The Amazon software engineer AI memo published on October 27, 2026, tells engineers to judge artificial intelligence by long-term outcomes, not by how much AI they use. Amazon revised the role guideline for software development engineers, known as SDEs, and said impact on customers, the business and teams is the test that matters. The memo does not tell engineers to stop using AI. It tells them to stop treating usage itself as proof of good work.
PetaTech24 Technology Desk | October 29, 2026
The Amazon software engineer AI memo came with an introduction from Terk Terkowitz, vice president and distinguished engineer for Worldwide Amazon Stores, on AboutAmazon. The memo was addressed to SDEs and software development managers, known as SDMs. CNBC reported that it had circulated inside Amazon in September before publication. The public version is presented as a model for customers, not only as internal instruction.
Amazon framed the change as early, not finished. The Amazon software engineer AI memo said, as reported by CNBC: “We’re just seeing the beginning of AI’s effect on software engineering.”
In Brief
- The Amazon software engineer AI memo measures outcomes, not tools used.
- Token usage, adoption, code changes and reviews are deeply flawed as targets.
- The same measures still correlate with delivery, so use them with care.
- Leaders must examine the 10th and 90th percentiles of adoption.
- Work that gains nothing from current AI tools is a finding, not a failing.
- No token caps have been announced, a spokesperson told CNBC.
What the Amazon Software Engineer AI Memo Actually Says
Amazon published the update on AboutAmazon, with Terkowitz explaining that senior engineering leaders had reaffirmed what it means to be an SDE at Amazon. The revised guideline drew on observations of the company’s most successful engineers. Their output, the introduction says, is not defined by the use of AI.
The memo gives that idea a name. Terkowitz wrote: “We’re now in the era of the Renaissance Developer, blending AI tools with human judgment and domain expertise to build entirely new things.” The phrase describes a blend, not a handover. Human judgment and domain expertise stay in the work.
The Amazon software engineer AI memo avoids fixing a definition of good AI practice into the role guideline. Secondary summaries report its reasoning in these words: “Any definition we write now will likely look antiquated by this time next year.” The guideline anchors the role in outcomes that are not changing, even as tools change faster than before. The memo presents AI as more fundamental than the cloud, microservices and serverless shifts.
Why the Amazon Software Engineer AI Memo Calls Input Metrics Flawed
The warning is about targets, not about data. The Amazon software engineer AI memo calls token usage, adoption and throughput measures deeply flawed when used as targets instead of measures, because they are disconnected from what matters. PYMNTS quoted the memo directly: “We are all aware of how deeply flawed these metrics are, primarily because they are disconnected from what matters.” A number meant to describe work can start to direct work.
Two examples tie the warning to Amazon leadership principles. The memo said, as quoted by PYMNTS, that using the metrics as targets could 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).” Tokenmaxxing, as CNBC described it, means using AI as much as possible with little regard for output. More tokens cost more, and more lines of code can mean a harder system to maintain.
The Amazon software engineer AI memo does not discard the numbers. It says the input measures currently correlate well with the outputs that matter, according to secondary reporting that quotes the memo. Teams that embraced AI most, called frontier teams, are delivering more, while teams that are not adopting AI are falling behind. The distinction is between using a correlation to ask questions and using a count to judge a person.
That distinction leads to the percentile guidance. The Amazon software engineer AI memo asks leaders to examine the 10th percentile of adoption, to find teams not embracing AI effectively and understand why, and the 90th percentile, to check whether heavy use matches higher delivery. A low number may point to a barrier or a task where AI helps less. A high number may point to strong delivery, or to activity that has not turned into results.
The Backstory in May
The Amazon software engineer AI memo did not arrive without context. CNBC reported that Amazon shut down an internal leaderboard in May after discovering employees were tokenmaxxing to climb the ranks. A leaderboard rewards the number it displays, so the episode showed the incentive problem the October memo warns about. The memo itself does not restate the May episode in detail in the excerpts reported.
The same CNBC reporting carried a limit that matters. An Amazon spokesperson told CNBC that the company had not instituted caps on employees’ token usage. The Amazon software engineer AI memo is therefore not a ban on heavy AI use, and it is not a token budget.
What the Amazon Software Engineer AI Memo Asks Leaders to Do
Leaders get a different job in this framing. Good leaders, the Amazon software engineer AI memo argues, deeply understand their team’s work, study many examples rather than only averages, and track outputs carefully instead of relying on surface-level measures. Secondary summaries describe leaders as expected to use AI tools hands-on in production work, so they can judge where the tools help and where they do not.
The Amazon software engineer AI memo also protects a specific kind of honesty. It says, as quoted by PYMNTS: “If your work does not benefit from current AI tooling, that is a finding, not a failing.” Engineers are asked to document why, share the finding with peers, bring it to a manager and work to find solutions. The memo adds: “An engineer who can explain where AI does not yet help their work is demonstrating the judgement we are asking for.” The spelling of judgement follows the quotation as published. Learning the tools remains expected, even when a particular task does not benefit yet.
What Is Still Unanswered
The Amazon software engineer AI memo leaves the hardest measurement problem open. Long-term outcomes are slower to observe than token counts, and the memo does not prescribe one replacement metric, a review formula or a timetable in the reporting seen here. It says better metrics and smarter goals should emerge over time. Readers should not treat that as a finished system.
The Amazon software engineer AI memo leaves other practical questions unanswered, including how reviews will weigh outcomes against input data. No cost figures, adoption percentages or delivery figures from the memo were published in the secondary reporting relied on here.
One limit applies to this article. The AboutAmazon page could not be fetched directly during research because the fetch service returned a rate limit error, so quotations here are only those reproduced verbatim in secondary reporting, principally PYMNTS and CNBC reporting as republished. Paraphrase is attributed where full memo wording was not reproduced. No quotation has been reconstructed from a summary.
Quick Answers
What does the Amazon software engineer AI memo say engineers should be measured by?
Long-term outcomes for customers, the business and teams. The Amazon software engineer AI memo says that test should replace input and throughput counts as the measure of good AI-powered engineering.
Is Amazon telling engineers to use less AI?
No. Engineers are urged to learn AI tools and keep improving their practice, and teams that do not adopt AI are described as falling behind. The objection is to scoring people on usage, not to using tools where they help.
What is tokenmaxxing?
As CNBC described it, tokenmaxxing means using AI as much as possible with little regard for output. Heavy use raises cost, and chasing the count can raise costs and complexity without proving better results.
What should an engineer do if AI does not help a task?
Document why, share the finding with peers, bring it to a manager and work to find solutions. A clear explanation of where AI does not yet help is presented as evidence of judgment.
The Takeaway
The Amazon software engineer AI memo is a warning about pressure in new clothes: when a measure becomes a target, people optimize the measure. Amazon’s answer is to keep the data and change the judgment.
For other teams, the usable lesson is narrow. Start with the outcome, choose AI where it moves that outcome, and keep a written record where it does not. The Amazon software engineer AI memo does not settle how long-term outcomes should be measured, and that unfinished work is the part to watch next.
Sources and Attribution
- Amazon, AboutAmazon, “An update from Amazon engineering leadership on the role of the developer in the age of AI,” Terk Terkowitz, October 27, 2026: AboutAmazon update.
- PYMNTS, “Amazon Tells Engineers Long-Term Outcomes Define AI Success,” October 27, 2026: PYMNTS report.
- The Register, “Amazon tells tokenmaxxing devs to slow their AI roll,” October 28, 2026: The Register report.
- CNBC reporting by Annie Palmer, October 27, 2026, on September circulation, the May leaderboard shutdown and no token caps, as republished in secondary coverage. No direct CNBC URL was verified, so no CNBC link is included.
