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Home » The Army Is Burning Through Its AI Tokens
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The Army Is Burning Through Its AI Tokens

News RoomBy News Room21 July 2026No Comments
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The Army Is Burning Through Its AI Tokens

A little over a month after the Department of Defense (DOD) bragged that nearly half of its 3.5 million employees were using AI at work, members of the Army’s Combat Capabilities Development Command (DEVCOM) received an email informing them that they were burning through tokens, and needed to limit use.

“Although the Army CIO announced in May 2026 that they were offering unlimited tokens, by mid-June the Army CIO pool was exhausted of tokens and had to re-establish limits,” the email reads. The email goes on to say that although the Army has chosen to renew token usage at “its current levels,” it’s unclear “if the Army CIO pool will be renewed after 1 Oct.”

The Army uses Ask Sage, a multimodal generative AI platform where users can run different large language models (LLMs), including Alphabet’s Gemini, Meta’s Llama, and OpenAI’s ChatGPT. “Apparently the whole Army burned through the whole year of tokens for just one service,” says an Army employee who spoke to WIRED anonymously because they were not authorized to speak to the press.

Ask Sage is used by the Army to “power its enterprise LLM workspace” and is “accredited for Controlled Unclassified Information.” It is also used by the DOD’s Chief Digital and AI Office (CDAO) for acquisitions. According to the Army’s website, Ask Sage was used to complete tasks like “reclassifying personnel descriptions, which involves defining and aligning job duties, experience and backgrounds.”

The Army employee says that the Army has been pushing its workers to lean into using generative AI. Employees were given an allotment of at least 200,000 tokens per month, according to emails viewed by WIRED, and were automatically allocated more if they burned through their initial allotment. Employees who had signed up for Ask Sage but were not regularly using it would receive emails encouraging them to use more of their allocated tokens.

In order to use Ask Sage, the Army had access to 100,000,000 tokens as part of an annual subscription to an “enterprise pack.” Tokens represent a unit of output, either in text or image, from an LLM. For the Ask Sage tool, a single token equates to about 3.7 characters, according to documents viewed by WIRED. The Defense Department burned through some 20 billion tokens per day during the 38-day Operation Epic Fury in Iran, according to Breaking Defense.

The Army and DOD didn’t reply to requests for comment; neither did Ask Sage.

It’s unclear if the tokens used by regular DOD employees are drawn from the same pool as those who might be using AI tools on classified or secret information. This hasn’t stopped the Defense Department’s emphasis on AI. On Monday, the Intercept reported that the Pentagon has continued to lean into AI tools, and has cut the staff at the Civilian Protection Center of Excellence, whose jobs entailed preventing civilian casualties in conflict zones. Instead, the DOD is developing an AI tool to speed up the assessments that the Center’s staff would normally make.

The Army is not the first eager adopter of generative AI to rethink their near unlimited use. After encouraging employees to “tokenmaxx,” Meta quietly took down its leaderboard tracking token usage and is now trying to curb use. Last week, Adam Mosseri, head of Instagram at Meta, floated the idea of capping token use per engineer at the company. According to reporting from Fortune, Uber also saw its engineers burning through a year’s worth of generative AI tokens in merely four months.

The Army employee says they have not found the generative AI tools to be particularly useful for their work, and that when they have used the tools, they have found them to be unreliable. One model even asserted that it had completed a task that it hadn’t, they say. “I think there are definitely several aspects of the bureaucracy of the US federal government that these tools might be helpful with. But an unthinking application and use is not going to result in an effective, efficient, and trustworthy rollout.”

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