Private credit is cracking just as AI infrastructure spend surges into the trillions.
The tremor is fueled by software and IT companies seeing sharp drops in valuation as fears mount that AI advancements will render their core products obsolete.
Blue Owl Capital set off alarms in the private credit market, which has extended billions in financing, by selling assets across three funds and tweaking redemptions amid withdrawals tied to AI-threatened tech loans and stalled data centers. As UBS strategists recently noted, the worst-case default rate for private credit could climb as high as 15 percent as AI disrupts traditional software companies.
In the race to secure GPUs, neo-clouds – specialized providers that focus almost exclusively on high-performance AI compute – are ready to deploy the hardware powering the next generation of LLMs, but are being sidelined by underwriting processes that take months to move and equity models that demand too much control.
The financing bottleneck and the asset-backed solution
With global AI capital expenditures projected to reach trillions this decade, the mechanisms used to fund that growth cause delays that create supply bottlenecks.
Filichkin, Compute Labs’ chief business officer, described the dynamic clearly: operators are currently caught between slow banks and the limitations of venture capital.
Under the traditional model, a neo-cloud must raise massive venture rounds just to afford the down payments required by banks, forcing founders to give up control of their companies simply to buy the hardware needed to operate.
Zhang added that underwriting processes and capital structuring take several months, delaying off-take customers and forcing them to go elsewhere simply because they need capacity now. “Many AI customers… will simply go to some other providers, or they will just go to the market and then buy the capacity at a very high spot price,” he explained.
Capital inefficiencies also increase computing costs. When neo-clouds cannot deploy on time, demand pressure builds on existing providers, which allows them to charge more.
To bypass these delays, Compute Labs, a fintech that bridges neo-clouds and investors, packages GPU clusters for asset-backed deals. The company vets partners, secures senior debt and fundraises the missing 20-30 percent cash slice from investors to complete each deal. This lets neo-clouds deploy without equity dilution, while investors gain direct hardware yield from the contracts.
GPUs: The yield-generating asset class
A whitepaper co-published by the team at Compute Labs and The Family Office Association in December 2025 pitched GPUs as a new yield-generating asset class for family offices, like digital power plants producing steady cash from AI rentals for training and inference.
“When we work with these partners, one of the first things that they worry about is diluting their equity, and we know of an interesting business model that allows an investor just direct exposure to the most fundamental asset, which is the hardware,” explained Filichkin.
He noted the dual value points this structure serves: the neo-cloud avoids dilution, and the investor gains the raw hardware component without worrying about the volatility of the equity markets.
“More fundamentally,” added Hosseinion, “when we refer to a venture bet, we’re talking about VCs…betting on the founders to find product market fit, whereas (Compute Labs is) allowing investors direct access to the actual chips that power AI.”
These assets are secured by three- to five-year off-take contracts, a structure where end-users pre-commit to buying the compute power before it is even deployed. “The financial profile is a lot more similar to project finance… high upfront capex, the deployment phase, and then just a long tail of predictable yield.”
However, much AI infrastructure funding still relies on venture-style equity, despite the fact that typical VC rounds are often too small for major hardware buys.
‘Carfax for GPUs’
For GPUs to mature into a genuine asset class, the market requires a level of transparency that traditional tech lending has historically lacked. The current hesitation in private credit often stems from a “visibility gap” that prevents lenders from easily verifying the health, location or even the existence of the hardware they are financing.
Solving this requires what the Compute Labs team described as a “Carfax for GPUs” that employs a registry system that tracks the provenance, thermal history and real-time utilization of a chip, which would provide lenders the same level of auditability found in real estate or aviation.
While this strategy provides technical transparency, Compute Labs’ “revenue haircut” – where the 20 to 30 percent revenue share is the first to be sacrificed if performance targets are missed – provides financial safeguards that protect lenders from operational failures. This ensures that even if a neo-cloud struggles, the investors remain at the front of the repayment line.
Operational buffers are also becoming a benchmark for these deals; the team stressed that daily running costs, specifically electricity and maintenance, must typically remain under a quarter of the total income produced by the chips in order to maximize returns.
While concerns about technical obsolescence persist, the current supply-chain reality offers a natural hedge. Zhang noted that while new chips are announced frequently, it often takes up to 24 months for them to reach the market in significant volume at a reasonable price, providing a predictable “useful life” window for current-generation hardware.
Infrastructure before innovation
Ultimately, the shift toward asset-backed GPU financing is about unblocking what the team calls the “innovation funnel.” At the top of this funnel sit the thousands of AI applications and agents that promise to reshape the global economy. However, these innovations are entirely dependent on the physical infrastructure at the base.
By moving away from the slow, small financing models of the past and treating GPUs as a stable, bankable utility, the industry can finally provide the consistent power required to sustain the AI revolution.
However, if the bottom of the funnel remains choked by inefficient capital, the intelligence at the top will inevitably stall.
Securities Disclosure: I, Meagen Seatter, hold no direct investment interest in any company mentioned in this article.
