Amazon Warns Against AI Tokenmaxxing as Costs Rise

Amazon is discouraging employees from “tokenmaxxing”, a trend in which workers, particularly in the technology industry, use Artificial Intelligence tools as extensively as possible. The move comes as companies reassess how they measure AI adoption and manage the rising costs associated with increasingly intensive use of these tools.

The guidance appears in an internal memo outlining AI usage guidelines for Amazon software engineers. Circulated internally in September, the memo was published on the company’s website on Tuesday, October 27.

In the document, Amazon warns that metrics such as AI token consumption, adoption rates and the volume of code changes or reviews can provide a misleading picture of how effectively teams use artificial intelligence. The company describes these measures as “deeply flawed” when used to evaluate teams or organisations, noting that they can encourage unnecessary complexity and increase costs.

Amazon has not instructed employees to disregard these indicators entirely. Instead, it says they should be considered carefully alongside professional judgement, with greater emphasis placed on long-term results rather than the volume of AI activity.

What is tokenmaxxing, and why is Amazon discouraging it?

Tokenmaxxing refers to the practice of using AI tools as extensively as possible, often with token consumption treated as a measure of productivity or technological adoption. The term has gained attention as companies encourage employees to incorporate AI into their everyday work.

Tokens are units used to process text and other information in AI systems. Depending on the model and service, usage can be measured through the amount of input and output processed during interactions. More extensive use can therefore translate into higher charges, particularly when companies pay according to consumption.

While increased AI usage can help employees complete certain tasks more efficiently, the amount of activity generated by an AI tool does not necessarily indicate how much useful work has been accomplished.

An employee might generate large amounts of AI output, make numerous code changes or repeatedly consult an AI assistant without producing a proportionate improvement in the final result. In some cases, excessive experimentation or unnecessary AI-generated changes could make a project more complicated.

Amazon’s guidance reflects this distinction between using AI frequently and using it effectively. The company is asking employees to consider whether an AI tool actually improves their work instead of treating consumption as an objective in itself.

The concern is particularly relevant when organisations use internal dashboards, leaderboards or performance indicators to encourage AI adoption. If employees are rewarded for increasing usage rather than improving outcomes, they may have an incentive to consume more tokens without delivering corresponding business value.

Amazon questions AI usage as a productivity metric

Amazon’s memo identifies several indicators that can be unreliable when used to assess AI adoption and performance. These include token usage, the rate at which employees adopt AI tools, and the number of code changes or reviews completed.

Such measurements can help organisations understand how employees interact with AI systems. However, Amazon argues that they are inadequate as standalone measures of productivity because they focus on inputs and activity rather than the quality or long-term value of the work produced.

High token consumption, for example, does not automatically mean that an engineer has solved a difficult problem or delivered better software. Similarly, widespread adoption of an AI tool does not establish that the tool is suitable for every task.

The company has not said that these metrics should be abandoned completely. Instead, it has emphasised that employees and managers can use them with care and judgement when considering career development and organisational performance.

“Using these input metrics, with care and judgement, as you think about your career and your organization’s performance is appropriate,” Amazon wrote in the memo.

The distinction is important because companies need ways to evaluate how AI tools are being used, but those measurements can produce unintended behaviour if they become targets in their own right.

Amazon’s position suggests that AI adoption should be evaluated through a broader assessment of productivity, quality and business outcomes rather than token consumption alone.

Why AI costs are becoming harder to predict

Amazon’s warning comes as businesses face changing economics around AI services. Although the price of individual tokens has been falling, the total cost of completing a task can rise when AI systems are used more extensively or when services move towards usage-based pricing.

Token prices and overall AI expenditure are not the same thing. A lower price per token does not necessarily result in a lower bill if employees and applications consume significantly more tokens to complete their work.

Companies may also find it difficult to estimate how much AI usage a particular task will require. A simple request might involve a short interaction, while a complex software engineering task could require multiple rounds of analysis, code generation, testing and revision.

As AI tools become more integrated into daily workflows, usage can expand across departments and applications. When costs depend on consumption, this creates the possibility of unpredictable expenditure, particularly when organisations do not have a clear understanding of which activities generate the greatest value.

The shift from flat subscriptions towards usage-based pricing can make this challenge more visible. Under a flat subscription, businesses may pay a relatively predictable fee for access to a service. Under consumption-based arrangements, costs can vary according to how much the service is used.

As a result, even when individual tokens become cheaper, higher usage per task can push total spending upwards. Organisations must therefore consider not only the cost of AI models but also how efficiently employees and automated systems use them.

How companies are responding to rising AI bills

Amazon is not the only major technology company reconsidering how employees use AI tools. Businesses across the sector have begun examining whether intensive usage delivers sufficient productivity gains to justify the associated costs.

Some organisations have introduced limits on employee token consumption to slow rising AI expenditure. Others have shifted certain workloads to cheaper open-source models when those systems can meet their requirements.

These approaches reflect different ways of managing the same challenge: ensuring that AI tools remain useful without allowing usage costs to grow without adequate business justification.

Usage limits can help companies control spending, but strict caps may also restrict legitimate work if employees need more computing resources for complex tasks. Choosing cheaper models can reduce costs for suitable workloads, although businesses must still consider whether a model delivers the required quality and performance.

Amazon’s approach, as described in the memo, places particular emphasis on judgement and long-term outcomes rather than imposing a universal limit on token consumption.

The company has not introduced caps on employees’ token usage, according to the supplied report. Its guidance instead discourages treating increased consumption as a reliable indicator of success.

This distinction means Amazon is not simply asking workers to use less AI. It is asking them to consider whether the technology is helping them accomplish their objectives and whether the benefits justify the resources consumed.

Amazon previously shut down an internal AI leaderboard

Amazon’s latest guidance follows an earlier episode involving an internal leaderboard designed to track employee AI usage.

According to a CNBC report cited in the supplied content, Amazon shut down the leaderboard in May 2026 after discovering that employees were engaging in tokenmaxxing to improve their rankings.

The episode illustrates how measurement systems can influence employee behaviour. A leaderboard intended to encourage AI adoption can become counterproductive if workers begin optimising for the ranking itself rather than for the quality of their work.

When employees are encouraged to maximise a visible metric, they may prioritise activities that improve that number even when those activities offer limited practical value. In the case of AI usage, that could mean generating more interactions or consuming more tokens simply to appear more active.

Amazon’s subsequent emphasis on long-term outcomes reflects a different approach to measuring performance.

“We believe that it is best to measure, and be measured by, long-term outcomes,” the company said in its memo.

The statement places the focus on the results of employees’ work rather than on how extensively they use a particular technology. It also suggests that AI adoption should be judged by its contribution to meaningful objectives instead of the volume of activity it produces.

Amazon says employees should not use AI for every task

The memo also addresses when employees should avoid using AI tools. Amazon has discouraged software engineers from using AI when it does not make sense for a particular task.

This guidance challenges the assumption that every workflow should incorporate AI simply because the technology is available. Some tasks may benefit from automation or AI-assisted analysis, while others may be completed more effectively through existing methods.

Amazon explicitly states that identifying situations in which current AI tools provide little benefit should not be viewed as a failure.

“If your work does not benefit from current AI tooling, that is a finding, not a failing […] An engineer who can explain where AI does not yet help their work is demonstrating the judgement we are asking for,” the company wrote.

The message places responsibility on engineers to evaluate the suitability of AI tools for the work they perform. Rather than adopting the technology indiscriminately, employees are expected to understand where it improves their processes and where it does not.

That approach recognises that AI capabilities vary across tasks. A tool may help generate or review code in one situation but offer limited value in another. The appropriate decision depends on the problem being addressed and the quality of the result.

For businesses, this distinction could be important as AI becomes more widely available. Encouraging employees to use the technology where it offers genuine benefits may help organisations avoid unnecessary expenditure and reduce the risk of introducing complexity into otherwise straightforward workflows.

How Amazon is redefining software engineering in the AI era

Amazon’s memo also revisits the roles of software development engineers and managers as AI changes how software is produced.

The company says the transformation is still in its early stages and that many practices are yet to emerge. However, it expects artificial intelligence to become a central part of software engineering.

“We’re just seeing the beginning of AI’s effect on software engineering. While we have a vision for what the future looks like, there’s a lot that’s still emerging. We are certain that whatever future best practices are, AI will be at the center of software engineering practice,” Amazon said.

The statement indicates that Amazon expects AI to remain an important part of software development, even as organisations learn how to use it more effectively.

For software engineers, this could mean greater emphasis on deciding when to use AI, evaluating its output and determining whether the results meet the requirements of a project. The volume of code generated may be less important than the ability to deliver reliable software and make sound technical decisions.

Managers also face the challenge of evaluating work in an environment where AI can influence both the speed and the methods used to complete tasks. If traditional activity-based measures become less informative, organisations may need to place greater emphasis on quality, reliability and the broader impact of completed projects.

Amazon’s memo does not present a final model for AI-era software engineering. Instead, it acknowledges that practices are evolving while establishing a clear principle: AI should support effective engineering rather than become a target for usage in itself.

Andy Jassy’s warning about AI and jobs

Amazon’s effort to improve the effectiveness of AI use comes amid broader changes in the company’s workforce strategy.

Chief executive Andy Jassy has previously said that AI will “change the scope and speed” of everyday work. He has also warned that automation could reduce Amazon’s corporate workforce over the next few years, while noting that the technology will create other jobs.

These comments reflect the potential for AI to change how businesses organise work and allocate responsibilities. As some tasks become easier to automate, companies may reconsider staffing requirements, job responsibilities and the skills employees need.

However, the impact is not limited to reducing the time required for individual tasks. Organisations must also determine whether AI-assisted work produces sufficiently reliable results, how much oversight it requires and whether the cost of using the technology is justified.

Amazon’s tokenmaxxing guidance addresses one part of this broader challenge. If AI consumption rises without a corresponding improvement in outcomes, increased usage alone cannot establish that a team has become more productive.

The company’s position therefore connects two questions that businesses must address as AI adoption expands: how the technology changes work and how organisations measure the value it delivers.

Amazon’s latest job cuts add to workforce changes

The company’s AI guidance also arrives during a period of workforce reductions at Amazon.

According to the supplied report, Amazon let go of fewer than 1,000 white-collar employees earlier in October 2026, with most of the cuts affecting its retail unit.

The reductions are the latest in a series of smaller job cuts following a larger round of approximately 30,000 job cuts that began in 2025 and continued into January 2026.

Jassy’s earlier comments about AI and the corporate workforce have placed automation among the factors being discussed in relation to Amazon’s longer-term employment outlook. However, the supplied information does not establish that the latest job cuts were directly caused by tokenmaxxing concerns or by the new AI usage guidelines.

The distinction matters because workforce reductions can occur for multiple reasons, and the available details do not establish a direct causal link between these specific measures and the company’s staffing decisions.

What Amazon’s tokenmaxxing policy means for businesses

Amazon’s guidance highlights a broader issue facing companies adopting generative AI: measuring usage is easier than measuring value.

Token consumption, adoption rates and code activity can show how frequently employees interact with AI systems. But those figures do not independently establish whether the technology has improved quality, reduced the time required to complete a task or delivered a meaningful business benefit.

Companies that reward usage without considering outcomes may encourage employees to increase consumption simply to meet expectations. That can lead to higher costs and more complicated workflows without necessarily improving productivity.

At the same time, discouraging unnecessary usage does not mean that AI should play a smaller role in every business. Where the technology improves results, additional usage may be justified. The challenge is distinguishing productive consumption from activity that offers little benefit.

Amazon’s memo provides a clear indication of the principle guiding its approach: employees should use AI with judgement, understand where it helps and evaluate their work through long-term outcomes.

The company has not introduced token caps, but its decision to discourage tokenmaxxing signals that unrestricted enthusiasm for AI usage is not enough. As businesses face increasingly complex pricing models and expanding adoption, they must establish whether the technology is delivering value that justifies its cost.

The bottom line

Amazon’s warning against tokenmaxxing reflects a shift in how major technology companies are approaching artificial intelligence. Rather than treating token consumption or widespread adoption as proof of productivity, the company is urging employees to focus on meaningful results.

The guidance does not amount to a ban on AI usage, nor has Amazon imposed a general cap on employees’ tokens. Instead, it asks engineers and managers to use judgement, avoid unnecessary AI interactions and recognise that some tasks may not benefit from current tools.

With AI costs becoming harder to predict and software engineering practices continuing to evolve, the central question for Amazon is no longer simply how much AI its employees use. It is whether that usage helps them produce better work and achieve stronger long-term outcomes.