The token bill comes due: Inside the industry scramble to manage AI’s runaway costs

The era of unlimited AI spending is ending. Across the technology industry, companies that eagerly embraced artificial intelligence tools in early 2025 are now facing a harsh reckoning as token consumption outpaces budgets at an alarming rate. Uber burned through its entire 2026 AI coding budget by April. Microsoft revoked Claude Code licenses months after enabling them. A Priceline employee told Bitcoin World that a routine Cursor contract renewal came back four to five times more expensive. Even as per-token prices have fallen, the push for broader AI adoption and increasingly autonomous agents has driven token usage to unsustainable heights.

From experimentation to cost crisis

Companies that gorged on all-you-can-eat subscriptions in early 2025 are now scrambling to understand where their money is going, pull back spending, and figure out whether they can salvage any return on investment. The shift in tone is stark. “Six months ago, I would have a conversation with a customer and it would be all about ‘What can it do? Is it good enough?'” Alexander Embricos, OpenAI’s head of enterprise, told Bitcoin World at an event in New York City this week. “Now the conversations are about, ‘Hey, we’re spending so much. What visibility do you have? What auditability do you have? What token controls do you have? What is the efficiency of your models?'”

It’s against this backdrop that the Linux Foundation this week unveiled plans for the Tokenomics Foundation, a new standards body that aims to instill the same cost discipline around AI tokens that FinOps did for cloud spend. “In April and May, I started hearing from companies: ‘Oh my god, we are 3x over our entire 2026 token budget and it’s only April,'” J.R. Storment, executive director of the FinOps Foundation, a project under the Linux Foundation, told Bitcoin World. “We started hearing existential crises, and the whole conversation shifted from tokenmaxxing and ‘go fast’ to ‘we need guardrails, how do we control this?'”

The agentic consumption explosion

The cost crisis followed fervent demands from CEOs pushing their teams to use the best models and move fast, costs be damned. New models released in November — including Anthropic’s Claude Opus 4.5, OpenAI’s GPT-5.1, and Google’s Gemini 3 Pro — brought significant improvements to agentic tools, which have multiplied consumption. It’s how one company reportedly found itself with a $500 million Claude bill after forgetting to set usage limits for employees.

“It’s like the crack-cocaine epidemic,” said Chris Reed, senior director of IT finance at Priceline, noting the company had begun placing token limits on certain groups. “They let you try it to get you hooked on it, and now you’re kind of beholden to it.” Vitaly Gordon, CEO of engineering operations platform Faros AI, said he recently spoke to a CTO who told him: “One of my engineers spent $40,000 on tokens last month, and I genuinely don’t know whether I should stop him or should I go and tell everyone else to be like him.”

Measuring the productivity paradox

A March survey by Faros found that among 20,000 developers, output was rising, but so were bugs and rewrites. Jellyfish, an engineering management platform, similarly found engineers who used the most tokens were about twice as productive as those who used AI less, but they spent 10 times the number of tokens to get there. Nicholas Arcolano, head of research at Jellyfish, told Bitcoin World via email that expenditure on AI is exploding in large part due to agentic features, with per-developer consumption rising about 18.6 times in nine months.

All in all, these stats make the productivity case murkier than the spending suggests. “Whether extreme spend pays off comes down to the ultimate business value of shipped code (e.g. revenue), which most companies still can’t measure,” Arcolano said.

A trillion-row data problem

At least some of that measurement issue is the sheer scale at which AI is being used today. “Tracking cloud costs is a hundreds-of-millions-of-rows-a-month data problem,” Storment said. “Tracking token costs is a trillions-of-rows-a-month data problem. You can’t just stick that into whatever spreadsheet or even basic tool. You’ve got to fundamentally rethink your tooling, your specs and your accounting systems to do that.”

At Priceline, Reed is already seeing discrepancies. He noted issues between a vendor’s reported usage and Priceline’s internal data. “I started my career in telecom expense management, and I’m seeing all the same parallels, from telecom to cloud to AI,” he said. “Anytime you introduce something new, it’s ripe for billing errors and audit and optimization opportunities.”

The emerging market for token control

A market is beginning to form around this problem. There are pure-play companies like Pay-i, which tracks, measures and optimizes the costs and performance of GenAI investments. Paid, meanwhile, lets developers track costs, measure usage and bill users based on actual value rather than subscription fees. Then there are companies like Jellyfish, Waydev and Faros AI, which all provide AI agent monitoring to prove the ROI of developer tools. Storment says most of the 180 vendors within the FinOps Foundation are leaning towards this space.

Companies with existing distribution are also adding new features to capitalize on this new market. Ramp has recently moved into AI spend management; Datadog and New Relic have tacked on services like cloud cost management, token-level observability, and GPU monitoring. At the FinOps X conference next week, AWS is expected to introduce new financial management features geared toward enterprise AI spending. Tiffany Luck, a partner at NEA, thinks token efficiency and observability will likely be added in at the “harness or app layer.” She pointed to Factory, a startup that makes AI agents for enterprises, which this week launched a model router that automatically picks the right model for every task.

Gordon expects frontier labs and other model providers to adopt OpenRouter-style optimization to drive queries to the cheapest models — a trend already showing up on enterprise Claude bills. “The financial report for how much you spend on Anthropic, even if you call the Opus model, some of the spend will be on Sonnet or Haiku, because they are smart enough to do it,” Gordon said. “I think this will become more and more of a thing.”

Building a common language for tokenomics

But all these tools are being built without a common language or shared definitions for how much a token costs, what it produces, and how to compare spend across vendors. That’s where the Tokenomics Foundation hopes to prove useful. The Foundation is building a canonical definition and framework for “tokenomics”; open standards, specifications and metrics for AI token usage and billing; as well as new metrics for AI economics, like cost-per-intelligence or tokens-per-watt. It also plans to define metrics across token factory effectiveness and consumption efficiency. The group is planning a formal launch in July, and is about to announce more members at the FinOps X conference next week.

“Token economics is fundamentally more abstract and opaque than anything we’ve managed at this scale before,” Nishant Gupta, chief availability officer at Salesforce, said in a statement. “It requires a different operational muscle than the one the industry built for cloud.”

Conclusion

Goldman Sachs projects global token usage to multiply by 24 times by 2030. The companies already over budget need solutions now, and the foundation’s first deliverable is still months away. “Maybe we created a steam engine, but we still haven’t figured out the assembly line,” said Gordon. According to Arcolano, the smart move is broad, moderate adoption. “The best ROI comes from moving the broad middle from low to moderate usage, not pushing heavy users higher,” he said. The industry is now racing to build the guardrails that should have been in place from the start.

FAQs

Q1: Why are AI token costs rising so fast?
Even though per-token prices have fallen, the adoption of autonomous AI agents and the push to use AI across more business functions have driven token consumption far higher than anticipated. Newer, more capable models also consume more tokens per task.

Q2: What is the Tokenomics Foundation?
The Tokenomics Foundation is a new standards body under the Linux Foundation. It aims to create open standards, metrics, and definitions for AI token usage and billing, similar to how FinOps standardized cloud cost management.

Q3: How can companies control AI spending now?
Companies are using a mix of approaches: setting token usage limits per employee or team, adopting model routers that automatically choose the cheapest effective model, and deploying third-party tools for token-level observability and cost tracking. The key is moving from unlimited experimentation to measured, moderate adoption.