Exceeding company calculations
Uber teams are heading “back to the drawing board” as it counts the costs of surging AI usage, says Praveen.
Engineers were encouraged to adopt tools such as Claude Code and Cursor, even ranking staff on internal leaderboards based on usage. This strategy led to Claude Code becoming the dominant tool, while interest in Cursor plateaued.
This forms part of a wider industry trend known as “tokenmaxxing”. As tokens become a measurable unit of Gen AI usage, some organisations informally track how much engineers spend on these tools, often turning it into a competition within engineering teams.
As per reports, even companies such as Meta use internal dashboards to rank employees based on token usage. However, critics argue that high token consumption does not necessarily translate to better outcomes but wasteful spending.
This also becomes crucial at a time when AI budgets are already under pressure. At Uber, the rising usage is taking company costs to surging heights. To meet the growing need, Uber is now preparing to test OpenAI’s Codex in order to expand its AI stack.
The situation has created a scenario where the AI tools proved too successful to afford at scale as engineers reported monthly API costs between $500 and $2,000 per person.
Will it slow down hiring?
The numbers clearly reflect rising usage of AI tools within engineering teams. About 1,800 code changes implemented every week are written entirely by Uber’s internal AI coding agent without direct human input.
Additionally, nearly 95% of the company’s engineers use AI tools every month and close to 70% of the code that gets committed is generated by these systems.
