Amazon Posts Internal Memo: Calling Token Usage Metrics 'Deeply Flawed,' Discourages 'Tokenmaxxing'
Amazon releases updated SDE role guidelines, explicitly rejecting token consumption and code generation volume as primary AI adoption metrics in favor of long-term outcomes.
US tech giant Amazon published an internal memo on its official website on October 27, 2026, aimed at redefining the role of software engineers in the age of artificial intelligence. Developed by Amazon's engineering leadership, the memo states that measuring team or organizational AI usage solely through token usage, AI tool adoption rates, or code change counts is "deeply flawed," as these metrics are disconnected from what matters and can lead to increased costs and complexity.
The memo specifically criticizes "tokenmaxxing," a behavior where developers use AI tools as much as possible with little regard for output. Amazon shut down an internal leaderboard in May after discovering employees were engaging in this practice to climb ranks. While the company has not instituted hard caps on employee token usage, the new guidelines advise using these input metrics with "care and judgment" and emphasize focusing on "long-term outcomes."
Marc Brooker, Vice President and Distinguished Engineer at Amazon Web Services, told CNBC in an interview that the company is shifting towards metrics like "feature velocity"—such as the number of successful changes in production and launch frequency—to assess AI's true impact on engineering productivity. He described this as a return-on-investment analysis, advocating for investing resources into solving important problems for customers and the business rather than wasting effort on tasks that don't matter.
Additionally, the memo notes that if your work does not benefit from current AI tooling, "that is a finding, not a failing." An engineer who can explain where AI does not yet help their work is demonstrating the judgment the company is asking for. This stance reflects the latest management thinking among large tech companies grappling with surging AI costs and the difficulty of reliably measuring productivity gains.