Big Tech's Hidden Debt Problem

0:00A few weeks ago Nikiasia reported that the five biggest US tech companies are carrying $1.65 trillion of debt that doesn't appear anywhere on their balance sheets. Not the debt that you can see, a second larger pile hidden behind it. A few days later the Financial Times found another$50 billion in leases that Nvidia had signed for a single data center in Texas, stuffed full of its own chips. A commitment nobody had known about. And the number keeps moving. Nikkei did their count before most of these companies had even reported earnings. And when they did report days ago, three of them alone signed nearly$900 billion of new AI commitments in a single quarter.

0:47So$1.65 trillion is already an underestimate. Which is why, if you spend any time on financial YouTube, you already know what people are calling this. The word being thrown around is Enron. Commentators, the ones with big social media followings and no obvious background in accounting, have looked at these numbers and concluded it's Enron all over again. So I've been practicing my shocked face in the mirror. It turns out to be surprisingly hard to hold that frozen open mounted pointing at a redline expression while also looking like you understand what a lease is. Because, here's the question this video is actually about.

1:30Is any of that true? Is this fraud? The real numbers being hidden from investors, the way Enron hit them right up until the whole thing fell apart. Or is it something much more boring and much more interesting? Let's find out. First though, because a lot of you weren't following the financial news in 2001, a quick word on Enron since the entire accusation rests on it. Enron was an American energy giant that turned out to be a fraud. It had been hiding enormous debts and losses in a web of secret off the book's entities. The accounts investors could see were essentially a fiction. When it unraveled, the company collapsed in a matter of weeks.

2:17It took down Arthur Anderson, one of the five biggest accounting firms in the world, and wiped out the retirement savings of thousands of its own employees. It is still the definitive corporate accounting fraud. So when someone points at Big Tech and says Enron, they aren't complaining about confusing bookkeeping, they're alleging deliberate fraud on a criminal scale. That's the charge that we are going to test. When you see a headline claiming that tech giants are hiding over a trillion dollars in debt, it's natural to assume a crime is being committed. But when you dig a bit deeper, a lot of this debt turns out to be long-term purchase agreements for graphics cards and leases on data centers that haven't been built yet.

3:06Under standard accounting rules, if the goods haven't been delivered or if the building isn't running, you don't record it as a liability on the balance sheet. You disclose it in the footnotes. When you sign a two-year phone contract, you've committed to paying the network something like$50 a month for the next 24 months. That's a real obligation. You can't just stop. And if you added it up, you're on the hook for over$1 ,000. But you don't sit down and record a$1200 liability on your personal balance sheet the day you sign. You pay for it month by month as you use it. The tech companies are doing the exact same thing, just with more zeros.

3:50Instead of a phone contract, it's a 15-year lease on a data center in Ohio. For decades, financial commentators have been complaining about tech companies hoarding cash, that they use their cash flow to buy back shares instead of investing in anything new. Now, these same companies are issuing securities to invest in new infrastructure, and the same commentators have found a way to be unhappy about that too. Which raises the question, what does it actually signal when a company chooses to borrow instead of raising equity? Because it tells you quite a lot. Investors pay close attention to how a company pays for things.

4:30This is because the choices they make send clear signals to the market. Here is the intuition. If you actually thought you'd build a machine that turns$1 into$5, you wouldn't sell half of it to strangers to raise the money to build it. You'd instead try to borrow the money, build the machine, keep the entire$5 profit and pay off the loan. You want to keep all of the ownership stake yourself, in such a high conviction investment. You only want to sell a percentage of the business when you are less sure about the likelihood of it making a lot of money. So debt isn't always a bad sign. Borrowing to build tends to signal that management thinks the return on investment is worth keeping for the existing owners.

5:16Although, and this is where it gets interesting, these firms are also raising equity. In June, Alphabet completed the largest equity raise in corporate history, almost$85 billion, dollars anchored by a 10 billion dollar check from Berkshire Hathaway. Berkshire might be the last name you'd expect on that list. A firm that generally regards buying back its own shares as more sensible than funding somebody else's moonshot and a firm which drives a notoriously hard bargain. It reportedly bought its Alphabet stock at around a 6 % discount to the market price because of course it did. This is not a firm that overpays for a story.

6:01But they decided that the AI build-out was worth a$10 billion investment anyway. When you see big tech raising money by every route available all at once, record debt, record equity, convertibles, the lot, the signal isn't in which one they picked, it's in the scale of the capital raise itself. You don't raise capital like a company fighting for its life unless you think there is something on the other side worth the fight. Whether they are right about that is the rest of this video, but it is hard to argue they are hiding the spending when they announced part of it in the largest stock offering ever filed.

6:42And, a lot of this resolves itself over time. The leases that haven't started yet will come onto the balance sheet as real liabilities once the data centers switch on. That part is just a timing difference. Meta alone signed$233 billion in new commitments last quarter. $96 billion of it leases that will move on to the balance sheet as the data centres come into use. The purchase commitments mostly turn into chips and buildings the companies actually own. What doesn't tidy itself up so neatly is the cleverer stuff. the joint ventures and off-balance sheet vehicles engineered on purpose to stay off the books.

7:28But even that isn't hidden in the Enron sense. The details are all there in the accounts, you just have to go looking for them. So it isn't Enron-like fraud, it's camouflage which only really works on people who aren't paying much attention. Now none of this means that big tech plays it straight. They are plenty aggressive, they just do it in plain sight on the front page of the earnings report under a heading that says adjusted earnings. If you are spending billions on data centers and chips, those assets wear out and need replacing, but many of these firms would rather talk to you about EBITDA, earnings before interest tax depreciation and amortization.

8:13Charlie Munger suggested that every time you read the word EBITDA, you should replace it in your head with BS earnings. His point about depreciation was that it's a kind of reverse float. You pay the cash upfront for the equipment, and the expense shows up later as the thing wears out. Leaving it out is just assuming that physical objects last forever, which is a lovely thought and very rarely true. And you can see the strain in the real cash numbers. When the latest earnings landed, the four biggest hyperscalers posted their lowest combined free cash flow in a decade. Seven billion dollars between them, and Alphabet went cash negative for the first time since it went public.

9:01Then there's stock-based compensation. Tech companies love paying staff in stock, and then taking that expense straight back out of the earnings they show investors on the grounds that it's non-cash. Oswald de Moderen at NYU has called adding back stock-based compensation one of the worst abuses in modern reporting. His point is that it isn't a non-cash expense in the way depreciation is. It's a barter. If a company sold shares on the market and used the cash to pay employees, everyone would call that a cash expense. Handing over the shares directly instead of selling them and paying cash doesn't make the cost disappear.

9:46Warren Buffett has been asking the same question for years. If options aren't a form of compensation, what are they? If compensation isn't an expense, what is it? And if expenses shouldn't go into the calculation of earnings, where in the world should they go? To stop all that stock they're handing to staff from inflating the share count, the companies use real cash to buy their own shares back, which they present to investors as returning capital. Really, they're running on an expensive treadmill just to stay the same place. And here's the catch, a buyback only actually rewards the remaining shareholders if the shares are bought cheaply.

10:30But a company mopping up its own stock-based compensation doesn't get to wait for a good price, it has to keep buying on a schedule whatever the shares cost that quarter, which lately has not been cheap. While tech executives might be aggressive with their accounting. The actual cash they are spending on AI is very real, and it is the way they are spending it that has some investors worried. Nvidia is currently working on a round of AI deals worth more than$750 billion. It is in talks to backstop$250 billion to help OpenAI lease computing power and to finance another$350 billion of OpenAI's chip purchases.

11:15It threw$5 billion at a secretive new startup run by former OpenAI chief scientist Ilya Sutskver. Google has agreed to backstop lease payments for Anthropic, effectively handing it a$35 billion loan. SoftBank committed$65 billion to OpenAI and took out a$40 billion bridge loan just to finance the bet. If you draw the diagram of who owns what, the companies at the centre of the AI boom turn out to be mostly investing in each other. Now, if you were a car salesman trying to hit your monthly quota, it might occur to you that lending a customer the money to buy a car from you and then booking that as a sale is a very effective way to move inventory, at least until the customer stops making the payments.

12:09And the fear in the market is that AI has turned into one enormous version of this, a web of companies funding their own revenue. NVIDIA's CEO Jensen Wong has called the suggestion that any of this is circular ridiculous, which is a strong word to reach for while backstopping a quarter of a trillion dollars of purchases of your own product. But, to be fair to him, he has a point. As the Financial Times pointed out, this is really just old-fashioned vendor financing. Telecom equipment makers and plane makers have been writing checks to help their customers buy their products for decades. And the argument for NVIDIA doing it is just as reasonable.

12:55The AI boom is moving fast enough that a company like OpenAI couldn't raise enough ordinary debt or equity to build the computing power it thinks it needs. So by stepping in, Nvidia locks in a customer, makes sure its chips actually get used, and if the bet pays off, ends up owning a slice of something that could be worth a fortune. The trouble with vendor financing is what happens when it doesn't. Then it's a double blow. You don't just lose the customer, you lose the money you lent them to be your customer. And the credit guarantees make it worse. If an equity stake goes to zero, that's just money wasted.

13:39Annoying, but survivable. But a promise to cover a customer's debts if things go wrong can turn a valuation problem into a solvency problem. Right now, Nvidia throws off something like$200 billion a year in cash. So if one or two of these startups trip, it can take the hit. The question is what happens as the guarantees climb into the hundreds of billions, and a company that used to avoid debt is suddenly standing behind everyone else's. You don't have to take my word that this matters. The clearest sign is in Nvidia's own credit market. The cost of insuring its debt against default just jumped by the most on record in a single day, right as this round of deals landed.

14:29So, the people whose actual job is to price the risk of Nvidia not paying its bills had a look at all of this and got noticeably less relaxed. Because the real risk was never just that the AI market turns out smaller than hoped, it's that the people buying the chips and the people making the chips are increasingly the exact same people. All of this circular financing is happening because everyone involved is convinced that the market for AI is going to be so astronomically large that whatever they spent today will look like a rounding error tomorrow. Aswalt de Modrin has a name for what happens next.

15:11He and his co-author Bradford Cornell call it the big market delusion. The way it works is that a new technology shows up attached to a massive potential market. A crowd of companies crop up to serve it, and investors price each company as if it's going to be the winner. This is not about the companies talking themselves up, it's about the people buying the shares. Each cluster of investors looks at their chosen company and sees it as the obvious future giant. The problem is that they can't all be right. If you take these companies and add up what the market expects each of them to earn, you get a number bigger than the market itself.

15:57Everyone's been priced to come in first in a race that can have only one winner, Which is how a whole market can be priced for a future that mathematically can't happen. The story is doing all the work and nobody's minding the numbers. So, how big is the story here? The Economist estimates that the AI build-out is on track to be the largest investment surge in history. Around$900 billion this year alone being spent on chips, data centres and power, with more than 400 billion of it borrowed. And then they calculated what it would take to pay for all of that. Their estimate is that the industry would need to be earning something like two and a half trillion dollars a year in AI revenue, which is more than the entire global technology sector earns from everything it does today.

16:54The actual figure is not close. Adoption is real. around a fifth of American firms report using AI in some way, but a lot of them are using the free versions. According to a Bank of England study, the average American executive spends about 100 minutes a week using AI. That's not a typo. The largest capital investment in the history of the species is being justified by an hour and a half per executive per week, so somewhere between lunch and a drive home. And when users do pay, they don't pay much. The fintech firm Ramp went through actual company spending and found that the median firm was spending per employee per month $10.66.

17:44$2.5 trillion a year being spent to capture$10.66 per employee. The most damning number that came out of the Bank of England's research was that 9 out of 10 executives said that AI had made no difference to their company's productivity over the past three years. When a technology takes over the economy, people usually tend to notice. But that's the view from the top. Look at the other end of the economy and the picture flips. The people getting real value out of AI aren't the giants spending hundreds of billions on it, they're the small ones. According to a survey by the payroll firm Gusto, the share of new business founders who used AI to get started doubled to 60 % in two years.

18:38They are not using it to cure a disease or replace a department, but to build a website, handle the local paperwork, and do the things that used to mean hiring someone. Now some of this new business activity is people incorporating their hobbies, and a shrinking share of these firms will ever employ anyone but the founder. So let's not oversell it, but the clearest real world win for AI so far isn't the company burning billions on it, it's the person starting a one-man business paying about$20 a month. When a market gets priced as optimistically as AI has been, the people whose job is to sound a note of caution sometimes decide to do the opposite.

19:24Take SpaceX. When it went public in June, it wasn't shy about the AI framing. Its own prospectus claimed a total addressable market of$28.5 trillion, dollars and of that 26 and a half trillion or 93 percent of it was attributed to AI or Grok which leaves about two trillion for everything else the rockets the launches the satellites the global broadband network twitter the actual space company is the rounding error at the bottom That was the case for pricing the shares at$135. You'd think that that would be ambitious enough for anyone, but within weeks a Wall Street analyst put a target of$800 a share on it, which would value the company north of$10 trillion on a business that did under$19 billion of revenue last year.

20:25Now, you might wonder why an analyst would look at a company losing money on 19 billion in revenue and decide it's worth 10 trillion dollars. As it happens, there is a reason and it's a good one. The reason is what SpaceX is about to do next. One analyst went through the prospectus and added up the spending the company is committed to. Commitments that the filing discloses but never totals in one place, and they got to something like$235 billion by 2030. The IPO funds raised by SpaceX covered only a slice of that cash requirement. A gap of around$170 billion still needs to be filled by SpaceX, issuing more stock and more debt, again and again for years.

21:17Which is a great deal of underwriting business for Wall Street. And here's the thing about those targets. According to Fortune, analysts at 18 of the banks that underwrote the IPO put out their price targets at almost the exact same time, around 25 days after the stock started trading, which is when the rules let them start talking. The notes were, in Fortune's words, almost uniformly bullish. Morgan Stanley called SpaceX AI's final frontier. Bank of America said it was paving the superhighway to the stars. Raymond James compared it to the invention of electricity, the railroads, and the internet.

22:02These are supposed to be equity research notes. Out of 30-odd analysts covering the stock, exactly one rated it a sell, and that one works at an independent research firm that doesn't do any underwriting business. There was for about 20 years a rule that made this sort of arrangement awkward, but the SEC scrapped that rule last December. It was called the Global Research Analyst Settlement, and it dates to 2003 in the wreckage of the dot-com bubble. It built a firewall between the investment bankers and the research analysts at the same firms. The idea being to stop an analyst publicly raiding a stock a buy to help his bank win a fee, while privately emailing colleagues to call the same company a pig, which isn't a hypothetical.

22:56In the cases that led to the settlement, one analyst did exactly that. Another described a stock he was recommending as a POS, which I'll let you expand for yourself. The firewall was taken seriously. Bankers and analysts at the same firm weren't allowed to talk business without a chaperone on the line, two of the highest paid people in Manhattan needing a babysitter on the phone in case they said something a bit too useful to each other. The whole thing was championed by the New York Attorney General at the time, Eliot Spitzer, better known as client number nine of the Emperor's Club VIP, a man with a well-documented understanding of the value of keeping certain transactions off the books.

23:46The SEC scrapped the settlement last December, citing the need for lower compliance friction, which is the regulatory way of saying that the rule had become a hassle to enforce, So, they stopped. And it's not an isolated decision. By almost any measure, American enforcement of financial crime has been falling apart for years. White collar prosecutions have been drifting down over the last 30 years. They are now running at about half the level of 20 years ago. According to The Economist, the Justice Department has cut its laws by a fifth and moved investigators onto immigration and drugs. The SEC brought a grand total of 10 enforcement actions against auditors last year, a fifth of its usual rate, running down the exact oversight that was built after Enron.

24:41And since most of these crimes carry a five-year statute of limitations, the trick is often just to keep the plates spinning long enough that the clock runs out. One study reckons only about a third of corporate fraud is ever caught at all. But, here's the turn, and it's really the whole point of this video. For the companies we're actually talking about, none of this matters. The metas, the oracles, the hyperscalers, they aren't committing fraud, they don't need to. Everything they're doing is legal and nearly all of it is disclosed, which is a far more interesting situation than will they get caught, because they won't.

25:22The real question is this, if it's all sitting there in the open, the adjusted earnings, the 50 billion in leases, the stock-based compensation added back, does it actually work? Does dressing up numbers that anyone is technically free to read still fool people? Are investors really being taken in? It turns out that this is one of the most heavily studied questions in all of finance, and the answer is a deeply unsatisfying yes a bit. And here's exactly how. Take Aswad de Modrin, who we heard from earlier. His view is that markets are roughly efficient over time, but that presentation still matters, because most investors anchor onto whatever number is put in front of them.

26:12Show them an adjusted figure and that's the only one they'll use. His objection to adding back stock-based compensation is exactly this. It's a real cost, dressed up as a non-cost, and a lot of people simply accept it at face value. Then, the harder evidence. In 1996, an accounting professor named Richard Sloan published one of the most famous papers in the field. He split company earnings into two parts, the cash the business actually took in and the accruals, the softer, judgment-based part that depends on management's assumptions. And he found something the market apparently hadn't. The accrual part is much less reliable than the cash part.

26:59It tends not to last. Companies whose profits leaned on accruals went on to disappoint. Companies whose profits were backed by real cash went on to do better. But the market was treating both kinds of profit as if they were identical, which meant that you could earn excess returns for years simply by betting that it would eventually notice the difference. In plain terms, cleaner earnings beat dressed-up earnings. The polish doesn't hold. So, why doesn't the market just spot this immediately? That's the second idea. A professor named Robert Bloomfield has a nice explanation that he calls the incomplete revelation hypothesis.

27:46In theory, a market instantly prices in all public information, but Bloomfield's point is that public and usable are not the same thing. The number you need is technically out there, it's on page 83 of a 200-page filing, split across four footnotes in a form you have to reassemble yourself. And extracting it costs time, effort and attention, which aren't free, so the harder a fact is to dig out, the less completely it shows up in the price. It isn't that the information is hidden, it's that reading it is annoying and most people don't bother, which is the entire game. The debt isn't hidden, it's just filed somewhere tedious enough that you won't look.

28:34At which point you might reasonably ask, fine, but if there's money to be made reading the footnotes, why don't the professionals just do it? Read the filing, short the overpriced stock and collect. And that's the third idea, the limits of arbitrage, a field that the economists Mitchell and Pulvino did much of the foundational work on. The problem with being right about a footnote is that being right isn't enough, you also have to stay solvent long enough for everyone else to catch up. If you short a beloved narrative stock because you've found an ugly commitment buried in the accounts, and a few million people buy it anyway because they like the founder, that stock can keep climbing for a very long time, and your short position can wipe you out well before the market ever gets round to caring.

29:26The smart money is aware of this, so a lot of the time it simply doesn't bother getting involved in hype stocks at all. The mispricing survives not because nobody can see it, but because the people who can see it can't afford to bet against the people who can't. Which pulls the whole thing together. The people not reading the accounts outnumber the people who are. The people who are reading them can't move the price on their own. And so, a company that buries an inconvenient number in a footnote is making a perfectly rational bet that the crowd won't read it and the professionals who do won't be able to do much about it.

30:10It's disclosed, it's legal, and it works often enough to maybe be worth doing. So, to bring this all together, the current panic over hidden tech debt isn't the discovery of the next Enron. It's people finally reading the footnotes and realizing how much money is actually being spent. As Aswath DeModern puts it, evaluation is a story disciplined by numbers. Right now, AI is a very expensive story told by companies lending each other the money to buy their own products and reported through adjusted figures that leave out the most expensive parts. The debt isn't hidden, it's just filed somewhere tedious enough that most people won't look.

30:57The problem isn't that AI is a fraud, it's obviously useful, and the businesses getting the most out of it seem to be the small ones. The solo founders and the one-person businesses paying their$20 a month, which is a wonderful deal if you're the one renting. It's a rather worse deal if you're the one who spent billions building it, and is still waiting for that$20 a month to add up to the$2.5 trillion a year it would take to pay the thing off. Until it does, the companies building all of this will keep borrowing. They'll keep filing the commitments in the footnotes and the market will keep not reading them, right up until one day it decides to.

31:41Jamie Dimon put it well recently. Will AI pay off? Probably the way the internet did. Will it pay off the way you expect on the timeline you expect? Definitely not. Which means that I should probably get back to the mirror and keep practicing my shocked face. I have a feeling I'm going to need it. Thanks for tuning into this week's podcast, which is financed by viewers like you on Patreon. If you want to support the podcast, I've left a link in the show notes. Have a great day and talk to you again soon. Bye.