– Encouraging unlimited AI usage (“tokenmaxxing”) is proving to be an expensive failure, with companies like Microsoft and Uber now actively limiting it.
– Major tech firms like Meta and Amazon have removed internal AI usage leaderboards, signalling a shift away from incentivizing maximum AI consumption.
– Unchecked AI spending can lead to catastrophic costs, such as a single client accidentally spending $500 million in one month.
– Corporate AI adoption often fails to turn a profit or improve productivity, with 95% of public deployments failing to meet performance or financial goals.
Not long ago, headlines were abound concerning a move from executives to encourage that AI used as much as possible to improve productivity, often while also cutting employees. The most infamous example of that mentality at work was from Nvidia CEO Jensen Huang, who stated that he’d be deeply alarmed if Nvidia engineers were not burning half their $500K salary in AI tokens to get the job done. He even went so far as to compare it to a chip designer using paper and pencil instead of CAD.
The Costly Reality of Tokenmaxxing
Here in reality, it turns out that’s an egregiously expensive way to run a business even when you fire employees to pay for it, and megacorporations like Microsoft, Uber, Meta, and Amazon are reining in the excess of “tokenmaxxing.” While the first two are more directly limiting AI usage (with Microsoft canceling most of its Claude Code licenses and Uber operations chief Andrew Macdonald stating it’s “getting harder to justify” AI spending,) the latter two haven’t outright stated AI is too expensive or that they’re reining it in. What we have seen in response to leaks of internal AI token usage leaderboard leaks is that those leaderboards have been taken down, meaning tokenmaxxing is no longer being encouraged at those companies.
Accidental Spending and Mixed Results
It’s more than just Big Tech, as well. Per Gizmodo, an anonymous AI consultant informed Axios that one of its clients had accidentally spent half a billion USD in a single month by failing to limit Claude usage for its employees. We’ve also seen at best mixed reports of AI usage actually improving productivity: a report from OpenAI only indicates productivity improvements by “an hour a day” and an MIT study of 350 public deployments indicates that 95% of them fail to turn a profit or achieve performance goals.
A Turn in the Corporate Conversation
It’s an interesting turn for the conversation regarding AI in the enterprise, and indeed one that heavily contradicts the words of zealots who profit from its adoption at the expense of existing workers. Especially at the corporate level, AI is not free.
