An eyebrow-raising trend has emerged this year: tech leaders rating their employees’ productivity based on the number of AI tokens they use.
The trend, ribbingly dubbed “tokenmaxxing,” has sparked discourse for symbolizing the Silicon Valley’s unbridled infatuation with using AI as much as possible — and, quite literally, at all costs.
But what’s so far been a free or at least low-cost ride could be coming to a screeching halt. Setbacks plaguing the construction of AI data centers have brought the industry’s biggest chokepoint to the forefront: access to the precious computing power that makes frontier models tick.
As costs continue to ramp up, enterprise consumers could soon be left holding the bag, with companies like OpenAI and Anthropic looking to ramp up prices to stem at least some of the bleeding. It’s a notable shift after years of complimentary access to cutting-edge AI, a practice that has long belied the tech’s true costs.
“Is the era of basically free or close-to-free AI kind of coming to an end here?” Georgia Tech professor Mark Riedl asked The Verge. “It’s too soon to say for certain, but there are some signs.”
Most recently, Anthropic cut off millions of users from AI agent tool OpenClaw after it forced its systems into overdrive.
“We’ve been working hard to meet the increase in demand for Claude, and our subscriptions weren’t built for the usage patterns of these third-party tools,” Anthropic’s head of Cluade Code, Boris Cherny, tweeted earlier this month. “Capacity is a resource we manage thoughtfully and we are prioritizing our customers using our products and API.”
The company transitioned to a pay-as-you-go billing system to use its application programming interface (API), which charges users per token instead of more open-ended usage limits.
To generate enough money and cover the trillions of dollars being poured into AI data centers, AI economics expert and Gartner senior director analyst Will Sommer told The Verge that AI companies would need to get close to $2 trillion per year in revenue by 2029, in “historic returns” that would dwarf current figures.
Based on current economics, Gartner calculated that with a ten percent profit margin per token, the industry’s token consumption would need to grow anywhere from 50,000 to 100,000 times its current rate by 2030.
Scaling up operations that fast could prove extremely difficult. For now, companies are still taking a massive hit on making more tokens available in large part due to the soaring costs of extremely resource-intensive data centers. Worse yet, as AI models become more complex, they’re expected to require even more compute, a trend exacerbated by the recent popularization of AI agents.
For now, companies continue to fight over market share, with Anthropic most recently surging past a trillion-dollar valuation, overtaking OpenAI. Yet, aggressive price hikes or implementing ads could risk scaring away customers, tamping down further growth.
“On one hand, they want to see more tokens being generated but they have to either suck up the costs, which they can sort of do as long as venture capital is flowing, or pass the costs back on to [customers],” Riedl told The Verge. “Maybe the economics are a little upside down right now.”
In short, AI companies find themselves caught between a rock and a hard place: either continue doubling down on bringing out the latest and greatest in AI at the risk of soaring token costs — or risk falling behind the competition by dumbing things down to keep costs low.
Companies will need to walk a tightrope while trying to gauge how much of these costs to pass on to customers and how much new capital to raise.
Without a feasible long-term plan to keep the ball rolling, experts warn the business model could soon collapse in on itself — a catastrophic outcome not just for markets, but potentially for the entire economy as well.
More on AI economics: You’ll Snort-Laugh When You Learn How Much AI Actually Added to the US Economy Last Year
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