AI financing through SPVs moves massive risks off balance sheet. This analysis shows how tech giants structure billions outside direct view.

The strange thing is that everyone talks about AI, but almost nobody talks about the way all those billions are actually moving.

Meta is building an AI campus in Louisiana that almost feels symbolic for this moment, but the real story is not in the servers. It is in the financing. The Hyperion campus runs on an SPV that holds roughly twenty-seven billion dollars in debt and around two and a half billion in equity. That is rounded up to thirty billion, but the underlying structure is clearly detailed.

Blue Owl provides eighty percent, Meta contributes twenty percent and Morgan Stanley puts the whole deal together. Because the SPV remains the legal owner, the debt stays there.

That means Meta uses the project but does not have to show the loan on its own balance sheet. Bloomberg even says that half of the sixty billion Meta has raised for AI will never appear as corporate debt.

Meta leases the campus back through a long-term contract. Including a residual value guarantee, meaning Meta promises to carry part of the end value if the project is worth less by the time the lease expires.

It is not a trick, it is project finance. And importantly, Meta disclosed all of this in its investor filing. So nothing is being hidden. The debt simply sits in a different place than most people expect.

This setup lets Meta accelerate huge investments without polluting the balance sheet. In a sector where investors scrutinize every quarterly update, that matters. Private credit funds fit neatly into this gap. They have capital that can remain locked up for years and are not restrained by Basel rules that limit banks.

Where banks hesitate on long maturities, funds like KKR, Apollo, Blackstone and Brookfield step in instantly.

Meanwhile, xAI works with lease contracts for GPUs that run into the billions. Bloomberg and the Financial Times confirm that xAI leases ten to fifteen billion dollars' worth of compute capacity, including large volumes of Nvidia H100 and B100 chips. The reasoning is the same.

The company avoids putting the hardware on the balance sheet and can scale compute without classical debt. Google does something similar through structured financing lines for data centers, where banks and funds co-finance the builds and Google signs long-term offtake agreements.

Pension funds and sovereign wealth funds are in these deals as well, because they want stable, long-running cash flows. A data center with guaranteed rent is more attractive to them than low-yield government bonds.

The flow of capital is broad. Not just tech and private credit, but large institutional investors are riding this AI infrastructure wave.

UBS warns that AI-related debt is growing by roughly one hundred billion dollars per quarter. Morgan Stanley expects that AI infrastructure will require almost three trillion dollars by 2028. Around one and a half trillion must be financed externally and eight hundred billion of that is likely to come from private credit.

That is no longer niche. It is a parallel shadow system powering the AI boom.

The quiet mechanism behind AI financing

The playbook is simple.

A bank or fund sets up an SPV. Large investors fill it with capital. The data center gets built and the tech company leases it back through long contracts. Because the SPV remains the owner, it also carries the debt. It is elegant and incredibly fast. And it works well because AI companies need to show more growth than their balance sheets can carry.

But the risks do not disappear. They shift. Citi warns that AI hardware becomes outdated faster than the loans used to finance it. Data centers can lag behind new chip generations before they are fully utilized. The Guardian reports that some centers may never reach full capacity because the infrastructure gets overtaken too quickly. A small nuance from my side here is that the exact speed of obsolescence varies by source, but the direction is clear.

Why does it matter?

Because almost everyone is doing it. Meta. xAI. Alphabet. The hyperscalers. The banks. The private credit funds. The whole sector runs on structures that are legitimate, massive in scale and difficult for outsiders to see.

It is not 2008, but some analysts use that comparison to warn about the pace and opacity of the growth. Regulators are watching, but they trail behind the speed of the market.

The core is that AI infrastructure is growing faster than traditional financing can keep up with, which is why SPVs, leases and private credit have become the quiet foundation under the biggest AI expansion ever.

And as long as demand keeps rising, this system keeps running almost without friction. Only when momentum slows will we see how strong these structures really are.

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