this post was submitted on 03 Jul 2026
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[–] Grandwolf319@sh.itjust.works 2 points 2 days ago* (last edited 2 days ago) (7 children)

For anyone who actually wants to read the article:

https://archive.is/jqVhE

Edit, here is the article since people have been having issues:

Companies Are Throttling Employees’ AI Use Because It’s Too Expensive

Jul 2, 2026 at 6:00 AM

Companies Are Throttling Employees’ AI Use Because It’s Too Expensive

Photo by Sebastian Herrmann on Unsplash and collage by 404 Media with company logos.

Companies across tech, entertainment, banking, and many other industries are throttling their employees’ use of AI and pleading with workers to use less powerful models to stop AI costs from spiraling out of control, according to leaked Slack chats, screenshots of internal dashboards, emails, and more material obtained by 404 Media from half a dozen companies including Atlassian, Adobe, and Amazon. In at least one case, AI spending has tripled to more than $15 million a month.

The news shows the looming fallout from companies adopting AI as quickly as possible, and AI providers’ moves to charge enterprises based on how much they use AI rather than a flat fee. Emails obtained by 404 Media even show some companies cutting off access to some AI models altogether in an attempt to stop burning through their AI tokens, and big tech companies like Adobe are ending unlimited access to Claude.

“A lot of people had ideas about how to adjust workflows with lower-reasoning models for certain tasks in order to mitigate token consumption,” an Adobe employee told 404 Media. “But I am not sure that they fully absorbed the news, and I'm not sure the full ramifications are going to be clear to everyone until it goes into effect.” 404 Media granted multiple employees at companies using AI anonymity because they weren’t permitted to speak to the press.

Citi, for example, has shut off access to Claude’s and ChatGPT’s latest models entirely, according to an internal Citi email obtained by 404 Media. That includes Claude Opus 4.6 and 4.7, and GPT-5.5.

💡

Do you know anything else about token spend inside companies? We would love to hear from you. Using a non-work device, you can message Joseph securely on Signal at joseph.404 or Emanuel at emanuel.404

“These models consume significantly more AI Credits per interaction and have been the primary driver of elevated enterprise consumption,” the email reads. The email says Citi disabled the models on June 24 and plans to re-enable them on July 1.

Before shutting off access, Citi sent employees another email asking them to not use the more powerful models unless they absolutely had to.

“⚠️ Action needed: Choose the right model for the task (reduce Opus 4.7),” one section of the email reads, referring to one of Claude’s more recent, and token hungry, models. Since AI tokens are now pooled across Citi, the email says, developers with heavier AI-assisted workflows draw more from the shared pools, while lighter users ideally contribute their unused portion, freeing it up for the developers who may need their tokens. “We need everyone to be intentional about model selection to ensure fair access for all users across the enterprise.”

The email points again to Opus 4.7, saying, “Every interaction with Opus 4.7 (and other models in its class such as GPT 5.5) consumes significantly more credits than standard or mid-tier models.” It then provides a breakdown of what Citi employees should use each model for: GPT-5.3-Codex for quick questions, explanations, or simple code generation; the same model or Claude Sonnet 4.6 for code review and “standard chat;” then higher models like Claude Sonnet 4.6 for “architectural reasoning.”

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Citi’s changes come directly in response to GitHub moving from a flat subscription model to a usage-based billing one in June, according to the email. The email says Citi is also monitoring daily Copilot usage to find “excessive or anomalous usage patterns early” and has budget controls in place. Citi told 404 Media it has not disabled models and the company is not taking steps to curb usage by allocating workers a certain number of AI tokens. This is despite the email and other screenshots clearly showing Citi blocking access to certain models.

Atlassian, the company behind the popular software product development tool Jira, recently ended unlimited use of AI tools at the company and introduced a dashboard where employees can track how much their AI use costs the company. 404 Media has seen the dashboard, which shows Atlassian went from spending $5 million on things like AWS, Google Cloud, and OpenAI LLMs in the month of August 2025, to more than $15 million in May 2026. The company is on track to spend more than $120 million on AI tools for the fiscal year, the dashboard shows. Atlassian told 404 Media these numbers don’t accurately reflect its AI usage, but declined to say which of the figures were wrong and how.

“I’ve seen a lot of people complaining that they changed their workflow to maximize AI usage, and now they can run out in 2-3 days, especially when using agents or similar or using the latest Claude model. Lots of angsty messages in Slack like ‘now how do I do my job,’” an Atlassian employee told us. “For what it's worth I think it’s insane they were allowing huge amounts of spending on it before, it was only a matter of time before that had to end.” 

Inside GitHub things are a bit different. Employees don’t have a limit on token spend, but workers were recently told the company is looking into decreasing token spend by using open source models, a GitHub employee said. The employee told us that GitHub plans to test user-based billing, meaning budgeting AI tool use to individual people instead of teams, projects, or unlimited usage.

At Adobe, unlimited Claude access is not being renewed and will expire on June 30, an Adobe employee said. Workers there were told instead, in essence, try to get everything you can done before that date.

As 404 Media previously reported, Amazon recently shut down an internal company leaderboard which ranked employees based on how much they used AI tools at work. Multiple Amazon employees told us they suspect Amazon shut down the leaderboard because it was encouraging wasteful and expensive AI usage. After Amazon shut down the leaderboard, 404 Media saw a discussion on Amazon’s internal Slack where an employee shared a screenshot showing they had hit a token limit employees seemingly didn’t know existed previously. 

“Crazy, we go from no more leaderboard to actual usage limits in two weeks,” one Amazon employee said in a reply on Slack.  

An Amazon spokesperson told 404 Media in an email “We encourage employees to use and experiment with AI, and our guidance around AI usage hasn't changed.”

Other companies have burned through their AI tokens. An employee at an entertainment company told 404 Media, “We hit our limit for ChatGPT token use this month for the first time. One developer used almost half the entire company’s allocated pool with no obvious ROI [return on investment].” 

Last week 404 Media reported consulting giant Accenture found that much token usage, or ‘chewing,’ is not from supercharged engineers creating lots of code, but people converting PDFs into presentation slides. Accenture is seeing “soaring token spend” among its clients, according to leaked audio 404 Media obtained.

There is an obvious irony—or cold calculation—in Accenture pointing this out. In the audio, senior Accenture staff explained they told their clients to adopt AI as quickly as possible. Now that AI costs have skyrocketed or become unpredictable, Accenture is positioning itself also as the solution to that problem, with one of the employees saying Accenture has a new opportunity regarding its clients: “to really think about token economics.”

Accenture continues to use AI internally for trivial projects, though. Screenshots obtained by 404 Media show an internal tool that lets employees predict which team will win the World Cup. The tool was made with AI, a source with knowledge of the tool said.

“They are still trying to ram AI down our throats at all levels and areas of work,” the source said. “Everyone seems to be trying to outdo each other in finding new ways to waste water and no one is telling us to slow down.”

Adobe, GitHub, and Accenture did not respond to requests for comment.

[–] BarneyPiccolo@lemmy.today 1 points 2 days ago

Im self-employed, and my employer has no idea what they'd do with AI, so it's not an issue for me.

I thought a token was a credit for an inquiry, but from reading about this, I'm getting the idea that a token is a word or phrase that forms the prompt for the AI to respond to. So a single prompt could cost multiple tokens if there are multiple words or phrases. Further, since the more parameters you give the AI, the more likely you'll get a decent response, so a good prompt may cost a lot of tokens. Is that correct?

If so, then using more tokens to get a better response is likely to be a more efficient use, than multiple inquiries with mediocre results. But now we seem to be entering a era where they are more focused on the costs than the results, which is always stupid.

For a buncha geniuses, this AI stuff all seems pretty fucked up. Nobody seems to know what they're doing, or even what they want out of it, but they're spending literal fortunes on it. A scenario like that will NEVER have a good outcome.

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[–] jaschen@lemmy.world -5 points 1 day ago (1 children)

I been using open models for 100% of my coding. 1/2 of the time I'm using local open models like qwen 3.6 or Dwarfstar if it's sensitive code I don't want the internet learning from.

I don't miss using frontier models at all. GLM5.2 and Deepseek V4 pro are both equal or beats sonnet. I haven't had to use Opus for awhile now.

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[–] pixxelkick@lemmy.world 4 points 3 days ago (4 children)

Thus just in: handing your employees a bunch of nail guns with near zero effort put into training them on how to use the nail guns has resulted in terrible outcomes, to no one's surprise.

Its possible to use the tools efficiently at a low cost.

But the average dev Ive worked with has zero clue how to do this and has had zero training and will just rip through tokens willy nilly.

[–] artyom@piefed.social 3 points 3 days ago (1 children)

It's nothing to do with that. The costs are going up because the fees are going up. It's the natural progression of any startup. Give everything away for free but at some point the bean counters come calling and the marketing department doesn't cut it anymore.

[–] pixxelkick@lemmy.world 1 points 2 days ago (1 children)

No, I work in the industry and am vety actively entwined eith systems where we contract out to train and show companies how to use LLMs better.

And a lot of our clients now are of the "how do we use less tokens" variety, and you walk into the project and see the way they currently operate and go "oh god"

The average developers have absolutely zero clue wtf they are doing, they'll burn a million tokens on something that outta take 10k.

We often can get token usage down easily 90%+ in the first month just by on boarding and offering some basic training and helping install some basic guard rails, skills, etc.

[–] artyom@piefed.social 1 points 2 days ago (1 children)

Yeah I can see that you work in the industry...

[–] pixxelkick@lemmy.world 1 points 2 days ago (1 children)

Its not complicated. People have become extremely insulated away from what the real work looks like of a dev over the years.

The reality os starkly contrasted to public perception.

Most software developers heavily use LLMs now. They sucked 5 years ago, we're meh 3 years ago, decent 2 years ago, but over the past year and a bit have rapidly become genuinely more efficient when used right and skillfully than doing about 90% of your work load by hand.

Bits and pieces still require doing it by hand, but the vast majority of work for the average dev now is via moderating an LLM (with skill) to success.

Unfortunately a fuck tonne of devs lack that "with skill" part still, and what this comes out as is them costing their companies tremendously more money to do the same job.

A loooot of companies (stupidly) hedged their bets that if they just gave their devs wild west access to using LLMs without training they'd magically just "figure it out" along the way.

Which is nonsense, why would a dev feel compelled to conserve tokens or improve efficiency with zero incentive?

So now companies are scrambling as they realize their devs, who just spent 12 months going hog wild with LLMs, still havent learned how to use them well and in dact have developed arguably worse poor habits that they now need to unlearn

Thats where the industry is at now largely.

Meanwhile companies like the one I work at predicted this as a natural thing and we're preparing for it long in advance. When token prices shot up we already had set ourselves up with lots of training so the price increase was not nearly as noticeable.

I think when Im fully optimized out on a project I only spend about $10~$15 a day, despite going full steam for 5 hrs or so.

And despite that my productivity is probably higher than unskilled devs who burn through 10x~20x that. I get more work down in way less time and way less tokens.

Training and the resource/knowledge pool go a long way here. It cannot be understated

[–] artyom@piefed.social 1 points 2 days ago (1 children)

Give it up brother, you're not fooling anyone here

[–] pixxelkick@lemmy.world 1 points 2 days ago (2 children)

Theres not really any fooling here. Theres tonnes of interesting examples you can find.

Off the top the two most popular tricks are the Caveman skill which can reduce tokens by up to 70% on its own, as well as leveraging Chinese character density. Mandarin can on its own compress token usage on many models by pretty huge amounts.

Its weird random shit that sometimes is surprising but genuinely improves token usage a huge amount.

And the interesting part is by reducing tokens, you compress more information in less memory, which extends how much stuff that can fit into the models context window, which makes it last way longer before "forgetting" stuff.

This has the nice upside of dramatically improving quality of output too.

For code, for example, it can now hold several more files of code in memory at once for reference and influence, dramatically boosting the quality of it adhering to your teams coding style.

Thats just one example you learn on how to make the tool less stupid.

Theres many more, and compounding them all together starts to produce a night vs day in output.

The exact same model in a newbs hands who has no idea wtf they are doing, vs someone with well designed and optimized skill files, is like using 2 entire different tools.

Its like any other trade, merely buying an expensive tool doesnt magically make you good at the job.

Knowing how to use the tool is way more important

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[–] water1309@lemmy.world 1 points 3 days ago (1 children)

Any tips on how to learn using the tools efficiently?

[–] pixxelkick@lemmy.world 1 points 2 days ago

Start by learning all the critical things like Skills, MCP, Agents, etc.

Then look up skills and MCP tools that reduce token usage, improve recall, improve searching, improve parsing, etc

Then learn how to use sub agents bound to cheaper models for more expensive operations (the largest of which us always search and find ops)

Swapping to a cheaper model for a subagent with the job to just go find a specific thing alone can reduce costs like 30%, equipping it with tools that can search and find faster can push that to 70%

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[–] fubarx@lemmy.world 1 points 3 days ago
[–] cleverdrift4349@lemmy.1095.me -1 points 3 days ago (1 children)

@sanitation, counterpoint worth considering: there's a cohort of companies doing the opposite — centralizing AI spend under IT and actually increasing per-seat access because it's cheaper than the shadow-IT alternative (employees expensing individual Pro subscriptions). The math flips when you account for ungoverned spend. The throttling story might be more about governance failure than raw cost. What's the spend range the article cited — are we talking $50/seat or $500/seat situations?

[–] UnfortunateShort@lemmy.world 2 points 2 days ago (2 children)

We just bought everyone individual Claude Max subscribtions (Anthropic has done nothing about it so far lol). The most expensive tier is 200$/M I think - that's already much more than most people could possibly spend in tokens.

I am more than happy with the 100$ tier. For a business, that's a rounding error. Given the productivity boost, I'd say it's a no-brainer (although you should train your people on it as well).

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