Sam Altman Admits AI Token Costs Are a Huge Issue as Companies Blow Through Annual Budgets

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

Across the industry, companies are starting to balk at the price of AI. Uber blew through its entire 2026 AI coding budget by April. Microsoft revoked its developers' Claude Code licenses months after enabling them. A Priceline employee told TechCrunch that a routine Cursor contract renewal came back 4-5x more expensive. Even though per-token prices have fallen, the push for more AI adoption and increasingly autonomous agents have driven token consumption higher and higher. Companies that gorged themselves in early 2025 on all-you-can-eat subscriptions are now scrambling to understand where their money is going, pull back spending, and figure out whether they can salvage some ROI from the wreckage of their budgets. Meanwhile, a market is forming to meet them there. Startups, established vendors, and a new standards body are all racing to give companies the tools and language to track what they spend. "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 TechCrunch at an event in New York City this week. "Our conversations are never about that now. 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 TechCrunch. "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 cries heard round the tech world followed fervent demands from CEOs pushing their teams to use the best models and move fast, costs be damned. New models released in November like 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," says Chris Reed, senior director of IT finance at Priceline, when asked about the pricing issue in using AI. "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." 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 than those who used AI less, but they spent 10x the number of tokens to get there. Nicholas Arcolano, head of research at Jellyfish, told TechCrunch via email that expenditure on AI is exploding in large part due to agentic features, with per-developer consumption rising about 18.6x 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. 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." A market is beginning to form around this problem. There are the 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," Gordan 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," he said. Russell Brandom and Tim Fernholz contributed to this reporting.

OpenAI CEO Sam Altman admits AI token costs are becoming 'a huge issue' -- company seeks improved value as overspending becomes a meme

Are companies not getting more value out of the tokens they spent on AI? OpenAI CEO Sam Altman has said in an interview that companies are now concerned about the growing costs of AI use. Speaking during the Intelligence at Work event, he said this is the first time that OpenAI's clients raised the issue and that the startup is now looking for ways to make its models more efficient. "People are really saying, you know, it's kind of a meme now, but 'My company spent my entire 2026 budget in Q1. Can you make this more efficient?'" Altman said on stage. "We are continuing to push on that more with models. I think we'll have a lot of ways we can help people get more value for less spend. But that went from, at the beginning of this year, an issue that never came up (people were totally happy with the amount they were spending) to, all of a sudden, a huge issue." There have recently been a lot of stories of companies getting massive AI bills as they experiment with "tokenmaxxing." A few company leaders believed that AI use would increase the productivity of their workers, thus increasing revenue. Nvidia CEO Jensen Huang famously said that his engineers should use AI tokens that are worth at least half their annual salary, or else he'd be "deeply alarmed." We also saw another example with OpenClaw creator Peter Steinberger, whose team spent $1.3 million on OpenAI API tokens in a month, totaling 603 billion tokens. However, it seems that this move is starting to backfire on some companies. Amazon employees admitted that they were using AI agents for unnecessary tasks just to stay on the internal AI leaderboard, while Microsoft has reportedly cut back on Claude Code licenses due to increased costs. Even the Uber CEO admitted that there is currently no link yet between going all-out on AI and delivering successful products. Despite that, Altman projects that AI token usage will continue to increase. He said that six-and-a-half years ago, the top token spender at the startup used 100,000 tokens a month -- today, that is the global per capita average token usage, and that OpenAI's token leader uses about 100 billion a month. The OpenAI chief also admitted, to his own embarrassment, that someone else uses even more. So, if token usage were to grow linearly, then he would expect the global per capita token usage to hit 100 billion monthly. However, this is likely under the assumption that token prices will decrease faster compared to the increase in the number of tokens used across the globe. Because, at the moment, some are finding that it's now more expensive to run AI models compared with hiring people. Jevons paradox says that the cheaper a particular resource becomes, the more people will use it, and we're seeing this with AI. But as agentic AI becomes more popular and sophisticated, the number of tokens these systems use has been increasing exponentially, seemingly outpacing the efficiency gains that AI labs have been making on training and inference. Follow Tom's Hardware on Google News, or add us as a preferred source, to get our latest news, analysis, & reviews in your feeds.

Revenge of the AI bubble

Reckoning: Companies discovered that AI can be extraordinary when aimed precisely -- and ruinously expensive when treated as a universal productivity machine. Why it matters: The first phase doubted the technology. The second phase worshipped it. The third phase -- currently gaining steam across Corporate America -- questions whether AI's immense power is worth the price. Zoom in: The case against AI used to come from outsiders -- Luddites, "doomers," short sellers betting on a crash. Its newest skeptics are emerging from inside the boom. * Uber capped employee AI usage after burning through its annual Claude Code budget in four months. A top executive said the spending was getting "harder to justify," with no clear link between token use and more useful consumer features. * Amazon shut down an internal token leaderboard after employees gamed it with throwaway tasks to climb the rankings. An Amazon executive told staff, "Please don't use AI just for the sake of using AI." * GitHub moved Copilot, the AI coding assistant used by millions of developers, to usage-based billing as part of its effort to create a "sustainable" business. The change shocked users who were suddenly confronted with the true cost of heavy AI usage. * Bain surveyed 951 large companies and found AI savings falling well below projections, even as most firms planned to spend more. "The technology worked. The value didn't arrive," the report concluded. The intrigue: Even OpenAI CEO Sam Altman has acknowledged the new concerns, calling the question of whether AI spending will show up in revenue "the most fair criticism" of the moment. Reality check: The companies sounding the alarm are the early adopters. Most of the economy is still at the starting line, while the pioneers are the ones absorbing the cost shocks, wasted tokens and employee backlash. * AI is already creating real value for chipmakers, model labs and some power users. The harder question is whether that value spreads across the companies paying to deploy it. By the numbers: Wall Street got a fresh reminder Friday of how much AI optimism is baked into markets. * The Nasdaq plummeted 4.2%, its worst day in more than a year, while the Philadelphia Semiconductor Index plunged 10.3%, its worst day in more than six years. * One culprit was Broadcom: The chipmaker reported explosive AI growth, but failed to raise its longer-term AI revenue outlook -- disappointing investors looking for signs that demand was still accelerating. The bottom line: AI can make the right worker dramatically more productive, but those gains depend on knowing exactly where and how to apply it. The real bubble may have been the assumption that AI could be sprayed across companies, employees and workflows and reliably pay for itself.

Rising token costs are no laughing matter. OpenAI's Sam Altman isn't smiling...

The soaring cost of tokens is causing concern among AI implementators, admits a serious looking OpenAI CEO Sam Altman, but he doesn't understand why? In an chat hosted by OpenAI's Chief Revenue Officer Denise Dresser, Altman said that the second biggest theme he hears about from customers right now is cost: People are really saying, - it's kind of become a meme now - that, 'My company spent my entire 2026 budget in Q1 to kind of make this more efficient.'..That has become that way from the beginning of this year. [Before that] it was an issue that never came up. [It went from] people were totally happy with the amount they were spending to all of a sudden a huge issue. But we have seen plenty of evidence of token resistance kicking in of late. As we noted earlier in the week, recent times have seen US supermarket giant Walmart reportedly capping staff usage of an AI agent called Code Puppy, Uber reporting that it burned through its annual $3.5 billion AI budget in four months, and even Microsoft pulling Claude Code access for around 100,000 engineers after finding the costs impossible to justify. CEO Sundar Pichai, took time out during his address to the Google I/O developer conference to acknowledge the problem: We've heard that many companies are already blowing through their annual token budget; it's only May! To be fair to Altman, he does recognize that people are using a lot more tokens, but this elicits the comment that: If you all buy a ton of tokens from us, we're very happy. And what he says is couched as a, presumably, amusing anecdote, although he doesn't seem to smile when he tells it: To give people a sense of just how big the magnitude of the challenge in front of us is, six-and-a-half years ago, which was the earliest I could find data for, the token leader at OpenAI used about 100,000 tokens a month. It was probably very likely the token leader in the world today, six-and-a-half years later, that is about the per capita average in the world. [Today] the token leader at OpenAI uses about 100 billion tokens a month. To my embarrassment, that is not the token leader in the world - we found someone that uses even more! Hah hah, er, hah! (Still no smile...) More to come The OpenAI CEO plows on with a warning: It's still a one million times increase in six-and-a-half years. If the same trend happens again, where we project forward six-and-a-half years, and that becomes the global average of tokens per capita, or AI use per capita...You can start to wrap your head around the infrastructure challenge in front of us, and what it will take to be ready for that moment. The trouble is that the world is not set up for everyone who wants to use AI en masse to be able to do so. That's a challenge for the next phase of the revolution, suggests Altman: We have to build infrastructure to let people, companies, scientists, everyone use AI at the scale it deserved from that. whatever they want, whatever they need, whatever they like to do with it, and have this just be something that we think of as a resource that is seeped into everything. This last point is familiar Altman territory. Earlier this year at an appearance at BlackRock in Washington, he told his audience: We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter. As an analogy it begs so many questions, not least what happens if you can't afford to fill the meter? Sam cuts you off from intelligence? From knowledge? From information? What are the societal implications of that idea taken to extremis - or indeed the political ones? How soon before there's a two-tier world of knowledge haves and knowledge have-nots based on whether you can pay the bill? But the controversy his remarks sparked back then either passed him by or he just doesn't accept there's any issue here as he returns to the topic undeterred, with a remark that some might think suggests that Altman's connection with people's day-to-day real lives has been broken: You probably don't think about the price of electricity or water that much. You pay a bill for them, but you just know they're there. Try telling that to someone who just got their latest electricity bill, someone who hasn't made billions out of running a loss-making AI start-up that shows no signs of turning a penny profit for years to come, despite a ludicrous theoretical market valuation. The brown envelope from the water company may not fill anyone in the Altman household with dread, but for those out there in the real world it's a different matter. But Altman's plans for OpenAI will carry on regardless it seems. This is a guy who happily says: If you all buy a ton of tokens from us, we're very happy. Now he predicts that in the next phase: We really have to become this hugely expensive piece of infrastructure that the world will use for all this stuff. Will the cost of that will be taken on board by AI providers and not passed on to customers? Sam? Sam!?! Needless to say, he's still not smiling. My take I'm not entirely convinced that Altman's comments are necessarily a terribly good pre-IPO conversation to engage with, but that's his problem. Then again, I really also just don't think he gets what the token cost problem really means for so many. Maybe you could argue that ultimately that will be his problem as well, but as with so much of what he says, I fall back on my personal reading that he seems to views so much of the world as an academic exercise or a theoretical landscape, detached from signs of human empathy. To be fair, he does say: We want you all to be able to use AI and never worry about it being great and affordable, and there being a lot of it, and we got to go do that. OK, but will that happen? And how? Will the likes of OpenAI rise to the challenge and live up to the claims of CRO Dresser that: Our ambition is to bring intelligence to every human being in the world for good. We are committed to this. But at what price? Something needs to change here. The build-out costs of the scale that Altman alludes to aren't sustainable to produce profitability to the hyperscalers or indeed to end users, who aren't just balking at the size of the token bills they're being presented with, but with the lack of associated value that they perceive they're getting from paying out such amounts. It's that value association recognition that Salesforce is trying to address with its proprietary Agentic Work Unit (AWU) idea. This is a metric devised by Salesforce CMO Patrick Stokes which is pitched as measuring the actual work performed by AI agents across the Salesforce platform. While conventional token-centered AI metrics focus on the raw data processed by a model, the claim for AWUs is that they will quantify the end-to-end tasks completed, and as such will provide a clearer picture of AI's actual business ROI. Co-incidentally as Altman was making his latest remarks, Salesforce co-founder Parker Harris addressed this issue at the Evercore Global TMT Conference in San Francisco where he said of the tokens metric: This is how much we've paid model providers and helps with their S1[regulatory filing]. It's kind of like leaderboards for vibe coding - are you token maxing and who's using the most tokens to write code? People say, 'Well, that's a terrible metric because people are just going to try to use the most tokens' and it's not really a right metric for output. AWU is definitely the right metric. Convincing the rest of the industry to buy into that Salesforce metric isn't a stated objective to date - there's no push to make it a de facto standard, let alone a de jure one - but how receptive people on the buy side are to a value-centered metric that they can believe in will be (a) interesting to observe and (b) illustrative to other vendors as an approach to emulate. In the meantime, you just excuse me, I need to go and argue with my electricity provider about my latest quarterly bill...and that's nothing to smile about I can assure you!