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Amazon Tells Engineers Outcomes, Not Tokenmaxxing, Define AI Success

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Amazon Tells Engineers Outcomes, Not Tokenmaxxing, Define AI Success

For software engineers and managers, the way to determine good artificial intelligence (AI)-powered engineering practices is through long-term outcomes, Terk Terkowitz, vice president and distinguished engineer, Worldwide Amazon Stores, wrote in a memo shared with software engineers across Amazon and posted online Tuesday (Oct. 27).

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Terkowitz contrasted that metric with input metrics such as token usage or adoption of AI tools, and with throughput metrics such as code changes and reviews.

“We are all aware of how deeply flawed these metrics are, primarily because they are disconnected from what matters,” Terkowitz wrote in the memo. “We know that using these metrics could, 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 [lines of code] is at odds with invent and simplify).”

Amazon has seen that teams that have embraced AI have increased productivity and delivery, while those that have not adopted the technology have fallen behind, Terkowitz said.

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However, good leaders understand their team’s work rather than just surface-level metrics, he added.

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


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