Why it matters: Companies that rushed to embrace AI are now confronting ballooning IT costs, uncertain productivity gains and growing employee skepticism.
Driving the news: Microsoft canceled most of its Claude Code licenses, in part over costs, according to The Verge, and Uber’s COO said AI costs are getting “harder to justify.”
An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees.
Companies are citing AI's ability to automate jobs as a cause for layoffs, though Anuj Kapur, CEO of CloudBees, told Axios that workforce cuts may simply be "the only lever they can pull" to offset their AI bills.
Consumer sentiment around AI is also nosediving, and employees are rebelling against the use of the technology at work.
What they’re saying: The enterprise is undergoing a “healthy swing” away from AI overuse — or “tokenmaxxing,” the push to burn as many AI tokens as possible — Ali Ansari, CEO of model training firm Micro1, told Axios.
Ansari hopes this correction will push companies toward more efficient AI use.
While the market views these tools as working equally well across the enterprise, Ansari says "the reality of AI right now is that it only works for coding."
That disconnect can drive up IT bills without leading to high return on investment in agents, he said.
Friction point: Corporate AI adoption is running into four unique problems.
Use cases: "Most people default to automating tasks they dislike rather than tasks most valuable to the company," Sophia Velastegui, CEO of Velastegui Ventures and former chief AI officer at Microsoft told Axios. Instead, they should focus on using AI to drive revenue.
Costs: One CTO told Axios that employees were using AI models to check the weather. That gets expensive fast: Enterprise AI plans are not truly 'all you can eat,' and even simple chatbot queries can carry heavy token costs.
Isnt that the whole area of AI though. Its not based on fundamentals. Its not based on proven productivity gains. Sure, anecdotally, some engineera are increasing their output dramatically. But also many people are feeling prodictice while producing slop.
AI has the potential to reduce so many monotonous tasks and streamline processes that currently need a human.
Companies and managers are rushing in based on that potential, trying tonfigure out how to meet it as its new. However, the big driver seems to be a fear of missing out. Thats a bubble.
The facr that antropic seems to have overtaken openai and google has overtaken them in the consumer space says that maybe nobody will be ledt that far behind, if they just wait for a use case.
Sure, anecdotally, some engineera are increasing their output dramatically.
And it’s questionable whether they really are.
A) Are they actually producing more? Some studies on the subject have found that coders who use AI think they’re more productive that way, but their productivity actually goes down when objectively measured.
B) Yes, they’re churning out lots and lots of code, but is the code any good? Does it even work? Is it riddled with bugs that will have to be fixed later?
C) Is all that code they’re churning out maintainable? Does anyone working on it actually understand how it works? Will they be able to make updates and changes to it over time? … Or is all this ‘productivity’ coming at the cost of piling up huge amounts of technical debt in the future that will have to be paid when future devs have to wade through and fix all the AI slop code?
The amount of “what does this regex do?” - “dunno, ChatGPT wrote that for me” I see in code reviews these days doesn’t inspire much confidence in either B or C. I don’t have direct observational evidence for or against A but at least there’s maybe a claim for people who restrict its use to very specific use cases.
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Isnt that the whole area of AI though. Its not based on fundamentals. Its not based on proven productivity gains. Sure, anecdotally, some engineera are increasing their output dramatically. But also many people are feeling prodictice while producing slop.
AI has the potential to reduce so many monotonous tasks and streamline processes that currently need a human.
Companies and managers are rushing in based on that potential, trying tonfigure out how to meet it as its new. However, the big driver seems to be a fear of missing out. Thats a bubble.
The facr that antropic seems to have overtaken openai and google has overtaken them in the consumer space says that maybe nobody will be ledt that far behind, if they just wait for a use case.
And it’s questionable whether they really are.
A) Are they actually producing more? Some studies on the subject have found that coders who use AI think they’re more productive that way, but their productivity actually goes down when objectively measured.
B) Yes, they’re churning out lots and lots of code, but is the code any good? Does it even work? Is it riddled with bugs that will have to be fixed later?
C) Is all that code they’re churning out maintainable? Does anyone working on it actually understand how it works? Will they be able to make updates and changes to it over time? … Or is all this ‘productivity’ coming at the cost of piling up huge amounts of technical debt in the future that will have to be paid when future devs have to wade through and fix all the AI slop code?
The amount of “what does this regex do?” - “dunno, ChatGPT wrote that for me” I see in code reviews these days doesn’t inspire much confidence in either B or C. I don’t have direct observational evidence for or against A but at least there’s maybe a claim for people who restrict its use to very specific use cases.
We all know the answer to C. In a couple of years it will be a good time to open a “tech debt reduction” consulting company.