For years, companies learned to monitor the consumption of servers, storage, software licenses, and cloud infrastructure. With the popularity of generative Artificial Intelligence, a new metric has started to influence technical and financial decisions: tokens.

Although invisible to most users, tokens have become the basic unit of operation for language models. They determine how much an AI processes, how much each interaction costs, and, increasingly, how much value a company can extract from its AI initiatives.

The problem is that many organizations began adopting generative models even before developing governance practices to control their use.

The result is an uncertain scenario: while some companies encourage employees to generate more and more tokens, others are already facing difficulties in controlling the costs of this strategy.

Understanding this movement is the first step toward using AI in a sustainable way.

Why tokens are important

In language models, tokens are the units used to process text. Each question sent to an AI consumes input tokens. Each generated response consumes output tokens.

The larger the conversation, the analyzed document, or the executed task, the larger the processed volume.

During the early years of generative AI, the discussion was focused on the quality of the models. Today, as these tools become part of companies' operations, attention is shifting to the cost of utilization.

This happens because tokens have ceased to be just a technical measurement and have instead begun to represent an economic unit. In many corporate environments, they are already monitored in the same way as cloud infrastructure resources.

What is Tokenmaxxing?

One of the most recent discussions emerged within Amazon. The company developed an internal tool called MeshClaw to automate corporate tasks and encourage the daily use of AI among employees.

The goal was to increase productivity. In practice, however, some employees began using the tool for tasks without operational relevance just to boost usage metrics.

This behavior became known as tokenmaxxing.

The term describes the practice of maximizing token consumption to demonstrate AI adoption, regardless of actual efficiency gains.

According to reports published by the international press, Amazon began monitoring the use of these tools through internal dashboards and established adoption goals for developers. Consumption ended up turning into a visible metric for managers, creating incentives to boost numbers instead of necessarily generating results.

Similar cases were also observed at Meta, where teams began competing informally to produce ever-larger volumes of tokens. The episode shows that measuring usage is not the same as measuring value.

The economics behind AI

If tokenmaxxing is related to user behavior, tokenomics is related to resource management. The concept describes the economy of tokens within Artificial Intelligence ecosystems.

In practice, it involves issues such as:

• cost per interaction;

• model efficiency;

• return on investment;

• systems architecture;

• consumption of computing resources.

Recent research on AI agents already treats tokens as central elements of the economy of intelligent systems. After all, each automation, assistant, or agent directly relies on them to function.

As AI advances into complex corporate environments, understanding this relationship between performance and cost ceases to be a technical concern and becomes part of the business strategy.

The Token Crunch phenomenon

If tokenmaxxing encourages consumption, and tokenomics seeks to understand it, the token crunch arises when costs begin to pressure operations.

In recent months, companies have begun to report accelerated growth in expenses related to the use of language models.

An escalation that can be the result of simple thinking: a single interaction usually seems cheap. Thousands of them per day remain manageable. Millions of interactions in production represent a completely different reality.

In some cases, organizations discovered that features initially implemented as experiments began generating significant expenses when scaled to their entire user base.

The token crunch represents precisely this moment of adjustment, when companies need to find a balance between innovation, productivity, and financial sustainability.

Curiously, the market is currently experiencing a paradoxical situation. In just a few weeks, the discussion went from encouraging maximum token consumption to debates on cost control and operational efficiency.

This speed helps explain why governance is becoming such a relevant topic.

The risk that goes beyond costs

The challenges related to tokens are not limited to the budget. As employees use AI tools to gain productivity, the circulation of corporate information on external platforms also grows.

Projects, contracts, source code, financial information, commercial strategies, and customer data frequently end up being shared in prompts without the organization having visibility into it.

Recent surveys show that the majority of employees have already used AI tools with corporate data during professional activities.

The problem is not necessarily the use of technology itself. The risk arises when it happens without clear policies, without audits, and without control over the destination of the processed information.

How to use tokens consciously

The efficient use of AI is not linked to a higher volume of consumption, but to the capacity to generate consistent results with the least waste possible.

Some practices help in this process:

• define clear AI usage policies;

• monitor consumption by team and project;

• establish metrics linked to results and not just usage volume;

• restrict the sharing of sensitive information in public tools;

• continuously evaluate cost versus return on implemented automations;

• create audit and traceability processes.

The goal is not to reduce the use of technology, but to ensure that it happens in a sustainable manner.

CodeAdvisor: governance, security, and consumption control in a single platform

As the use of AI expands within companies, the need to consolidate tools, data, and processes in controlled environments also grows.

CodeAdvisor was developed following this logic. Instead of allowing each area to use different tools, the platform centralizes access to AI within a single corporate structure, with governance, auditing, and consumption control.

The proposal is to support technical teams and offer a management layer for the entire organization.

This allows for tracking usage, controlling costs, and reducing risks related to the decentralized use of external tools.

Among the available features are:

• consumption monitoring through dashboards;

• access control by teams;

• complete traceability of interactions;

• usage of models in a controlled environment;

• segregation of corporate data;

• management based on actual usage volume.

The plans are structured by token consumption, allowing the investment to scale with the company's actual demand.

This model offers greater financial predictability and facilitates the expansion of AI use without the need to maintain fixed licenses for all employees.

The future of AI goes through token management

For a long time, the main question for companies was how to adopt Artificial Intelligence. Now, a new question is starting to gain ground: how to manage this usage efficiently.

Recent debates on tokenmaxxing, tokenomics, and token crunch show that the maturity of AI is no longer measured solely by model capability, but is instead being evaluated by how organizations manage its use.

The trend is for technology to continue advancing rapidly. What changes are the priorities. One month, the market safety discusses consumption maximization. In the next, it talks about cost containment. Shortly after, the agenda shifts to governance and security.

In this scenario, companies that manage to balance productivity, cost control, and data protection will be better prepared to transform AI into a long-term strategic asset.