Sure it's a rerun, but it's got a Frazetta cover.

I'm still finishing up a post on the obscure and convoluted history of the intellectual property behind the eagerly anticipated Coyote vs. Acme (really hoping that this one knocks it out of the park). A story that no one outside of a couple of particularly obscure fan sites has noticed.

In the meantime, I thought I'd set the stage by revisiting one of my favorite cases of cartoon, or in this case comic book, characters being, not to put too fine a point on it, stolen and repackaged to considerably greater success.

The first post is from our long-dormant sister site Mippyville. The next two from the blog a few years ago.

 

Tuesday, February 23, 2010

Ghost Rider -- the character Stan Lee stole twice

Stan Lee's claims in the Sixties that he didn't read other comic books were a lot like Milton Berle's claims that he didn't listen to other comics' material. Lee had spent two decades in a closely-knit industry jumping on trends and borrowing ideas, starting with DC and EC derived books and ending with an unacknowledged appropriation of Julius Schwartz' successful formula for rebooting Golden Age heroes with science fiction origins and streamlined looks.

The difference between Berle and Lee was that a large part of Lee's audience didn't get the joke. Many of them had gotten serious about comics in the mid-Sixties when Marvel had hit its stride and really was putting out the best and most innovative comics. For them, Lee's comically over-the-top claims seemed entirely reasonable.

It's safe to assume that in 1967, few of those teen aged fans had any idea that Marvel's new Western themed hero, Ghost Rider, had a strong resemblance to another company's hero, a resemblance that included having the same name, costume, concept, atmospheric look, and artist. The retread only ran for seven issues but that was enough for Marvel to claim ownership of the name and, a few years later, launch another Ghost Rider.

If you had to lift a character you could certainly do worse. Ghost Rider was one of the last great superheroes of the Golden Age, sharply written by Ray Krank (with guidance from Vince Sullivan) and beautifully drawn by Dick Ayers with an occasional assist by Frank Frazetta. Don Markstein described him as "perhaps the most visually striking comic book hero of the decade," but you can judge for yourself.

Wednesday, July 17, 2013

Intellectual property and Marvel

(I told you I'd connect Stan Lee to this)

For about the past fifty years the company which is now Marvel entertainment, has made a spectacular amount of money and has done it in virtually every medium from comics to television to film to video games to  novels to even, Heaven help us, the stage.

This is all the more remarkable when you consider that shortly before its period of dominance the company was a third rate imprint that was, by some accounts, on its last legs.

The rise was a remarkable achievement both in American publishing and pop culture. In economic terms alone, it shows how a small company with almost no resources or structural advantages can come to dominate an industry and generate billions of dollars.

One aspect of that story which is particularly relevant given our recent posts on the subject is the way Stan Lee used public domain (and in some cases, not-quite-public domain) intellectual properties as an important part of his business model.

First a quick and hopefully painless bit of comic book history.

Superheroes were the first big original content hit of the medium. Starting with Superman in 1938, they dominated that side of the industry for almost a decade. Licensed titles (like Dell's Disney line) were, by some sources, bigger sellers but if you were creating characters specifically for comics in the early Forties, superheroes were where the money was. By the end of the decade, though, the boom was largely over and other fads such as crime, horror, Western, funny animals, funny teenagers and (with a very unlikely origin) romance took turns as the next big thing.

Of course, comic book publishers kept trying to bring back the genre that had started it all. Lots of companies tried to introduce new superheroes or dust off old ones but without real success. Among others, the company that would become Marvel was particularly badly burned by a superhero push in the mid-Fifties). The big exception here is Magazine Enterprise's Ghost Rider in 1949 but as Don Markstein pointed out, that character blended the faded superhero genre with the up-and-coming genres western and horror.

It was not until 1956 that a team working under DC editor Julius Schwartz came up with a workable formula: take a dormant mid-tier character from the Forties; completely rework the character (sometimes keeping only the name) with a science fiction origin, streamlined jump-suit inspired costumes and a heavy emphasis on space age themes.

In rapid succession and generally with great success,  Schwartz applied this rebooting approach to a number of properties and soon other companies were trying their hand. As early as 1959, Archie Comics (which had been known for a relatively successful and very violent collection of superhero titles in the Forties) had hired Joe Simon and Jack Kirby to rework their character the Shield. As the Sixties got going almost everyone was in on the act.

In 1961, Marvel Comics joined in. Marvel was a small part of Martin Goodman's middling publishing company but it did have a couple of significant assets: a few well-remembered Golden Age characters (the Human Torch, Namor and Captain America) and comics auteur Jack Kirby. Given the market conditions of the time, Kirby's brand was extremely valuable. There was a tremendous demand for all things associated with the previous era of superheroes and Kirby had been a major player with an exceptional level of prominence. In an era when most stories went unsigned, his name was a big enough selling point to be featured prominently on the covers.

Much myth has accumulated around the creation of the Fantastic Four, partially because of the impact the title would go on to have and partially because none of the people involved (Goodman, Lee, Kirby) can be considered reliable narrators, but if you simply look at the book itself and what had been going on in the industry at the time, FF #1 is about what you would expect, combining Schwartz's formula with the group dynamics of Kirby's team comics and elements of the monster comics Marvel had been producing (the two most unusual aspects, the lack of costumes and secret identity were completely dropped when Marvel introduced Spider-man less than a year later).

I don't mean any disrespect for Marvel here. This is usually how companies really work. You find an existing business model, modify it slightly, then use it to establish a revenue stream and a loyal customer base. That's what Lee did. That's what Sam Walton did with Ben Franklin stores. The big ideas and innovation tend to come only after you have a successful stable operation (which was certainly the case with Marvel). That leads to a much bigger point.

We have seen over the past few years a tendency to grant intellectual property protection to ideas that would previously have been considered general parts of a business plan (for example, offering free wi-fi to customers). What if the ability to borrow and recombine elements of business plans in kind of a de facto genetic algorithm is an important part of a creative economy? What if being derivative is the first step for coming up with something original?

There are also some interesting IP questions involving the creation of Spider-man (Wikipedia has a good summary), but that's a discussion for another time. The part of the story that's most relevant comes a couple of years later.

As mentioned previously, from approximately 1956 to 1966, the big thing in comics was to modernize and reboot Golden age characters. This left Marvel with a problem: With the exception of Captain America, the Human Torch and Namor, the company had a very thin bench. You very soon got down to really obscure characters. The whole purpose of the reboot model is to cash in on name recognition so rebooting the virtually forgotten is of limited value. (You have to wonder how many readers in the Sixties had ever heard of the Thirties Tarzan knock-off Ka-Zar.)

Lee's solution was to launch characters using at least the names of three of the biggest sellers of the Golden Age: Daredevil; Ghost Rider; and Captain Marvel, none of which actually belonged to Marvel, but were instead arguably in the public domain. It is the third one that required considerable nerve.

Captain Marvel had been, by some standards, the most successful character to come out of the Golden Age, outselling even Superman (nuisance suits from DC were a big factor in the decision to eventually cancel the series in 1953). What's more, the publisher, Fawcett was big and, though out of the comics business, still very active, publishing title including Family Circle, Woman's Day, Mechanix Illustrated and Gold Medal paperbacks.

Lee was betting (correctly as it turns out) that Fawcett either wouldn't notice or wouldn't bother to sue an obvious copyright infringement. It was a bold but not a reckless move. Attitudes toward copyrights have changed greatly and many of those changes involve the earlier emphasis on active properties and going concerns. Up until recently, the primary reason you acquired and held copyrights was because you wanted to do something with those properties. As a result, if someone went out of the comics business and no one had an immediate interest in their properties, the copyrights were often allowed to lapse or (in the case of Fawcett) go unenforced.

There's a lesson here about creative destruction. Companies, particularly those in the creation business, often start out by borrowing business plans and skirting copyright and patent laws. You can certainly argue that this lowers the value of the intellectual property they are making use of, but I think you can also argue, as or more persuasively, that the returns on tolerating this behavior from small, young companies far outweigh the cost.

For more on the IP beat, click here, here, and here.

 

 

Wednesday, June 12, 2013

More adventures in intellectual property --- Ghost Rider rides again

Not long ago, Marvel managed to get a couple of unlikely hit movies out of a fairly obscure motor-cycle riding character called. Ghost Rider. That unexpected success has led to some lawsuits over who owns the character.

NEW YORK (Reuters) - A comic book writer who claims he created the flaming-skulled character called Ghost Rider got another chance to press his claim on Tuesday when a U.S. appeals court revived his lawsuit against Marvel Comics.

The 2nd U.S. Circuit Court of Appeals vacated a 2011 district court ruling that found the rights to the character belonged to Marvel Comics, owned by The Walt Disney Co. It returned the case to the lower court for trial.

Former Marvel freelancer Gary Friedrich, who claims he created the character, will pursue the case "aggressively and vigorously," his lawyer Charles Kramer said.

Ghost Rider, a motorcycle-riding vigilante whose head is a flaming skull, first appeared as a vigilante superhero in 1972.

Sorta.

It turns out that the 1972 character was something of a reboot of a Western-themed Ghost Rider Marvel launched in 1967 (again with the involvement of Gary Friedrich) and that's where the intellectual property question gets amusing because that incarnation was perhaps the most blatant example of plagiarism in what has always been a plagiarism-fueled medium.

Stan Lee's claims in the Sixties that he didn't read other comic books were a lot like Milton Berle's claims that he didn't listen to other comics' material. Lee had spent two decades in a closely-knit industry jumping on trends and borrowing ideas, starting with DC and EC derived books and ending with an unacknowledged appropriation of Julius Schwartz' successful formula for rebooting Golden Age heroes with science fiction origins and streamlined looks.

The difference between Berle and Lee was that a large part of Lee's audience didn't get the joke. Many of them had gotten serious about comics in the mid-Sixties when Marvel had hit its stride and really was putting out the best and most innovative comics. For them, Lee's comically over-the-top claims seemed entirely reasonable.

It's safe to assume that in 1967, few of those teen aged fans had any idea that Marvel's new Western themed hero, Ghost Rider, had a strong resemblance to another company's hero, a resemblance that included having the same name, costume, concept, atmospheric look, and artist. The retread only ran for seven issues but that was enough for Marvel to claim ownership of the name and, a few years later, launch another Ghost Rider.

(Yes, that is a Frazetta cover)

And here's the Marvel version:

Of course, the idea to introduce a character by this name in 1949 may have had something to do with a hit song introduced in 1948 (and covered a billion times since).

 

LLMs are not nothing.

 Given the state of things in 2026 and the two "next big things" that preceded generative AI, this is by no means a trivial point.

The metaverse was next to nothing. Web3 effectively was nothing. In both cases, an astounding and profoundly embarrassing level of hype was supported by nearly non-existent substance. 

Large language models are not like the other two. They are a powerful set of tools and represent a fantastic leap forward in natural language processing. There is plenty here to be legitimately excited about without resorting to exaggeration. Unfortunately, bullshitting is habit-forming, particularly when you have the same lack of guardrails and even worse incentives.

With almost 20 years having passed since the debut of the last world-changing piece of consumer technology, boosters have grown extraordinarily adept at building economic mountains out of technological molehills, and the press has gone beyond mere complicity to become active collaborators in the construction of these imaginary peaks.

Many have given themselves over completely to fanboy gush, but the more respectable press, represented as always by the New York Times, adopted a veneer of sobriety that, if anything, made matters worse. No matter how obviously absurd and self-serving the crap Silicon Valley tried to sell was, writers like Kevin Roose would pass it along with a framing of just asking questions/we need to keep an open mind/important if true. When faced with obviously absurd and self-serving crap, we do not need to keep an open mind; we need to be ready to call bullshit and to recognize the person behind it as a grifter.

For the hyperloop generation of tech and business reporters, hyping non-stories into major innovations was their job,  what readers wanted, what editors demanded. When handed a major innovation, perhaps it's not surprising they instinctively hyped it into something epochal.

Great video but keep your finger over the mute button. Aschenbrenner's laugh will haunt your dreams.

Random Patrick Boyle quote:"If I left a million dollars on the table over a non-disparagement clause, I'd bang out a million dollars worth of disparagement that same afternoon"  

His Wedding Guests Were Arriving—Just as His $45 Billion Fund Was Falling Apart

Berber Jin, Peter Rudegeair, Gregory Zuckerman, Anissa Gardizy 

July 31, 2026 12:41 pm ET

When the week began, Leopold Aschenbrenner was preparing for his wedding. The plan was for a multiday celebration in Carmel, a seaside town in Northern California, with the ceremony at a Tuscan-style villa and the send-off at a spa in the forest. There would also be a pre-wedding colloquium to discuss ideas in panels and breakout sessions. The couple’s only request: no gifts.

The 24-year-old investor had amassed a fortune by promising he could see into the future, building a $45 billion investing powerhouse that primarily bought stocks in the AI trade. For months, the holdings of his hedge fund Situational Awareness shot up in value, as did Aschenbrenner’s standing in the upper echelons of San Francisco’s elite.

But by the time guests began to arrive, his fund was unraveling—and Wall Street was closing in. 

Aschenbrenner borrowed too much money to make his AI bets, leaving him at risk as they faltered. With the value of his portfolio tumbling, he scrambled to raise cash to satisfy his lenders, appealing to some of the largest hedge funds and selling billions of dollars in holdings in a fire sale to Ken Griffin’s Citadel. 

 

In order to ... make sense may be too strong a term, but at least be prepared for what you're going to hear, there are a couple of things that you need to know about Silicon Valley culture, and they both involve stories.

Among Tech messiahs and their acolytes, few ideas are as cherished as that of the young tech bro with no background in the field showing the experts how it's done. (Seriously, these people hate experts for some inexplicable reason.) We saw that in the push for quack medical treatments in the pandemic and in the decision to hand DOGE over to completely unqualified and not very bright college dropouts.

Other than being an actual college graduate, young Leopold was the stuff of dreams for these people, a genuinely gifted young man who came out of the world of Sam Bankman-Fried and OpenAI who, with absolutely no relevant training or experience, was managing a fantastically successful hedge fund.

The West Coast tech crowd desperately wanted to believe his story; they also desperately wanted to believe the story he was telling, one of a future of unimaginable AI-driven abundance just around the corner. 

From Boyle:

The cornerstone of the whole empire, however, was a 165-page essay he self-published in June 2024 called Situational Awareness. I read all 165 pages for this video, and I can't tell you how badly I wanted to read an AI summary instead, which, given the subject matter, might have been the intended way to consume it. You'll notice that everyone who mentions this essay brings up the page count. Nobody ever tells you that George Orwell wrote a 224-page book called The Road to Wigan Pier, which possibly tells you something about how gripping Leopold's essay is by comparison. I'd also be curious how many of the people who cite this essay as scripture actually reached page 165. My suspicion is that Situational Awareness is the most referenced and least finished document in Silicon Valley since the terms and conditions.

...

Now, having read the whole thing, I can report that it is less of a prophecy and more of a very long summary of what everyone in San Francisco had already been saying at dinner for about three years. He says AGI arrives in 2027, that the world will change faster than anyone believes because of recursive self-improvement, that the free world survival is at stake, and there's a great deal about China and robots, that sort of thing.

There's nothing about the essay that struck me as innovative or even interesting. It's just that describing the tech industry's group chat back to itself in 165 pages is not usually what we mean by seeing the future.

...

Tim Ferriss crowned Leopold the Nostradamus of AI, which is more fitting than Ferriss probably intended, given that Nostradamus was a man whose famous predictions only look like predictions once you already know what happened.

 

Investors were breaking down the door.

In these stories, when the bold young hero does something that makes the old hands in the industry shake their heads in dismay, it always turns out to be a brilliant, original thought that succeeds spectacularly. Actual examples of things turning out that way are decidedly rare.

Case in point, the, as it turns out, ironically named Situational Awareness was not so much of a hedge fund as an anti-hedge fund. The basic idea of a hedge fund, albeit in oversimplified terms, is that you try to control risk by hedging your bets. Hence the name. Patrick Boyle gives a characteristically sharp and informative explanation of the concept and what Situational Awareness chose to do instead, but the short version is you generally want to have some of your money spread out among investments that are inversely correlated, so that whichever way the market turns, you will have some winners in the group.

For example, if you think that good news for AI stocks translates to bad news for software-as-a-service companies like SAP, and vice versa, you might want to put some money into both sectors so that if OpenAI hits a road bump and SAP rallies, you won't take quite as large a hit.

Needless to say, if you eliminated the chances of having all losers, you've also pretty much eliminated the chance of having all winners. You have reduced your risk but also your margins. This is why hedge funds tend to be heavily leveraged. If you have a small but safe return on an investment, it makes sense to borrow as much money as you can to optimize your returns.

Young Leopold did embrace that aspect of running a hedge fund. He was leveraged to the max. But rather than distributing his bets to protect against downturns in the AI market, he simply increased his exposure. With borrowed money, he went long on AI companies and shorted software-as-a-service companies so that if the former stumbled and the latter surged, he would be absolutely screwed. Guess what happened?

Here's where we get to the part that would be the most incredible if you forgot what I said at the beginning of the post. The kind of West Coast techno-optimist investors that we are talking about wanted so very badly to believe in the story of this young man and in the story he was telling. Thinking that these things were true and that this was the way that things were going to work out was such a load-bearing belief, such a fundamental part of their worldview, that a mere $30 billion loss was not going to shake that conviction.

Again from Boyle: 

Here's the part that tells you everything about the two coasts. One week after nearly blowing up his fund, Leopold isn't fending off furious investors demanding their money back. He's fending off new investors trying to give him more.

According to Bloomberg, Silicon Valley money has been clamoring to get in since the collapse, and the fund is, for the moment, politely turning them away. Think about what that means. On Wall Street, losing two-thirds of your fund in a month is a career-defining catastrophe. In Silicon Valley, it's a buying opportunity.

Pat Grady, a partner at Sequoia, was asked about the turmoil by Bloomberg and said, "Our suspicion is that he's going to be a fixture in Silicon Valley for a long time to come." And he wasted no time getting back to it. Days after Citadel bought his collapsing public book on a midnight fire sale, Leopold wired $400 million into a single privately held startup backed by Sequoia, on top of $100 million he had put into the same company a month earlier.

So the man who had been very nearly destroyed by one enormous concentrated bet marked the occasion by making another one. Now, the wedding went ahead as planned. The guests presumably enjoyed the breakout sessions, and Leopold rides off into the Silicon Valley sunset to invest his remaining billions, a little wiser, a little more married, and forever haunted by the mathematical gap between the mean and the median.

 

 

Not another Cushing post, but Cushing-relevant

 

First, let's get some important disclaimers out of the way. Neither Joseph nor I are any kind of experts on international energy markets, so anything we tell you is pretty much based on what we read in the papers.

When it comes to military questions, Joseph is actually pretty good, but, unfortunately, he's not the one writing this post. Nothing I have to say on that subject is backed by anything more than hopefully a little common sense and the articles cited. Reader beware.

Our sporadic thread on oil reserves is based mostly on two observations: first, that the news we've been seeing from reputable sources seems increasingly worrisome; and second, that the story doesn't seem to be getting anywhere near the attention that it should.

Spencer Kimball writing for CNBC: 

The SPR fell by 6.1 million barrels to 298.7 million barrels last week, according to data released by the Department of Energy on Monday. The reserve, created in 1975, is at its lowest level since January 1983. 

...

The SPR stood at around 415 million barrels before the U.S and Israel attacked Iran on Feb. 28. U.S. government stocks will fall to around 243 million barrels when the release Trump ordered is completed.

The SPR's operational capability is at risk due to aging infrastructure, according to a May report from the Government Accountability Office. More than a quarter of its inventory was "not available for drawdown due to a combination of construction outages and cavern outages" as of December 2025, GAO investigators found.

That implied that a minimum of 103 million barrels in the SPR today are not available for use, according to a July analysis by Rapidan Energy.

 

This is by no means an isolated phenomenon. As we previously discussed in our post about Cushing, Oklahoma, oil reserves, both public and private, are headed dangerously close to the tipping point.

With a few exceptions, numerous experts have observed that, after the initial reaction, the oil futures markets have tended to remain remarkably calm throughout the war, arguably to the point of denial. Part of this is credited to an increasingly questionable faith that things will resolve soon, but another component has been the steady release of oil from various reserves, which has softened the shocks considerably.

Assuming that when demand exceeds supply for an extended period of time, and assuming supply cannot be readily increased, we would expect prices to go up until demand once again matches supply. If we hit the tipping point before the end of the war, it seems like we should expect considerably more pain at the pump.

That is by no means the worst-case scenario. Between the Strait of Hormuz and the Red Sea/Suez Canal, a big chunk of the world's oil supply is being slowed or stopped, but not a majority. There are still a lot of potential targets for terrorist or cyberterrorist attacks, including other choke points, infrastructure, shipping, and storage facilities, not to mention the potential for supply shocks due to industrial accidents or natural disasters.

As I said at the beginning, I'm no expert, but this does not seem... good.

The Ides (ish) of March -- checking in on the Ellison Media Empire

By the standards of 2026, March is a long ways away, particularly with respect to this story. For starters, there is that ticking fee, a penalty which Paramount agreed to pay for every day after the end of September that the deal doesn't go through.

One interesting aspect of that fee which probably hasn't gotten the attention it deserves is that Paramount/the Ellisons only have to pay it if the deal goes through, which adds yet another moving part to what is already a very complicated piece of financial machinery.

If the deal does not go through, Paramount will have to pay $7 billion, certainly not a trivial amount even for rich people, but if the deal does go through, Larry Ellison personally is going to have to cough up somewhere between $40 and $50 billion. That is by any standard a hell of a lot of money to pay so that your nepo baby son can have his own media empire, but when you take into account that about a year ago, Larry Ellison was worth around $400 billion, it possibly didn't seem all that insane.

Since then, his net worth has dropped by more than half. More significantly, his ability to raise cash has taken even more of a hit. Both Ellison and Oracle are heavily in debt and have genuinely ugly credit ratings. What's worse, the current state of the company is widely considered precarious, especially if OpenAI is not able to turn things around.

Like I said, March is a long ways away.

Not Enron, but not good either.

[Picking up from this and this]

I realize that my recommendations for Patrick Boyle videos tend to be a bit heavy-handed, but even by his standards, this one is essential for anyone trying to get a handle on the AI bubble and its increasingly likely ramifications. It is an extraordinarily pithy half hour, hitting a number of important topics. I really need to do at least one or two more posts on this.

For today, let's focus on what may be the key point in the entire 2026 AI market discussion: the unresolvable disconnect between the trillions of dollars in capital expenditure and debt accumulated (both on the books and off) and realistic estimates of the total addressable market.

Side note: I also need to revisit our long-running economics of streaming thread in the context of Cornell and Damodaran's remarkably prescient The Big Market Delusion: Valuation and Investment Implications. 

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. Annoying, 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.

 

So 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 spend today will look like a rounding error tomorrow.

Aswath Damodaran has a name for what happens next. He and his co-author, Bradford Cornell, call it the "big market delusion." [One of those papers that deserves a lot more attention -- MP]

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.

Everyone'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 buildout is on track to be the largest investment surge in history. Around $900 billion this year alone is being spent on chips, data centers, 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 $2.5 trillion a year in AI revenue, which is more than the entire global technology sector earns from everything it does today.

The 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 the 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.

$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 nine out of ten 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.

They 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.

 

Is it a bad sign when people start making Enron comparisons? It seems like a bad sign.

Following up on yesterday's post. If you're not interested in the AI bubble, you might want to come back next week because this thread is going to be running through Friday.

Here is Ed Zitron discussing the AI industry's huge off-the-books debt. Before we jump in, however, there's one point I want to emphasize (we'll dig into this even deeper tomorrow when we discuss Patrick Boyle's analysis). This story is Enron-esque. It is Enron-reminiscent. It is "a little bit Enron." It is not, however, another Enron.

The executives at that company engaged in criminal accounting fraud. People went to jail. As far as I can tell, no one is seriously accusing any of the major AI players of that kind of Enron-level behavior. The key phrase here is "at least on a balance sheet basis." Oracle and all the rest are hiding their debt from people who do not read the footnotes. Legally, that's a huge distinction, the kind that determines who goes to jail and who doesn't, but the difference can be smaller than you'd expect (more on that tomorrow).

The problem with these SPV-based deals is that they allow companies to, at least on a balance sheet basis, hide the scale of their debts. Meta’s long term debt sits, as of its latest quarter, at around $58.7 billion. It’s as if the $39 billion in debt for gigawatts’ worth of AI data centers doesn’t exist out of the payments it’ll eventually have to make. 

This is all legal, worrying, and yes, a little bit Enron.  

 

Per Amanda Iacone of Bloomberg:

Enron Corp. exploited US accounting rules to hide from investors and lenders hundreds of millions in debt it had bundled into off-balance sheet entities — obligations that contributed to one of the biggest corporate collapses in US history.

Alphabet Inc. and Meta Platforms Inc. each have turned to vehicles known as variable interest entities (VIEs) as part of the financing mix needed to construct data centers and related energy infrastructure.

Meta, the parent of Facebook, last year formed a joint venture, a VIE, to build a Louisiana data center through a partnership with Blue Owl Capital. The social media titan’s maximum exposure for the venture is $46 billion, according to its filings with the Securities and Exchange Commission. The company announced last week that it would expand its planned campus and is expected to spend as much as $250 billion on the project, Bloomberg News has reported.

To be clear, a Variable Interest Entity is a type of SPV where you have control over the entity, and you must consolidate it into your balance sheet…unless you are not considered the “primary beneficiary,” which Meta argues isn’t the case despite being the primary tenant and reason that Hyperion is being built. Per Bloomberg:

Meta determined it shouldn’t bring billions in debt from the Louisiana project onto its own balance sheet because it isn’t responsible for finding tenants to replace or join it at the nearly 4,000-acre campus — a critical job that impacts the entity’s economic performance, the social media company said in its most recent quarterly SEC filing. Meta said its role is limited to construction management, along with administrative and property management services.

Auditor Ernst & Young raised a “red flag” (per the WSJ) about this arrangement, flagging it as a “critical audit matter,” adding that it “...was especially challenging due to the significant judgment required in determining the activities that most significantly affect the VIE’s economic performance.” Nevertheless, it was approved, it happened, and everything is fine and normal. 

This is why Google backstopped Fluidstack and Cipher Mining’s 300MW data center and another for TeraWulf. Both will, eventually, operate as data centers that Google will lease to provide compute to Anthropic, booking revenue for doing so, acting as the sole tenant and the entire reason that the debt was raised, yet because Fluidstack and TeraWulf and Cipher Mining are the actual entities involved, nothing shows up on Google’s balance sheet. 

What’s also important to note is that none of the money going into these SPVs counts as capital expenditures. For example, across the space of five quarters (Q1 2025 through Q1 2026), Meta spent around $88.6 billion in capital expenditures, but that doesn’t include any of the debt or purchases of GPUs or anything else done in its name as part of the Hyperion SPV, despite it having (per its own fillings) $45.95 billion of exposure. 

 

 

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]

 

Carl Brown explains why being an AI developer is the best job in the whole wide world.

Here, Brown does a characteristically fine job of walking us through the weeds of the OpenAI/Hugging Face hack. The TL;DR version is that OpenAI made a bunch of rookie mistakes setting up their sandbox, while Hugging Face did a comparably bad job on their end. He also explains what concepts like "zero-day," and "escape"mean in this context (spoiler: not what tech reporters think it means), and why, in the world of frontier models, every screw-up is a marketing opportunity.

Yeah, I think this is one of those cases of "never attribute to malice what can be adequately explained by incompetence"—or "stupidity," depending on which phrasing of the saying you use. I think it's Hanlon's razor.

What it means is that people, especially in the AI industry, are learning that they don't actually have to try, or they don't have to try very hard, because if they don't do a good job and the thing ends up failing in a way it shouldn't have if they had done a good job, then all they have to do is put out a press release saying, "Look how smart the AI is."

 And if you're curious about the kind of sensationalistic, badly reported coverage Brown is complaining about... 

 Did ChatGPT go Rogue and HACK Hugging Face? - Emergency Episode - OpenAI Hugging Face hack explained

Bonus video from Cal Newport including some great quotes from the Financial Times. 

OpenAI, SoftBank, and Nvidia

Following up on yesterday's post, Yesterday's Alarmism is Tomorrow's Consensus

 

Longtime and even casual readers of this blog will know that we are big fans of Talking Points Memo and particularly of Josh Marshall (for my money, the best political commentator of the past 20 years). There have been plenty of times when I have disagreed with Marshall on minor and sometimes major points of analysis, but this recent piece on the AI bubble is a first.

Except for one or two general and largely obvious observations about the absurdity of the current situation, this piece and the Semafor article it's based on managed to get virtually everything wrong about the relationships between OpenAI, Nvidia, and SoftBank.

For lack of a better explanation, Elizabeth Hoffman, who penned the Semafor piece, seems to have seen the title of Ed Zitron's recent post comparing the data center bubble to the subprime crisis but does not seem to have actually read it. Zitron, at great length, laid out the disturbing parallels. It is a highly recommended piece. Hoffman's analysis mainly consists of the analogy AIG:lenders::Nvidia:OpenAI, a comparison so tortured that she abandons it mid-paragraph.

Here's Hoffman:

Nvidia’s $250 billion backstop to OpenAI will let the money-burning AI lab lease space at the largest data center ever built. OpenAI doesn’t have an investment-grade rating, so Nvidia is essentially lending its own. Broadcom did the same thing for Anthropic a few weeks ago; I wrote at the time that it was “like getting your parents to cosign the lease on your first apartment.” Nvidia’s backstop for OpenAI is literally that — OpenAI is trying to sign a lease and its landlord, SoftBank, doesn’t like the tenant risk. So Huang is cosigning.

...

The notion of Nvidia-as-AIG is right in one respect: The company most to blame for the 2007 bubble wasn’t a bank writing bad loans, but the insurer that backstopped them, spreading that risk throughout the financial system. Risky mortgages went into AIG and came out stamped AAA. Risky AI stuff is going into Nvidia, Broadcom, and Google and emerging similarly shined up.

Spreading risk around is sometimes prudent — it’s how mutual insurance works. But it also brings players that might have sat out a crisis into the thick of it. AIG didn’t need to be a part of the mortgage crisis. Nvidia does need to be a part of the AI buildout, but it is testing its balance sheet to finance its customers. (OpenAI will put Nvidia chips inside the Ohio data center.) That has echoes of General Electric and General Motors, which were nearly toppled by their finance arms in 2008.

 

Here's Marshall:

 This is not like getting your parents to cosign the lease on your first apartment. It’s more like getting your top employee to cosign your first lease because you pay that employee huge sums of money despite the fact that your company, which pays his salary, actually makes no money. A bank would likely see the problem with the top employee co-signing the mortgage on the boss’s fancy home. But it doesn’t seem clear to people in this case. Or rather, it seems completely clear. But we seem to have decided this is just how AI works: the technology is so amazing that it requires this kind of mutual leverage with no floor beneath it.  

Not entirely sure what Marshall is going for here.  The employee co-signing the boss's mortgage sounds like a coerced kick-back. The part about the company not making money sounds like like money laundering. Even the employer/employee analogy breaks down under scrutiny. Nvidia sells chips to OpenAI, but most of its sales come from companies like Microsoft, Alphabet, Amazon, Meta, SpaceX, Oracle, CoreWeave, etc. (Various governments are also big customers, particularly until recently, China.) Some of these companies use these chips to OpenAI models, some to run Anthropic models, some to their own, some to run something else like open-weight models. Not sure how you'd get OpenAI employee out of that.

Then how do we make sense of the enormous company giving the much smaller one what amounts to a blank-check credit guarantee? The employer/employee relationship doesn't explain it, at least not the one that Marshall proposes. You'd get closer reversing it and thinking barker and shill. 

[Any excuse to plug Cool and Lam.]

Nvidia indirectly giving OpenAI money, which is then indirectly spent on Nvidia chips, is good business in much the same way that it was good business for a snake oil salesman to give the shill the money to publicly buy a bottle of miracle tonic, but even that doesn't quite capture it.

What would a correct reading look like? Let's start with SoftBank, which appears to be more or less a neutral, independent, and minor figure in both these pieces, a "landlord" merely concerned with the creditworthiness of its business partners.

About that...

From CNBC:

The company participated in OpenAI’s funding round last year at a reported $300 billion valuation and has continued to deepen its involvement. It secured a $40 billion bridge loan in March to help fund additional investments in OpenAI and for general corporate purposes.

As of the end of 2025, SoftBank had about 16.3 trillion yen (about $104 billion) in stand-alone interest-bearing debt, according to its financial statement.

S&P Global in March estimated that OpenAI would account for roughly 30% of SoftBank’s investment portfolio, similar to Arm Holdings’ share, following the group’s additional $30 billion investment in the ChatGPT maker.

S&P Global Ratings revised SoftBank’s credit outlook to negative in March, saying the company’s asset liquidity and quality of its portfolio, as well as its financial capacity are “likely to deteriorate because of its additional huge investment in OpenAI.”

 

There's an essential bit of context that we need to include here. As late as the beginning of this June, the consensus in the financial markets was that OpenAI would have an IPO, probably north of a trillion dollars in 2026. Among other things, that would have made the early investors whole or better and would still have given the company plenty of cash on hand.

By mid-July, those expectations had done a complete 180 for a variety of reasons that we'll get into one of these days. Suddenly, SoftBank had gone from being on the verge of a windfall to facing serious questions about its viability as a company, with its fate tied to an increasingly unreliable Sam Altman. If you ask analysts what companies have the greatest exposure to an OpenAI collapse, the two names you will hear most often are Oracle and SoftBank. Both of these companies would gladly have extended a massive line of credit if it meant keeping the status quo stable, but neither now has the wherewithal.

OpenAI is losing $20 billion a year. Its potential sources of funding are going away. Why should the world's largest company care? 

Since the beginning of 2023, the share price of Nvidia stock has increased by more than 1,200%, overwhelmingly due to the AI bubble. While OpenAI is not the primary customer for Nvidia chips, it is one of the foundational blocks in the Jenga tower that has made Jensen Huang one of the world's richest men. The death of OpenAI might not kill Nvidia or even cost it the majority of its revenue, but if it pops the AI bubble, it could easily shave one or two trillion dollars off the behemoth's market cap. You don't need fancy analogies to see why Huang stepped up.

But while the analyses of Hoffman and Marshall are flawed, they are still informative.

I'm not sure what's going on with Hoffman—I don't normally read Semafor—but I do follow pretty much everything Marshall writes, and I think I have a pretty good handle on where he's coming from.

With respect to this story, I strongly suspect Marshall is a normie. I doubt he spends hours a week poring over the latest massive missives from Ed Zitron or following Cal Newport, Paul Kedrosky, Gary Marcus, Cory Doctorow, et al. I'll bet he doesn't annoy friends and acquaintances with emailed articles from the Financial Times explaining the latest excesses of the AI bubble.

In other words, I suspect he has a life.

When it comes to the AI bubble, Marshall, like The New York Times, represents the well-informed mainstream. And for years that group was heavily under the sway of the techno-optimist/Silicon Valley messiah AI narrative propagated by people like Kevin Roose or Casey Newton, while the skeptics, who were by most standards more grounded, were relegated to the fringe.

Now the mainstream is starting to embrace that fringe, with central bankers echoing the arguments of Zitron and Newport writing op-eds in NYT. Marketplace runs features with names like "What happens if the AI bubble pops?" As the normies start to wrap their heads around the magnitude and absurdity of the current situation, they sometimes get the nuances and key details wrong, but the very fact that they are asking what kind of bubble this is is a huge development.

Yesterday's Alarmism is Tomorrow's Consensus

A couple of years ago, skepticism about the AI boom was something of a fringe position. Today... not so much.

From FT Alphaville [love that last line]: 

Over at Jefferies, head of equity strategy Chris Wood has for some time been offering clients a sum of all fears in one simple, easily ignorable package.

His latest outlines his expectation of “massive capital destruction”, as token parsimony replaces tokenmaxxing, and as Chinese open-source models divert spending away from the US majors. It also covers default risk on hyperscaler debt, a lot of which is sitting off-balance-sheet via data centre lease commitments, and the artificial earnings boom from non-cash unrealised gains in investments, compute sales being recognised upfront, and depreciation costs being kept unrealistically low. On top of all that, Wood cites a viral blog from earlier this month about how commitments from hyperscaler tenants like OpenAI should be viewed as liabilities because all they’ll ever do is refinance, not repay:

There is a potential “2008 real estate” analogy in AI infrastructure. Hyperscalers and neo-clouds have built data centers based on promises of future compute purchases, creating a credit-like structure tied to tenants whose long-term profitability is uncertain.

It might not be a complete surprise to know that Ed Zitron, the hyper-online unofficial voice of big-tech antipathy, was a recent guest speaker at Jefferies’ offices; the biggest difference between his body of work and the above summary is in the profanity count.

 

And more recently:

Fitch Ratings-New York-27 July 2026: The global credit risk environment has evolved heading into 2H26 but continues to be driven by two main sources of short-term risk, according to Fitch Ratings: rising vulnerability to an AI-related market correction and persistent geopolitical uncertainty in the Middle East. This is on top of a broader context of slowing US consumer momentum, high inflation risks stemming from the 2Q energy shock and structural public finance pressures limiting the ability to respond to risk events.

The scale of the AI investment boom and the accelerated global technology cycle has been a significant driver of US equity market valuations and corporate bond issuance over the past year. The effects on real economic indicators are profound. The 18% yoy rise in IT capital investment directly added 1.4pp to 1Q26 GDP growth. The wealth effect from AI-related investor optimism and equity market gains has also been a meaningful support for US consumer spending growth, which has been broadly slowing.

That said, the medium- and long-term potential of the underlying technology is highly uncertain, as with previous tech cycles. The combination of revenue uncertainty and the extent to which capital markets and economies have become intertwined with AI have created a vulnerability for credit in the event of a re-evaluation of long-run returns potential. Very short-term spikes in market volatility for individual equities and tech-heavy stock indices have already occurred, but a larger, more protracted correction could have wider market, macro and credit effects depending on its scale, duration and contagion.