Amazon just issued new guidelines for software engineers in the AI era: Prioritize business outcomes over raw usage.
Terk Terkowitz, Amazon’s vice president and distinguished engineer, said in a memo published Tuesday that AI is changing software development more quickly and fundamentally than earlier shifts such as cloud computing. But engineers should be judged by their impact on customers and the business, he wrote, not simply how much they use AI.
“We all need to embrace this change and take the time to learn, or we risk being left behind,” Terkowitz said.
AI coding tools and agents are upending the software industry and changing what it means to be a coder. Amazon calls this the “era of the Renaissance Developer,” where human judgement and machine intelligence combine to create new products and services faster and more efficiently.
“This is a time of change for our industry, and a time of change for what it means to be a software engineer,” Terkowitz wrote in the memo.
The updated guidelines reflect Amazon’s growing effort to make AI adoption part of how it assesses engineers and engineering managers. The company has tracked measures including AI usage, token consumption, and lines of code internally, and used leaderboards to rank teams by some of those metrics.
But the memo warns that using such measures as targets can produce bad outcomes, including “tokenmaxxing” rather than better software or customer experiences.
“Using these metrics could, if used as a target instead of a measure, drive negative outcomes,” Terkowitz wrote in the memo.
Amazon has been experimenting with ways to measure and encourage AI use across its engineering organization.
Earlier this year, the company was tracking engineers’s use of internal AI tools and some teams used leaderboards tied to measures such as token consumption. The programs created friction among some employees, who worried that pressure to use AI could reward wasteful activity rather than productive work.
The new guidance offers Amazon’s clearest public acknowledgment of that risk. Rather than lay out a detailed AI playbook that could quickly become obsolete, Amazon said its revised developer guidelines focus on long-term impact on customers and the business.
Still, the company said AI should be “at the center of software engineering practice” and urged engineers to incorporate the technology into their daily work.
Leaders are expected to use AI tools on production work themselves, understand where they help and where they fall short, and explain that judgment to their teams, according to the memo.
Terkowitz said Amazon has found that metrics such as token use, AI-tool adoption, and code reviews can correlate with the outcomes it values. But he said the measures must be used carefully. More token use can raise costs, while a focus on lines of code can create unnecessary complexity.
He also recommended that managers pay particular attention to teams at both ends of the AI adoption spectrum: those in the bottom 10%, to understand why they are not using AI effectively, and those in the top 10%, to determine whether heavier use is translating into faster delivery.
Terkowitz said Amazon’s most AI-forward “frontier teams” have shown stronger productivity and delivery. He also said teams and individuals that are not adopting AI or increasing output metrics are falling behind.
But his memo left room for engineers whose work may not yet benefit from current AI tools. In those cases, he said, employees should document why and work toward a solution by sharing their findings with peers and managers.
“An engineer who can explain where AI does not yet help their work is demonstrating the judgement we are asking for,” the memo said.
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