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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.
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