Ten banks lend $22B to fund Alphabet and Blackstone's TPU cloud
The compute buildout just got its largest chip-backed loan yet — and the collateral is silicon only one company's software can use.
A consortium of 10 banks is providing a $22 billion loan to Crux AI, the cloud venture Blackstone and Alphabet formed in May, to buy Google's custom Tensor Processing Units, Reuters reported Wednesday, confirming Bloomberg's earlier account. Goldman Sachs, Sumitomo Mitsui Banking Corp, Barclays, BNP Paribas and Bank of Nova Scotia are among the top lenders, according to a person familiar with the deal who was not authorized to speak publicly. None of the banks, Blackstone, Alphabet or Google would comment.
The structure is the interesting part. The debt is secured by the value of the TPUs themselves plus Crux AI's customer contracts, Bloomberg reported, and the banking group is still syndicating the facility to spread the risk. The report also says the loan could later be refinanced into long-term institutional money via the investment-grade bond market — a sign the lenders see this as a bridge, not a permanent exposure.
Crux AI formally launched last week with $5 billion of equity from Blackstone; Google contributes the TPUs, software and services. The venture said it will bring its first 500 megawatts of capacity online in 2027, and it is pitching dedicated compute at AI labs, enterprises and governments. The chips it is now borrowing against are the whole product.
Why it matters: chip-backed lending is becoming the default way to finance AI capacity, and it carries a risk a data-center mortgage does not. Nvidia-powered clouds already ran this playbook — Lambda raised roughly $1 billion of short-dated private debt in August against GPUs that Microsoft would lease. Crux AI is the same instrument with a different vendor. We flagged the shape of this financing wave earlier — The Take — The $3T isn't Enron. It's a no-exit clause — and the bull case here is that the debt is matched to contracted revenue rather than to a hope.
The bear case is the collateral. A $22 billion facility secured against TPUs assumes those parts hold resale value, and the secondary market for Google's silicon outside Google's own stack is thin to nonexistent — Nvidia's GPUs at least trade across clouds. The repayment side leans on customer contracts signed with labs that are themselves under pressure to explain their spending, in a month when the industry's own executives spent more time talking about pacing than about buying more compute. Cheap credit does not make demand certain; it just moves the moment of truth to a syndication book and a bond prospectus.
Nvidia has shipped CUDA Rust, two official tracks for writing GPU kernels natively in Rust instead of wrapping code written somewhere else. The tile track compiles Rust to PTX and lets the compiler decide how work maps onto each architecture, needing only CUDA 13.3, stable Rust 1.89, compute capability 8.0 or later, and no custom LLVM; the SIMT track, which is the one for readers who want to manage memory and threads by hand, still wants a pinned nightly toolchain.
The part worth caring about is what the compiler now catches. Thousands of threads touch the same buffers in no guaranteed order, so when one of them writes while another reads, the result depends on timing — the class of bug that passes every test and then fails in production. Both tracks refuse to compile a kernel whose output buffer is passed as one of its own inputs, whether or not that kernel would actually race. Nvidia is leaning the whole pitch on that bargain: the systems layer around AI is increasingly written in Rust, and the kernel was the last holdout. For readers who want the vocabulary underneath this, we explained what a kernel actually is back in August — AI 101 — What is a learned kernel?. The caveat is adoption: this is early, it is NVlabs work published as open repos, and neither track's source is the language most CUDA code in production is written in today.
What to watch: whether Crux's syndication fills at $22 billion and what spread the remaining lenders demand; and whether Nvidia's tile track gets adopted by a major inference stack, which would be the first real evidence that GPU code can leave C++ behind.
Would you lend $22 billion against chips only one company's software can run? Tell us in the comments.
