How to burst the AI bubble: Strike at its roots

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all is not lost

Sci-fi author/tech journalist Cory Doctorow on his new book, The Reverse Centaur’s Guide to Life After AI.

Last year, we featured a lengthy interview with tech journalist/science fiction author Cory Doctorow about his book, Enshittification: Why Everything Suddenly Got Worse and What To Do About It. The prolific Doctorow is back with a provocative new book that serves as a follow-up of sorts, focusing on AI and related issues: The Reverse Centaur’s Guide to Life After AI. Doctorow doesn’t actually enjoy talking about AI, but he’s constantly being asked to comment on it. “I made the tactical error of being sick of talking about AI,” Doctorow told Ars. “So I wrote a book about why I think it’s a dumb thing to keep asking people to talk about, and now I have to talk about it.” Reverse Centaur is Doctorow’s attempt to “sort out the bullshit from the material reality.” In automation theory, per Doctorow, a “centaur” describes a human augmented with a technology, like machine learning, or even just driving a car or using autocomplete. A reverse centaur “is a machine head on a human body, a person who is serving as a squishy meat appendage for an uncaring machine,” Doctorow said in a speech last December. He gave the example of an Amazon delivery driver, surrounded by AI cameras monitoring their driving, who essentially serves as a peripheral to the delivery van. Being a centaur is generally viewed as a positive thing; few people relish being a reverse centaur. And yet the AI industry seems intent on using those tools to create more reverse centaurs. It’s one thing to incorporate AI tools into the medical field to help radiologists process X-ray images and spot potential tumors they might otherwise miss. It’s quite another to fire nine out of 10 radiologists and let AI make the diagnoses, with the remaining radiologist solely responsible for checking the AI’s work—and, ultimately, taking the blame for any errors.

Doctorow is not virulently anti-AI; he uses AI tools regularly and sees potential in many of those tools as useful plugins or cool new apps. But he is nonetheless alarmed at all the hype surrounding AI, the enormous capital expenditures, the unrealistic expectations and self-serving messaging, and the potentially catastrophic economic consequences when the AI bubble inevitably pops. “The bubble doesn’t want cheap useful things,” Doctorow said. “It wants expensive ‘disruptive’ things: big foundational models that lose billions of dollars every year. When the AI investment mania halts, most of the models are going to disappear, because it just won’t be economical to keep the data centers running. The collapse of the AI bubble is going to be ugly. Seven AI companies currently account for more than a third of the stock market, and they endlessly pass around the same $100 billion IOU. AI is the asbestos in the walls of our technological society, stuffed with wild abandon by a finance sector and tech monopolists run amok. We will be excavating it for a generation or more.” Naturally, Doctorow has some ideas about how to push back against the prevailing narrative of AI’s inevitability. Ars caught up with him to learn more.

Ars Technica: We touched briefly on AI last year when we chatted about your prior book. Reverse Centaur seems like a natural outgrowth of that. Cory Doctorow: Enshittification is primarily a thesis about how firms in the absence of constraint get tilted to the bad, but it’s also a thesis about how the constraint of competition, when it falls away, produces all kinds of perverse outcomes. One of those perverse outcomes is that firms that have saturated their markets can no longer grow, and they have to find other markets. There’s a ticking bomb when you saturate your market because it’s only a matter of time until investors start to worry that you’re not a growth stock, you’re a mature stock. Mature stocks trade at a small fraction of the multiple that growth stocks do.

There’s an enormous amount of liquidity in growth stocks, which means that you can use growth stocks to grow.

You can buy other companies with shares, and shares are an endogenous substance that you make on the premises by typing zeros into a spreadsheet. Firms with growth stocks can grow by typing zeros, whereas firms that are mature, they have to use money if they want to grow, and you’re not allowed to make money on the premises. If you do, the Treasury Department shows up and takes you away in handcuffs. So you can see why firms would be very anxious to maintain the perception that they have room for growth even after they have 90 percent market shares. “The capital markets have the object permanence of a toddler, and they would lose a game of peekaboo if they were drafted to play in the league.” That’s why those firms started promoting stories about how they were going to conquer imaginary markets. Imaginary markets have no agreed-upon valuation because you just made them up. Unless you can turn an imaginary market into a real market pretty quickly, you need to come up with another imaginary market and announce that this is the new imaginary market you’re going to conquer. It’s easier than you’d think because the capital markets have the object permanence of a toddler, and they would lose a game of peekaboo if they were drafted to play in the league. So you can say, “Oh, actually, it’s not metaverse. It’s crypto. It’s not crypto. It’s Web3. It’s not Web3. It’s something else.” And the markets will forgive you, provided you do it quickly enough. But something different happened with AI. It is much, much bigger in terms of capitalization than anything we’ve ever seen—not just bigger than other tech bubbles, bigger than other bubbles. When I wrote the book, capital expenditure (CapEx) globally was $700 billion, now it’s $1.4 trillion. Meta wasted $60 billion on the metaverse. They spent $150 billion in the last three years on AI, and they say they’re going to spend another $150 billion this year.

So this is a much bigger bet, and it raises the question: If the material basis for this is creating a narrative so that you can continue to grow by dint of having a highly liquid growth stock, what’s the ideological basis?

Why are people willing to make such a bigger bet? Some of it is that there’s more “there” there with AI. It’s real computer science. It was remarkable 10 years ago, when a couple of computer scientists and their grad students took some existing techniques, applied them in a new way, and got a very surprising result that turned out to not only produce dividends the first time around, but to have somewhat linear returns on investment, which is not usually the case. There was a lot of low-hanging fruit in AI, although it’s tapering off now because, as they say in finance, anything that can’t go on forever has to stop. So we’re losing the end of that growth period in terms of returns to scale. Ars Technica: Why do you think AI is so appealing to political and business leaders in particular?  Cory Doctorow: It’s not just that it makes for a good demo. AI really appeals to a fantasy that I think all of us have to some extent but that powerful people really have, of a world without people in it—because hell really is other people. You can’t get stuff done without other people helping you. You can’t have romance without a romantic partner. You can’t have social media without people to socialize with. You can’t play a board game, or do a startup, or build a bridge, or build a house, or do politics without other people. And other people stubbornly refuse to organize everything they do to make you happy.

Particularly if you’re rich and powerful, it’s very galling. So AI is very attractive. One of the reasons DOGE fired so many government workers was because it played into the fantasy that you can have a government without government employees. In the corporate sphere, it’s the fantasy of a business without workers, because every corporate leader is haunted by the secret fear that if they don’t show up for work, everything goes on just fine. But if the workers don’t show up, everything shuts down. Maybe they’re not really driving the car, maybe they’re strapped in the backseat with a toy steering wheel. If that’s the case, AI will let them wire the toy steering wheel directly into the drivetrain.

So you can have an amazing idea as a corporate visionary, and you don’t have to have any ego-shattering confrontations with people who know how to do things, who tell you you’re actually an idiot. You just type some stuff to the chatbot, and it shits out your product. If you combine those two things—the material necessity to have a growth narrative and the ideological attractiveness of a world without people—you get $1.4 trillion in CapEx for a sector that is turning over $50 billion a year and has to replace all of its assets every 24 to 30 months. Ars Technica: You raised an interesting point recently on your blog: Workers actually wanted earlier technological breakthroughs and often had to fight to get them into the workplace. With AI, people are more likely to feel that the technology is being shoved down our throats; some workers are even required to use it. Cory Doctorow: I think that’s entirely right. One of the things that I’ve been attending to a lot lately is the difference between the bubbles that we had before and the bubble that we’re having now. People will say, “Oh, Amazon wasn’t profitable, and it became profitable. And the web wasn’t profitable, and it became profitable. The web was a bubble.” Of course the web was a bubble. You don’t get pets.com and all those Super Bowl ads without a bubble. But it is a very obvious error of logic to say, “Once, there was a thing that lost money and then it made money, therefore, if you are losing money, someday you’ll make money.”

“AI is the money-losingest thing our species has ever done. We have never lost as much money as we’ve lost on AI.” The thing that made the web profitable was not that it was unprofitable; it was things like good unit economics, where every time someone started using the web, the web got less unprofitable. Every time a web user used the web again, the total profits generated went up. Every generation of web technology made the web more profitable. That’s the opposite of AI. Every AI customer loses money for the company, every use of AI by that customer loses money for the company, and every generation of AI loses more money than the last one.