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On October 28, 2026, Amazon updated its working guidelines for software engineers for the AI era. According to a TechSpot report, the guidelines require teams to measure their value by the software delivered with the help of AI coding tools rather than by token consumption, and warn against treating AI adoption rates, token usage, code changes, and code reviews as performance metrics; the company will still look at usage data, but it must be combined with other metrics. The report said the guidelines target tokenmaxxing behavior—deliberately inflating AI consumption to appear efficient on internal dashboards—and allow engineers to conclude that a given task is not suited to the available tools. 36Kr·AI reported on October 30 that this guidance document was disclosed by Vice President Terk Terkowitz and introduces the concept of the "Renaissance developer," requiring technical leaders to personally use AI in production work and to explain the scenarios in which it is effective and ineffective.
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Amazon releases engineer guidance document, opposing the use of Token consumption as a performance metricDevelopments
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Oct 30
36Kr · AI
Amazon releases engineer guidance document, opposing the use of Token consumption as a performance metric
Amazon has released a guidance document for software engineers that explicitly opposes using metrics such as Token consumption and AI tool adoption rates as performance goals, arguing that this would drive up costs and increase system complexity. The document was disclosed by Vice President Terk Terkowitz and introduces the concept of the "Renaissance developer," requiring technical leaders to personally use AI in production work and explain the scenarios in which it is effective and ineffective. Previously, Microsoft Executive Vice President Jay Parikh also asked his team in August to use AI rationally and set the lower-cost GPT-5.6 as the internal default model.
Oct 29
Analytics India MagazineRelated discussion
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TechSpot
Amazon asks engineers to stop gaming tokens and measure AI tools by delivered outcomes
Amazon has updated its engineering guidelines, requiring teams to measure the value of AI coding tools by the software they help deliver rather than by token consumption, and warning against treating AI adoption rates, token usage, code changes, and code reviews as performance metrics. The company says it will still look at usage data, but it must be combined with other metrics: too little usage may indicate missed opportunities for improvement, while too much usage should prompt the question of whether delivery has improved accordingly. The guidelines target tokenmaxxing behavior—deliberately inflating AI consumption to appear efficient on internal dashboards; earlier this year employees used the internal AI agent MeshClaw to inflate token counts, and in May Amazon shut down an internal leaderboard because its rankings were being gamed, saying it had not set token limits for employees. The guidelines also allow engineers to conclude that a task is not suited to existing tools, calling that a discovery rather than a failure.
Oct 28
Simon Willison
Terk Terkowitz: AI Not Being Helpful Is Also a Finding; Engineers Should Document and Share It
Amazon engineering leader Terk Terkowitz said in an internal update that if current AI tools are not helpful for the work, that is a finding rather than a failure, and one should document the reasons, share them with peers and managers, and look for solutions. He pointed out that engineers who can explain where AI cannot yet help them with their work are demonstrating the judgment that is expected.
IT Home · Artificial Intelligence
Amazon Updates Engineer Work Guidelines: Evaluations Look at Business Value, Not AI Usage
Amazon has updated its working guidelines for software engineers in the AI era, emphasizing that evaluations should look at how much business value engineers create, rather than how much AI they use. Vice President and Distinguished Engineer Turk Tekowitz stated that data such as token consumption, AI tool usage, and code reviews are somewhat related to work outcomes, but one cannot look only at the numbers; treating these metrics as goals may lead to negative results. The new guidelines also require managers to focus on examining the teams ranked in the bottom 10% and top 10% for AI adoption, and allow positions that are temporarily unable to benefit from AI tools to record the specific reasons.
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