Structured financing secured by chips is becoming the central funding engine for global AI expansion and is rapidly developing into a new asset class that could exceed a trillion dollars in scale. This market traces its origins to a $2.3 billion private credit facility that Blackstone provided to CoreWeave in 2023, which became the landmark first transaction in chip-backed debt. Now Broadcom is in talks with Wall Street institutions to arrange $60 billion in chip financing for Anthropic, SpaceX is reportedly seeking $40 billion in financing for Nvidia chips, and Nvidia itself has announced a $500 billion cooperation framework with six major lenders. JPMorgan analysts estimate that total chip spending before 2030 could exceed $3 trillion, with most of it relying on debt financing. However, this debt expansion also brings risks that cannot be ignored. Because most transactions are private in nature, investors lack convenient exit channels, and cross-exposure among chipmakers, cloud computing giants, and emerging cloud service providers is highly concentrated. CoreWeave's corporate-level junk bonds have recently fallen sharply, with some dropping to 80 cents on the dollar and yields climbing to about 15%, reflecting deep market doubts about AI's commercial returns.

A Single Financing Deal Creates a New Paradigm

The starting point of this revolution was the Blackstone Tactical Opportunities team's cautious assessment of an unfamiliar concept. According to CoreWeave co-founder Brannin McBee, who participated in due diligence at the time, Blackstone executives directly asked: "What is a GPU?" and "Is AI real?" After receiving satisfactory answers, Blackstone led the completion of a $2.3 billion private credit facility for CoreWeave in 2023, setting a precedent for chip-backed debt financing. The core logic of this structure is to establish a special purpose vehicle (SPV) to purchase and hold chips, repay debt with cash flows generated by target customer contracts, and match the debt term to the five-to-six-year depreciation cycle of the chips, thereby locking hardware depreciation risk within a predictable range. CoreWeave's model was born from experience in commodity markets, similar to the development model in liquefied natural gas where contracts are signed first and then financing is arranged to build facilities. The first transaction was secured by a Microsoft contract, and at that time the financing cost was as high as a mid-teens interest rate. McBee admitted it was the most difficult due diligence of his career. As CoreWeave's customer roster continued to expand, its financing costs declined accordingly. In March of this year, the company completed an $8.5 billion debt financing backed by a Meta contract, with the borrowing rate falling to about 6%, as Wall Street priced the risk according to Meta's high rating rather than CoreWeave's own junk rating.

Chip Costs Far Exceed Data Centers

To understand the scale of this financing revolution, one must first understand the magnitude of chip spending. Chips cost two to four times as much as the data centers that house them, yet they depreciate completely within five to six years, creating a huge financing gap that traditional capital markets cannot digest independently. Take AI cloud service provider Nscale's flagship project in West Virginia as an example. The supporting power plant is expected to cost $6.5 billion, the data center and equipment $18 billion, while GPU procurement costs are as high as $47 billion. Investment-grade cloud computing giants such as Alphabet's Google, Microsoft, Amazon, and Meta can cover these hardware costs with ample cash or cheap bond financing, but OpenAI, Anthropic, and a host of emerging cloud service providers do not have such balance sheet strength. OpenAI Chief Financial Officer Mike Liberatore admitted: "The scale of infrastructure construction is unprecedented. There is no playbook telling you how to go from zero to artificial general intelligence in the traditional way under extremely capital-intensive conditions." To this end, the industry transplanted mechanisms from project finance and structured finance: isolating the risk of each transaction through an SPV, borrowing based on the quality of the transaction itself and customer credit rather than the issuer's own rating, and often keeping it off the corporate balance sheet.

Chipmakers Step In Directly to Backstop

The deep involvement of Broadcom and Nvidia has pushed this market to a new order of magnitude. Both companies provide backstop commitments for related debt transactions: if the borrower defaults, the chips will be re-leased or sold to repay the remaining debt, and the chipmaker is responsible for filling the gap. This mechanism allows senior bonds issued by SPVs to be linked to the chipmaker's investment-grade rating, significantly reducing financing costs. Apollo and Blackstone recently jointly completed a $35 billion "Big Sky" transaction to support Anthropic. This is also the first transaction to land on the "AI XPV" platform jointly built by Broadcom, Apollo, and Blackstone. Bank of America strategists estimate that if the platform reaches 20 gigawatts of financing capacity, it may require $235 billion in backstopped debt by mid-2029. In the Anthropic transaction, two senior bonds backstopped by Broadcom received investment-grade ratings, with the largest tranche at 5.75%; the riskier $4.5 billion subordinated tranche was priced at 8.5%, still far below Anthropic's standalone financing cost as an unrated private company. In August of this year, Nvidia assembled Apollo, Blackstone, BlackRock, Brookfield, KKR, and Goldman Sachs to jointly provide financing sources for AI infrastructure, with Nvidia itself committing to backstop up to 25% of the total.

From Private Placement to Syndicated Market

To achieve true scale, chip financing must break through the boundaries of the private market and enter the syndicated loan and even investment-grade public bond markets, one of the largest and lowest-cost capital pools in the world. CoreWeave took the lead this year in completing chip-backed syndicated leveraged loans, with both transactions receiving solid subscriptions from institutional investors and trading near par in the market, in sharp contrast to the same company's corporate bonds. Rich Myers, global head of structured products and private capital markets at Morgan Stanley, said: "These financings are directly linked to the most valuable assets in the market, namely the computing power generated by chips. A large body of evidence shows that demand for computing power will continue to grow and exceed supply." Chris Edson, head of global asset allocation at Apollo, said the traditional public debt market cannot meet the financing scale of digital infrastructure on its own and requires participation from multiple types of capital, including project finance, private placements, insurance funds, and commercial real estate. Obtaining an investment-grade rating is the key to opening the largest capital pools, such as insurance funds.

Rising Risks Cannot Be Ignored

However, as capital accelerates its inflow, risks are accumulating in tandem. Jason Pompeii, senior director at Fitch Ratings, pointed out that the current scale of vendor financing is unprecedented and risk is highly concentrated. "This only increases the possibility that if AI ultimately disappoints, the entire supply chain will be hit." The private nature of the transaction structure raises questions about liquidity. Because most chip financing transactions do not circulate in public markets, investors find it difficult to exit conveniently, and the transactions deeply bind a small number of chipmakers, cloud computing giants, and emerging cloud service providers, creating highly concentrated cross-exposure. Dave Snyderman, managing director of alternative credit and fixed income at Magnetar, warned: "The giants will only get bigger. Only they can obtain financing channels." The legal force of customer contracts is also in question. Akhil Bansal, head of asset-backed finance at Carlyle, said bluntly: "Everyone assumes these contracts are ironclad, but what I focus on is the practical level. If only seven institutions provide all the credit support for GPUs, who really holds the cards?" Laura Parrott, head of private fixed income at Nuveen, said she is currently holding off on participating in large investment-grade chip transactions and will reassess after the market structure matures further. "This is a completely new structure. Once some kind of shock occurs, we do not yet know how the outcome will unfold." In addition, there are macro-level variables: local opposition in Texas has already prompted a suspension of new projects, Oracle's project in New Mexico has been forced to delay payments due to regulatory obstacles, and OpenAI and Anthropic are also discussing slowing the pace of deployment over safety issues.

Moving Toward a "Computing Power Asset Class"

Despite the challenges, market participants remain optimistic about the long-term potential of this paradigm. John Watson, senior managing director at Blackstone Tactical Opportunities, compared the current moment to the early years of the automobile industry: those who financed the Ford Model T also faced the question of whether people would really drive, and today auto finance has developed into a large and mature market. At the level of chip market evolution, early research and actual usage data show that the actual service life of chips is exceeding the years reflected in current accounting treatment, which will improve their financeability. Karen Fang, global head of infrastructure and sustainable finance at Bank of America, expects that semiconductor valuation tools similar to used-car price guides will gradually emerge, thereby creating a more active chip leasing and hedging market. The ultimate vision described by Watson is to turn a unit of computing power into an asset class that can be traded and financed globally: "We understand the economic value of computing power, and therefore we finance it." Achieving this goal still depends on the commercialization of AI truly living up to market expectations.