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.