Companies cap AI spending as token costs triple enterprise budgets despite price drops

Tokenminimizing: firms cap staff AI use as bills bite

A year ago firms ranked staff on leaderboards by how much AI they burned. Now AT&T, Meta, Uber and Walmart are capping it, and Amazon has scrapped the leaderboard entirely. A year ago, the smart move inside a big company was to use as much AI as humanly possible. Some firms even ranked employees on leaderboards by how many tokens they burned, a status game that earned its own name: tokenmaxxing. That era is ending. The same companies are now capping AI use, and the new buzzword is its mirror image: tokenminimizing. The latest is AT&T, which has started limiting some employees' access to GitHub Copilot, according to The Information. Meta is reportedly reining in staff spending on Anthropic and other AI tools, a sharp reversal from the months when workers raced each other to consume the most. The bill came due The trigger is simple: the spending got frightening. The most AI-obsessed firms now spend $7,500 per employee per month, and agentic tools that call a model over and over have tripled enterprise AI bills even as per-token prices collapsed. Uber blew through its entire 2026 AI coding budget by April and now caps employees at $1,500 a month per tool. Walmart has capped use of its in-house AI agent. Amazon scrapped the internal leaderboard that ranked staff by AI usage after people gamed it, sending compute costs up. Even individual engineers were a problem: Microsoft found some spending $500 to $2,000 a month on Claude Code tokens alone. Cue the 'I told you so' Some companies are enjoying the moment. "We never celebrated tokenmaxing," Box chief executive Aaron Levie said. "We never had leaderboards, so we didn't get ahead of our skis on... incentivizing the wrong thing." Not everyone is pulling back. At Databricks, an engineering leader said the AI budget for engineers is still unlimited, "so tokenmaxxing still exists", a sign that firms confident their staff use AI efficiently see less reason to ration it. That is the tension under the trend. Caps control costs, but they can also throttle the productivity gains that justified the spending in the first place. The real winners are the cost-cutters' tools The more lasting shift is what tokenminimizing pushes companies toward. To cut bills without cutting use, firms are swapping expensive frontier models for cheaper or open-source ones on simpler tasks. That hands an opening to the plumbing. Microsoft and Databricks have launched 'gateway' tools to monitor and cap staff AI spending, and Nvidia-backed Factory, valued at $1.5bn, just launched a model router that shunts cheaper tasks to cheaper models. Satya Nadella captured the mood in a weekend essay, arguing AI models should be swappable rather than dominant. "The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see," he wrote. Coming from the boss of a company whose software is under pressure from the very labs it depends on, it is also a tell about where this is heading.

Tokens are getting cheaper, but companies are spending even more on AI as a result, top economist warns | Fortune

The ghost of a 19th century English economist may be haunting yet another part of the AI boom. In 1865, William Stanley Jevons observed that when the Watt steam engine made coal use more efficient -- decreasing the amount required to a task -- coal consumption actually skyrocketed. More than 150 years later, one economist is citing this phenomenon, dubbed Jevons paradox, to explain why the cost of AI will continue to creep up. Despite the price of a single token dropping more than 90% since 2023, spending on large language models has doubled since late last year, according to the Silicon Data Token Expenditure Index. Essentially, token price -- or the cost to process the most basic unit of AI -- has gone down, but companies are spending more than ever on AI. Apollo chief economist Torsten Slok said it's yet another example of "Jevons paradox in action." "As tokens get cheaper, companies don't spend less but instead run more AI agents, automate more workflows and generate more code, pushing aggregate expenditure higher even as the unit cost of intelligence collapses," Slok wrote in a recent blog post. The cost of tokens has become a major concern for companies racing to leverage AI. The trend of "tokenmaxxing," in which employees blitz to increase their AI use, has emerged as companies like Meta and Amazon incentivize the technology's use. However, the deployment of AI just for the sake of it is proving unsustainable. Uber president and chief operating officer Andrew Macdonald recently said the rideshare company burned through its entire AI budget in the first four months of the year amid the company's increasing use of Claude Clode. Bloomberg reported the company has now capped monthly AI spending to $1,500 per employee. Others are reckoning with AI -- which tech leaders promised would boost productivity -- still costing more than human labor: "For my team, the cost of compute is far beyond the costs of the employees," Bryan Catanzaro, vice president of applied deep learning at Nvidia, recently said in an interview with Axios. Jevons paradox and the AI boom With token prices dropping as new AI models become more efficient, the era of tokenmaxxing may be over, but that won't necessarily solve companies' AI budget crises. A group of Bain and Co. analysts confirmed Slok's point in a brief last week, finding that while token costs were halved from December 2024 to December 2025, the tokens consumed grew by 450% in the same period. The analysts attribute this paradox to companies feeling compelled to upgrade their AI models to take advantage of the upgraded technology, rather than stick with their current models and pocket the savings. Moreover, tokens per query have increased as agents become more capable of complex tasks. And to Slok's point, once a team believes AI can complete these more significant tasks, they will ask more of the technology and subsequently use more tokens. "The models get cheaper. The usage gets heavier. The bill stays stubbornly high," the brief said. Token costs are just one area of the AI boom where economists have seen paradoxical economic data emerge. Slok similarly found that despite AI being able to automate 86% of tasks for customer service workers, employment for call center workers in the Philippines has actually nearly doubled over the last decade. A similar trend can be seen in radiologists, another profession deemed endangered because of AI's ability to automate it. The number of radiologists in the U.S. has actually increased by 10% in the last 10 years. "Lower cost per interaction does not mean fewer interactions," Slok wrote last month. "It means more customers served, more channels opened and more markets worth reaching. The technology that was supposed to shrink the industry is fueling its expansion." Bain and Co. sees an AI future where a company's operating expenses come 70% from human headcount, and 30% from tokens. In order to make this shift sustainable, analysts warned companies will have to navigate uncertainty regarding costs of AI by not just creating a budget for AI spend, but determining the true financial returns from employing certain AI tools to assess if the tokens are worth it. "The opex shift from headcount to tokens isn't a budget problem," analysts said. "It's a structural transformation."

Why Companies Are Already Blowing Through Their 2026 AI Budgets in Just Two Months

"The future is going to be good for the AIs regardless; it would be nice if it would be good for humans as well." That's a quote from Ilya Sutskever, an AI researcher who cofounded OpenAI and became its chief scientist. It reminds us that though AI is powerful, it's very controversial -- for a long list of reasons. One of the biggest concerns is that as companies race to deploy AI, they may be displacing real people from real jobs as AI takes on some workplace duties. A new report at CNBC points out that displacing people may actually be happening for a different reason: the increasing cost of using AI systems means company leadership is having to question exactly where it spends the money needed to keep the business running. The news outlet spoke to two "enterprise AI CEOs" who are busy rolling out AI to their workers, and they both highlighted that AI costs are steeply rising, and that AI prices are soaring to levels above what anyone expected. At the center of the issue is the price of AI tokens -- the basic unit of input processed by an artificial intelligence system like a large language model, the core tech at the heart of systems like ChatGPT.

Accenture Had Linked Promotions to AI Use; Now CEOs Are Reversing This Position

in barely four months, enterprise CEOs have shifted from being an AI evangelist to cutting corners on AI spends Back in February, Accenture CEO Julie Sweet made news asking staff to get friendly with AI if they wanted future promotions. Now, four months later, big businesses are reconciling AI costs with their AI evangelism mandate and quietly asking their employees to go slow with AI coding tools as their costs have suddenly spiralled. In an article published by The Economist, the discussion has veered around to the growing problem faced by enterprise around the use of AI via agentic solutions. These use massive processing power and have caused AI bills that have made their use uneconomical. The article quotes an unnamed executive to suggest that the problem is only going to grow. "Big companies, the executive points out, typically use hundreds of software programs. If each of those offer agents (as they probably will), AI costs could easily spiral out of control," the official told the magazine. Does this mean that the era of AI maximalism is over or getting there inexorably? Maybe so because now CEOs are forcing staffers to reduce AI usage in certain tasks. They are moving away from evangelising AI to cutting corners as several corporate honchos forgot to check their AI billing tabs, like we heard from the Uber CTO recently. The irony is not lost when we consider what Accenture's Sweet had said while appearing in a podcast back in February. She began by explaining that Accenture had integrated AI into its workflow over the past few years and the result was that employees had to now use these tools as part of their regular work routines. Learning to work with AI is increasingly tied to career progression within the company, she had noted then claiming that the company had spent billions of dollars training their staff in AI use. Wonder if she still thinks her idea of linking promotions to AI use is workable. We won't know for sure till Accenture shares their compute bill from the expenditure column. Of course, the current reticence to spend on AI might be a temporary phase as it plays out in parallel to another trend whereby companies are claiming that improved AI efficiencies has resulted in them handing out pink slips to redundant workers and replace them with coding agents. In fact, Amazon even set up a leaderboard ranking staffers by the number of AI tokens they used. Of course, the board disappeared as suddenly as it had appeared. Somewhere during the course of these four to five months, the meme-worthy phenomenon called token-maxing also surfaced. Stories of token utilisation without a clearly defined outcome or one could say an overkill of AI usage started emerging. We heard of an employee who spent $150,000 a month on AI tokens. Someone at Nvidia claimed he spent more on AI costs for research than on his entire staff salaries. Another company blew away $500 million on Claude usage fee within a month. In fact, there was one research conducted by Ramp Economics Lab that created a few ripples among those occupying the corner office and wanting to be part of the AI evangelism club. It said the top 1% of companies spend $7500 per employee per month on AI expenses. Compared to the $16,000 a month an average software engineer makes, it is still decent. Of course, those are the actual power users. The top 10% of companies are spending about $611 per month per employee on an average while the median are only dishing out a measly $11.38, which is equivalent to the cost of a seat on an enterprise plan. However, the study also noted that AI spends grew 14.1% per employee per month in recent times. These numbers are concerning as we might think. And the immediate response is that most CEOs now appear to have ditched their exuberance for a modicum of caution. They all know that the problem isn't AI itself, but where and when one uses it and how enterprises can find ways to maximise both its potential and their profits by using the technology. In recent times, we've seen AI consultants go around asking companies to impose token limits, ensure a more selective approach to AI deployment and usage of cheaper models. As we said earlier, the first step was to discard the euphoria as Amazon showed by removing the AI leaderboards. We aren't sure Accenture has followed suit on reducing the euphoria just yet. The fact remains that customers are reducing AI usage in consonance with the phenomenon of services companies like OpenAI and Anthropic pushing their unbridled capex on to costs for the users. Some experts warn that token costs could be the cheapest that they will ever be, while others point to Microsoft who are looking for cheaper alternatives, even if they come from the forbidden land of China! Looks like the AI ecosystem is facing its first big challenge. While getting customers to use AI indiscriminately for all sorts of tasks, they forgot to ascribe a cost to such usage. On their part, customers went all guns blazing into the "valley of death" without reminding themselves that there are "no free lunches" in corporate America.