Amazon became the latest tech giant to admit that maybe all AI all the time is not the best idea.
The retail giant has reportedly shut down its employee-led AI leaderboard (known as Kirorank), an internal mechanism the company used to encourage employees to use AI more often.
According to a report by the Financial Times, Amazon’s decision was based on two factors. The first was cost. More companies are using more AI; the additional usage, measured in tokens, is causing prices to skyrocket. The second reason is due to employees engaging in a practice known as tokenmaxxing, where they make AI do menial tasks to increase token usage so that they score better on the leaderboard. In short, Amazon was spending a lot of money on AI that wasn’t really doing anything.
These two problems reportedly cost Amazon enough money that the company decided to scale back its AI usage by removing the leaderboard and calling for a stop to tokenmaxxing.
“One of the internal dashboards, called Kirorank, was recently created by a group of employees who wanted to drive awareness for how AI can accelerate work, and was never intended to promote the use of AI for usage’s sake,” an Amazon spokesperson told CNET in an email. “The beta dashboard was not a formal or approved tool, and has since been deprecated. We’re focused on AI adoption and sharing best practices to celebrate innovation and operational efficiency gains across the company, and we’re proud of the way our teams are embracing this technology.”
Soothsayers probably saw this one coming as Amazon’s battle with tokenmaxxing was made public earlier in May. A leaked employee memo from Dave Treadwell, Amazon senior vice president, asked employees to stop “using AI just for the sake of using AI” in response to employees overusing AI to get to the top of the leaderboards. Amazon says it measured token usage to understand cost and efficiency but discouraged using those metrics to measure developer productivity.
Companies are pulling back on AI overuse
Amazon is one of several companies to admit that maybe they’re using a little too much AI. Meta forcibly closed down an employee-run AI leaderboard in April after employees engaged in tokenmaxxing to compete for the “Token Legend” status. In a Rapid Response interview last week, Uber Chief Operating Officer Andrew Macdonald admitted that the rideshare company was struggling to justify further AI costs after a viral interview from Uber Chief Technology Officer Praveen Neppalli Naga revealed that the company had blown through its entire 2026 AI budget in just a quarter of a year.
It’s difficult not to label these decisions as a trend. Microsoft began canceling Claude Code licenses in early May. According to the Wall Street Journal, Salesforce, DoorDash and several other big names have also gone from throwing AI at everything to rationing it amid soaring costs with lackluster returns.
Despite the pullback, generative AI use remains at an all-time high. Google announced that Gemini had jumped from 480 trillion tokens per month in May 2025 to 3.2 quadrillion tokens per month as of May 2026. One cause is that agentic AI, coding tools and always-on tools like OpenClaw — all of which have surged in popularity this year — burn through far more tokens than basic text prompts and responses from chatbots.
“I would say [companies pulling back on AI usage] isn’t surprising, but probably not enough of a slowdown that it is going to burst the generative AI bubble that we seem to be in,” said Jackie Rees Ulmer, dean of the Ohio University College of Business, in an email. “As companies get better at sorting the applications that provide real value versus using AI just for the sake of using AI, demand will only increase.”
This seems to be the rallying cry of these businesses. Will McGough, chief investment officer at Prime Capital Financial, told the Wall Street Journal the same thing, stating that companies were still very much “figuring things out” when it comes to effective AI use.
Ulmer says that the path forward is education, and that she encourages her students to learn more about AI applications relevant to their fields, but to always “double down” on “human skills, such as critical thinking and communication.”
