Why would some of the most profitable companies in the world feel the need to load up on debt? Why would some of the most creditworthy companies in the world borrow money at a higher interest rate?

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 financing for the project. And for which the company pays a significant premium over investment grade interest rates. How much more? As much as 200-300 basis points, or 2-3%. This is a juicy return on investment-grade company debt.

So, why would an investment-grade company agree to do that? They do it because the capital needed for these buildouts is 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 as their debt. It is the debt of those guys 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 financial engineering, will work until it doesn't—and when it doesn't it could be a very big 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.

 

 

“Today is August 4, 2026”

In the living room the voice-clock sang, Tick-tock, seven o’clock, time to get up, time to get up, sever o'clock! as if it were afraid that nobody would. The morning house lay empty. The clock ticked on, repeating and repeating its sounds into the emptiness. Seven-nine, breakfast time, seven-nine!

In the kitchen the breakfast stove gave a hissing sigh and ejected from its warm interior eight pieces of perfectly browned toast, eight eggs sunnyside up, sixteen slices of bacon, two coffees, and two cool glasses of milk.

“Today is August 4, 2026,” said a second voice from the kitchen ceiling, “in the city of Allendale, California.” It repeated the date three times for memory’s sake. “Today is Mr. _ Featherstone’s birthday. Today is the anniversary of Tilita’s marriage. Insurance is payable, as are the water, gas, and light bills.”

Somewhere in the walls, relays clicked, memory tapes glided under electric eyes.

Eight-one, tick-tock, eight-one o’clock, off to school, off to work, run, run, eight-one! But no doors slammed, no carpets took the soft tread of rubber heels. It was raining outside. The weather box on the front door sang quietly: “Rain, rain, go away; rubbers, raincoats for today . . .” And the rain tapped on the empty house, echoing.

 

The opening of "There Will Come Soft Rains" 

Ray Bradbury 1950 

 

 

One of the great things about living in the 2020s is all of the fascinating ethical problems to discuss

 More fun stuff from Matt Levine's newsletter.

There are a couple of stories here, both interesting but only one important.

The inconsequential one is a ingenious variant on the paradox of the heap. As Levine cleverly points out, under certain circumstances, selling insider information becomes legal, arguably even ethical, if you can just sell the same piece of information to a sufficient number of people.

The second story is about the end result of a decades-long effort to neuter and discredit regulators. We've already discussed how this applies to the idea of insider trading, but this also needs to be seen in the larger context of this amazingly successful campaign. Lax enforcement of financial laws. Ignoring flagrant anti-trust cases. Dismissing legitimate environmental concerns. None of these things happened independently. 

I'm not quite ready to kick this hornet's nest and start naming names (other than to say that the word abundance should always set your Spidey sense tingling), but most of the centrist mainstream press and an alarming number of liberal commentators and bloggers who really should know better have uncritically swallowed a line of libertarian, anti-government propaganda without ever thinking about the implications or contradictions.

All of which may have had something to do with the news story that got Levine thinking about this. 

I think from time to time about the line between “investigative journalism” and “insider trading.” If you are good at befriending people who work at public companies and getting them to reveal important secret information to you, here are two ways you could monetize that secret information:

1. You could trade the companies’ stock before the secret information becomes public, or
2. You could publish the information in a newspaper and charge people money to read it (or serve ads against it).

The first is insider trading and generally illegal; the second is journalism and generally fine. [2]  (Not legal or journalistic advice, etc.) But there are other ways that fall somewhere in between. For instance:

1. You could sell the information to a hedge fund, which could trade on it and give you money, or
2. You could sell the information to five hedge funds, which could trade on it and give you money, or
3. You could “publish” the information in a “newsletter” with a subscriber base of 10 hedge funds, each of which pays $100,000 a month for a “subscription” to the “newsletter.”

I put a bunch of scare quotes in that last one, but I don’t really mean them. A newsletter with 10 subscribers is a newsletter, even if the subscribers are hedge funds that pay a lot. But selling the information to one hedge fund is surely insider trading. The point is that there’s a range. If you get inside information about a company and give that information to exactly one customer, who trades on the information and pays you a lot of money, that’s probably insider trading. If you get inside information about a company and give that information to 1 million customers, some of whom trade on the information and all of whom pay you $49.95 per year for a subscription, that’s probably journalism.

Somewhere in between there’s a line. There’s some number of customers that is high enough, some subscription price that is low enough, to make the thing “journalism” rather than “insider trading.” We have talked about this problem before, and my very rough guess is that the dividing-line number of subscribers is on the order of 100 (or a bit less) and the subscription price is on the order of $100,000 per year (or a bit more). If you sell information to three hedge funds for $10 million a year each, bad. If you sell information to 500 hedge funds for $10,000 a year each, fine. I cannot emphasize strongly enough that this is not advice of any kind, I have just made it up based on vibes and gut feeling, and I know of no real law about this. [3]