You’re about to feel the AI money squeeze

Earlier this month, millions of OpenClaw users woke up to a sweeping mandate: The viral AI agent tool, which this year took the worldwide tech industry by storm, had been severely restricted by Anthropic.

Anthropic, like other leading AI labs, was under immense pressure to lessen the strain on its systems and start turning a profit. So if the users wanted its Claude AI to power their popular agents, they’d have to start paying handsomely for the privilege.

“Our subscriptions weren’t built for the usage patterns of these third-party tools,” wrote Boris Cherny, head of Claude Code, on X. “We want to be intentional in managing our growth to continue to serve our customers sustainably long-term. This change is a step toward that.”

The announcement was a sign of the times. Investors have poured hundreds of billions of dollars into companies like OpenAI and Anthropic to help them scale and build out their compute. Now, they’re expecting returns. After years of offering cheap or totally free access to advanced AI systems, the bill is starting to come due — and downstream, users are beginning to feel the pinch.

Over the past few years, most top AI labs have introduced new subscription tiers to court power users. OpenAI and Anthropic shifted their pricing plans for enterprise. OpenAI introduced in-platform advertisements. Anthropic, of course, restricted third-party tools.

In some ways, this is a tale as old as time, and particularly, a clear echo of the tech boom of the ’10s. Venture capitalists helped startups subsidize fast growth in all kinds of areas: ride-hailing apps, e-commerce, takeout and grocery delivery. Once companies cemented their power, they raised prices, added new revenue streams, and delivered a return to investors. Or they didn’t — and they crashed and burned.

But AI companies have gone through more investor money at a faster pace than any other sector in recent history. AI companies have broken ground on data centers around the world, dedicating billions of dollars with promises of better models, lower costs, and AI for everyone. Even stemming the flow of losses will be difficult — let alone making the kind of money investors are hoping for. “When you sink trillions of dollars into data centers, you’re going to expect a return,” said Will Sommer, a senior director analyst at Gartner, who specializes in economic forecasting and quantitative modeling.

“When you sink trillions of dollars into data centers, you’re going to expect a return.”

“Is the era of basically free or close-to-free AI kind of coming to an end here?” said Mark Riedl, a professor in the Georgia Tech School of Interactive Computing. “It’s too soon to say for certain, but there are some signs.”

Gartner’s Sommer studies long-term economic market trends related to generative AI, including calculating just how much money is at stake. Between 2024 and 2029, he said, Gartner estimates that capital investment in AI data centers will reach about $6.3 trillion — a “massive amount of money.”

To avoid a write-down of these assets, major AI model providers would ideally generate a return on invested capital (ROIC) of about 25 percent, Sommer said. (That’s about what Amazon, Microsoft, and Google tend to earn on their overall capital investments.) On the other hand, if the returns fall below 12 percent, institutional capital loses interest — there’s better money elsewhere, Sommer said. Below 7 percent, you’re in write-down territory, which is “an unmitigated disaster for all of the investors in this technology,” Sommer said.

To reach that bare minimum of 7 percent, Gartner forecasts that large AI companies would need to earn cumulatively close to $7 trillion in AI-driven revenue through 2029, which is close to $2 trillion per year by the end of the period. In order to achieve “historic returns,” the providers would need to earn nearly $8.2 trillion in the same period.

OpenAI has already made $600 billion in spending commitments through 2030, the company said in February, which Sommer says is already a “massive step down” from the $1.4 trillion it had planned before. Based on OpenAI’s revenue forecasts and potential compound annual growth, Sommer said that even in the best-case scenario, he predicts that the lab would only hit a fraction of the overall spend required to hit that 7 percent ROIC.

How do model providers like OpenAI make this money? By selling access to what are known as tokens. A token is essentially a unit of data input that an AI model can understand and process — it could be text, images, audio, or something else. One token is generally worth about four characters in the English language — the word “bathroom,” for instance, would likely be processed as two tokens. One paragraph in English is generally about 100 tokens, and a 1,500-word essay may be about 2,050 tokens, per an OpenAI estimate.

To hit investors’ revenue expectations, providers would need to process a “mind-bending” number of tokens, Sommer said.

By most measures, companies’ numbers are already pretty big. Google announced it was processing 1.3 quadrillion tokens in October, for instance. If you add all the providers’ estimates up, Sommer said, you get 100 to 200 quadrillion tokens a year. But to achieve the the $2 trillion in annual spend Gartner calculated, providers would need to be generating, by conservative estimates, a cumulative 10 sextillion tokens per year. (To make that slightly less abstract, a quadrillion has 15 zeros, and a sextillion has 21.) Even assuming a very generous profit margin of 10 percent per token, that would mean that token consumption between now and 2030 would need to grow by 50,000–100,000x.

To hit investors’ revenue expectations, providers would need to process a “mind-bending” number of tokens

Right now, constantly seeking more data centers and strapped for compute, companies aren’t capable of processing this many tokens. Even if they could, they’d face a problem: they’re likely taking a loss on them. Sommer estimates that if you only account for the direct cost of infrastructure and electricity, “every company is making very reasonable margins on every token.” But that margin is probably tighter or nonexistent with newer, more token-hungry models. And it’s eaten up completely by indirect operation costs, like building out more compute and the “ungodly” expense of constantly training the next big model.

“As soon as you then add all of the infrastructure that needs to be built for the next generation of model, and you look at how these models are going to scale, it becomes increasingly untenable,” Sommer said.

Sommer predicts that many companies “won’t be able to sustain their burn rate,” and says market consolidation is virtually inevitable — in his eyes, no more than two large language model providers in any regional market will survive. And the era where nearly every service has a fairly generous unpaid tier probably isn’t going to last.

“For the [labs] that have a lot of users that were free, I think the question was never really if you’d monetize the free tier but it was when, and how badly do you do it,” Jay Madheswaran, cofounder of legal AI startup Eve, which is a client of both OpenAI and Anthropic, told The Verge.

Even if you do find a way to square the math, building customer loyalty can be just as complicated. Top labs are constantly leapfrogging each other on model debuts, feature releases, strategy shifts, hiring announcements, and more. It can be tough to stay on top long enough to corner any part of the market — engineers and developers are famous for switching which model they’re using on any given day, and it’s easy to do so.

So labs are increasingly emphasizing the importance of locking users into their platform and tools. Anthropic, which primarily builds for enterprise clients, has been going all in on its coding efforts, and OpenAI has recently pledged to mirror Anthropic’s focus on coding and enterprise, ahead of both companies