Fans of Dr. Seuss will know by heart the key stanzas of Green Eggs and Ham.

Do you like
green eggs and ham?

I do not like them,
Sam-I-Am.
I do not like
green eggs and ham.

For those who have never had to read a bedtime story, allow me to explain. An irrepressible little creature, Sam-I-Am, spends the entirety of the book pitching green eggs and ham—on the face of it, an unappetizing dish—to a skeptical and increasingly irascible larger creature. With every page, the pitch grows more elaborate. Would you like them on a boat? With a goat? In the rain? On a train? Surely, there must be some context in which green eggs would be appealing fare. By the time Sam prevails, his hapless victim inhabits a scene of chaos.

When you come to think of it, there is often someone called Sam trying to sell you something you don’t initially want. In the 1920s, as I learned from Andrew Ross Sorkin’s 1929: Inside the Greatest Crash in Wall Street History—and How It Shattered a Nation, it was Sam Crowther’s article, “Everybody Ought to Be Rich”—exhorting housewives to buy stocks with margin credit. A few years ago, it was Sam Bankman-Fried with his crypto exchange, FTX. At the height of his fame, Bankman-Fried declared, “I want FTX to be a place where you can do anything you want with your next dollar. You can buy bitcoin. . . . You can buy a banana.” And you could also have bought green eggs and ham—until FTX blew up and Sam landed in prison.

A lot of the applications of generative artificial intelligence remind me of green eggs and ham. Take OpenAI’s Sora 2.0. With a few prompts, you can generate soft-porn videos of scantily clad girl manga elves. This is also one of the ways Elon Musk tries to sell xAI’s Grok. But why would I want to watch such videos, any more than I want to eat green eggs and ham?

Financial history can help us here. If you’re unsure if there’s an AI bubble, refer to the historian Charles Kindleberger’s five-stage model:

1. Displacement: Some change in economic circumstances creates new and profitable opportunities for certain companies.
2. Euphoria or overtrading: A feedback process sets in whereby rising expected profits lead to rapid growth in share prices.
3. Mania or bubble: The prospect of easy capital gains attracts first-time investors and swindlers eager to defraud them.
4. Distress: The insiders discern that expected profits cannot possibly justify the now-exorbitant price of the shares and begin to take profits by selling.
5. Revulsion or discredit: As share prices fall, the outsiders stampede for the exits, causing the bubble to burst altogether.

We are currently at stage 3.

It is impossible to read the first part of Sorkin’s 1929 without being reminded of our own times. We too easily forget that underpinning the stock-market boom of the 1920s were the tech stocks of the day—Radio Corporation of America, for example, the company at the cutting edge of the new mass entertainment on radio, on vinyl, and on celluloid.

Investors rush to withdraw their savings during the 1929 stock market crash. (Hulton Archive via Getty Images)

Today, we’re being offered something even more alluring than the cornucopia of the Jazz Age. According to a project from economist Ezra Karger aiming to predict the progress of AI, more than 18 percent of American work hours will be AI-assisted by 2030. Ten years later, AI will be as important to this century as electricity or the car were to the previous one. Indeed, there is a one-in-three chance that AI is going to rank alongside the printing press as a technology that “changed the course of human history.”

Even if AI falls short of that, Reuters reported last week that 97 percent of listeners cannot tell the difference between AI-generated and human-composed songs. The song currently topping the country charts—Breaking Rust’s “Walk My Walk”—was generated by AI, according to the Financial Times.

AI—or rather the promise of AI—is now the principal driver of both the U.S. economy and the stock market. Between a sixth and two-fifths of the rise in gross domestic product over the past year is attributable to investments in computer and communications equipment, including chips, data centers, grid upgrades, and AI software.

Financial Times columnist Ruchir Sharma estimates that AI companies account for 80 percent of the gains in U.S. stocks this year. Blogger-economist Noah Smith notes that “more than a fifth of the entire S&P 500 market cap is now just three companies—Nvidia, Microsoft, and Apple—two of which are basically big bets on AI.” The so-called Magnificent Seven (those three companies plus Alphabet, Amazon, Meta, and Tesla) account for more than a third of the S&P 500’s market capitalization. Quarterly capital expenditures by these companies now exceed $110 billion, roughly three times what it was two years ago. Nearly two-fifths of that total consists of purchases by everyone else of Nvidia’s graphics processing units (GPUs).

The standard analogy to the supposed AI bubble is the dot-com bubble of the early 2000s. The standard counterargument is that the value of Nvidia is much lower relative to the company’s earnings than was true of Cisco 25 years ago. Unlike most other stock markets, the growth in U.S. market cap reflects rising earnings, not just rising valuations. Moreover, in the late 1990s, capital expenditures—much of them in fiber optic cables—ran far ahead of demand for the internet. The same is not true of demand for GPUs. Nvidia cannot keep up with AI-driven demand for additional computing capacity. Nor can the American power grid. The fact that electricity bills are up 7 percent this year is seen by some commentators as one of the unintended consequences of the AI capital expenditures boom.

Add to that the sheer speed of AI adoption. More than 18 billion messages are sent to ChatGPT every week. The rate of adoption is far higher than that of the world wide web in the 1990s.

In short, AI is changing the 2020s economy faster than the internet changed the 1990s economy. An August paper shows that, since the widespread adoption of AI, workers ages 22–25 in the most AI-exposed occupations (such as legal services) “have experienced a 13 percent relative decline in employment even after controlling for firm-level shocks.” Talk to anyone in investment banking, and they will tell you that their programs to hire entry-level analysts are being slashed.

For all these reasons, 19th-century railroads may be a better analogy to AI than 1990s telecoms. Think of today’s capital expenditures on data centers being like capital expenditures on railroads 150 years ago. And there’s the rub. Two things can be true at the same time: a) the data centers to power AI could be as economically worthwhile an investment as railroads, and b) we could still experience at least one stock market crash along the way to its general adoption.

In 1873 and 1893, investors in railroads realized that their returns on capital expenditures wouldn’t be quite as rapid as they had previously expected. If AI investors realize the same, or that returns won’t accrue to the companies doing all the big-ticket investment, a crash is likely. Moreover, history tells us that the economic hit will be proportionately larger depending on how much of capital expenditures are being financed by debt, as opposed to equity or cash flow from other sources.

The biggest companies—Microsoft, Amazon, Meta, and Alphabet can finance the lion’s share of their capital expenditures from free cash flow. And they’re likely to continue to invest in Nvidia, so long as Jensen Huang’s chip design and software remain state of the art. But OpenAI is another matter.

According to The Wall Street Journal, Sam Altman “recently told employees that OpenAI wanted to build 250 gigawatts of new computing capacity by 2033. . . a plan that would cost over $10 trillion by today’s standards.” That would be equivalent to a third of current U.S. peak energy usage.

OpenAI CEO Sam Altman speaks at a joint press conference at the Plaza Hotel in Seoul, South Korea, on February 4, 2025. (Chris Jung/NurPhoto via Getty Images)

Yet OpenAI is not quite 10 years old. Its flagship product, ChatGPT, is only three years old, and its burn rate (the amount of money it loses each quarter) may be the highest in history. How does Altman propose to pay for 250 gigawatts of new computing capacity? The answer is only partly by taking out bank loans ($4 billion to date). But the rest of it involves debt of another kind—from just about everyone else in the AI game.

Altman has signed a $22.4 billion cloud contract with CoreWeave. He has signed a $38 billion deal with Amazon Web Services. He has agreed to buy Broadcom’s custom chips and networking equipment. The only hitch is that “OpenAI is in no position to make any of these commitments,” as one analyst told the Financial Times last month. Why? Because while Altman says the company’s annualized revenue is “well more” than $13 billion, its losses in the last quarter amounted to $12 billion. The company’s claim that revenue will grow to $100 billion by 2028 seems implausible. It would certainly be unprecedented.

Some of OpenAI’s financing is being provided by Microsoft, with which it has a revenue-sharing agreement. There are also deals with Google and Nvidia. Perhaps the most important part comes from Larry Ellison’s Oracle, one of the parties to the Stargate Project, a joint venture announced in January to invest $500 billion in AI infrastructure for OpenAI. Other participants include SoftBank and investment firm MGX.

These deals are complex. The Nvidia-OpenAI deal, for example, entails an agreement from Nvidia to lease up to five million of its chips to OpenAI. In return, Nvidia will invest up to $100 billion in OpenAI over time to help the company pay for the chips. In that way, Nvidia serves as both an investor in and supplier to OpenAI.

Similarly, OpenAI may be on the hook to pay more than $20 billion to CoreWeave, but it also owns part of CoreWeave, “having made a $350 million equity investment in the company before its initial public offering.”

Other deals involve similar circular financing. The Wall Street term for this is roundabouting. The phrase house of cards also comes to mind, as clearly anything that caused a significant equity market correction would pose serious problems for the stability of this structure.

What might lead investors to revise downward their expectations about the money to be made out of generative AI?

I can think of four good reasons for disappointment:

1. The realization that ChatGPT is more of an upgrade on Google Search than a productivity-raising miracle. The bulk of ChatGPT use is by people seeking practical guidance, information, or technical help. By contrast, according to an MIT study, 95 percent of organizations are getting zero return on their AI investments. That’s because employees are using it to generate what the Harvard Business Review has dubbed “workslop,” i.e., AI-generated verbiage.
2. OpenAI has serious competition. A year ago, its share of the generative AI market was 86.6 percent. Today it’s 72.3 percent. Google’s Gemini is gaining rapidly. And Anthropic is beating OpenAI when it comes to enterprise AI.
3. All the U.S. AI players face competition from China’s open-source models, which have rapidly outstripped their U.S. counterparts when it comes to worldwide adoption. More and more U.S. companies—for example, Airbnb—are quietly using Chinese models because they are cheap. When Huang is telling the Financial Times, “China is going to win the AI race,” that seems like bad news for Sam-I-Am.
4. If GPUs are serving implicitly as collateral for AI debt, that’s a potential headache, too. Unlike railroads, GPUs are short-lived assets with a useful life of perhaps five years. Or is it eight? Or two? No one knows.

As I said, mild disappointment can cause a crash even when the technology is awesome, or even if the investment will ultimately be worth it for society as a whole. That was scant consolation to those who lost their shirts on railroad securities in 1873 and 1893. How far an equity sell-off today would cause a wider shock—even a recession—depends on the extent of the financial contagion. Step forward Oracle, which has about $96 billion of long-term debt, up from $75 billion a year ago, with a potential total of $290 billion by 2028, according to Morgan Stanley. Oracle’s debt-to-equity ratio has surged to 500 percent, compared with Amazon’s 50 percent and Microsoft’s 30 percent. The price of Oracle credit default swaps (a derivative you buy to protect you against a company failing to honor its debts) has doubled since September.

It’s all fine, just fine, in current financial conditions, which are as easy as they have been in three years. But you have to ask yourself: Are the 1920s coming to ask for their financial history back?

Certainly, when I read in The Wall Street Journal that “the fates of the world’s biggest semiconductor and cloud companies—and vast swaths of the U.S. economy—[are tied] to OpenAI, essentially making it too big to fail,” I have only one response:

I do not like them,
Sam-I-Am.
I do not like
green eggs and ham.