The problem: a trillion-dollar market with one deal template
By 2029, AI debt outstanding will approach $7T, second only to US mortgages. Yet nearly every dollar lent today fits a single template, and the template is running out of road.
The template: A Neocloud signs a 5-year take-or-pay compute contract with an investment grade hyperscaler. Lenders don't underwrite the GPUs, the tokens, or the operator. They underwrite Microsoft's promise to pay, add roughly 90bps for execution risk, and fund at 70-80% loan-to-value. This is why CoreWeave borrowed at 5.9% against a Meta backstop while its own unsecured bonds yield around 10%. That 410bps gap is the market saying: we can price the hyperscaler; we cannot price you.
Three forces that break the template:
| Force | Why it breaks the template |
|---|---|
| Backstops are finite | Hyperscaler balance sheets cannot guarantee trillions. Nvidia stepping in as backstopper of last resort (the "central bank of AI") is a bridge, not a destination, and it takes roughly an 18% revenue share for the privilege. |
| Demand is short, funding is long | Inference providers won't sign beyond 1 year; startups want bursts of capacity between funding rounds. A 5-year-offtake-only market rations out exactly the customers growing fastest. |
| Lenders lack tools | No public price index history, no residual value curve, no operator quality standard, no demand model. Banks "hide behind the shield" of the IG offtake because they literally have nothing else to look at. |
The opportunity, and the subject of this paper, is the discipline that fills that gap: underwriting Neoclouds as merchant businesses, the way banks learned to underwrite merchant power plants, memory fabs, and ship charters. Every one of those markets started exactly here: long dated assets, volatile spot prices, lenders demanding a utility grade offtake, until someone built the risk framework that let credit flow without one.
The five layers of GPU credit risk
Every GPU loan decomposes into five stacked risks. The \ structuring is deciding which layers the deal transfers away (via offtake, backstop, insurance, hedges) and which the lender actually holds. Toggle the deal structures below and watch who holds what.
| Layer | The question | Primary evidence |
|---|---|---|
| 1 · Offtaker credit | Will the buyer of the compute pay? | Credit rating, financials, contract enforceability, parent guarantees |
| 2 · Market / price | What will re-let rates be when contracts roll? | Rental price indices, forward curve, supply pipeline, token demand growth |
| 3 · Execution / operator | Can this team stand up and run the cluster? | Track record, uptime SLAs, network architecture, ops bench, ClusterMAX-style ratings |
| 4 · Asset / residual | What is the hardware worth if we repossess? | SKU depreciation curves, secondary market prints, next-gen launch cadence |
| 5 · Structural | Does the paper actually protect us? | SPV isolation, cash sweeps, cross-default, security over contracts not just chips, datacenter lease terms |
The key insight most newcomers miss: layers 1 and 2 are substitutes. A strong offtake makes market risk irrelevant for its tenor; no offtake means you are underwriting the market itself. Today's lenders can only handle layer 1. The \game is learning to price layer 2, which is exactly what the forward market in Paper 2026-07 exists to make tractable.
The two numbers: DSCR & LTV. Size a loan
Everything in GPU credit reduces to two ratios. DSCR (debt service coverage ratio) is cash available for debt service divided by debt payments each period. The covenant floor is 1.30×, and it is tested in the downside scenario, not the base case. LTV (loan-to-value) is loan divided by cluster cost. Market standard is 70-80%, but the binding constraint is almost always DSCR, not LTV. Size a deal yourself:
Bars show DSCR per year at the max loan. The red line is the 1.30× covenant. Notice the shape: early years are fat (high rents, low amortization progress), late years pinch as prices decay against fixed debt service. Now push decay to 30%: the max loan collapses even though year-1 economics look identical. The lender's real exposure is the decay assumption, and nobody agrees on it. This single slider is why the market needs a forward curve.
payback period = cluster cost ÷ year-1 net cash. Under about 2.5 years, the deal survives almost any decay assumption; over 4 years, you are making a bet on terminal demand. Most disagreements between bulls and bears are really disagreements about which side of 3 years the payback sits.Collateral that melts: residual value
In equipment finance, repossession is the backstop. In GPU finance, the collateral is deflating at silicon speed: each new generation makes the old fleet worth less per FLOP, and a wave of off-lease supply can crater secondary prices exactly when defaults cluster. This is the same wrong-way risk that burned aircraft lenders in 2009 and auto lenders in every recession.
Orange: GPU fleet value. Blue: loan principal outstanding. The shaded gap is the lender's unsecured exposure at each point. Flip to a 40% balloon and watch the underwater window widen. Balloons are how deals "pencil," and they are the single largest hidden risk in the market. Any repayment you rely on after the collateral crosses the loan line is a bet on cashflow, not collateral.
Two conclusions. First, residual value is a cushion, never the thesis: treat any recovery assumption above 40% of original cost past year 3 with suspicion. Second, the depreciation slope is itself uncertain, and the 36-month forward from Paper 2026-07 is the only market cleared estimate of it that could exist. Until then, underwriters triangulate from secondary market prints, rental price indices, and next-generation launch cadence.
Reading a Neocloud's book: the rating engine
When there is no hyperscaler shield, the underwriter's real object of study is the Neocloud's customer book, the portfolio of rental contracts. Four dimensions matter: who (credit quality of renters), how long (tenor mix vs. the loan), how concentrated (top-customer share), and how well run (operator quality: uptime, networking, support; the thing ClusterMAX-style ratings measure). Below is a toy scoring engine. Build a book and see how a rating desk would grade it:
Building the spread: price the loan
Spread over the risk-free rate is built layer by layer. Each risk you hold demands compensation; each risk transferred away removes a block. The two market anchors: hyperscaler backstopped paper at roughly SOFR+225 (about 5.9% all-in) and top-tier Neocloud unsecured at about 10%. Every real deal prices somewhere between.
*The borrower's side of the same number: at 70-80% LTV, moving all-in cost from 5.6% to 10% crushes a Neocloud's pre-tax margin from about 15% to about 5%. Financing cost IS the business model, which is why every Neocloud contorts itself into whatever structure prices tightest, and why the underwriter who can justify a tighter spread on a merchant book wins the deal flow.
What actually kills the loan: stress tornado
Underwriting is asking "what breaks first?" in order. The tornado below shocks each variable one at a time by a realistic adverse amount and ranks the damage to worst-year DSCR (using your 3 deal). This ordering, not the base case, is the mental map of the market:
Bars measure DSCR destruction from each standalone shock. The consistent lesson across parameterizations: price/decay risk dominates everything, utilization is second, opex (power) is the sleeper, and rate risk, the thing traditional lenders instinctively model, is usually near the bottom. GPU credit risk is a compute-price problem wearing a credit costume. Adjust the section 3 sliders and watch the ranking hold.
Twelve nuances underwriters miss
The checklist of things that don't show up in the model but decide outcomes. Tagged by type: Kill Risk can zero the loan, Price Risk moves economics, Structure is fixable.
1 · Take-or-pay is not take-or-pay until you read the outs Kill risk
2 · The datacenter lease can outlive the revenue Kill risk
3 · Prepays create phantom equity Structure
4 · Power is the opex wildcard Price risk
5 · Vendor concentration cuts both ways Price risk
6 · Depreciation is scheduled; obsolescence is lumpy Price risk
7 · Utilization is not revenue quality Price risk
8 · Security over chips is weak; security over contracts is strong Structure
9 · Cross-default with the parent Structure
10 · The 1.30× DSCR is tested on the wrong scenario if you let it be Structure
11 · Tokenomics is the demand model, and it is learnable Price risk
12 · Correlation is the portfolio killer Kill risk
That is the map. Two ratios (3), five layers (2), one melting collateral curve (4), a book-quality lens (5), a spread stack (6), a stress ordering (7), and twelve traps (8). Anyone holding this frame can sit across from a Neocloud's CFO, or a credit committee, and know which questions matter. The tools to answer them at scale (the index, the curve, the rating engine) are the businesses waiting to be built.