SpaceX, the rocket and satellite company, is reportedly negotiating to borrow $40 billion to buy computer chips from Nvidia, the company whose chips run most artificial intelligence tools.
Apollo, a giant investment firm, would lead the lending, so the chips would be bought with borrowed money rather than money SpaceX already has.
Why it matters: when buying chips depends on loans this large, the AI boom also depends on lenders getting repaid, and AI chips lose value quickly as newer ones arrive.
SpaceX is in talks to borrow $40 billion to buy Nvidia chips, Bloomberg reported. The Financial Times, in a report relayed by Reuters, said Apollo Global Management is leading the financing. No signed agreement has been announced. SpaceX and Apollo have not disclosed terms, the chip quantities, or a timeline.
Executive Summary
The reported financing would put a $40 billion debt package, led by one of the largest private-credit managers, behind a single company’s purchase of AI chips. Private credit means loans made by investment firms rather than banks. Nvidia’s graphics processing units (GPUs) are the specialised processors that train and run most large AI models, and they are the most expensive line item in an AI data center.
It matters because of how the purchase is paid for, not only because of its size. A buyer that pays for chips from operating cash or new equity bears the risk itself. A buyer that borrows $40 billion takes on fixed repayment obligations, and its lenders take on part of the risk. If the talks conclude, how fast SpaceX adds AI computing capacity would depend partly on lender terms, collateral and repayment schedules.
Why a $40 Billion Loan Turns Compute Into a Credit Question
A company that buys AI chips with its own cash answers mainly to its shareholders. If the chips earn less than expected, the owners absorb the loss. A company that borrows $40 billion for the same purchase faces a different test. Interest and principal fall due on a schedule whether or not the hardware is fully used. The lenders, here a group led by Apollo, have to decide in advance how much the chips will earn and how quickly that cash will come in.
That changes the questions that decide the deal. Before committing, lenders typically ask what revenue the computing capacity will produce, how quickly the chips lose value, what they could recover if repayment faltered, and how long the loan runs compared with the hardware’s useful life. None of the terms have been made public. Still, at $40 billion, the answers to those questions would shape the pace and cost of SpaceX’s chip purchases as much as Nvidia’s supply would.
Several groups are exposed. SpaceX would take on fixed obligations. Apollo and any co-lenders, along with the investors whose money they deploy, would take on part of the hardware risk. Nvidia would book demand that rests partly on borrowed money rather than the buyer’s own funds.
Why a Private-Credit Manager Would Lead
Private-credit firms raise long-term capital from insurers, pension funds and other institutions. They can commit large sums to a single borrower and set terms privately, without the public pricing of a bond sale. That makes them a natural fit for a borrower seeking a very large, bespoke package. Reports describe Apollo as leading the financing, which suggests other lenders could take part, though no participants have been named.
Apollo’s balance sheet shows why insurance-linked capital matters to firms like it. In an 8-K filed October 5, Apollo said Athene, its retirement-services subsidiary, holds most of its alternative investments, and estimated third-quarter alternative net investment income of about $375 million pre-tax, an annualised return of roughly 10%. That filing previews quarterly results and does not mention SpaceX or Nvidia. Whether any of the SpaceX exposure would end up on Athene’s books has not been disclosed.
The Depreciation Problem Lenders Have to Price
AI chips lose value faster than most assets usually financed with long-term debt. Nvidia releases new GPU generations frequently, and each one cuts the market value of the last. A loan secured against chips, or repaid from the revenue they generate, is only as sound as its assumptions about how long the hardware stays competitive.
Lenders in AI financings have used the chips themselves as collateral (security that a lender can seize if a loan is not repaid). SpaceX and Apollo have not said whether this deal would be secured that way, or backed instead by SpaceX’s broader business. The difference matters. A loan backed by GPUs ties the lender’s recovery to second-hand chip prices. A loan backed by the whole company ties it to SpaceX’s overall cash flow.
What Follows Downstream: Sites, Power and Cooling
Chips bought with $40 billion of debt only start earning once they are installed and running, so delays in data center capacity become delays in repaying the loan. Each rack of high-end GPUs needs floor space, a reliable electricity supply and, increasingly, liquid cooling, because air alone cannot remove the heat dense AI hardware produces.
For data center developers, utilities and cooling suppliers, debt-financed purchases make the timetable less forgiving. Hardware that sits in a warehouse waiting for power still accrues interest. SpaceX has not disclosed where the chips would be deployed or how much electrical capacity they would require.
Background
Nvidia’s GPUs, originally built for graphics, became the default hardware for training and running large AI models. They are now the most expensive single component of an AI data center, and buyers have funded them through operating cash, equity and, increasingly, debt. SpaceX is best known for rocket launches and satellite services.
Apollo Global Management is a New York-based alternative asset manager. Its businesses include private credit and retirement services through its subsidiary Athene. Apollo is scheduled to report third-quarter results on November 3, 2026, according to its October 5 SEC filing.
An Australian company called Sharon AI has borrowed 356 million US dollars and pledged the computer chips it is buying as security, much like a mortgage uses the house.
The chips are powerful Nvidia processors that run artificial intelligence. The loan costs about 10 percent a year, and Goldman Sachs is among the lenders.
The twist: lenders get repaid from what customers pay to rent those chips. So the loan really rests on Sharon AI’s customer deals, and it is only the first of several loans behind a plan to install 68,000 chips by mid-2027.
SharonAI Holdings Inc. (NASDAQ: SHAZ), an Australian provider of AI computing infrastructure, announced on October 1, 2026 that it has entered its first GPU-backed debt facility: a US$356 million committed, senior secured loan held in a special purpose vehicle and priced at a fixed 9.95%, excluding fees. The facility is secured against the GPUs and their associated cash flows, and proceeds will fund compute capacity dedicated to customer contracts.
Lenders include Goldman Sachs and select large private credit funds alongside Australian, Asian and global investors, with Jarden Australia acting as sole financial advisor and arranger. Sharon AI called it the first in an expected series of GPU financings supporting more than 68,000 NVIDIA GPUs deployed by mid-2027, and said it has now secured more than US$2.6 billion of debt and equity capital over ten months against a customer contract book worth more than US$8.8 billion.
Executive Summary
Sharon AI has opened a new funding channel for its GPU fleet: debt secured directly by the chips and the customer revenue they generate. The US$356 million facility is modest against the company’s 68,000-GPU build-out plan, but Sharon AI presents it as the template for a series of similar financings, shifting part of its hardware funding from equity to borrowed money at a fixed 9.95% rate before fees.
The significance lies in the structure. Because the security package combines hardware and contracted cash flows, the lenders are effectively underwriting Sharon AI’s customer contracts, with GPU resale value as the fallback. That makes the quality, length and enforceability of those contracts, rather than raw chip count, the variable that determines how much Sharon AI can borrow and at what price.
The deal also lands days after Sharon AI disclosed a change of auditor to Ernst & Young and announced a confidential-computing collaboration with VAST Data aimed at regulated customers, two developments that bear on how lenders and investors will judge the company’s reporting and its contract pipeline.
The Chip Is the Collateral, the Contract Is the Repayment
Sharon AI describes the facility as senior secured and GPU-backed, held in a special purpose vehicle (SPV), a separate legal entity set up to own specific assets and borrow against them. The security is “the GPUs and associated cash flows,” and proceeds will fund compute “dedicated to customer contracts.” Senior secured means these lenders are paid first and hold a direct claim on the pledged assets.
Put those pieces together and the lenders’ position becomes clear. In normal operation, the debt is serviced from what customers pay to use the specific GPUs inside the SPV. The chips themselves matter mainly if something goes wrong: if a customer stops paying or the SPV defaults, lenders can claim the hardware and its revenue stream. From the lender’s seat, each GPU is worth largely what its attached contract is expected to earn over the life of the loan, with resale value as the backstop.
That has consequences beyond Sharon AI’s balance sheet. Graphics processors are a fast-moving asset class, and each new NVIDIA generation can change what older chips fetch on the secondary market. A lender relying on resale value alone would carry that risk directly. Tying security to contracted cash flows moves the central credit question from what a chip will be worth at the end of the loan to whether the customer will keep paying, which makes the strength and duration of the customer book the main driver of how cheaply an AI cloud operator can finance hardware.
What a Fixed 9.95% Buys, and What It Leaves Out
The 9.95% rate is fixed, so Sharon AI’s interest bill will not rise if benchmark rates do, a useful match for revenue that comes from multi-year contracts at agreed prices. The figure explicitly excludes fees, however. Arrangement and commitment fees on structured facilities can be meaningful, so the all-in cost of this borrowing is higher than 9.95% by an amount the company has not stated.
Chief Executive James Manning said the structure is “designed to enhance return on equity.” The mechanism is simple: if the GPUs earn more than the cost of debt, borrowing lets shareholders keep the difference on a smaller equity base. The arithmetic also runs in reverse. If utilization, pricing or customer payments fall short, leverage magnifies losses for equity holders, who rank behind senior lenders. Whether the claim holds depends on contract economics Sharon AI has not published.
The lender roster adds context. Goldman Sachs and “select large private credit funds” sit alongside Australian and Asian investors. Private credit, meaning lending by investment funds rather than banks, commonly provides asset-backed loans of this kind, and a committed facility (money promised up to a limit) gives Sharon AI drawable capacity as deployments come online.
$356 Million Is the Opening Tranche of a 68,000-GPU Plan
Sharon AI calls the facility “the first in an expected series of GPU financings” behind a scheduled build-out of more than 68,000 NVIDIA GPUs by mid-2027. It has not said how many of those chips this US$356 million covers, but the framing is clear: this is a repeatable structure, not the whole bill.
Two company figures set the scale. Sharon AI says it has secured more than US$2.6 billion of institutional debt and equity over the past ten months, and that its customer offtake, meaning contracted commitments to buy compute, stands at a total contract value (TCV) above US$8.8 billion. TCV sums the full value of contracts across their entire terms, so it is not annual revenue, and its worth to a lender depends on contract length, cancellation rights and counterparty strength. Sharon AI lists hyperscalers (the largest cloud platforms), AI-native companies, government, enterprise and research organizations among its customers, without naming those behind this facility.
The company is also building product features for regulated buyers. On September 23 it announced a collaboration with VAST Data to offer VAST DataEnclave, a confidential-computing environment in which, the companies say, customer data and model weights remain encrypted even from Sharon AI as operator, targeting banks, government agencies and other regulated organizations. If such features help win long-dated contracts from institutional customers, they feed directly into the cash flows that secure facilities like this one.
Reporting Discipline Matters More Once Debt Is Layered In
Lenders monitor borrowers through their financial reporting, so audit quality becomes more important as debt is added. In an 8-K filed September 24, Sharon AI disclosed that its Audit Committee approved ending HoganTaylor LLP’s engagement on September 20 and engaged Ernst & Young on September 23 for the fiscal 2026 audit, following a competitive process involving several firms.
The filing states there were no disagreements with HoganTaylor and that its audit reports for 2024 and 2025 carried no adverse or qualified opinions. It does list one reportable event: a material weakness, meaning a significant gap in internal controls over financial reporting, identified by HoganTaylor and disclosed in the 10-K filed March 31, 2026. Moving to a Big Four auditor is a common step for a growing public company, and the filing gives no indication of a dispute. It does not say whether the material weakness has been remediated, and for a borrower planning a series of GPU-backed facilities, reliable controls over cash flows and asset records are part of what lenders are underwriting.
Background
SharonAI Holdings Inc. trades on Nasdaq under the ticker SHAZ and describes itself as an Australian “neocloud,” a specialist provider that rents GPU computing power for AI rather than offering general-purpose cloud services. Through its AI Factory platform and a network of technology and co-location partners (data center operators that host its equipment), it serves organizations training and running AI models, with an emphasis on sovereign infrastructure that keeps data within national borders. It reports to the SEC as an emerging growth company.
Companies in this segment typically need large sums up front to buy GPUs before customer revenue arrives, which makes financing structure central to their growth. Sharon AI says its ambition is gigawatt-scale compute capacity across Australia, New Zealand and the broader Asia-Pacific, a gigawatt being roughly the output of a large power station.
Two companies that rent out powerful computers for artificial intelligence recently borrowed money, and one is paying far more for it.
CoreWeave took a $2.6 billion loan that costs 5.5 percentage points above a standard benchmark interest rate. Its rival Nebius borrowed $775 million at just 2.5 points above that same benchmark.
The difference comes down to what backs each loan. Nebius tied its loan to one financially strong customer’s contract, while CoreWeave’s covers many customers. If demand for artificial intelligence cools, cheaper borrowing could become a real advantage.
Dealroom reported that CoreWeave closed a $2.6 billion delayed-draw term loan in August, priced at Term SOFR plus 5.50% and maturing in September 2031, with $1.2 billion drawn so far. That margin is three percentage points above the SOFR plus 2.50% Nebius Group secured on a $775 million facility in July. Nebius has described that facility as backed by deployed GPU infrastructure and cash flows from an investment-grade customer.
Separately, Nebius closed $5.75 billion of convertible notes in August. In a Form 6-K filed September 8, it announced that Palantir had named it a preferred sovereign AI infrastructure partner.
Executive Summary
Two of the best-known neoclouds, the specialist providers that rent GPU computing for artificial intelligence, have borrowed against their buildouts within weeks of each other on very different terms. CoreWeave’s $2.6 billion facility carries a 5.50% margin over the Secured Overnight Financing Rate (SOFR), the US benchmark for floating-rate loans. Nebius’s smaller $775 million facility carries 2.50%.
The gap does not show lenders favoring one company’s AI prospects over the other’s. It shows lenders pricing the specific collateral and customer contracts behind each loan. Nebius’s loan sits on a single contracted deployment with an investment-grade customer, meaning one with a strong credit rating. CoreWeave’s loan finances committed deployments across a broader customer pool and gives the company more flexibility, and it pays more for that flexibility.
This matters beyond the two companies. AI infrastructure is being built largely with borrowed money. When credit markets start pricing each buildout on its own contracts and collateral, the cost of capital becomes a competitive variable, and it could separate winners from strugglers if GPU rental prices or utilisation weaken.
Same Industry, Very Different Price of Money
Floating-rate corporate loans charge a benchmark rate plus a fixed margin, or spread. The benchmark here is SOFR, which tracks the cost of overnight borrowing secured by US Treasuries. Because both CoreWeave and Nebius pay SOFR, the spread is the part that reflects how lenders view the risk. CoreWeave’s 5.50% is 2.2 times Nebius’s 2.50%, a gap of three percentage points. Describing it as ‘triple’ is loose shorthand. Once the shared benchmark is added back, the difference in all-in borrowing cost is proportionally smaller, though still substantial.
The structure also matters. A delayed-draw term loan lets the borrower tap committed funds in stages as spending arrives, which suits GPU deployments that come online in phases. CoreWeave has drawn $1.2 billion of its $2.6 billion. As simple arithmetic before fees, three extra percentage points on the drawn balance comes to roughly $36 million a year. On the full facility it would be roughly $78 million a year.
Collateral and Contracts, Not Confidence, Set the Spread
Nebius’s facility resembles project finance. It is secured by hardware already deployed and by cash flows from an investment-grade customer, and Nebius said it covers more than 100% of that deployment’s capital expenditure. Lenders are underwriting a known asset with a known payer, which is why the margin is low. The model works best if Nebius can repeat it, and the company points to more than $40 billion of additional customer commitments as the pipeline for doing so.
CoreWeave’s loan spreads risk across a broader set of committed deployments and runs to September 2031. That breadth gives CoreWeave room to serve shorter contracts and higher-margin enterprise work that a single-customer structure would not accommodate. The trade-off is a higher hurdle rate, the minimum return each financed GPU must earn before it adds value for shareholders. Neither structure is inherently better. One buys cheap money through narrow contracts, and the other pays for flexibility.
Capital Hunger on Both Sides of the Comparison
Neither company is borrowing from a position of surplus. Nebius’s $5.75 billion convertible note sale in August dwarfs its $775 million facility. Convertible notes are debt that investors can later swap for shares, so they carry potential dilution. Nebius is also adding demand-side relationships. Its September 8 filing describes Palantir naming Nebius its preferred sovereign AI infrastructure partner and the two working to deploy new capacity, including modular data centers at sites where power is already available. That filing disclosed no financial terms, contracted capacity or timeline.
Investor positioning is mixed. CoreWeave’s hedge fund holder count rose to 71 in the second quarter of 2026 from 63, even as Magnetar Capital trimmed its stake 24% to 52,062,927 shares. Nebius’s count rose to 86 from 60, while Orbis Investment Management cut 22% to 6,494,719 shares. On August 31, 17.89% of Nebius’s float was sold short. With billions of convertible debt outstanding, part of that short interest likely comes from convertible holders hedging their positions rather than outright bets against the company.
Why the Pricing Gap Could Matter More If Demand Cools
While AI compute is scarce and rental prices hold, a three-point spread difference is easily absorbed. The risk appears when utilisation or pricing slips, a concern that resurfaced after September 14. Expensive debt then consumes a larger share of revenue, and strong top-line growth can coexist with weak returns to shareholders.
The likely beneficiaries are providers that can attach financing to long contracts with highly rated customers, and the lenders who have learned to tell those deals apart. Providers relying on broader, flexible facilities are not disadvantaged today, but they carry more exposure to a downturn. If lenders keep pricing buildouts deal by deal, a lower cost of capital becomes a durable advantage that is hard for rivals to copy quickly.
Background
CoreWeave and Nebius are leading examples of neoclouds, companies that build and rent out GPU computing capacity for training and running artificial intelligence models. CoreWeave, which listed on Nasdaq in 2025, is US-based. Nebius Group, listed on Nasdaq as NBIS and headquartered in Amsterdam, is led by founder and CEO Arkady Volozh and was formed from the international businesses of the former Yandex N.V.
Both companies need large amounts of capital, because GPUs, power and data center space must be paid for well before customers’ rental payments arrive. That has made debt markets central to the sector, and lenders have increasingly looked at the specific contracts and collateral behind each deal rather than lending against the AI growth story as a whole.
The investment firm Blue Owl, alongside Pacific Investment Management Company, is lending $2.4 billion to a company called IREN. The loan is secured against the chips themselves — the specialised processors that train artificial intelligence systems.
IREN once mined Bitcoin. It now rents computing power to artificial intelligence firms, Microsoft among them. It disclosed the loan on 27 August alongside annual results showing a $703 million loss, mostly from scrapping old mining machines.
Why it matters: the money funding the artificial intelligence boom increasingly comes from private lenders, secured on hardware that loses value fast.
IREN Limited (NASDAQ: IREN) disclosed on August 27, 2026, in a press release filed as an exhibit to a Form 8-K, that it has secured $2.8 billion of new GPU financings for non-investment-grade customer deployments. The largest tranche is a $2.4 billion facility led by Blue Owl and Pacific Investment Management Company LLC, as adviser to certain investors, at a 9.0% fixed rate. IREN says the facility funds 90% of the GPU capital expenditure associated with its air-cooled expansion at Mackenzie, and that combined with customer prepayments it provides funding in excess of GPU capex, freeing cash for other data center spending.
The disclosure came inside IREN’s full-year results for the fiscal year ended June 30, 2026, filed the same day alongside its annual report on Form 10-K. Yahoo Finance and 24/7 Wall St. reported that IREN shares climbed about 4% on the news, with fellow bitcoin-miner-turned-AI-host Cipher Mining up about 2%.
Executive Summary
The headline number is $2.4 billion, but the more revealing number is 9.0%. In the same results release, IREN disclosed a separate $3.6 billion “investment grade” GPU financing tied to its Microsoft contract priced at 6.0%. The two facilities buy substantially the same physical asset — racks of NVIDIA accelerators in IREN-owned data centers — yet they clear 300 basis points apart. The difference is not the hardware. It is who signed the offtake contract.
That spread is the story for anyone trying to understand how the AI buildout is actually being paid for. Lenders are underwriting GPU deployments as discrete, contract-matched financings rather than lending against a corporate balance sheet, and they are pricing each one off the credit quality of the customer at the other end. Private credit managers, not banks and not equity markets, are supplying the capital at the riskier end of that curve.
For IREN, the mechanics are aggressive by any standard. The company says recent three-year contracts are being struck at more than $20 million of revenue per megawatt of IT capacity, implying roughly a two-year payback, with customer prepayments covering 45–55% of GPU capex. Layer 90% debt financing on top of prepayments of that size and the equity cheque approaches zero — which is precisely the point, and precisely the risk.
Three Hundred Basis Points Is the Price of a Name
Read IREN’s two GPU financings side by side and you get an unusually clean market reading. The $3.6 billion facility supporting the Microsoft deployment is described as investment grade and priced at 6.0%; together with prepayments it funds 96% of the associated GPU capex. The $2.4 billion Blue Owl and PIMCO-led facility supports non-investment-grade customer deployments at 9.0% and funds 90% of GPU capex on its own. Same operator, same asset class, same vintage of silicon, roughly the same moment in the market.
What separates them is counterparty credit. When a hyperscaler with a top-tier rating signs the contract, the lender is effectively buying that hyperscaler’s payment stream with a data center wrapped around it. When the offtaker is a fast-growing but unrated AI developer, the lender is exposed to a company that may not exist in its current form when the loan matures — and charges accordingly. IREN’s recently disclosed customer list, which includes Cohere, Prometheus, Perplexity, Figure AI, Fal AI and Higgsfield AI alongside an unnamed frontier lab, is a plausible map of what sits behind that 9.0%.
This is a healthier signal than it might first appear. Credit markets differentiating between AI counterparties is what functioning risk pricing looks like; a world in which every GPU deployment funded at the same rate regardless of who was buying the compute would be far more worrying. The open question is whether 300 basis points is enough compensation for the gap between a hyperscaler and a Series C startup.
Collateral That Depreciates on a Two-Year Clock
GPU-backed lending inverts the usual logic of infrastructure credit. A data center shell, a substation and a power interconnection are long-lived assets that hold value across decades and multiple tenants. Accelerators are not. They are consumables on a rapid product cadence — IREN’s own Horizon 1 build for Microsoft runs GB300 NVL72 systems that did not exist a couple of generations ago — and their residual value depends on a secondary market that has never been tested at scale through a downturn.
The economics only work if cash comes back faster than the collateral ages. IREN’s disclosed contract terms are built for exactly that: more than $20 million of revenue per IT megawatt on three-year deals, active discussions at around $25 million, and a stated payback of roughly two years. If those contracts perform, the loan is largely repaid from revenue before hardware obsolescence becomes the binding question, and the collateral is a backstop rather than a repayment source. If a non-investment-grade customer stops paying in year one, the lender is left holding used accelerators and looking for a replacement tenant — the scenario the 9.0% is meant to cover.
Scale sharpens the point. A fully drawn $2.4 billion at a 9.0% fixed rate implies on the order of $216 million in annual interest before any amortization — close to IREN’s entire FY26 adjusted EBITDA of $245.7 million. That comparison is not a solvency warning; the facility is matched to new capacity that does not yet generate revenue. But it makes explicit that this debt is underwritten against contracted future cash flows, not against the business as it exists today.
The Cost of Leaving Bitcoin Behind
IREN’s FY26 accounts show a company mid-pivot and paying for it. Total revenue rose to $707.0 million from $501.0 million, with AI Cloud Services revenue up roughly eightfold to $128.8 million while bitcoin mining still contributed $578.2 million. The company reported a net loss of $702.6 million for the year, including $638.8 million of non-cash impairments that IREN attributes primarily to decommissioning bitcoin mining hardware as sites convert to AI workloads. Adjusted EBITDA slipped to $245.7 million from $269.7 million, and fell sharply in the June quarter to $19.2 million on higher headcount and platform investment ahead of the AI revenue ramp.
Those impairments are best read as the accounting cost of a strategic decision rather than a deterioration in trading. Converting a mining site to liquid-cooled AI capacity means writing off machines that still work but no longer fit the plan. The relevant test is whether the replacement revenue arrives: IREN says its 2026 capacity is largely sold out, that contracted annualized run-rate revenue for 2026 capacity stands at $4 billion against $1 billion operating today, and that it holds $14 billion in cash plus committed GPU financing and prepayments.
The gap between $4 billion contracted and $1 billion operating is where the entire thesis sits. It is a delivery problem — Horizon 2 is commissioning, Horizon 3 and 4 are in late-stage construction targeting Q4 2026 delivery, and cumulative targets of 0.3GW of IT capacity in 2026 rising to 0.8GW in 2027 depend on power, supply chain and construction all landing on schedule across sites in Texas, Oklahoma, British Columbia, Australia and Spain.
Private Credit Becomes the Buildout’s Balance Sheet
Step back from IREN and the structural shift is the real news. Blue Owl and PIMCO are not project-finance banks; they are alternative asset managers deploying institutional capital into an asset class that barely existed three years ago. A $2.4 billion single-asset-class facility at a fixed rate, sized to a specific deployment at a specific site, is closer to how aircraft and shipping fleets are financed than to how enterprise IT has traditionally been bought.
The appeal to operators is obvious. Ninety percent debt funding plus prepayments covering roughly half of GPU capex means expansion without commensurate equity dilution — a meaningful advantage for a company whose share count has grown through equity offerings, at-the-market facilities and convertible notes maturing in 2029 and 2030. The appeal to lenders is spread: 9.0% fixed on collateralised paper is attractive in a market where comparable yield is scarce, provided the underwriting holds.
The systemic question is concentration. If a large share of the world’s AI compute capacity ends up financed by a relatively small group of private credit managers, against collateral whose value is correlated to a single technology cycle and whose repayment depends on the durability of AI customer demand, then a broad slowdown would hit lenders, operators and chip demand simultaneously rather than sequentially. Nothing in IREN’s disclosures suggests distress. But the structure is new enough that no one has yet observed how it behaves when contracted revenue fails to convert.
Background
IREN Limited is one of a cohort of bitcoin mining companies attempting to convert accumulated power capacity, land and data center shells into AI compute hosting. The logic is that the scarce input for AI infrastructure is not chips but energized real estate — grid interconnections, substations and cooling — and miners spent years acquiring exactly that. The conversion is not simple: AI training racks draw far more power per rack than mining rigs, frequently require liquid cooling, and demand redundancy and network performance that mining sites were never built to provide.
IREN reports two segments, Bitcoin Mining and AI Cloud Services, with the latter growing from $16.4 million in FY25 to $128.8 million in FY26. In FY26 the company completed acquisitions of Mirantis and Nostrum, adding software and managed services capability and a European footprint, nearly tripled headcount, and made five C-suite appointments. It has a stated pipeline of more than 5GW across sites including Childress and Sweetwater in Texas, Kiowa in Oklahoma, Mackenzie, Canal Flats and Prince George in British Columbia, Bundey in Australia and Badajoz in Spain. Its capital structure includes dual-class shares, convertible senior notes due 2029 and 2030, an at-the-market equity facility and now several billion dollars of asset-backed GPU debt.
Blue Owl Capital and PIMCO have structured a $2.4 billion debt facility for IREN Ltd, the Nasdaq-listed operator that is converting bitcoin-mining sites into AI compute campuses. Reporting on the deal indicates the proceeds are earmarked for purchasing Nvidia accelerators — the specialised processors that run AI training and inference workloads. Separately, Core Scientific announced $600 million in new credit facilities.
The two financings land alongside IREN’s statement that its 2026 capacity is sold out and that it is now negotiating contracts for 2027 and 2028. Together they mark the maturing of a financing structure in which the chips themselves, and the contracted revenue they generate, carry the debt.
Executive Summary
The headline number is $2.4 billion, but the more consequential detail is the structure. Blue Owl and PIMCO are both large private-credit managers — firms that lend directly to companies rather than arranging syndicated bank loans — and they have built a facility specifically tailored to GPU procurement. That framing implies a financing secured against a hardware fleet and the contracts that fleet serves, rather than against a diversified corporate balance sheet.
This matters because it decouples AI infrastructure buildout from equity issuance. A neocloud — an operator that rents out GPU capacity without the broader service portfolio of a hyperscaler like AWS or Azure — has historically had two ways to buy chips: sell shares, or fund from cash flow. Neither scales to multi-billion-dollar fleets. Asset-backed debt is the third path, and it is now open at institutional size.
The trade-off is symmetrical. Pre-selling capacity years forward gives lenders visible cash flows to underwrite against; IREN’s claim that 2026 is fully contracted is precisely the kind of evidence that makes such a facility underwritable. But it also fixes revenue in advance while leaving the borrower exposed to the residual value of assets that depreciate on a schedule nobody has yet observed across a full technology cycle.
What It Means to Pledge a Chip
Collateralised lending is old; the question is always what the lender can recover if the borrower stops paying. Real estate works as collateral because buildings are immobile, long-lived, and trade in a deep secondary market. Aircraft and shipping containers work because they are standardised, tracked, and re-leasable. GPUs are a genuinely new asset class in this respect: they are standardised and in acute demand, which argues for strong recovery values, but they are also installed inside purpose-built facilities with specific power and cooling requirements, which complicates repossession in any literal sense.
In practice, facilities of this type tend to rely less on physically seizing hardware and more on capturing the contracted revenue that hardware produces — the customer agreements, and the entity that holds them. That is why the sequencing in IREN’s case is notable: the company’s statement that 2026 capacity is sold out precedes and supports the financing logic. Lenders are underwriting a contracted book, with the chips as backstop rather than as primary recovery.
None of the public material specifies the security package, the advance rate against hardware cost, the tenor, or the pricing. Those terms are where the actual risk allocation lives, and their absence is the single largest gap in what has been disclosed.
The Residual Value Problem Nobody Has Solved
Every asset-backed structure embeds an assumption about what the asset is worth at the end. For GPUs, that assumption is unusually hard to defend. Nvidia has been shipping new accelerator generations at a cadence far faster than the multi-year amortisation periods typically applied to data centre equipment, and each generation has delivered large performance-per-watt improvements. A chip that is two generations old is not worthless — inference workloads, smaller models, and price-sensitive customers all provide a floor — but its rental rate is not the rate it commanded at launch.
This creates a specific mismatch. If a facility amortises over, say, a longer horizon than the period during which a chip commands premium pricing, the borrower must either re-contract older hardware at lower rates or refinance into a fleet upgrade. Both are manageable in a market with excess demand. Neither is comfortable if demand normalises while the debt schedule does not. The honest position is that no one has yet observed a full GPU depreciation cycle under sustained competitive supply, so residual-value assumptions in these deals are estimates, not history.
It is worth being even-handed here. The counterargument — that compute demand has repeatedly outrun supply forecasts, and that older accelerators have found ready secondary uses — is not unreasonable. The point is not that these facilities are unsound; it is that their soundness rests on a forward-looking judgment that has not been stress-tested, and that lenders are being compensated for taking it.
Winners, Losers, and the Private-Credit Angle
The clearest beneficiaries are the neoclouds themselves. IREN and Core Scientific both originated as bitcoin miners, meaning they already controlled the scarcest input in AI infrastructure — energised sites with interconnection agreements and power contracts. What they lacked was the capital to fill those sites with accelerators. Debt of this kind converts a land-and-power position into a compute business without diluting shareholders at every step.
Nvidia benefits indirectly and substantially: financing capacity is now a gating factor on GPU sales, and structures that unlock institutional debt expand the buyer pool beyond hyperscalers with investment-grade balance sheets. Private credit managers benefit from a new, large, yield-generating asset class at a moment when they hold substantial dry powder. Traditional banks are, for now, less visible in these transactions — which is itself informative about where regulatory capital treatment and risk appetite currently sit.
For buyers of AI capacity, the second-order effect is availability. More financed hardware means more contractable capacity, and IREN’s stated pivot to 2027 and 2028 negotiations suggests operators are trying to lock in demand well ahead of delivery. Enterprises signing multi-year GPU contracts should nonetheless treat counterparty durability as a real diligence item: a highly levered provider whose debt is secured against the very fleet serving your workload is a different credit risk than a hyperscaler, and contract terms should reflect that.
Background
Both IREN and Core Scientific began as bitcoin miners, businesses defined by the pursuit of cheap electricity at scale. That pursuit left them holding something the AI buildout badly needs: sites with signed grid interconnection agreements and multi-year power contracts, in a market where new interconnection queues can run for years. When AI compute demand accelerated, converting those sites to GPU hosting became a more attractive use of the same infrastructure. Core Scientific emerged from Chapter 11 bankruptcy protection in 2024 and continued that pivot; a proposed all-stock acquisition by CoreWeave was rejected by its shareholders in 2025, leaving the company independent.
The financing question followed directly. Site and power are capital-intensive but financeable through familiar channels; filling those sites with accelerators requires very large equipment purchases that neither company could fund from operating cash flow. Equity issuance dilutes shareholders. That gap is what facilities like the Blue Owl and PIMCO structure are designed to fill, and it explains why the terms of these deals — not just their headline sizes — are the thing worth watching.
On August 28, 2026, Bullish (NYSE: BLSH), an institutionally focused digital asset platform, announced a $100 million stablecoin-based liquidity facility for USD.AI, a protocol that lends against high-performance computing hardware. Bullish frames the deal as its strategic entry into middle-market AI infrastructure financing; the release was issued under USD.AI’s name.
USD.AI, developed by Permian Labs, uses the capital to extend non-recourse loans secured solely by the GPUs being financed. Bullish also plans to list sUSDai — USD.AI’s yield-bearing token — across multiple trading pairs on Bullish Exchange with a dedicated market-making program, and the two firms are expanding a joint research effort on capital formation for AI capital expenditure.
Executive Summary
The headline number is modest by AI infrastructure standards, but the structure is the story. A $100 million facility denominated in stablecoins — digital tokens designed to hold a fixed value against the dollar — is being deployed as debt against graphics processing units, the chips that train and serve AI models. The borrower’s borrowers are not being asked to pledge their companies. They pledge the hardware.
That matters because the AI buildout has so far been financed overwhelmingly with equity: venture rounds, strategic investments, and public-market raises that dilute founders and existing shareholders. Debt secured by the machines themselves is cheaper on paper and non-dilutive, which is precisely how truck fleets, aircraft, and construction equipment have been financed for decades. The open question is whether GPUs behave like those assets.
The second half of the announcement — listing sUSDai on Bullish Exchange with market-making support — is an attempt to build a secondary market where compute-backed credit can be priced continuously rather than marked by a lender’s internal model. If that works, it is genuinely new market infrastructure. If it does not, the listing is a liquidity venue in search of participants.
Compute Is Being Reclassified From Capex to Collateral
For most of the last three years, buying GPUs has been an equity decision. An operator raised money, bought chips, and hoped utilization arrived before the cash ran out. USD.AI’s pitch inverts that: the chips are income-producing assets that can service their own debt, so they should be financed like assets rather than like ideas. The company describes its loans as non-recourse and secured exclusively by the underlying GPU infrastructure, which means a default is supposed to cost the borrower the hardware and nothing more — the corporate balance sheet stays insulated.
The economics are attractive to the middle of the market: regional cloud providers and specialist AI hosts, often called neoclouds, that have real customer demand but cannot raise hyperscaler-sized equity rounds. Non-dilutive capital lets them add capacity without surrendering ownership. This is the same logic that built the equipment-leasing industry, and Bullish’s Thomas Cowan, its Head of Tokenization, positions the facility as evidence that "credible, well-structured real-world assets belong onchain."
Whether that logic survives contact with GPU economics is the substantive question, and the release does not attempt to answer it. Aircraft hold value for decades and trade in a deep, documented resale market. GPUs face a fast product cadence, and their resale value depends on power availability, hosting contracts, and whether a newer generation has made the previous one uneconomic for frontier work.
The Depreciation Curve Is the Whole Trade
Asset-backed lending works when the collateral’s decline in value is slower than the loan’s repayment schedule. If a borrower stops paying in year two of a three-year facility, the lender needs the recovered hardware to be worth more than the remaining principal. That is the pressure point in every GPU-backed structure, and it is sharpened by the non-recourse feature: a rational borrower whose chips have fallen below the outstanding balance has an economic incentive to hand back the hardware rather than keep paying.
Recovery is also physically awkward in a way that auto lending is not. A repossessed car can be driven to an auction lot. A repossessed GPU cluster sits in someone else’s data center, drawing power under a contract the lender may not control, and its value in the resale market depends on whether it can be redeployed somewhere with megawatts already energized. Lenders in this space typically address that with hosting-agreement step-in rights, utilization covenants, and conservative advance rates — none of which the release discloses.
None of this makes the structure unsound. Equipment finance handles depreciating collateral routinely by lending less than the asset is worth and amortizing quickly. It does mean the interesting terms are the ones not in the announcement: loan-to-value, tenor, and whether the underwriting assumes a functioning secondary market for used accelerators or assumes none at all.
One Firm, Several Roles in the Same Market
Bullish is doing four things here at once. It is the lender providing the facility. It operates the exchange that will list sUSDai. It is arranging the market-making program that supplies liquidity for those pairs. And, per its own description, it is the parent company of CoinDesk, a widely read digital asset news and data provider. The release states plainly that Bullish was an early investor in the protocol before this facility.
This is not unusual in digital asset markets, and vertical integration is often what makes a nascent asset class tradable at all — somebody has to stand up the venue and quote the first prices. But it is worth naming, because the release’s claim that the listings will improve "price discovery" for GPU-backed debt is strongest when prices come from many independent participants and weakest when they come from an affiliated market maker in a thin book. The stated goal, a transparent market for the cost of compute, is a real and valuable one; readers should judge it on the breadth of participation it eventually attracts rather than on the launch announcement.
The credible version of the argument is USD.AI’s own: onchain settlement means loan positions, collateral, and repayments are visible to anyone rather than buried in a private credit fund’s quarterly letter. That transparency is a genuine differentiator from conventional private credit, where mark-to-model valuations have drawn scrutiny across the industry. It is a claim that can be verified over time by watching the chain.
Read the Market-Size Claim Carefully
The release asserts that AI infrastructure financing has become one of the largest sectors in private credit, at a scale that "eclipses legacy debt markets such as auto loans and home equity lines of credit." No figure, source, date, or definition accompanies that statement, and the distinction matters enormously: announced financing commitments, annual originations, and outstanding balances are three very different measures, and auto lending and HELOCs are long-established consumer credit markets with decades of accumulated balances.
The directional point — that debt is arriving in AI infrastructure quickly and at serious size — is well supported by the pattern of deals, including USD.AI’s own prior transactions: a $34 million three-year facility for NexGen Cloud’s GPU deployment in Sweden, and a joint venture with Singapore-based BSQ Capital Partners to finance $300 million of AI compute across Asia-Pacific. Against that pattern, $100 million is a middle-market facility, not a landmark, and the release describes it as exactly that.
For buyers of infrastructure capacity and for investors, the useful takeaway is not the comparison but the trend it gestures at. When an asset class attracts dedicated lenders, tokenized instruments, and exchange listings within a short window, the cost of capital for that asset falls — and so does the barrier to building capacity that may or may not find tenants. Cheaper financing accelerates supply. Supply eventually meets demand, in one direction or the other.
Background
The AI buildout has been financed largely with equity so far — venture rounds, strategic investments, and public raises — because the assets involved were new, the demand curve was unproven, and lenders had no basis for valuing used accelerators. As GPU clusters began generating contracted revenue, a private credit market formed around them, borrowing structures from equipment and asset-based finance: lend against the machine, size the loan below its value, and amortize before the technology turns over.
USD.AI, built by Permian Labs, applies that model with blockchain settlement, making loan positions and collateral visible onchain rather than reported quarterly. Bullish, a New York–listed digital asset platform that operates an institutional exchange and owns CoinDesk, has been an investor in the protocol and is now extending it a balance-sheet facility — part of a broader industry push to bring "real-world assets" onto public ledgers, where the collateral is physical hardware rather than a financial instrument.
MLive reported on April 25, 2026 that the large data center campus planned for Saline Township, in Washtenaw County, Michigan, has secured financing through Blackstone, the world’s largest alternative-asset manager and a major private-credit lender. Saline Township is a rural farming community roughly south of Ann Arbor, and the site has been the subject of local debate since the project was first proposed.
The report is headline-level. The coverage available to us does not state the size of the facility, the amount or structure of the financing, the identity of the anchor tenant, or the construction schedule. What is established is the fact of a financing commitment from a private-capital provider rather than from a bank syndicate or a utility-led arrangement.
Executive Summary
A financing close is the moment a data center stops being a land-use argument and becomes a construction project. Site control, zoning approvals and power studies can all exist without a single dollar of committed capital; a lender writing a check is the first hard signal that a third party with money at risk believes the project will generate cash. That is why this particular disclosure matters more than its length suggests.
The identity of the lender matters as much as the event. Blackstone has become one of the largest financiers of digital infrastructure through its credit and real-assets platforms, and its involvement places Saline Township inside a broader shift: the capital funding America’s AI-era compute buildout is increasingly private credit — money lent directly by asset managers — rather than utility balance sheets, investment-grade bonds, or traditional construction lending. Private credit moves faster, tolerates more complexity, and prices that flexibility into the interest rate.
The consequence is a redistribution of risk. When a regulated utility builds generation and transmission for a large customer, cost overruns and demand shortfalls can end up in rate cases, where regulators decide how much lands on other ratepayers. When a private lender funds a merchant campus, the first loss sits with the sponsor’s equity and the lender’s loan. Which of those two models Saline Township follows is the single most consequential question the reporting does not yet answer.
Why a Private-Credit Lender, Not a Utility, Is the Story
For most of the last century, the entity that financed heavy electrical load in a place like Washtenaw County was the local utility. It raised capital, built the wires and the plants, and recovered the cost from customers over decades under a regulator’s supervision. The model was slow, but it was durable, and it socialized risk across a large base of ratepayers who had little say in the matter.
Data centers built for artificial-intelligence workloads do not fit that rhythm. The demand signal arrives in months, not decades, and it is concentrated in a handful of hyperscale buyers whose plans can change. Private credit — non-bank lending in which asset managers lend directly from their own funds — has filled the gap because it can underwrite an idiosyncratic asset quickly, structure around construction milestones, and accept collateral that a bank credit committee would struggle with. The borrower pays for that speed in spread.
The trade is real in both directions. A sponsor who takes private credit gets certainty of execution and avoids the political timeline of a rate case. It also accepts covenants, tighter reporting, and a lender that can enforce quickly if lease-up or delivery slips. Reading Blackstone’s involvement as validation of the Saline Township site is reasonable; reading it as a guarantee of completion is not, because financing commitments are typically conditioned on milestones that have not been disclosed here.
The Capital Structure Decides Who Eats the Power Risk
Whether a campus of this scale is financially safe depends less on the headline amount than on what sits behind it. Two structures dominate the sector. In the first, the developer signs long-term leases with a creditworthy tenant before drawing debt; the lender is effectively underwriting the tenant’s credit, and power costs are passed through under the lease. In the second — a merchant or speculative build — the developer takes capacity risk, betting that demand will appear at attractive rates. The interest cost of the two differs sharply, and so does the consequence of being wrong.
Power is where those structures are tested. A large campus needs a firm interconnection, a tariff that sets what it pays per megawatt-hour, and often a commitment to pay for a minimum volume whether or not the servers are drawing it. That last provision — a take-or-pay or minimum-demand charge — is the mechanism by which regulators try to ensure that a large customer, not the general ratepayer base, funds the network upgrades built on its behalf. Whether such terms exist here, and how strict they are, is not in the reporting.
The winners in the current arrangement are relatively easy to identify: landowners who sell into a rising market, contractors and electrical trades, lenders earning wide spreads on secured assets, and local governments that collect property tax on very expensive equipment. The exposed parties are harder to see in advance. They include equity holders if AI compute demand normalizes before the campus is leased, and residential ratepayers if grid investment is later judged to have been undersubscribed by its intended customer. Neither outcome is predictable from a financing headline, which is exactly why the terms matter.
Michigan’s Calculation: Tax Base Now, Load Growth Later
Michigan has actively courted data center investment as part of a broader effort to attract capital-intensive industry, and southeast Michigan offers a genuine set of advantages: cool climate for much of the year, abundant fresh water in the Great Lakes basin, existing transmission built for a manufacturing economy that has shrunk, and proximity to engineering talent around Ann Arbor and Detroit. Those are structural, not promotional.
The fiscal case for a rural township is also real but narrow. A hyperscale campus generates substantial property tax relative to farmland and comparatively few permanent jobs — typically technicians, security and facilities staff, against a much larger but temporary construction workforce. Communities that evaluate these projects as employment engines are usually disappointed; those that evaluate them as tax-base plays are usually not, provided the assessment holds and abatements are modest. The distinction is worth making plainly because it is where local expectations most often go wrong.
The longer-term question for Michigan is load. Adding gigawatt-scale demand to a grid changes generation planning, transmission queues and reserve margins for everyone connected to it. That can be managed well — with large-load tariffs, staged energization, and on-site or contracted generation — or managed poorly. The financing announcement tells us capital has arrived. It tells us nothing about which of those paths the electricity side is on.
A Contested Site, and How to Read Both Sides
The Saline Township project has drawn organized local opposition, as most large rural data center proposals now do. Residents raise farmland conversion, water use, noise from cooling equipment, traffic during construction, and the durability of tax promises. These are legitimate, checkable questions, and dismissing them as reflexive opposition would be lazy — several of them have been substantiated at other sites, particularly noise complaints near residential parcels.
The same standard applies to opposition claims. Water consumption varies by an order of magnitude depending on whether a facility uses evaporative cooling or a closed-loop design, so a figure quoted without the cooling architecture attached is not informative. Ratepayer-impact estimates depend entirely on the tariff, which is a public document once filed. And in a national debate where template campaigns circulate between communities, it is fair to ask of any local group — as of any developer — who is speaking, what the specific local evidence is, and whether the numbers cited come from this project’s filings or from someone else’s. Asking is not an accusation, and there is no basis here for speculating about anyone’s funding.
The most even-handed reading is that both sides are currently arguing about a project whose material terms are not public. The developer has not, in the reporting available, published capacity, water design, or power arrangements; opponents cannot fully assess impact without them. A financing close usually precedes more disclosure, not less, because lenders require documentation that eventually surfaces in permits and utility filings. That is where the argument should be settled.
Background
Blackstone is the world’s largest alternative-asset manager, with major platforms in real estate, infrastructure and private credit. It has become one of the most significant financiers of digital infrastructure globally, lending to and owning data center assets as demand from cloud and artificial-intelligence workloads has outpaced what traditional bank and utility financing could supply on the required timeline.
Saline Township sits in Washtenaw County, southeast Michigan, an agricultural community adjacent to a metropolitan corridor with legacy industrial transmission. Large data center proposals in such places have become a recurring national pattern over the past several years: developers seek land, power and water at rural prices near urban fiber, while residents weigh tax revenue against land use, noise and grid effects. The Saline Township project has been locally contested since it was proposed, and the April 2026 financing report is the point at which the debate moved from land-use approvals toward committed capital.
