There’s No Guarantee AI Will Ever Be Profitable

Tech companies like to make two grand pronouncements about the future of artificial intelligence. First, the technology is going to usher in a revolution akin to the advent of fire, nuclear weapons, and the internet. And second, it is going to cost almost unfathomable sums of money.

Silicon Valley has already triggered tens or even hundreds of billions of dollars of spending on AI, and companies only want to spend more. Their reasoning is straightforward: These companies have decided that the best way to make generative AI better is to build bigger AI models. And that is really, really expensive, requiring resources on the scale of moon missions and the interstate-highway system to fund the data centers and related infrastructure that generative AI depends on. For a product as important as fire, they say, any spending is worth it. Sam Altman, the CEO of OpenAI, has described his firm as “the most capital-intensive startup in Silicon Valley history.” Dario Amodei, the CEO of the rival start-up Anthropic, has predicted that a single AI model (such as, say, GPT-6) could cost $100 billion to train by 2027. The global data-center buildup over the next few years could require trillions of dollars from tech companies, utilities, and other industries, according to a July report from Moody’s.

Now a number of voices in the finance world are beginning to ask whether all of this investment can pay off. OpenAI, for its part, may lose up to $5 billion this year, almost 10 times more than what the company lost in 2022, according to The Information. Over the past few weeks, analysts and investors at some of the world’s most influential financial institutions—including Goldman Sachs, Sequoia Capital, Moody’s, and Barclays—have issued reports that raise doubts about whether the enormous investments in generative AI will be profitable. As Jim Covello, Goldman Sachs’s head of global equity research, told me, “If we’re going to justify a trillion or more dollars of investment, [AI] needs to solve complex problems and enable us to do things we haven’t been able to do before.” Today’s flagship AI models, he said, largely cannot.

When judged by almost any standard other than the revolutions caused by electricity or the internet, generative AI has already done extraordinary things, of course—advancing drug development, solving challenging math problems, generating stunning video clips. But exactly what uses of the technology can actually make money remains unclear. At present, AI is generally good at doing existing tasks—writing blog posts, coding, translating—faster and cheaper than humans can. But efficiency gains can provide only so much value, boosting the current economy but not creating a new one. Right now, Silicon Valley might just functionally be replacing some jobs, such as customer service and form-processing work, with historically expensive software, which is not a recipe for widespread economic transformation.

Even if generative AI has not yet seriously changed many people’s lives, proponents say that as the technology improves, it will solve long-standing scientific problems, unlock huge productivity boosts, and create entirely new sectors of the economy. In only a few years, various generative-AI models have gone from fumbling over simple sentences to writing entire essays. Plenty of investors and analysts are all in. Tony Kim, the head of technology investment at BlackRock, the world’s largest money manager, told me he believes that AI will trigger one of the most significant technological upheavals ever. “Prior industrial revolutions were never about intelligence,” he said. “Here, we can manufacture intelligence.” McKinsey has estimated that generative AI could eventually add almost $8 trillion to the global economy every year. One JPMorgan researcher recently said AI is more seminal “than the internet or the iPhone.”

Amid the hype, it’s important to remember that this future is not guaranteed. Many of the productivity gains expected from AI could be both greatly overestimated and very premature, Daron Acemoglu, an economist at MIT, has found. AI products’ key flaws, such as a tendency to invent false information, could make them unusable, or deployable only under strict human oversight, in certain settings—courts, hospitals, government agencies, schools. A lot of human labor is manual, which software isn’t close to replacing. Whether scaling up AI models will continue to yield significantly better results is highly contested. And analogizing AI to the atomic bomb, though evocative, is not a road map for a sustainable business model. For all the talk of generative AI as a truly epoch-shifting technology, it may well be more akin to blockchain, a very expensive tool destined to fall short of promises to fundamentally transform society and the economy.

Yet tech companies are spending as if those transformative uses are a foregone conclusion. Researchers at Barclays recently calculated that tech companies are collectively paying for enough AI-computing infrastructure to eventually power 12,000 different ChatGPTs. Silicon Valley could very well produce a whole host of hit generative-AI products like ChatGPT, “but probably not 12,000 of them,” the researchers wrote—and even if it did, there would be nowhere enough demand to use all those apps and actually turn a profit. David Cahn, a partner at Sequoia Capital, has put the financial gap differently: Some of the largest tech companies’ current spending on AI data centers will require roughly $600 billion of annual revenue to break even, of which they are currently about $500 billion short.

Tech proponents have responded to the criticism that the industry is spending too much, too fast, with something like religious dogma. “I don’t care” how much we spend, Altman has said. “I genuinely don’t.” In other words, the industry is asking the world to engage in something like a trillion-dollar tautology: AI’s world-transformative potential justifies spending any amount of resources, because its evangelists will spend any amount to make AI transform the world. Kim, the AI optimist at BlackRock, captured the sentiment perfectly: “You need to believe that these technologies and capabilities keep going, which requires lots of investment,” he told me.

The tech industry has long walked a precarious line between grand vision and grand delusion; frequently, the only difference between the two has been what pays off in the long run. But in the AI era in particular, a lack of clear evidence for a healthy return on investment may not even matter. Unlike the companies that went bust in the dot-com bubble in the early 2000s, Big Tech can spend exorbitant sums of money and be largely fine. At some point, however, the enormous bank accounts of Microsoft, Google, Amazon, and Meta could begin to thin, especially if the economy worsens. If their balance sheets ever get shaky, shareholders and investors might lose some of their enthusiasm, Raj Joshi, a senior vice president at Moody’s Investor Services who analyzes the technology sector, told me.

Even if generative AI is a bubble, that still doesn’t mean all this investment is for nought. Chatbots seem unlikely to yield $600 billion in annual revenue in the next few years, but that doesn’t mean other sorts of AI won’t transform society by 2040, or some decade after that. The spending frenzy might just be far too concentrated and far too early. Amazon, Google, Meta, and Microsoft burning hundreds of billions of dollars to build data centers means future tech start-ups might be able to use those computing resources at lower costs.

For now, attitude is more important than any product—that tech companies are willing to spend so much is their proof that AI will pay off. And perhaps even more important in Silicon Valley than a messianic belief in AI is a terrible fear of missing out. “In the tech industry, what drives part of this is nobody wants to be left behind. Nobody wants to be seen as lagging,” Joshi said. Amazon, Google, Meta, and Microsoft are defending their empires. Go all in on AI, the thinking goes, or someone else will. Their actions evince “a sense of desperation,” Cahn writes. “If you do not move now, you will never get another chance.” Enormous sums of money are likely to continue flowing into AI for the foreseeable future, driven by a mix of unshakeable confidence and all-consuming fear.