The Financing Gap Behind the AI Boom: How On-Chain Credit Absorbs the GPU Debt Traditional Banks Can't Finance?
Author: Pink Brains
Compiled by: TechFlow
TechFlow Digest: The AI computing power race is evolving into a massive debt game. As top banks tighten risk controls and restrict credit only to tech giants with independent cash flows, over 200 small-to-medium-sized emerging cloud providers (Neoclouds) face liquidity droughts and brutal consolidation, forcing accelerated innovation in computing power mortgage loans.
Chain-based lending protocols like USD.AI and GLX are entering the fray, using stablecoin savings pools to provide high-yield capital and Special Purpose Vehicles (SPVs) to achieve transparent ownership of hardware and first-lien positions. Moving from simple holding to secondary debt distribution, on-chain finance is reconstructing the liquidity foundation for physical computing assets.
Elon Musk previously stated publicly that for emerging cloud providers (Neoclouds) lacking independent business cash flows outside of AI data centers, the doors to the traditional debt market have almost completely closed.
Musk bluntly stated on social media: "Over the past few weeks, the debt market has almost cut off funding to companies without substantial revenue outside their data centers. SpaceX did recently complete a $40 billion debt financing round for GPUs, but that's because we hold investment-grade credit and are backed by Starlink and rocket launch operations."
SpaceX signed a legally binding $40 billion lease financing agreement to procure NVIDIA Vera Rubin architecture chips, led by Apollo and Goldman Sachs, with expected closing before year-end. SpaceX calls it the largest single-GPU financing deal in history.
In Musk's view, the hundreds of emerging cloud providers across the U.S. are inevitably sliding toward a brutal wave of mass bankruptcies and mergers.
The credit market has already factored this risk into asset pricing. CoreWeave's credit default swaps (CDS) hit record highs in mid-October, implying a greater than 60% probability of default within the next five years. Gartner even predicts that over 80% of emerging cloud providers will fail to maintain independent operations by 2030.
However, the underlying frenzied demand for computing power continues to grow. According to Silicon Data's Emerging Cloud Index, the hourly rental price for B200 chips on specialized GPU cloud platforms rose from $5.78 to $6.47 between September 28 and October 27.
High rental costs drive cloud service providers to purchase more GPUs, while the funds for these hardware purchases primarily come from debt financing. While leading cloud giants can still secure credit from Wall Street banks, mid-to-small operators with only hundreds of GPUs simply cannot get through the bank doors.
As Social Capital pointed out, even NVIDIA H100 chips listed in 2022 now command rental prices 45% higher than late last year. Despite two newer architectures being released since then, the huge gap in computing power demand has actually driven up prices for older models.
Chart: Trend of NVIDIA H100 rental cost increases
This has spawned a massive credit vacuum. Physical GPUs are being transformed into pledgeable financial underlying assets, while chain-based lending protocols funded by stablecoin savings pools are rapidly stepping into blind spots beyond traditional banking reach.
Financing Gap Behind the AI Computing Boom
Before diving deeper, it's necessary to clarify a few core concepts:
GPU: The core chips for training and running AI models; NVIDIA B200 and B300 are currently the mainstream computing power workhorses.
Neocloud: Cloud service providers specializing in renting out GPU computing power externally, such as CoreWeave, Nebius, Lambda, Crusoe, and hundreds of smaller operators; whereas Amazon AWS, Google Cloud, and Microsoft Azure are known as hyperscalers.
SOFR (Secured Overnight Financing Rate): A U.S. dollar overnight borrowing rate secured by U.S. Treasury bonds, serving as the pricing benchmark for the vast majority of commercial loans in the U.S. Loan rates are typically quoted as "SOFR + x%"; the lower the spread x, the higher the lender's trust in the borrower.
Today's AI computing power is essentially a debt-driven leverage game. According to data from American Compute, there have been 110 publicly disclosed GPU and data center financing projects since 2023, injecting $197 billion in new capital. Of this, 92% was raised in 2025 and 2026.
Wall Street major banks tightly control this track. Of the 49 deals where lenders were explicitly disclosed since July 2025, 32 were led by JPMorgan Chase, Goldman Sachs, Morgan Stanley, or MUFG.
Debt pricing focuses on tenant credit rather than the chips themselves. In 2026, loan rates collateralized by investment-grade lease contracts generally ranged from SOFR + 2.25% to 3.0%; while unranked or below-investment-grade qualifications carried rates as high as SOFR + 4.5% to 5.5%, or fixed rates of 9% to 10%.
CoreWeave secured the largest single loan on the public list while also paying the highest risk premium (a full 3 percentage points higher than Nebius). At a rate of SOFR + 5.50%, this largest emerging cloud giant in the U.S. is effectively borrowing at sub-investment-grade costs.
Chart: Distribution of GPU computing power debt spreads in 2026
Hyperscale buyers usually bypass standard corporate credit loans, opting instead for Special Purpose Vehicle (SPV) structures. An SPV is an independent legal entity responsible for funding chip purchases and leasing them back to operators. This architecture rigorously isolates hardware assets, lease contracts, and corresponding liabilities in a separate container, completely decoupling from the parent company's other businesses.
Chart: Traditional computing buyer SPV isolation structure
This structure runs smoothly at the industry's top tier, but hits economic bottlenecks when penetrating downward. Building a bank-grade SPV requires $200,000 to $500,000 in legal and financial setup costs, with approval cycles lasting months. For financing amounts under $50 million, the fixed costs are too high, and banks simply refuse to handle them.
This creates a massive "missing middle": hundreds of medium-sized cloud service providers with hundreds to thousands of chips struggle in the traditional financial system due to a lack of investment-grade lease backing.
On-Chain Mortgage Loans: Bringing Wall Street SPVs On-Chain
The rise of on-chain lending aims precisely to fill this middle gap. Protocols like USD.AI lightweight and code-ify Wall Street SPV mechanisms, sinking them into mid-scale computing asset lending.
To the latest statistics, USD.AI has cumulatively initiated 20 financing rounds, disbursing $531 million in loans. Individual loan amounts range from hundreds of thousands of dollars to $128.9 million.
Chart: Cumulative scale of USD.AI computing credit disbursements
Interest paid by borrowers is significantly higher than traditional major banks. USD.AI's interest rate ladder starts at 7% for those with investment-grade leases and climbs to 15% for those without long-term lease agreements.
Among 15 USD.AI loans tracked between January and August 2026, actual executed rates fell between 10% and 15%, with a median of 12%. In comparison, banks offer fixed rates of 9% to 10% for subprime borrowers. The core selling point of on-chain loans isn't cheapness, but availability and extreme speed: USD.AI claims loan approvals can close in as fast as 7 days.
Chart: USD.AI lending interest rate ladder
Chart: Comparison of interest rates for credit of different-rated computing power
If a borrower defaults, the recovery agent first attempts to keep the SPV running under alternative operator management, or sells the entire entity at a price that fully repays the lenders. The borrower's SPV holds ownership of the GPU hardware, while USD.AI holds a first-lien position on the servers and all equity of the SPV.
Loan amounts typically cover 70% to 80% of hardware procurement costs, with the rest self-raised by the borrower. Funds are held in third-party escrow until servers are racked in data centers and performance verification is complete. Afterward, revenue from renting out computing power will prioritize loan principal and interest repayment. Only after all restructuring avenues fail will servers be auctioned off one by one through IT Asset Disposition (ITAD) specialists.
The flow of the entire business operation is clear and controllable:
Chart: Full process execution of USD.AI computing mortgage loans
One might question: Traditional homes last for centuries, while the golden window for advanced GPUs is only 2 to 3 years. How can such mortgage-like loans be viable? In reality, GPU mortgage loans are closer to auto loans, facing the reality of rapid underlying asset depreciation.
Precisely because of this, USD.AI generally keeps loan terms around 3 years, never issuing long-term liabilities. Meanwhile, the latest risk control rules only accept top-tier architecture chips as eligible collateral: NVIDIA B200, B300, GB200, GB300, VR200, and AMD's MI455X and MI430X. Additionally, the protocol introduced a 150 basis-point annual residual value guarantee insurance (deducted from pledge yields) to hedge against the discount risk of liquidating second-hand chips.
sUSDai Yield Sources and Liquidity Mismatch
USDai is backed 1:1 by PayPal's stablecoin PYUSD, whose underlying reserves include U.S. Treasuries and do not inherently generate interest.
Users deposit USDai into the staking contract to earn interest-bearing receipts called sUSDai. The sUSDai savings pool funds computing loans and captures borrowing interest. Only sUSDai holders truly bear the credit default risk of the GPUs. To match asset-side liquidity, the redemption mechanism processes in batches every 30 days, while the loan asset cycle lasts about 3 years.
The baseline yield target (CHIP Hurdle Rate) established for this vault is 7%.
On November 1, sUSDai's annualized yield rate was 6.53%. USD.AI immediately injected 127,927.66 USDai into the vault to top it up to 7%. From January to early June 2026, sUSDai's actual annualized yield rate consistently hovered around 7.0%.
A spread of roughly 5% exists between the borrowing interest paid by borrowers (median around 12%) and the yield received by depositors (around 7%). These funds are allocated to several core components: a 150 basis-point annual residual value insurance fee, a 10% net interest split taken by the protocol, and cash yields on unloaned idle funds under Treasury rates.
Data from DefiLlama shows USD.AI generated $4.87 million in total fees and $487,000 in protocol net income in October. Income accounts for 10% of total fees, perfectly matching the net spread ratio collected by the protocol from sUSDai yields. Over the past year, the protocol has cumulatively generated $24.75 million in gross income.
Chart: USD.AI protocol fee and income trends
However, capital inflows once far exceeded actual lending capacity. When vault deposits approached the historical peak of nearly $700 million, the actual lent computing assets amounted to only about $1 million. The massive influx of capital was mainly driven by expectations of token airdrops.
By mid-October, active loan sizes rose to $177 million, with another $134 million in escrow waiting for chip deliveries, aligning closely with DefiLlama's recorded $309.5 million in active loans.
While idle cash can earn Treasury yields, they fall far short of GPU loan yields. Accelerating the utilization of idle liquidity is precisely the core driver behind the birth of the secondary debt distribution network, GLX.
GLX Architecture: From Holding Loans to Distributed Circulation
For USD.AI, fully holding a single loan for 3 years means bearing default risk throughout the entire period. Moreover, once the borrowing cloud provider scales up and secures bank credit, they often shift to cheaper traditional refinancing channels. For example, Sharon AI completed a bank refinance at a fixed rate of 9.95%.
Maturity mismatch also brings implicit redemption pressure. Depositors can request withdrawals every 30 days, but the lent loan assets span 3 years. Similar asset-liability duration mismatches caused the collapse of former crypto lending platforms like Celsius. Although the 30-day queue mechanism delays bank runs, it doesn't eliminate the physical reality of liquidity mismatch at the foundational level.
Launched on October 1, GLX (GPU Loan Exchange) breaks the rigid one-to-one binding between deposits and loans. Under this new model, sUSDai only issues initial bridge loans. Once a loan passes several months of normal repayment tests and matures into a sound asset, the protocol packages and sells it to external professional asset management firms. The recycled cash is then used to originate new loans.
The full lifecycle circulation structure for loans under GLX operates as follows:
Chart: GLX debt circulation and relending loop structure
External institutions can even co-fund at the time of loan origination. On October 29, credit giant Silver Point Capital ($48B AUM) and Victory Park Capital under Janus Henderson provided a $106 million, 4-year senior secured loan to NexGen Cloud through GLX jointly with USD.AI, supporting the deployment of 1,728 NVIDIA B300 chips in Finland.
Chart: Announcement of USD.AI joint institutional $106 million financing for NexGen Cloud
The volume of computing assets circulating through GLX has surpassed $200 million. A USD.AI governance proposal sets strict risk control red lines for the sale of these secondary assets:
最低出售价格必须为票面价值平价（100），官方建议售价为 100.5 或更高；
仅当超过 30 天周期的赎回请求大于协议可用流动性时，才允许启动 99.5 的折价底线；
溢价销售利润的 60% 归属于运营实体 USD.AI Inc，40% 划入收益储备金库，用于支撑 sUSDai 维持 7% 的基准收益率；平价或折价出售则不产生分成；
买方在股权和利益关系上必须与发起实体严格隔离。
For example, a $10 million loan sold at 100.5 generates a $50,000 premium split 60/40 ($30k to the operating entity, $20k to the reserve pool). More importantly, leveraging USD.AI's 2% to 3% origination fee, recycling the $10 million principal for relending would bring the protocol $200,000 to $300,000 in fresh revenue. GLX's commercial essence lies not in earning meager asset spreads, but in ultra-fast circulation and lending scaling.
First Complete Exit Case: Duos Edge AI
On June 5, 2026, USD.AI announced a $98.1 million, 3-year loan to Duos Edge AI, a subsidiary of Nasdaq-listed Duos Technologies. The collateral consists of 2,304 NVIDIA B300 chips, managed and scheduled by infrastructure provider Hydra Host via its Brokkr platform.
This loan adopts a fully non-recourse SPV structure. Even if the loan turns bad, USD.AI can only dispose of the SPV assets and its chips, with no legal recourse against the Duos parent company.
Chart: USD.AI issues $98.1 million loan to Nasdaq subsidiary Duos Edge AI
This September, another computing operator, Axe Compute, bought out the SPV entity en masse from Duos. As a core part of the transaction terms, Axe Compute fully settled all loan principal and interest on the SPV's books, amounting to approximately $87.8 million. Through this, Axe acquired a mature computing cluster with a 5-year long-term lease generating an estimated $364.6 million in cash flow.
USD.AI thus achieved a 100% return of principal and interest in less than 4 months, recycling the principal into the next round of lending cycles.
CHIP Token Value Capture and Buyback Logic
For holders of the native governance token CHIP, the value accumulation of all these business flywheels ultimately converges on the protocol's 10% take on sUSDai's net spread. The higher the proportion of successfully lent funds in the pool, the larger the cash flow captured by the protocol.
GLX's two-way mechanism further amplifies this flywheel. Selling loans transfers subsequent interest returns, and origination fees belong to USD.AI Inc., but the recycled cash can quickly be relent at high rates of around 12%, preventing funds from lying idle under 4% to 5% Treasury yields.
The token buyback program is officially online. USD.AI has begun programmatically routing sUSDai's net spread income to open-market CHIP buybacks.
After deducting basic daily operational expenses for the CHIP Foundation, 100% of sUSDai's net spread will be entirely used to buy back CHIP, with no fixed buyback amount or schedule set currently. Additionally, USD.AI Inc. can use its excess origination fees for buybacks, with 60% of loan transfer proceeds directed for reinvestment by the entity.
Calculated at the October 26 buyback price, CHIP's circulating market cap is around $90 million (2 billion circulating supply multiplied by $0.0448). Before deducting Foundation costs, this buyback program could theoretically repurchase about 2.5% of the circulating supply annually (based on last year's data), or 6.5% if annualized at current business growth rates.
Two days before the buyback routing went live, on October 26, USD.AI Inc. took the lead by using $1 million USDC to buy back 22,343,372 CHIP tokens on the open market at an average price of $0.044756, accounting for 1.1% of the total circulating supply. A single strategic buyback size equals the sum of regular buybacks over the past five to six months.
Chart: USD.AI Inc announces $1 million CHIP token buyback
The protocol's long-term goal is to make the number of CHIP tokens retired per buyback cycle exceed the inflation issuance released as staking rewards by the protocol. CHIP's inflation rate has dropped from 3% in Q1 to 2.25% in Q2. Once buyback volume exceeds new coin output, token release will switch to a fully monthly mode driven by real protocol revenue.
Most traditional DeFi project buybacks rely on on-chain trading fees, while CHIP's buyback engine is deeply rooted in real-world GPU physical lending interest. Its expansion speed depends entirely on the pace of physical computing credit ledger growth.
Looking at the bigger picture, on-chain lenders currently account for less than 1% of the $48.4 billion in total GPU-secured debt raised nationwide in 2026. Assuming a ~12% lending rate and the protocol's 10% split, each 1% share of this market captured by on-chain capital generates about $5.8 million in annual buyback funds (roughly 6.5% of the current circulating market cap); if market penetration rises to 5%, annual buybacks will sweep through a third of the circulating supply.
The Future and Potential Risks of On-Chain GPU Finance
The premise for GPU collateral lending to work lies in the collateral itself generating stable rental cash flow, and current market computing rents remain on the rise. However, its greatest risk remains settled at the underlying physical asset layer: GPU hardware depreciates extremely fast. The launch of NVIDIA's new architectures often causes double halving of old chip rents and secondary market second-hand prices, while the small-to-medium cloud providers served by on-chain institutions are themselves high-churn risk epicenters.
Yet simultaneously, several key catalysts are driving accelerated expansion of on-chain computing credit:
传统商业银行在风控收紧下几乎向中小玩家关上了大门，而散落在各州碎片化的监管政策，显著利好能够以天为单位迅速完成过桥注资的链上机构；
美国中期选举后对大型集中式数据中心的土地与能耗审批收紧，正倒逼算力建设向更加分散的中小型集群迁移，这与链上协议单笔数百万至千万美元的灵活放贷规模天然契合；
海外市场的算力主权需求正在爆炸式增长。英伟达本年度主权 AI 收入增长超过三倍突破 $300 亿，由加拿大、法国、荷兰、新加坡和英国领衔。这些国家和地区的算力需求正大量由 NexGen Cloud 和 Sharon AI 等本土新兴云承接，而它们正是 USD.AI 等链上借贷协议的核心目标客户群。
No doubt, on-chain GPU finance remains a blue ocean today, accounting for less than 1% of the 2026 computing debt big picture. In this highly vertical track, USD.AI exclusively occupies about 93% of the on-chain market share, followed by GAIB, Forum Markets, and Falcon Finance.
This is not investment advice (NFA). Growth in the protocol's business scale does not necessarily directly equate to proportional surges in the token's secondary market price.