

Media coverage celebrated miners’ pivot to AI computing power provision as “risk mitigation” and “business model diversification.” Balance sheet analysis told a different story. Data from VanEck and TheMinerMag revealed that leading publicly traded mining companies had not substantially reduced net debt ratios in 2025 compared to 2021 cycle peaks. More strikingly, certain aggressive operators increased debt by over 500%.

How? Through balance sheet engineering:

Left side (assets): Retain highly volatile BTC and ETH holdings while simultaneously claiming future computing power revenue as implicit collateral.

Right side (liabilities): Issue convertible bonds and high-yield debt instruments to acquire H100 and H200 GPUs.

This isn’t deleveraging; it’s refinancing. Miners were executing what credit analysts term a “double leverage” play: using cryptocurrency volatility as collateral while simultaneously betting on GPU rental cash flows. In favorable macroeconomic environments, this arrangement doubles profits. Once conditions tighten, both triggers fire simultaneously—crypto asset prices compress while computing power hashrate rentals decline. In structured finance terminology, this is correlation convergence, the nightmare scenario for all derivative products.

The Collateral Illusion: When Repo Markets Vanish

What truly worries credit portfolio managers isn’t the default event itself—it’s the liquidation cascade that follows. During the 2008 subprime mortgage crisis, creditors could at least conduct property auctions when mortgagors defaulted. Computing power financing presents a distinct problem: if a miner defaults and creditors repossess 10,000 H100 graphics cards, who absorbs the inventory?

This secondary market doesn’t exist in meaningful form:

Physical infrastructure dependency: High-end GPUs function only within specific liquid cooling architectures and power density parameters (30-50kW per rack). They cannot simply be plugged into consumer-grade infrastructure.

Non-linear hardware obsolescence: The release of NVIDIA Blackwell and planned Rubin architectures creates accelerating depreciation curves for predecessor generations. What trades at book value today becomes electronic waste within quarters.

Absence of buyer capacity: When systemic liquidation occurs, no “lender of last resort” mechanism exists to absorb billions in selling pressure. The market for depreciated computing hardware lacks both depth and continuity.

This creates what might be termed a “collateral mirage”—the LTV (Loan-to-Value) ratios appear conservative on spreadsheets, but the secondary repo market that would actually absorb this selling pressure doesn’t exist materially. Stated collateral values are largely theoretical.

The Macro Signal: Credit Cycles Peak Before Tech Cycles

This analysis doesn’t deny AI’s technological trajectory or computing power’s genuine utility. The concern isn’t technological but financial: when deflationary assets (subject to Moore’s Law acceleration) are priced as inflation-hedging infrastructure; when miners who haven’t genuinely deleveraged are financed as high-quality utility operators—the market is conducting an unpriced credit experiment.

History demonstrates that credit cycles peak before technology cycles. The inflection point may arrive sooner than technology roadmaps suggest. For portfolio managers and fixed-income strategists, the operational priority before 2026 completion isn’t predicting which large language model achieves supremacy, but rather re-examining the actual credit spreads embedded in “AI Infrastructure + Crypto Miner” portfolio combinations. The computing power financing structure may represent the 2026 credit story that begins in Q1.