Companies scramble to control AI spending as tokenmaxxing era crashes over unpredictable costs

Companies are scrambling to stop employees from maxing out AI budgets with small tasks

The era of tokenmaxxing is over. After the AI industry encouraged companies to max out their AI budgets earlier this year, and some companies even built employee leaderboards to encourage internal AI usage -- they are now realizing just how easy it is to spend huge sums of money on AI and get little in return. We now appear to be entering the era of token rationing. Recent news has been rife with stories about AI cutbacks and now 404 Media reports that consulting firm Accenture has been attempting to stop its employees from depleting its token reserves by using AI to do basic tasks -- like converting PDFs into presentation slides. The cutbacks take place not long after Accenture threatened that employees would "risk losing out on promotions" if they didn't use AI, 404 writes. 404's reporting is based on leaked audio from a recent internal meeting involving Accenture's agentic AI strategy lead, Justice Kwak. "We're hitting this inflection point where AI is becoming material to the cost structure," Kwak says. "Spend is becoming very unpredictable; and leadership, especially at the CFO, COO, and CIO level, are still asking the question of whether they're getting value from what we're spending on in the context of AI." The cost of tokens has thrown into doubt the AI business model -- as evidenced by what's being called the "AI selloff" which has battered some AI-dependent businesses the last few days, especially memory chip makers. The AI industry has reached the stage where it can't just be exciting and new anymore. It has to prove its worth.

The AI tokenmaxxing party is crashing over spiraling costs -- leaked consulting firm audio suggests no one is sure how to measure AI effectiveness

"Leadership [...] are still asking the question of whether they're getting value from what we're spending." The era of AI tokenmaxxing may be well and truly over. Alongside stories of Amazon cutting its AI leaderboard and an unknown company blowing through $500 million worth of tokens in one month, leaked audio has emerged from consulting firm Accenture as it tries to figure out how to rein in rampant token spend at client companies, 404Media reports. In leaked audio, Accenture acknowledges that certain trivial tasks being offloaded to AI are causing massive token overspend, especially when agentic AI is part of the mix. The staff in the meeting clearly recognizes that not only is AI spend growing out of control at companies heavily adopting the technology, but that there is very little way to predict how much any tasks would cost, or whether there is real value in using AI to complete them. Accenture has previously been incredibly bullish on AI, even encouraging employees to use it so much that if they didn't, they risked missing out on promotions. But that seems like a policy destined for the AI history books, as Accenture is now clearly aware that it's overspending on AI, and many of its clients are too. From tokenmaxxing, to token hoarding For much of the past year, many companies have charged full speed into an AI-heavy business strategy. Amazon had an AI leaderboard, and Nvidia's CEO Jensen Huang said he'd be alarmed if engineers weren't spending at least 50% of their annual salary on AI tokens. Anecdotally, I know a number of software developers and data engineers who have been encouraged to use AI as much as they can. They have token limits, but they have been encouraged to use all of them and find new ways to do it, too. This is leading to runaway token spending, something Accenture is seeing in its client data. Accenture's agentic AI strategy lead, Justive Kwak, was quoted in the audio saying: "What we're seeing right now is just rapid escalation in AI token spend [...] as companies start to scale AI, moving from like simple chatbots into use cases that feature agentic workflows and automation and then enterprise-wide deployment of some of these tools like Copilot, Claude Code, and Codex." This isn't something that will be contained to just a few firms, either, he said. "It's really not a niche problem. It is a problem that every enterprise will face if they are bullish on AI, if they haven't already," he said, adding that token spend was increasing, "exponentially, as more and more people are starting to use AI." But that may be starting to change. Amazon canned its AI leaderboard - it's rumored to be the mystery company with a half-billion dollar AI spend in one month - Uber is capping AI use to cut costs, and Axios reported at the end of May that a number of CEOs and companies were switching to more affordable models, and more closely monitoring employee usage. Some software developers I know have been using the "caveman" trick to reduce token spend. Even OpenAI CEO Sam Altman said that he was aware token costs were becoming a huge concern for people. This all comes in the aftermath of the move by many of the major AI providers to token-based billing. Where previously subscriptions offered very favorable rates for AI use, suddenly companies were having to pay for the tokens they input, and the tokens the AI output - even when it was verbose, or made mistakes, or required follow-up correction. As the Accenture call shows, it's making even some of the most AI-bullish organizations question their usage, because measuring the spend and the return on that investment is proving all but impossible. As Kwak said in the leaked audio, "Leadership, especially at the CFO, COO, and CIO level, are still asking the question of whether they're getting value from what we're spending on in the context of AI." How do you measure return on investment? Although large language models are proving to be extremely useful in niche cases, their effectiveness at a broader range of tasks is more nebulous. Especially when it comes to financing it. When managers and executives look at AI budgeting and a return on that investment, it's hard to square away the numbers. When you can't know how many tokens a task will take to complete, or whether the task will be completed effectively on the first, second, or third attempt; when you can't completely control the length of the output, or know whether that output will be wrong, or a lie, or just a random hallucination, how do you measure return on the investment in that tool? "We're hitting this inflection point where AI is becoming material to the cost structure; spend is becoming very unpredictable," Accenture's Kwak said during the meeting. Although the overall bill of AI costs is visible, he suggested, finding the specific value attributed to that token spend was not. This seems to have created a culture of task hierarchy within Accenture, where some tasks are deemed more worthy of AI token use than others. When Kwak positioned himself to show some slides during the meeting, Accenture's client group lead, Stuary Henderson, joked that he hoped Kwak didn't use AI to convert a PDF into images and then markdown files. "I'm learning that's one of the big token chewers," he said. "Turning PDFs into markdown: is that right?" Kwak agreed that Accenture data did show some tasks being completed using AI that didn't really need it, and were using unnecessary tokens because of it. Much of that problem, he suggested, was down to non-technical staff overusing it. "We're seeing from some of the data internally at least that it's actually not our engineers that are driving the token consumption. It's a lot of the non-engineers that are doing some of those behaviors." Now that Accenture has encouraged heavy AI adoption among its clients, it finds itself in the bizarre position of having to discourage it or at least encourage more studious use of it. It now sees its next opportunity as a way to advise clients on how to "think about token economics." It's working on a tool called "Token IQ" to help advise clients, according to the call, but hasn't made any announcement so far. What's clear from the Accenture leak and actions of some of the major tech companies, which have previously been so bullish on AI use, is that the finances of mass AI adoption at the per-token scale don't line up. Without a clear way to measure the return on AI investment, we may find even the most tokenmaxxing companies look to restrict access and spend through the rest of 2026 as they re-address AI strategy.

Businesses face up to budget-busting AI bills

A shift to usage-based pricing and new models is making companies rethink spending In the AI whirlwind of recent years, businesses raced to implement the technology amid widespread discussion of its seemingly unbounded potential and ability to automate white-collar work at little expense. In sectors from manufacturing and pharmaceuticals to tourism and gaming, businesses encouraged staff to experiment with AI, free from the usual constraints on cost. In Silicon Valley, tech groups such as Amazon built internal leader boards ranking staff on AI usage, convinced this would help integrate the technology into work routines. But since the start of 2026, a shift has begun to take place. The world's largest AI providers have started to move businesses away from a flat-fee structure and on to usage-based pricing, as they prepare to go public and demonstrate the sustainability of their businesses. People are really saying . . . 'My company spent my entire 2026 budget in Q1' Meanwhile, so-called AI agents, which perform far more complex tasks than prompt-and-response chatbots but also burn through AI credits at a far greater rate, have surged in popularity. The combined effect has been to raise significantly the cost to businesses of using AI. Uber made waves earlier this year when it said that it had spent its entire AI budget for 2026 by April and other companies have been similarly shocked by the bills they are racking up. Uber is now limiting its employees to $1,500 per month per AI coding tool. Nearly half of 2,145 global business leaders surveyed by KPMG in May said they had scaled back use of AI agents because costs outweighed the benefits. "People are really saying . . . 'My company spent my entire 2026 budget in Q1'," said OpenAI chief Sam Altman on stage earlier this month. It "went from, at the beginning of this year, an issue that never came up . . . to, all of a sudden, a huge issue". While AI use is continuing to grow overall, businesses are now weighing how to use the emerging capabilities of agentic models without breaking the bank. Tech businesses are on the front lines of this conundrum and are coming up with a host of ways to rein in costs. Enterprise software group Atlassian is one of several companies that has put caps on the number of "tokens" -- units of data processed by models -- that each of its employees can use over a given period. Staff can ask for more tokens but that requires sign-off from a manager. "I see a lot of companies out there 'yolo'-ing the whole thing -- it's basically, pick the most expensive model, use it as much as you want," says Mike Cannon-Brookes, Atlassian chief executive, of the "you only live once" spending approach. "It's pretty dangerous because it also teaches very bad habits," he adds. Another concern for businesses is vendor lock-in, in which a company becomes overly reliant on a single provider for models, making it difficult to switch if prices rise or performance falls. "It's really threatening to companies that don't have the resources to 'spray and pray'," says Sarah Sachs, AI lead at productivity software group Notion, which, like other software vendors, has built routing tools that help pick the best model for a given task. Hostinger, a website hosting provider, has been using large language models (LLMs) for six years and says almost all of its 900 employees use them daily. To keep costs down, the company regularly benchmarks the latest AI models and has also built its own routing tool. "There are a lot of things to think about," says Mantas Lukauskas, Hostinger's AI tech lead. "[Anthropic's] Claude models right now are the best for coding, but for creative writing, OpenAI models are the best," and there are others still that are better for features like image generation, he says. One solution gaining traction is open-source AI models, which are free to use and can be hosted locally rather than using AI companies' servers. The models are especially popular with groups such as banks, telecom companies and other businesses for whom data security is important, says Costi Perricos, global generative AI leader at Deloitte. A recent study led by an academic from the Mozilla Foundation, an open-source-focused non-profit, estimated that open-source models achieved about 90 per cent of the performance of their more popular closed alternatives. Reallocating demand towards open-source could cut AI costs for businesses by up to 70 per cent, it said. But, for now, total spending on AI continues to rise and the world's top AI labs are continuing to release new models touting ever more advanced abilities. Despite growing controls on spending, Goldman Sachs predicted last month that by 2030, there would be a 24-fold increase in global consumption of tokens, driven by the use of AI agents. "Model capability is improving so fast that when a new model comes out, there are all these new things you can do," says Duncan Lennox, chief product and technology officer at software developer HubSpot. "It's a matter of discipline -- yes you want room for experimentation and innovation, but the big lesson for us has been to tie it to work outcomes."

OpenAI and Anthropic face new AI reality as companies shift from tokenmaxxing to efficiency

"Some of their largest enterprise customers may start limiting their out-of-control token spend," said D.A. Davidson analyst Gil Luria, regarding concerns around OpenAI and Anthropic. Flo Crivello's expenses were out of whack, and there was only one way to get them under control. Earlier this month, the 34-year-old CEO of AI startup Lindy switched his company off of Anthropic's Claude models, moving 100% of its traffic to DeepSeek, a Chinese company that makes cheaper, open-weight alternatives. "We did it, and you could see that cost curve go down, like, crash to the ground," Crivello said in an interview from his company's San Francisco headquarters. He said the decision will save Lindy millions of dollars within months, though he still expects the roughly 25-person company to spend more on AI than payroll. "It's a matter of survival for the business," Crivello said. "That's all it is." Crivello, who previously spent almost five years at Uber, is among a growing crop of founders and executives across the U.S. trying to rein in artificial intelligence spending. Bills for AI have ballooned - sometimes into the billions of dollars - since OpenAI first captivated Wall Street with its ChatGPT chatbot in 2022, kickstarting a rush by businesses to deploy the technology across areas like customer support, marketing and finance. In particular, costs ramped up in the realm of AI-assisted coding, as developers pumped tokens into the creation of new tools and services that previously would have required teams of coders. That led to the era of so-called tokenmaxxing and AI leaderboards, where employers have incentivized developers to use as much AI as possible without worrying about the results. The crackdown is underway. Uber said this month it had implemented a series of spending tiers on some AI tools, starting at a base level of $1,500 per month, though employees could request access to higher levels. In April, Uber CTO Praveen Neppalli Naga revealed to The Information that the ride-sharing company blew through its entire annual AI budget in just four months. OpenAI and Anthropic have been the principal beneficiaries of the spend-at-all-cost mentality, which has fueled their exponential growth rates and pushed both of the AI model leaders to valuations approaching $1 trillion. Now, as they gear up for potentially historic IPOs -- both filed confidentially in early June -- the mood around AI is shifting, and business leaders like Crivello are no longer willing to throw money at Anthropic or OpenAI without a clear picture of a return on their investment. "Current growth rates for Anthropic and OpenAI are the fastest they will ever be, which is mostly a matter of basic math," Gil Luria, an equity analyst covering tech companies at D.A. Davidson, told CNBC. "That is a good reason to go public now, as is the concern that some of their largest enterprise customers may start limiting their out-of-control token spend." Anthropic last reported a $47 billion annualized run rate in May, up from the roughly $10 billion in revenue it recorded for all of last year. OpenAI's run rate was pacing closer to $25 billion earlier this year, according to reports, up from the $13.1 billion in revenue it generated in 2025. Listing soon, while the numbers are still dazzling, could be strategic. "There has to be some period of time in the future where there's some rationalizing of spend by companies, and that may be a blip ahead for Anthropic and OpenAI," Luria said in an interview. "That creates some sense of urgency to go public before we see that." Anthropic declined to comment for this story. OpenAI didn't respond to a request for comment. Crivello said he's a big fan of Anthropic, but his company had been dealing with "unsustainable" AI costs for a long time. Lindy was built around the idea that the cost of tokens, or the units of data that are processed and generated by AI models, would decrease dramatically over time, Crivello said. That proved true for a while, but leading model developers, including Anthropic and OpenAI, have been slower to slash prices in recent months. Crivello said he'd be open to switching Lindy back to Claude models if the prices come down. "I hope that they cut the costs again at some point but, until then, we've got options," he said. Jeff Henry, president of consulting at Highspring, said some of his firm's clients are pulling back until they "can really start to prove an ROI," and others are still waiting another 12 to 18 months before making any big spending decisions. "Everybody is experiencing the same spend crunch on AI," he said. However, there are still countless mid-sized companies that haven't even started experimenting with AI yet, he said. "AI is not going away," Henry said. "There's no way that toothpaste ever goes back in the tube." Darren Kimura, CEO of enterprise AI company AISquared, said one area where AI spending is "absolutely" hitting a peak is in use of state-of-the-art models, also known as frontier models, for simple tasks that can be accomplished with cheaper alternatives. Some companies are turning to what's called model routing, which matches the appropriate task to the appropriate model. It's a technique so new that, according to Glean CEO Arvind Jain, roughly 95% of enterprise AI usage is still running on frontier models. Kimura said that approach will be "untenable" for most companies in the long run. D.A. Davidson's Luria said pricing in the market is still at an "unsophisticated" stage, but both OpenAI and Anthropic have been trying to adjust to an increasingly budget-conscious environment. OpenAI launched analytics and updated controls for enterprises earlier this month, allowing administrators to break down credit spend across the workplace, set usage limits and give employees visibility into their available budgets. Anthropic rolled out a series of controls in August that allow customers to provision users, view analytics and set spending limits at the organization and individual level. Finance departments are paying close attention after getting hit with surprisingly large AI bills, said Eric Glyman, co-CEO of expense management startup Ramp. "Most CFOs not only didn't plan for this in their annual plans -- the steep growth -- but don't have great tools to manage this," Glyman said in an interview. "Suddenly you have this third pillar that has showed up, which is spending through tokens and intelligence. It's not a clean area of spend." As companies become more price sensitive to AI, OpenAI and Anthropic have to contend with deep-pocketed competitors that are aiming to develop lower-cost models. Microsoft, which has poured more than $13 billion into OpenAI as much as $5 billion in Anthropic, unveiled a suite of new low-cost models earlier this month. The company has also emphasized that its AI coding product, GitHub Copilot, will route users to the most appropriate model for a task. In a June essay, Microsoft CEO Satya Nadella said the industry needs to avoid concentrating power in a handful of large providers. "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," Nadella wrote. "If all the value is accrued by only a few models, the political economy will simply not tolerate it." Amazon and Google are also ramping up their investments in models for business users. Peter DeSantis, Amazon's top AI executive, told CNBC this month that he hopes the company will be able to compete with OpenAI and Anthropic's frontier models in the "coming year." Like Microsoft, Amazon is an investor in both of those companies. DeSantis said in February that Amazon will rely on its in-house chips to develop models at a less expensive rate than its rivals. "AI has a cost problem," he told The Wall Street Journal in an interview. "If we ultimately want AI to transform everything, the costs have to be different." Google made a concerted effort to highlight affordable AI offerings at its annual developer conference last month. The company showcased Gemini 3.5 Flash, a lighter-weight addition to its model suite that's available at half, or in some cases close to one-third, the price of comparable frontier models, according to CEO Sundar Pichai. "Microsoft and Google have the infrastructure and capability - the entire stack - where they can come in and stiff-arm both OpenAI and Anthropic," PitchBook analyst Harrison Rolfes said in an interview. "They're probably waiting on the sidelines for them to battle it out, see where they're not doing well." As for going public, neither of the big model companies have provided an exact timeframe for their prospective debuts. The New York Times reported on Thursday, citing people involved in the deliberations, that OpenAI is leaning toward holding off until next year. Pressure to go public may revolve around the need for capital. With Anthropic and OpenAI increasingly competing against their biggest financial backers, the IPO market may be the best avenue for new money, especially as their capital needs have become too great for most venture and private equity firms. "A lot of the traditional pockets of capital are drying up," said Dharmesh Thakker, a general partner at Battery Ventures. "All the institutional investors who can invest in these companies have already taken their pound of flesh." Choose CNBC as your preferred source on Google and never miss a moment from the most trusted name in business news.

The Token Belt-Tightening Is Coming for PDFs

The tokenpocalypse is upon us, and it's coming for nontechnical staff. Many large companies have pushed employees to use AI for nearly everything, partly to justify the enormous sums they poured into the technology without any clear integration strategy. But a new report from 404 Media shows that some of those same firms are now tightening access after discovering that employees are burning tokens on tasks that never required AI in the first place. The publication reportedly obtained audio of a meeting at consulting giant Accenture, where staffers have been told they're seeing "soaring token spend" driven by pointless tasks like converting PDFs into presentation slides. Those uses are actually starting to burn more tokens than the work being done by technical staff. "We're seeing from some of the data internally, at least, that it's actually not our engineers that are driving the token consumption," an Accenture employee said in the audio, per 404 Media. "It's a lot of the non-engineers that are doing some of those behaviors." While it may seem a bit absurd to think that workers who focus on BS-ing their way through presentations would be using more tokens than their colleagues over in engineering, PDFs are an extraordinarily inefficient way to feed information into AI systems. Depending on the tool and the document, a model may have to extract and interpret not just the text but also every page's layout, images, charts, and other visual elements. The file format may be a boss's best friend, but that could change once the token bills start piling up. It's not exactly hard to figure out how Accenture found itself in this situation. According to a report published by the Financial Times earlier this year, the Fortune 500 consulting company went out of its way to push people to use AI tools, going so far as to track logins made by staff and tying promotions to their chatbot usage. In a memo to the staff, the company reportedly said that moving up in the company would require "regular adoption" of AI, signaling that using the technology would not be optional. Accenture, of course, is far from the only company to use some shockingly uncritical metrics to push AI usage. Big Tech firms like Meta and Amazon started leaderboards to track which employees in the company were burning through the most tokens and incentivizing them to push themselves onto the list. This, of course, just leads to people doing menial tasks with AI that they could otherwise do themselves, just for the sake of burning tokens. That approach is dumb but fine in the "free money" era of AI, where costs weren't linked to token usage. But as the major AI labs head toward IPOs, they've shifted to a usage-based model for pricing. The result has been some absurdly high bills. That is less sustainable, and now the companies are starting to ask their employees to cut back on AI usage instead of ramping it up. Weird how a company that tells other businesses how to operate more efficiently and effectively failed to see the very obvious and predictable token crash coming. Probably nothing to read into there.

Employees Are Using Their Jobs' Super-Expensive AI Tokens for the Most Hilariously Pointless Tasks Imaginable

Can't-miss innovations from the bleeding edge of science and tech Under all the bluster of AI hype lies a real conundrum: companies are charging out-of-this-world prices for a tool that still can't match the value of a competent human. As AI companies and their financial backers continue to dump billions of dollars into AI development, the consequences of that basic contradiction are spilling out into offices and shops throughout the US. The fintech firm Slash, for example, recently encouraged its employees to start using AI coding tools as much as possible, a phenomenon known as tokenmaxxing, with the ultimate goal of boosting productivity and lowering costs. Unfortunately, LLMs are too expensive -- and not quite useful enough -- to really make that a reality. At Slash, one employee wasted an astonishing $80,000 in AI tokens to vibe code a lackluster video game called "brainrot shooter." As Business Insider describes it, the first-person shooter is a barren experience where the player runs around shooting at enemies inspired by viral internet memes. "Pls play it so we can write this off as a marketing expense," Slash wrote on social media. Elsewhere, office workers who'd been told to use AI as much as possible are incinerating incredible sums of cash on tasks that didn't need LLMs in the first place. At consultant firm Accenture, for example, 404 Media reports that non-tech workers are using corporate AI budgets to do things like convert PDF files into PowerPoint presentations. "We're seeing from some of the data internally at least that it's actually not our engineers that are driving the token consumption," Accenture's head of AI strategy Justice Kwak said in an internal meeting, according to leaked audio files obtained by 404. "It's a lot of the non-engineers that are doing some of those behaviors." It's a particularly ironic development, because workers are doing exactly what they were told -- using the exciting new technology at all costs, thereby exposing just how little they're getting in return. For years, the titans of the tech industry have been subsidizing the mammoth costs of LLMs in a bid to spread their buzzy new tools to corporations far and wide. Once LLMs advance to a point where they can actually make companies money -- a scenario which is becoming increasingly difficult to imagine -- the market will theoretically settle on a price that makes everybody happy. Proponents of AI support this delayed approach. They point to examples like Uber, which operated at a loss for years in order to undercut the price of taxi cabs. Once Uber had destroyed the taxi industry, it could ratchet up prices as high as it wanted, and nobody could say no. One key difference, as tech industry analyst Ed Zitron observed in an interview with Bloomberg, is that Uber spent about $32 billion to eventually reach a market capitalization of $155 billion today. That's a lot of money, to be sure. But Uber is playing in the Little Leagues compared to AI: as Zitron notes, that $32 billion is "less than half of what Anthropic has raised in the last six months" alone. Now with AI companies forced to hike prices in order to keep up with exorbitant costs, the world's most expensive game of workplace malicious compliance might be coming to an end.

Companies Look to Cloud-Era Solutions to Control AI Spending | PYMNTS.com

As The Wall Street Journal (WSJ) reported Tuesday (June 30), artificial intelligence (AI) is increasingly being billed according to usage, with companies facing volatile pricing for tokens, the essential building block of AI computing. "With AI, you're putting the credit card in the hands of the end user. If you have no control over that, or if the end user is not educated enough, they're going to run up that tab," Chris Reed, a senior director of IT finance at online travel company Priceline, told the WSJ. According to the report, tech leaders at some companies have begun employing cost-control strategies developed during the rise of cloud computing. Kathy Kay, chief information officer of Principal Financial Group, said financial services companies are "putting governance and optimization practices in place, similar to what companies have done with cloud, to manage costs as we scale." For example, Principal is concentrating on using the right AI model for the proper task, so that "higher usage doesn't necessarily translate into higher costs," said Kay. "Given how quickly pricing and capabilities are evolving, we're designing for flexibility so we can adapt over time and continue to deploy AI efficiently," she added. Ravi Soin, CIO and chief information security officer of Smartsheet, said the company's FinOps -- a mix of financing, engineering and product -- team is charged with tracking overall AI spend. The software company has created automated alerts to notify employees that they're about to reach their token limits. "We have user dashboards available to the entire company, by department, by manager, so you have real-time visibility on how often and what your costs are, so it isn't a surprise at the end of the month," he said. Meanwhile, other companies are benefiting from this trend, PYMNTS wrote earlier this month. For example, Ramp, a financial operations platform, raised $750 million at a $44 billion valuation, almost tripling its worth in a year. "The company is betting that AI consumption, billed by the token and fluctuating with every prompt and agent action, has become a cost category that most enterprise finance teams can't track, allocate or control," that report said. "The problem Ramp is targeting didn't exist at scale two years ago," PYMNTS added. "AI providers initially priced access on flat subscription terms. As agentic models moved into coding, customer service, research and procurement, usage-based billing became standard. Every step an agent takes runs a meter."

The AI About Turn: Step Aside Token-maxxing; Tokenpocalypse is Here

In February Accenture said it would promote only those who use AI, now they are seeking ways to curtail AI usage Exactly a week ago, we had reported the new trend in AI usage where enterprise CEOs had shifted from being AI evangelists to AI misers. Now it appears that these head honchos have turned cost-conscious with a vengeance and want token rationing to become part and parcel of their operations. An article published by 404 Media notes that of all the companies, Accenture, which had at one time announced that future promotions would be based on token use, has reversed its position. The report claims access to a leaked audio where Accenture is trying to understand how to stop non-technical workers from blowing through AI token budgets. The report claims that the consulting giant has witnessed a trend where employees used AI tokens to do basic stuff like converting PDFs into presentation slides, which is worrisome as Accenture is seeing a "soaring token spend" across industries. This turnaround in Accenture's position is indeed fascinating and possibly explanatory of the hype that AI has brought in its wake. In February, CEO Julie Sweet had revealed that she had asked employees to get friendly with AI if they wanted future promotions. Now, it looks like the staffers got really friendly and ended up blowing all their tokens and more. Looks like the era of tokenmaxxing is coming to an end as companies are casting aside employee leaderboards built to encourage AI usage and moving back to a more practical position of using the new technology for tasks that delivered better returns. Looks like we are now in the age of token rationing where employees get to use them only for relevant tasks, not merely as shortcuts to reduce time and effort. The move seems to be quite an overarching one as reports said even Meta has rolled in to reduce AI usage in order to keep down costs, which a report said had crossed billions of dollars this year. According to the 404 media article, the leaked audio from an internal meeting had Accenture's agentic AI strategy head Justice Kwak. "We're hitting this inflection point where AI is becoming material to the cost structure. Spend is becoming very unpredictable; and leadership, especially at the CFO, COO, and CIO level, are still asking the question of whether they're getting value from what we're spending on in the context of AI," the report said. This trend exposes a further chink in the armour of the ongoing AI goldrush, especially since the cost of tokens has created concerns over the existing business model that companies are built on. In fact, even the stock markets appear to be concerned about this trend as evidenced by a report from CNN which called it an "AI selloff." The report quoted James Reilly, senior markets economist at Capital Economics, to suggest that the single-day drop of Nasdaq (2.21%) and S&P 500 (1.44%) caused largely by selling in both semiconductor chip stocks and other AI-related shares, may be a result of a growing trend of rising volatility, which is "evidence of excessive froth" and calls into question the sustainability of this rally. "Whatever the cause, with AI companies' sky-high valuations and incredible growth trajectories, it doesn't take much to set off investors. The Kospi is up more than 90% this year, so when the wind blows in an unexpected direction, it can lead traders - and, often more consequentially, trading algorithms - to head for the exits. They fear the top of the Jenga tower could tip over," the report said. Of course, it is not just the markets that are concerned. Microsoft CEO Satya Nadella called out the general frenzy around AI in an interview with The Wall Street Journal earlier this week. You can't say, hey, all white-collar jobs are gone and this could even be a weapon and we will use all the power to build datacentres. The public, he predicted, wouldn't tolerate just a few models and companies "doing all of the learning for the world," he told the WSJ. Meanwhile, his company also began exploring alternatives to the AI giants to power up Microsoft's own AI offerings. And the first stop they made is with China's DeepSeek as a partner for Copilot, albeit with some caveats such as having its servers in the US. This move also happens to run parallel to Microsoft's efforts to shift its Copilot Cowork to a usage-based pricing model across the entire enterprise AI space. The report says the Redmond-based tech giant might be considering a locally hosted version of DeepSeek as a cheaper option for its users. And barely days before these reports, we saw the company ask its employees to reduce Anthropic's Claude licenses. Then came reports that they are making money in the Chinese markets by selling OpenAI's ChatGPT as part of the Microsoft Azure offerings. Then came a report that quoted Suleyman as saying that he wants Microsoft to be the fourth AI lab after DeepMind, Anthropic and OpenAI. Whatever be the actual case, the fact remains that all these incidents coming one after the other could slow down the frenzy over AI as the next best thing after sliced bread. Maybe, it is for the global good that the hype is being replaced with a practicality that went up in smoke when the new age whiz kids announced AI as the magic wand for everything and anything.

Cheaper AI is better: Soaring bills are reshaping how businesses choose models

June 29 (Reuters) - Silicon Valley's powerful and pricey AI models have been a necessity for businesses looking to future-proof themselves. But now a growing number of tech CEOs are arguing that cheaper options would be crucial for their wider adoption. Top executives such as Microsoft's Satya Nadella, Palo Alto Networks' Nikesh Arora and Coinbase Global's Brian Armstrong have said smaller, cheaper models can handle a big share of corporate needs. This view is the result of a reassessment within companies that until recently encouraged heavy use of AI tools, often treating rising consumption as a proxy for productivity, dubbed "tokenmaxxing". Now, those bills are starting to bite. Prices of tokens - the units used to measure AI usage - are falling, but the cost of completing a task is rising as AI firms shift from flat subscriptions to usage-based pricing. That is leaving companies with unpredictable and often higher bills as usage per task becomes harder to estimate. Uber, for instance, burned through its entire 2026 AI budget in just four months after employees rushed to adopt AI coding tools, forcing management to cap usage, according to reports. "Changing the license model caught a lot of people by surprise," said Harold Byun, CEO of BlueRock, a startup that helps companies run AI systems safely. "Immediately after that, we had a number of reports from customers that we're seeing a 20% to 30% spike in terms of over-budgeting." BUSINESSES FRET OVER HUGE BILLS As companies use AI more, their costs are surging beyond initial estimates as tasks now involve more steps, more data and longer inputs. Gartner estimates AI coding costs will surpass the average developer's salary by 2028, while a survey by the research firm found three-quarters of executives see tech budgets rising this year, with nearly half of them projecting double-digit jumps. That has led businesses to embrace cheaper models and turn to routing tools such as OpenRouter, an AI marketplace, as they seek to assign tasks to the most cost-effective system while reserving premium models for complex work such as coding. Open-source tokens processed on OpenRouter jumped to 65% in June from 34% in January, according to a Citi note. That should benefit open-source model makers such as China's DeepSeek, which have won wide adoption among startups but struggled to break into large businesses due to security concerns. "If you want to win enterprise, you should be forward pricing tokens," Palo Alto Network's Arora wrote on X last week, urging AI labs to charge customers today at the lower rates that tokens are expected to command in a few years. OpenAI appears to be adjusting to the shift. ChatGPT maker has been reported to be weighing significant price cuts, including on token usage, in anticipation of similar moves from rival Anthropic. However, any shift to cheaper models could hurt their revenue growth, especially as they prepare for potential IPOs. "There will be a price-war dynamic when it comes to OpenAI and Anthropic as they both duke it out for a 'first to public market' IPO dates," said Christopher Brown, financial adviser in private wealth management at Synovus Securities, which owns shares in several Big Tech companies. Tech stocks sold off for much of last week as investors reassessed AI valuations as doubts about returns on massive spending were compounded by weak post-IPO show from SpaceX and reports that OpenAI may delay its listing. OPEN SOURCE, CHINESE MODELS DRAW ATTENTION The cost spike is pushing more businesses toward open-source models, including cheaper Chinese alternatives. The four most popular models on OpenRouter are all Chinese, with DeepSeek holding the top spot. Chinese models are closing the capability gap with top U.S. models while charging as little as 18 cents per million tokens, against $4 on an average for the top models, the Citi note showed. "They (open-source models) used to be more than a year behind (leading AI models). Now, probably the estimates are they're roughly four months behind. That the gap will continue to close," BlueRock's Byun said. Still, some analysts said that concerns about the security of Chinese models were likely to hamper enterprise adoption, especially in sensitive industries such as cybersecurity. Instead, they expect businesses to follow the cloud computing playbook, spreading across multiple providers in search of the best fit and price. Open-source models are showing that they are "90% as good at 10% of the price", said Val Bercovici, chief AI officer at WEKA, which helps companies run AI models faster and cheaper. "We don't need to spend the premium tokens on every level of effort." (Reporting by Aditya Soni in Bengaluru; Editing by Sayantani Ghosh and Arun Koyyur)