FOMO is strong around AI, with companies adopting the technology willy-nilly. Many have encouraged employees to tokenmaxx to their hearts’ content. Now they’re starting to realize that freedom comes with a price.

Just one in four companies say they have a comprehensive view of what artificial intelligence is costing them, according to an as-yet-unreleased KPMG survey reported by The Wall Street Journal. Only about half have even some visibility into the cost of their AI use. One in five have no visibility, or only see the damage once the bill arrives. “It’s a new resource that needs to be managed that didn’t exist quite that way, and we’re seeing exponential growth,” Steve Chase, KPMG’s global head of AI, told the Journal.

Part of the problem is pinning down what, exactly, AI costs. The basic unit of AI use—the token—is an unusual thing to budget around. Each token is a fragment of text, code, or data processed by a model when it reads a prompt or produces an answer, but it doesn’t map neatly onto a single word. Some tokens can be cached by AI models, meaning they are not charged again, while others must be processed as new. The result is uncertainty that may not become clear until the bill lands at the end of the month.

Multiply that across individual employees at a company, and it is little wonder that chief financial officers are being left with eye-watering bills. KPMG is working with companies that have blown through annual token and cloud-computing budgets in a matter of months, Chase told the Journal, while one client has seen token usage rise sixfold. Axios reported last month that one AI consultant’s client spent half a billion dollars in a single month after failing to put usage limits on employees’ Claude licenses.

“People are getting these massive bills,” says Sam Ransbotham, professor of analytics at Boston College’s Carroll School of Management. “They turn on usage, and suddenly the people paying the bill are not the people using the product, and whenever you have that sort of mismatch, there’s going to be problems.”

The challenge is made worse by a shift away from the “all you can eat buffet” phase of AI pricing, as vendors look to recover the enormous cost of providing access to powerful models. “Software-as-a-service for years has worked off per seat, per licensing, because companies need budget,” says Ransbotham. “They need to predict.” But AI is priced differently.

The idea that AI is a game changer for business, encouraged by bosses, is also creating perverse incentives. Amazon recently shut down an internal AI usage leaderboard after employees tried to boost their scores with needless activity. Some workers reportedly assigned AI agents to pointless tasks in an attempt to climb the rankings.

“The fact that you have the capacity doesn’t say that you are using it beneficially,” says Baruch Lev, Philip Bardes professor of accounting and finance at New York University Stern School of Business. Lev argues that companies need two measures: how much AI is being used, and what benefit that use produces. Without both, return on investment is mostly guesswork. “There is no resource, however beneficial, that you should use the maximum,” he says.

Part of the problem is that companies still haven’t figured out how they want to account for their investments in AI. Lev says some may want to treat AI spending as an expense, akin to rent or salary. Others may see it as an investment in capacity, like software infrastructure that can be scaled across the business. Whichever answer a company’s accountants land on matters—and will likely affect the willingness of boards to keep funding AI rollouts.

For companies hit with stinging bills, Lev says there is likely room to negotiate payment plans or cheaper alternatives. Some may need to pay from cash flow, borrow, or raise equity to cover the costs. But he says vendors have an incentive to be flexible. “The main vendors are willing to help,” says Lev. “They also want to show revenues on their balance sheet, so I think they’ll give them very favorable terms, at least at the beginning.” Without that flexibility, he adds, “people will not be able to pay.”

To avoid future headaches, Lev says companies need to do a better job of measuring the return on their AI investments.

Even without firm evidence, Boston College’s Ransbotham says it is striking that “none of them say, ‘All right, turn it off.’” Instead, he says, the reaction is more measured: “What they say is, ‘Let’s not get the Cadillac version of everything.’”

That more judicious approach—using cheaper models for simpler tasks and reserving the most expensive systems for work where their capabilities actually matter—is something companies, including Coinbase, are starting to consider, according to a recent post by CEO Brian Armstrong on X.

Companies may route simple queries to cheaper models, keep multiple suppliers alive, and use the most advanced models only when the work demands them. Asking the most advanced model on the market for tomorrow’s weather is a “zillion dollar hammer for this tiny task,” says Ransbotham.

Lev, for his part, pushes back against skeptics who see the big bills as evidence of an AI hype bubble coming due. He calls AI “a real revolution,” not another dotcom delusion. But even revolutions need meters.