Back a couple of years or so ago, when the evidence of an AI bubble was just starting to accumulate, the standard rebuttal to skeptics broke down into two basic categories. One was that large language models were going to have such a huge impact and make so much money that a massive ROI was all but guaranteed.
The second argument, specifically addressing the comparisons to the dot-com bubble, was that this time the capital expenditures were coming from some of the biggest and most successful companies in the world, run, almost universal belief had it, by some of the smartest people. Even if large language models turned out to be a commercial disaster worse than the metaverse, it's not like these companies would notice an extra $100 billion here and there.
It was an enormously effective one-two punch of an argument: immense potential rewards, minimal risk. What's more, it was an argument that lots of people really, really wanted to believe (such as Ezra Klein of The New York Times, but we'll get to that in another post). This was a genuinely exciting new technology supported by a convincing-sounding business case and embraced by an establishment that deeply wanted it to be true. It's not that surprising that the critics and skeptics found themselves marginalized in the debate.
It's also not that surprising that, when red flags started popping up and lifeless canaries started to accumulate on the ground, the major financial players and the business and tech press ignored the warning signs. Capital expenditures shot up far beyond anything seen before. Circular financing became so dominant and complex that any halfway accurate diagram automatically served as a punchline. Breathlessly announced breakthrough models continued to underwhelm. Losses started to reach mind-boggling levels. Companies like Meta and Google/Alphabet responded to the sirens going off by doubling down on projected data center spending.
Now people are starting to take those warning signs seriously, along with things like off-the-books debt, which brings us to SPVs.
[What Is a Special Purpose Vehicle (SPV)?]A Special Purpose Vehicle (SPV), also known as a Special Purpose Entity (SPE), is a separate subsidiary formed by a [parent company]to isolate and manage financial risks. By operating independently, SPVs secure obligations even in the event of a parent company's bankruptcy. However, if improperly used, SPVs can obscure debt, as revealed by the infamous Enron scandal. Understanding SPVs is crucial for evaluating potential investments and mitigating financial exposure.
Paul Kedrosky takes it from here: [Emphasis in the original.] But let's return to Meta's AI datacenter spending, because it is instructive. A friend asked me, "Why do that? Don't they have the money?" And that got me thinking. Yes, they do, but that "having the money" doesn't matter illuminates the current moment in instructive ways.
Consider this from the FT
[article]:Private investment groups have increasingly been pitching investment grade corporations on alternative financings to traditional corporate bonds or loans. Such deals, including the Intel transaction, are often structured as special purpose vehicles or joint ventures, where the asset managers take a large minority ownership share in the vehicle. The company contributes assets to the venture in exchange for the capital — either in debt or equity — that private investment firms provide.There is a lot here, so let's unpack it. It's saying that companies like Meta, which can raise money from banks at low rates any time they want to, increasingly choose ... not to. Instead, they turn to private investment groups—private equity, essentially—who can create
custom financingfor the project. And for which the company pays asignificant premium over investment grade interest rates. How much more? As much as 200-300 basis points, or 2-3%. This is ajuicy returnon investment-grade company debt.So, why would an investment-grade company agree to do that? They do it because the
capital needed for these buildoutsis so large that doing it with orthodox balance sheet debt, or by issuing sufficient equity, let alone spending your cash, would make a mess of your balance sheet.By structuring it this way, via
special purpose vehicles (SPVs)in which they have joint ownership, companies like Meta don't have to show the debt astheirdebt. It is the debt ofthoseguys over there, that SPV. Not us. Granted, they retain shared control, and they get to use the AI data center, and nothing there happens without their say-so, but still. It's not ours.This is accounting trickery, of course. It is a transparent attempt to raise large amounts of money without balance sheet damage by putting the debt in a vehicle you indirectly control, but that, for accounting reasons, doesn't have to be disclosed as your debt on your balance sheet. The accounting term of art is
"control without consolidation"....
This epic AI data center spending, partly on the back of f
inancial engineering,will work until it doesn't—and when it doesn't it could be averybig mess. Granted, not a mess on the scale of the global financial crisis after the housing bubble, but that is perhaps only because no one has yet had the bright idea of rolling up cash flows from SPV-controlled data centers and syndicating them. Maybe let's not suggest that.Meanwhile, there is a new risk regime growing in front of us, and, as usual, it is in the
empty spaces between regulations, at the intersection of non-bank finance and AI data centers. It will grow rapidly, and if something breaks, damaging insurance assets, people will wonder why they went along with using home and life insurance to pay for AI data centers.Is this GFC 2.0? No, not yet. This is not systemic risk in the mortgage-backed sense. But the components are familiar:
leverage hidden in plain sight,mispriced risk, and capital chasing yield through increasingly convoluted structures. We’ve seen how that story ends.Collateralized AI Obligations, anyone (CAOs)? I kid ... I hope.