Ep. 037 - Who's Funding the $11 Trillion AI Buildout? (Capital Markets)
Summary
- SemiAnalysis puts cumulative AI IT and data-center CapEx at $11.3 trillion for 2024-29, far above the familiar one-year hyperscaler figure. The estimate multiplies tracked shipments of GPUs, XPUs, TPUs, Trainium and complete systems by their total cost, then includes facilities often funded outside hyperscaler budgets. The old project “trinity” remains “capital, offtake, and data center,” but financing is becoming the binding leg.
- The buildout could leave roughly $7 trillion of debt outstanding by 2029, with about $1 trillion of net funding needed in the current year alone. From June through October 7, Nishball counted roughly $400 billion of AI loans and bonds either raised or announced, including $260 billion of major completed deals and another $150 billion reported but undocumented. His warning: forecasts only include confirmed capacity, so the estimate “will probably creep up.”
- Hyperscalers and NVIDIA are turning investment-grade credit into “balance sheet as a service” for labs and neoclouds that cannot finance projects independently. Microsoft can backstop Nscale capacity ultimately used by OpenAI, while managed-service treatment may keep commitments outside conventional debt accounting. Nishball’s test is simpler: “If it talks like a duck, quacks like a duck, it’s a duck”—investors and rating agencies will eventually count the economic exposure.
- GPU residual value currently bears little resemblance to textbook depreciation because scarcity has overwhelmed performance-based obsolescence. One-year H100 rentals fell near $1.30 an hour in 2025, yet some buyers later renewed around $2.50 and current deals approach $3, close to early-2023 pricing. With GB300 capacity sold out, demand “cascades down,” making immediately available H100s valuable despite GB300 delivering roughly seven times the inference speed.
- The willingness to finance AI rests on unusually strong economics throughout much of the stack. SemiAnalysis models up to $100 million of annual frontier-token revenue per megawatt against roughly $40 million to install GB300 capacity, albeit under optimistic utilization; its simplified token income statement implies about 90% frontier API gross margins. Nanos described the surprise not as AI fast takeoff, but as “revenue and profitability fast takeoff.”
- NVIDIA’s backstops can expand the market, educate lenders and generate revenue, but they cannot fund the entire buildout. Its AICP structure offers a six-year rental floor and takes 50% of revenue above that backstop; other arrangements secure NVIDIA deployments through residual-value guarantees. SemiAnalysis estimates NVIDIA could support as much as 46 gigawatts—not a forecast—versus roughly 240 gigawatts of incoming capacity.
- The investable endgame is a much broader debt-financing market able to underwrite AI projects without investment-grade guarantees. SemiAnalysis expects AI debt to become the second-largest debt-financing market after the U.S. mortgage-backed market and says 2026 is the last year it remains below 10% of all U.S. issuance. Lenders must learn GPU residual values, customer churn and site-level execution before the coming “zombie” waves move from “wave five” to “wave fifty.”
Deep dive
1. Financing has replaced compute and data centers as the binding constraint
Nishball’s organizing framework is the AI-project “trinity”: “capital, offtake, and data center.” Compute once looked like a fourth leg, especially as memory scarcity threatened rack shipments, but the location of the bottleneck keeps moving.
AI was heading toward roughly 70% of DRAM wafer demand, while rising memory content was pushing accelerator ASPs higher through Rubin Ultra. Instead, vendors began de-speccing systems: ROI mattered, but the immediate problem was simply that “there hasn’t been enough memory to go around to actually ship racks.”
Data-center supply has responded through newly activated powered sites, refurbished facilities and behind-the-meter work. Nishball expects supply and demand to look much more balanced by 2027-28, making facilities less of an obstacle just as funding becomes more acute.
The $11.3 trillion figure covers cumulative 2024-29 AI IT and data-center CapEx. SemiAnalysis derives it from tracked GPU, XPU, TPU and AWS Trainium shipments multiplied by full-system costs—including servers, networking and CPUs—then includes facilities built by hyperscalers, neoclouds and independent data-center companies.
2. The $11.3 trillion buildout implies a $7 trillion debt market
SemiAnalysis projects roughly $7 trillion of debt outstanding by 2029, with about $1 trillion of net new funding in the current year. Because its forward estimates recognize capacity only when confirmed, Nishball expects the total to “probably creep up.”
The existing pools are small by comparison: approximately $3 trillion of investment-grade issuance, around $400 billion of leveraged loans and roughly $400 billion of private equity. Hyperscalers and NVIDIA generally qualify as investment grade; neoclouds such as CoreWeave sit in riskier markets.
From June through October 7 alone, Nishball counted about $400 billion of AI-related loans and bonds raised or announced—roughly $260 billion of major funded deals and another $150 billion reported but not documented. That put the market broadly on track with the annual estimate.
Examples convey the breadth: Blue Sky Funding raised $24 billion plus a $6 billion delayed-draw loan for a Broadcom–Google–Anthropic TPU arrangement; a Meta–BlackRock facility raised $12.5 billion; CoreWeave added a $4.2 billion convert and $2.6 billion delayed-draw loan, while Nebius, Zencore and Anthropic raised $3.5 billion, $3 billion and $15 billion respectively.
3. Five-year contracts make residual GPU value an upside, not a necessity
A GB300 system costs roughly $31 million to $33 million per megawatt, versus perhaps $10 million-plus for the associated data center. Yet the capital side is modeled over roughly five years while a facility may last 15, supporting three IT cycles; that shifts the upfront CapEx split toward approximately 60/40 rather than the 75/25 implied by cost of ownership.
Financing usually requires a long-term offtake, typically supported by an investment-grade counterparty. “If you don’t have a five-year offtake, you’re not getting financing,” Nishball argued, making revenue and project cash flows unusually preordained compared with speculative merchant capacity.
Typical five-year deals are underwritten to roughly 20%-30% annual IRRs, at least under the economics discussed, without assuming any resale or sixth-year revenue. Whatever a GPU earns after its modeled life is therefore “like a bonus,” even though lenders still want a defensible residual-value framework.
4. Off-balance-sheet backstops turn credit quality into infrastructure
Nanos summarized $497 billion of NVIDIA commitments across AI-cloud agreements, third-party and self-use data-center leases, energy and LC guarantees, cloud-service agreements, component supply commitments and an as-yet unvalued residual-value-guarantee category. The variety makes the exposure difficult to reconstruct from headline balance-sheet figures.
In the Nscale example, capacity flows to Microsoft and then OpenAI. OpenAI’s credit alone may not support the construction loan, so Microsoft guarantees usage: “If OpenAI doesn’t pay the bill, Microsoft’s on the hook.” The project becomes financeable because lenders can underwrite Microsoft rather than the lab.
Microsoft gains capacity, may retain an economic cut and accesses outside capital without directly funding every asset. The accounting distinction matters: the speakers described an ASC 606 managed service as off balance sheet, while an ASC 842 operating lease appears on it, even when the underlying economic commitments look similar.
Nishball called the model “balance sheet as a service.” His caveat was that accounting presentation cannot make support unlimited: rating agencies and bond investors will still ask how a company could credibly backstop trillions. “No one has a crystal ball.”
5. Credit is beginning to price both sponsor exposure and execution risk
Amazon’s $25 billion, six-tranche July issue priced with an approximately 100-basis-point ten-year Z-spread and roughly 65 basis points at five years, then widened another 10-20. Nanos highlighted a 105-basis-point Oracle move; Nishball estimated Oracle’s curve had widened 30-50 since summer, with its ten-year spread near 250 versus Amazon around 100-110.
Nanos contrasted CoreWeave, whose spread was nearly 400 basis points, but Nishball separated corporate risk from project risk. A Meta-offtake project priced around 225 basis points over its benchmark—slightly under 200 basis points on a Z-spread basis—against Meta’s roughly 97-basis-point five-year curve. The difference compensates lenders for standing up and operating the cluster.
Nishball proposed ClusterMAX as an execution signal because lenders had already found it useful; a platinum operator should be “less likely to mess up.” Nanos pushed back that managed clusters and bare-metal sites are distinct products, then allowed that it could be a signal toward execution risk—but insisted each SPV, geography, team and construction schedule still requires its own assessment.
Procurement has meanwhile inverted. Buyers once compared five providers through two-week proofs of concept on 256 GPUs; now they “pick a dance partner six months before the GPUs get delivered” and may prepay 30%-50% of contract value. Firms oscillate from renting, to self-building, to exhausting their own balance sheets and effectively “mortgaging” future demand to outside providers.
6. Scarcity has broken the conventional GPU depreciation curve
Pure performance economics should crush older hardware: SemiAnalysis estimates GB300 offers about seven times H100 inference speed and three times the FLOPS. For marginal inference, Nanos asked, “Why would anybody want to buy a marginal Hopper over a marginal GB300?” Availability, however, makes that the wrong near-term question.
One-year H100 pricing fell below $1.80 and $1.40, with some weaker providers offering roughly $1.30. Sellers debated locking two years near $1.25; some later renewed around $2.50, while current one-year deals approach $3 per hour—close to first-half 2023 pricing.
Some researchers still value H100 and H200 familiarity, job throughput and numerical stability, avoiding migrations from BF16 to FP8 or FP4. More importantly, a newly funded startup chooses between waiting six months for sold-out GB300 or beginning immediately on H100. “It cascades down.”
SemiAnalysis’s performance-based curve tracked observed prices within about 2% for 18 months before dislocating around September and October, then “blew out completely” in late 2025. Reversing the model implies demand outran supply; Nanos expects that pressure to persist if construction delays, political moratoriums, endpoint usage or model quality keep demand ahead.
7. Better models and hardware are expanding margins simultaneously
Nanos argued that reasoning shifted progress away from simply increasing parameter counts toward spending more FLOPs through reinforcement learning and post-training. The Astra, Fable or Mythos generations may have grown, but not enough to outrun GB300 NVL72 gains in memory, bandwidth and FP4 performance.
His supporting example was the progression from DeepSeek V3 to DeepSeek V4, 4.1 Pro, 4.1 Flash, GLM 5.3 and Kimi K3. Sparse attention and only two-to-three-times parameter growth produced substantially better outputs without comparable FLOPS growth, allowing higher-value tokens and more throughput per GPU. He put current GB300 NVL72 gross margins for Chinese-model serving around 80%.
The speakers argued that even frontier labs did not anticipate their own profitability, user growth or ability to serve this volume, forcing premium contracts for immediate GPUs. Nanos rejected the “AI fast takeoff” label, but said the evidence looks like “a revenue and profitability fast takeoff.”
8. Frontier-token economics dwarf the cost of physical compute
SemiAnalysis models up to $100 million of frontier-token revenue per megawatt of GB300 against roughly $40 million to install it, though Nishball called the utilization assumption optimistic. Contemporary neocloud contracts were nearer $12 million per megawatt, while SpaceX and Google deals around $48 million initially looked shocking but fit the resale economics.
Its simplified income statement uses about $1.85 per million blended tokens, including cache hits, translating to approximately $38 of revenue per hour. Ownership costs roughly $2.80 per hour and neocloud rental perhaps $4-$5, leaving frontier API gross margins near 90%.
Unlimited subscription plans depend on underuse: their model assumed margins nearer 50%-60%, while heavy users and Chinese gray-market resellers can turn $20 plans into API supply. Nanos’s Planet Fitness analogy captured the incentive—“a profitable gym is the one that’s empty”—even though providers cannot reliably identify low-usage customers beforehand.
Nishball disputed the assumption that token prices are collapsing: frontier and open-model prices were rising, and flat prices would still expand margins as throughput improves. Nanos agreed blended spending is increasing, though token efficiency remains hard to measure because users spend more tokens attempting harder problems that were previously impossible.
9. AI-native demand is strong; legacy-enterprise adoption is uncertain
The speakers divided the funding question into willingness and ability. Profitability and user utility support willingness at the tip of the spear, while backstops, lender capacity and market structure determine whether that demand can actually be financed. Enterprise adoption remains less certain.
Nishball’s team assembled thousands of public debt facilities and relationships, work he estimated would otherwise require four people for four months to produce “half as good of a job.” Nanos cited flight refunds, email triage, travel and routine planning: “You’d have to pry this stuff out of my cold dead hands.”
Nishball remained least certain about traditional enterprises. Many executives display “a lack of discovery in Fortune 500 and lack of imagination,” reducing the question to summarization or “How do I fire people?” Adoption can still hinge on one leader saying embrace AI—or banning it.
Nanos’s harsher answer was that slow incumbents may simply be replaced. He argued that nine of the ten largest companies by market capitalization are AI-, semiconductor- or data-center-related and that successful AI-native companies could grow independently or transform their acquirers. His conditional proof point: an Anthropic IPO at $2 trillion would make it roughly a top-25 company.
10. AI debt is becoming a macro asset class before lenders understand it
In SemiAnalysis’s comparison, AI debt crosses auto loans, student loans, credit cards, non-agency mortgages, CMBS and HELOCs, becoming second only to the U.S. mortgage-backed market. Unlike those slowly growing categories, its line rises abruptly as hyperscalers, neoclouds, labs and data centers all arrive together.
Nishball expects AI financing to exceed $1 trillion and says 2026 is the final year it remains below 10% of roughly $12.7 trillion in total annual U.S. issuance. He linked that scale to higher long-end rates and wider credit spreads: lenders face a “zombie apocalypse” without knowing whether this is “wave five or wave fifty.”
Today, some lenders avoid learning the technology by underwriting NVIDIA, Microsoft or other investment-grade credit. Backstops buy time to study GPU rental prices, residual values, churn, cancellations and execution. Nanos’s Ontario Teachers’ Pension Plan anecdote made the endpoint tangible: pension funds ultimately supply capital, so explaining the risks is substantive work rather than financial ornament.
11. NVIDIA’s backstops are a bridge, not enough capital for the destination
Under AICP, NVIDIA promises to rent unused neocloud capacity for six years at an average floor near $3.25, allowing lenders to price NVIDIA credit plus execution risk. The guarantee is not intended to be exercised; it lets operators finance clusters while building more diversified customer books, including token-as-a-service activity.
NVIDIA also receives 50% of revenue above the backstop, making the guarantee a business rather than charity; Lambda uses a related revenue-share structure. Elsewhere, $105 billion of residual-value guarantees support 4.25 gigawatts—and potentially nearly nine across two phases—while locking the complex into NVIDIA GPUs. Nishball called that function a “land grab.”
For the proposed $500 billion capital partnership, SemiAnalysis illustrated a loss waterfall across equity, an unguaranteed B piece and successively senior A tranches. NVIDIA’s 25% residual-value guarantee could be attached to a selected tranche. Losses would have to exhaust junior capital before reaching the safest tranche, creating products for private equity, private credit, banks and insurers.
SemiAnalysis believes NVIDIA’s balance sheet could support as much as 46 gigawatts, explicitly “not our forecast,” against roughly 240 gigawatts of incoming capacity. With roughly 60-some-odd incremental gigawatts potentially arriving in 2029, the lending market ultimately must underwrite projects without backstops.