Amazon memo: Token consumption and other metrics are not suitable for measuring AI productivity
According to CNBC, Amazon publicly posted an internal memo on its company website on October 27, 2026 (circulated internally in September), guiding software engineers to avoid tokenmaxxing when using AI tools.
The memo said that token consumption, AI tool adoption rates, and code change or review volume are “seriously flawed” metrics for measuring how a team or organization uses AI, and may lead to higher costs and unnecessary complexity. The company said these metrics should be used cautiously and with judgment together with other data, with priority given to measuring long-term outcomes.
Amazon closed its internal AI usage leaderboard in May after finding that some employees were tokenmaxxing in order to climb the rankings. The memo said that if your work cannot benefit from the current AI tools for now, that is a finding, not a failing. Engineers who can explain why AI has not yet helped their work are demonstrating the judgment the company requires.
Original sources:
Title: Amazon's anti-tokenmaxxing memo says engineers should be pickier about using AI Publisher: CNBC Publication date: 2026-10-27 URL: https://www.cnbc.com/2026/10/27/amazon-ai-coding-tokenmaxxing.html Source nature: news report Verification status: key paragraphs read in full for this report Supported content: memo content, May leaderboard closure, tokenmaxxing, the finding, not a failing wording, and metric evaluation
Title: Amazon tells software engineers to avoid ‘tokenmaxxing’ AI use Publisher: Seeking Alpha Publication date: 2026-10-28 URL: https://seekingalpha.com/news/4658620-amazon-tells-software-engineers-to-avoid-tokenmaxxing-ai-use Source nature: news report Verification status: read in this report Supported content: memo points consistent with CNBC report
ABAB AI Insight
Amazon had previously pushed AI adoption and tracked usage. It closed the leaderboard in May after discovering that employees were increasing token consumption with unnecessary tasks to improve their ranking. This memo further makes clear that usage metrics alone cannot effectively reflect productivity.
The company proposes measurement methods oriented toward long-term outcomes such as feature velocity, and recognizes engineers’ judgment in determining when AI is not suitable. This move reflects a time when, amid rising AI tool costs, companies are beginning to distinguish usage from actual value.
Reports have also described similar internal usage pressure and metric issues at other tech companies. Amazon’s public stance this time provides the industry with a concrete example of shifting from usage-oriented to outcome-oriented measurement.
ABAB News · Cognitive Law
Once usage metrics become targets, they are prone to inducing behavior that diverges from actual value.
After closing the leaderboard, companies still need to define alternative ways to measure outcomes.
Recognizing that AI is temporarily unsuitable can reduce unnecessary resource consumption.