Amazon: judge AI by results, not by how much it is used

the outcome, not which tools are used. Tue, 27 Oct 2026

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Amazon believes sharing our vision for the SDE role of the future can serve as a model for our customers.

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Based on key learnings and observations from the most successful SDEs at the company, they developed an updated role guideline.

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The following is a memo shared with software engineers across Amazon.

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how you should be using AI, and how you should be measuring AI usage in your teams and organizations.

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AI is changing the work of software engineering both more quickly and more fundamentally than these previous trends.

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two decades of technology trends (the cloud, microservices, serverless, etc.) have reshaped the work that SDEs do.

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Any definition we write now will likely look antiquated by this time next year.

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we have decided to focus on what is not changing as we evolved the SDE roles. Specifically, impact on our customers, our businesses, and our teams.

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The outcomes that matter aren’t changing, even if the inputs, tools, and techniques are changing faster than ever.

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we have tackled those problems by using input metrics instead, like token usage or adoption of AI tools, or throughput metrics like code changes and reviews. We are all aware of how deeply flawed these metrics are, primarily because they are disconnected from what matters.

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if used as a target instead of a measure, drive negative outcomes, increasing costs (tokenmaxxing is at odds with frugality), and increasing complexity (measuring LOC is at odds with invent and simplify).

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we have found that these input metrics correlate well with the outputs that matter and expect they will continue to do so for some time.

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Using these input metrics, with care and judgement, as you think about your career and your organization’s performance is appropriate.

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We recommend paying special attention to the 10th percentile (finding those teams who aren’t embracing AI effectively and understanding why) and the 90th percentile (finding those teams who are heavily using AI and understanding whether they’re seeing delivery increases to match) of AI adoption.

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If your work does not benefit from current AI tooling, that is a finding, not a failing. Document why, share it with your community of peers, bring it to your manager, and work to find solutions.

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Sufficient understanding means you have used these tools on your production work, can describe a task where they helped and a task where they did not, and can explain that judgement to your team.

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working with them hands-on rather than relying on what you hear

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making sure that your team or organization is using AI to the best effect to improve delivery for customers and your business.

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We believe that it is best to measure, and be measured by, long-term outcomes.

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