Across the technology sector, companies are beginning to push back against the rising expenses tied to artificial intelligence. Uber exhausted its entire 2026 budget for AI coding by April. Microsoft revoked Claude Code access for its developers just months after enabling it. Although the cost per token has decreased, the drive for broader AI adoption and more independent agents has caused token consumption to surge. Businesses that indulged in unlimited subscription plans in early 2025 are now urgently trying to track their spending, cut costs, and determine if they can recover any return on investment from their shattered budgets. Meanwhile, a new market is emerging to address this need. Startups, established vendors, and a fresh standards organization are all competing to provide companies with the tools and terminology to monitor their expenditures. "Six months ago, my conversations with customers were all about 'What can it do? Is it good enough?'" one industry insider noted. "Now, those discussions never happen. Instead, we talk about 'Hey, we're spending too much. What visibility do you have? What auditability do you offer? What token controls are in place? How efficient are your models?'"

EMBED_PLACEHOLDER_0

It is against this backdrop that the Linux Foundation has announced plans for the Tokenomics Foundation, a new standards body designed to bring the same cost discipline to AI tokens that FinOps brought to cloud spending. "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,'" said J.R. Storment, executive director of the FinOps Foundation. "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 outcry across the tech world followed intense pressure from CEOs urging their teams to use the best models and move quickly, regardless of costs. New models released in November—such as 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 dramatically increased consumption. This is how one company reportedly ended up 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 that the company has started 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."

EMBED_PLACEHOLDER_1

A March survey by Faros found that among 20,000 developers, output was increasing, but so were bugs and rewrites. Jellyfish, an engineering management platform, similarly discovered that engineers who used the most tokens were about twice as productive as those who used AI less, but they consumed ten times the number of tokens to achieve that. Token consumption per developer grew 3.6 times in nine months. Overall, these statistics make the productivity case less clear 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," said Kathryn Arcolano, a partner at Madrona Ventures. At least part of that measurement challenge stems from the sheer scale of AI usage 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."

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 toward 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 added 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 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."

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."

That said, 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," she said. Russell Brandom and Tim Fernholz contributed to this reporting.