Hey there, hi there, ho there folks, and welcome back to Wednesday. Today I thought I’d spotlight an excellent piece by Matteo Wong and Charlie Warzel in The Atlantic that dives into the economics of AI. It explained the AI economy in a clear and helpful way without being too full of hype or worry. Here’s How the AI Crash Happens

The amount of energy and money being poured into AI is breathtaking. Global spending on the technology is projected to hit $375 billion by the end of the year and half a trillion dollars in 2026. Three-quarters of gains in the S&P 500 since the launch of ChatGPT came from AI-related stocks; the value of every publicly traded company has, in a sense, been buoyed by an AI-driven bull market. To cement the point, Nvidia, a maker of the advanced computer chips underlying the AI boom, yesterday became the first company in history to be worth $5 trillion.

Here’s another way of thinking about the transformation under way: Multiplying Ford’s current market cap 94 times over wouldn’t quite get you to Nvidia’s. Yet 20 years ago, Ford was worth nearly triple what Nvidia was. Much like how Saudi Arabia is a petrostate, the U.S. is a burgeoning AI state—and, in particular, an Nvidia-state. The number keeps going up, which has a buoying effect on markets that is, in the short term, good. But every good earnings report further entrenches Nvidia as a precariously placed, load-bearing piece of the global economy.

America appears to be, at the moment, in a sort of benevolent hostage situation. AI-related spending now contributes more to the nation’s GDP growth than all consumer spending combined, and by another calculation, those AI expenditures accounted for 92 percent of GDP growth during the first half of 2025. Since the launch of ChatGPT, in late 2022, the tech industry has gone from making up 22 percent of the value in the S&P 500 to roughly one-third. Just yesterday, Meta, Microsoft, and Alphabet all reported substantial quarterly-revenue growth, and Reuters reported that OpenAI is planning to go public perhaps as soon as next year at a value of up to $1 trillion—which would be one of the largest IPOs in history. 

[…]

The yawning gap between data-center expenditures and the rest of the economy has caused whispers of bubble to rise to a chorus. A growing number of financial and industry analysts have pointed out the enormous divergence between the historic investments in AI and the tech’s relatively modest revenues. For instance, according to The Information, OpenAI likely made $4 billion last year but lost $5 billion (making the idea of a $1 trillion IPO valuation that much more staggering). From July through September, Microsoft’s investments in OpenAI resulted in losses totaling more than $3 billion. For that same time period, Meta reported rapidly growing costs due to its AI investments, spooking investors and sending its stock down 9 percent.

Much is in flux. Chatbots and AI chips are getting more efficient almost by the day, while the business case for deploying generative-AI tools remains shaky. A recent report from McKinsey found that nearly 80 percent of companies using AI discovered that the technology had no significant impact on their bottom line. Meanwhile, nobody can say, beyond a few years, just how many more data centers Silicon Valley will need. There are researchers who believe there may already be enough electricity and computing power to meet generative AI’s requirements for years to come.

[…]

Here is where the bubble dynamics get complicated. Tech firms don’t want to formally take on debt—that is, directly ask investors for loans—because debt looks bad on their balance sheets and could reduce shareholder returns. To get around this, some are partnering with private-equity titans to do some sophisticated financial engineering, Paul Kedrosky, an investor and a financial consultant, told us. These private-equity firms put up or raise the money to build a data center, which a tech company will repay through rent. Data-center leases from, say, Meta can then be repackaged into a financial instrument that people can buy and sell—a bond, in essence. Meta recently did just this: Blue Owl Capital raised money for a massive Meta data center in Louisiana by, in essence, issuing bonds backed by Meta’s rent. And multiple data-center leases can be combined into a security and sorted into what are called “tranches” based on their risk of default. Data centers represent an $800 billion market for private-equity firms through 2028 alone. (Meta has said of its arrangement with Blue Owl that the “innovative partnership was designed to support the speed and flexibility required for Meta’s data center projects.”)

In this way, the data-center financing ends up being a real-estate deal as much as an AI deal. If this sounds complicated, it’s supposed to: The complexity, investment structure, and repackaging make exactly what is going on hard to parse. And if the dynamics also sound familiar, it’s because not two decades ago, the Great Recession was precipitated by banks packaging risky mortgages into tranches of securities that were falsely marketed as high-quality. By 2008, the house of cards had collapsed.

Data-center build-outs aren’t the same as subprime mortgages. Still, there is plenty of precarity baked into these investments. Data centers deteriorate rapidly, unlike the more durable infrastructure of canals, railroads, or even fiber-optic cables. Many of the chips inside these buildings become obsolete within a few years, when Nvidia and its competitors release the next wave of bleeding-edge AI hardware. Meanwhile, the returns on scaling up chatbots are, at present, diminishing. The improvements made by each new AI model are becoming smaller and smaller, making the idea that Silicon Valley can spend its way to superintelligence more tenuous by the day.

Worth reading the full thing for the full explanation! Otherwise, be kind and thoughtful today. Cheers.