For those interested in the spectacle of rival billionaires fighting over money and power, I present to you the Oakland courtroom where Elon Musk, the world’s richest man, is claiming that Sam Altman and Greg Brockman, two of his fellow co-founders of OpenAI, cheated him by converting the organization, which began as a not-for-profit, into a capitalist enterprise after he left it in 2018. (OpenAI claimed that the lawsuit is an “unlawful campaign of harassment” and countersued Musk in early April.) Musk is asking the court to unwind OpenAI’s conversion, remove Altman and Brock, and award him up to a hundred and fifty billion dollars (no typo) in damages. Among the things that we have learned from the evidence and testimony is that, in a 2017 e-mail addressed to his OpenAI colleagues, Musk wondered whether he was “just being a fool who is essentially providing free funding for you to create a startup.” But we also learned that earlier, in 2015, Musk had suggested setting up a for-profit entity alongside the not-for-profit foundation.
It remains to be seen whether Musk, who is worth six hundred and fifty-one billion dollars, according to the Bloomberg Billionaires Index, will be able to persuade the jury to rule in his favor and award him another huge fortune. In any case, generative A.I. is now fiercely capitalist and competitive, with OpenAI, Anthropic, Google, Meta, Musk’s own xAI, and other firms racing to establish a grip on what they all agree is the defining industry of the twenty-first century. As the civil trial was beginning in Oakland, the Wall Street Journal reported that OpenAI, which has been the industry leader since launching ChatGPT, in November, 2022, had failed to hit ambitious revenue targets, which it needs to meet in order to support its heavy spending on data centers and its hopes to issue shares to the public. This news prompted a brief sell-off in A.I.-related stocks. But, later in the week, the Nasdaq and the S. & P. 500 both rallied to new highs after four established tech giants—Alphabet, Amazon, Meta, and Microsoft—reported healthy growth in revenues and profits, some of them connected to A.I., during the first quarter of 2026. The big four have estimated that they will spend more than seven hundred billion dollars on A.I. investments this year.
That’s not a typo, either. Each of the tech giants is developing its own generative-A.I. models. But much of their enormous outlay is being devoted to their cloud-computing divisions, which they hope will provide the computing backbone for an A.I.-powered economy. To put these financial commitments into perspective, the total business investment in the United Kingdom, the world’s fifth-largest economy, was about four hundred and thirty billion dollars last year. The four companies, which are often referred to as hyperscalers, aren’t the only firms betting heavily on A.I. infrastructure. Other players include Oracle and so-called neo-cloud companies such as CoreWeave, Nscale, and Lambda.
Meanwhile, firms of all shapes and sizes are busy deploying A.I. tools. Although OpenAI may be struggling to hit its revenue targets, Anthropic has seen spectacular growth since last year, when it released Claude Code, its agentic coding agent, and subsequently released a web-based version. Last month, the firm said that it had more than three hundred thousand business customers, and its “run rate revenue”—annualized revenues, extrapolating from the most recent month—had risen from about a billion dollars in January, 2025, to thirty billion. Two years ago, the firm had just a dozen customers who were spending more than a million dollars each with it on an annual basis. By this February, there were more than five hundred of them.
This rapid spread of A.I. tools also raises the question of when they will lead to higher profits for A.I. users, an issue I wrote about last year. A much cited survey of A.I. from McKinsey, which was released in November, found that ninety-four per cent of respondents disclosed not yet seeing significant value from their investments. This may be because many A.I. deployments are still in their pilot phase, and it takes time for the financial benefits to show through. But in a separate survey of corporate chief information officers by the firm Dataiku, more than two-thirds of the executives said that A.I. budgets are likely to be frozen or cut if financial targets aren’t reached by the middle of this year. The profit paradox, as it is known, has sparked a lot of debate. A.I. optimists note that some large corporations do report that A.I. is already making them more efficient, and that some Big Tech companies which are now hugely profitable lost money for many years as they were building up their businesses. (Amazon is the classic example.) Pessimists point to previous tech-driven booms that ended in busts, such as the railway boom in the middle of the nineteenth century and the dot-com boom in the late nineteen-nineties.
Even the savvier optimists acknowledge that there are legitimate questions about the finances of some A.I. companies. OpenAI and Anthropic are both burning through money, which means they are dependent on successive capital injections from outsiders—some of which have attracted attention because of their circular nature. The hyperscalers are different. They generate hefty profits from their existing businesses—in the latest quarter, nearly a hundred and fifty billion dollars in net income combined—some of which they can use to finance their huge A.I. investments. But they are also borrowing a lot of money, as are Oracle and the neo-clouds, which have less stout balance sheets. According to Bloomberg, the A.I. debt binge has now topped three hundred billion dollars. As long as new flows of equity capital and credit are readily available, the A.I. build-out can continue, benefitting hardware companies like Nvidia and cloud providers like Amazon and Google. Ultimately, however, the actual A.I. applications will need to generate sufficient revenue and profits to justify all the spending on infrastructure and model building.
Keeping track of all the developments in the A.I. industry can be hard, but it’s helpful to bear in mind two basic questions: What’s the potential size of the market for A.I. products? And who will profit from it? In a recent lecture at Stanford University, where he is teaching a course on the economics of A.I., Apoorv Agrawal, a venture capitalist at Altimeter Capital, said that Alphabet, through its Chrome browser, Android operating system, and YouTube video-sharing service, reaches about four billion people worldwide and generates, mainly through advertising, revenues of about a hundred dollars per user per year. Meta, through Facebook, Instagram, Messenger, and WhatsApp, reaches about 3.5 billion people, and generates about seventy dollars per user per year, Agrawal estimates. OpenAI recently said that it has more than nine hundred million weekly active users, but, according to Agrawal’s calculations, ChatGPT only yields about ten dollars per user per year. In an e-mail, Agrawal told me that he thinks of himself as very much an optimist on A.I.’s future. Referring to users of the technology, he told his students, “The question is how do we get the one billion up to four billion? I’m not sure knowledge work is the answer.” He continued, “The second question is: How do we get the ten dollars per user per year up from ten to one hundred? And I’m not sure subscription is the answer.”
The vast majority of ChatGPT users employ the free version. Agrawal suggested that advertising could eventually provide OpenAI and its rivals with a big revenue stream and help “unlock” the A.I. economic model. Since A.I. firms gather a lot of information about their users, they should be able to target ads effectively. Alphabet has incorporated A.I. into its Google search engine. When it released its latest quarterly results last week, its C.E.O., Sundar Pichai, said that revenues in this part of the business rose nineteen per cent and search queries hit an all-time high, “with A.I. experiences driving usage.”
The other big question is how competitive the A.I. industry will be in the long run. Size doesn’t guarantee riches. The retail sector is a huge industry, but it’s fiercely competitive and has small profit margins. Ditto food and travel. Traditionally, the tech sector has been controlled by a few very large firms that generate big margins. Increasing returns to scale and network effects could well push A.I. in the same direction. If Google ended up dominating consumer A.I. and Anthropic ended up dominating business A.I., the industry would resemble other digital markets that have tipped into monopolies or oligopolies, like search and social media. But what, then, would be left for other players, like OpenAI and Meta, and all the infrastructure they are building?
Right now, there’s no shortage of competition. After Anthropic’s success in attracting business users, OpenAI is beefing up its Codex coding agent and other work tools. Last month, Denise Dresser, the firm’s chief revenue officer, said that it had nine million paying business users, and payments from businesses accounted for more than forty per cent of its revenues. Meta, xAI and others are also investing heavily in their models. There are also countless A.I. startups looking to establish a niche, and cheaper open-source models, like OpenAI’s own GPT-OSS or the DeepSeek-R1, future iterations of which could gain traction in some business realms and territories.
In short, the long-term outcome is far from certain. And at a moment when the A.I.-driven stock market is breaking through the stratosphere, it’s perhaps worth looking at some cautionary tales from the past. In an article from 2021, the economist and venture capitalist Bill Janeway used the term “productive bubbles” to describe periods like the railway and dot-com boom-busts, which bequeathed to future generations invaluable productive assets, such as rail networks, power grids, and fibre-optic networks. (Unproductive bubbles include tulip mania in seventeenth-century Amsterdam, and the meme-stock phenomenon of 2021.) Last year, in another piece, Janeway argued that generative A.I. could be the latest productive bubble, and when I corresponded with him last week, I found that recent developments hadn’t changed his thinking much. He pointed out that following the Great Panic of 1873, more than a hundred and twenty railroads defaulted on their bonds. After the dot-com bust of 2000-01, countless internet companies went under. So did WorldCom and Global Crossing, two big providers of telecom infrastructure, Janeway reminded me. “Why will it be different this time?” he wrote. “I get that Microsoft and Alphabet have enormous flows of monopoly rents from their established business, but OpenAI and Anthropic as well as CoreWeave et al?”
In the A.I. gold rush, the participants don’t have time to ponder such questions. They are too busy staking their claims before others can, and some of them aren’t above trying to handicap competitors. If Musk did win his case in Oakland, the future of OpenAI would be thrown into doubt. But the over-all future of A.I., and the financial boom it has engendered, hinges on larger economic forces. ♦