{"id":691264,"date":"2026-05-24T10:04:14","date_gmt":"2026-05-24T10:04:14","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/691264\/"},"modified":"2026-05-24T10:04:14","modified_gmt":"2026-05-24T10:04:14","slug":"ai-cost-crisis-hits-tech-giants-as-employee-tokenmaxxing-backfires-sparking-corporate-pullback-at-microsoft-meta-and-amazon-agentic-ai-eats-up-to-1000x-more-tokens-than-standard-ai","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/691264\/","title":{"rendered":"AI cost crisis hits tech giants as employee ‘tokenmaxxing’ backfires, sparking corporate pullback at Microsoft, Meta, and Amazon \u2014 agentic AI eats up to 1000x more tokens than standard AI"},"content":{"rendered":"

Many tech companies are pushing their employees to use AI tools and increase their productivity, but it seems that this initiative has begun to backfire. According to The Verge<\/a>, Microsoft<\/a> has been reportedly pushing its people to switch to its own Copilot CLI rather than Claude Code because it wants to use an internal tool rather than a third-party one. However, sources say the primary reason is that the cost of using Claude Code has been steadily increasing as more people use the AI tool.<\/p>\n

Microsoft is not alone in this, as Fortune<\/a> reports that other companies are also pulling back on AI usage. While it\u2019s true that the cost of training AI models is falling, making AI tokens more affordable, people have started using more tokens in their day-to-day tasks. This is particularly true for agentic AI, which can use a thousand times more tokens compared to querying an LLM, depending on the number of steps needed to accomplish your instructions. For example, OpenClaw creator Peter Steinberger claimed that his team spent more than $1.3 million in token costs<\/a> in just a single month. Because of this, it\u2019s now apparent that using AI is more expensive than hiring people<\/a>, especially since it offers only limited productivity gains at the moment.<\/p>\n

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Decreasing token costs, paired with increased usage, reminds us of the Jevons Paradox, in which increased efficiency has led to more people using a particular tool or technology. There are many examples of this throughout history \u2014 the introduction of efficient steam engines during the Industrial Revolution led more firms to deploy these tools to increase productivity. This is also evident in the airline industry: as planes became more fuel-efficient, lower ticket prices led to higher demand, and air travel demand is now on track to double by 2050, according to IATA.<\/p>\n

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It seems that this is also true with AI tools, especially as many companies are deploying them in a bid to increase productivity. Nvidia CEO Jensen Huang famously said that its engineers should use AI tokens <\/a>worth at least half their annual salary<\/a> each year to be fully productive, even going so far as to say, \u201cAre you insane?\u201d<\/a> to managers who discouraged AI use. This phenomenon, called \u201ctokenmaxxing,\u201d has led many employees to use AI for just about anything to hit internal targets. This was evident at Amazon<\/a>, where some team members admitted to using the tool for unnecessary tasks to inflate internal usage scores<\/a>, and it has also been reported at other companies, such as Microsoft and Meta. Incidentally, these companies are among the biggest spenders on AI development.<\/p>\n

It\u2019s unclear yet whether these companies will change their policies now that increased token use, which comes with associated costs, has become an issue. AI is indeed a useful tool, but some companies are using it to replace people<\/a> in a bid to cut labor costs. If the number of tokens needed to accomplish tasks outpaces the speed at which these tokens become cheaper, then that move might just backfire.<\/p>\n

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