Voluntarily exhausting your AI credits to the last drop, and bragging about it. Tokenmaxxing is coming from the USA – and behind the absurdity there is a much more serious organizational symptom.
When I discovered the concept of tokenmaxxing, I initially thought it was a joke.
The principle is simple: some conversational AI users now consider that exhausting all of their monthly credits is a form of success. Advice is circulating on the networks – asking the AI to rephrase the same thing twenty times, starting endless conversations, producing text unnecessarily – only to empty a subscription to the last drop. And apparently, some are proud of it.
Then I started to see serious discussions happening. Screenshots. People who explained how to “make money” from their subscription by making the AI talk for hours. And I realized it wasn’t a joke – it was a symptom.
What a token really is
Before going any further, a useful point of context: a token is a fragment of text that language models use to cut up and process what is written to them. A word, part of a word, sometimes a punctuation mark. Each time we send a request to an AI, we consume tokens. Every time she responds, she consumes it too. Everything is counted.
And this consumption has a real cost – financial, energy, computational. Behind every answer there are servers, calculations, electricity, data centers. This cost is justified when it produces something useful. It becomes absurd when it is an end in itself.
In my daily use, I very rarely see the message “you have reached your usage limit”. And honestly, that reassures me. That means I use the tool when I need it – not to spin it.
The company that asks its teams to “prove that they use AI”
My brother called me a few weeks ago, stressed. His manager had just asked the entire team to write down in writing how AI increased their productivity.
I understand the stress. Because the request is strange.
It’s a bit as if, in the 2000s, each employee was asked to write a report proving that the Internet improved their work. Or as if we measured the value of a hammer by the number of blows given during the day. At some point, some tools just become normal tools. We do not measure their relevance by their level of consumption.
But this logic has taken hold in many organizations since the media explosion of generative AI: we must use AI. It doesn’t matter why. No matter how. You have to be able to say – and show – that you use it. So we deploy tools, we ask teams to adopt them, and we begin to measure adoption in tokens consumed, in sessions opened, in reports produced thanks to AI.
The result is predictable: people use AI. Sometimes without real need. Sometimes just to show that they are “good employees of the future”. And some, pushed to the limit by this logic, end up tokenmaxxing – consuming for the sake of consuming, because that is what they were implicitly asked to do.
What I do before each automation
As an entrepreneur, this logic seems completely absurd to me. Because I actually pay for the tools, subscriptions and API calls.
Before launching an automation, I systematically ask myself the same questions: how many tokens will this task consume? Does the gain justify this cost? Is there a simpler, faster, less expensive alternative?
Sometimes the answer is no. I regularly test an automation, calculate what it actually consumes – and decide not to deploy it. Not because it doesn’t work. Because a simple script, or even a well-established manual process, does the same thing for ten times less cost. These decisions are not sexy. They don’t take good screenshots. But they are exactly the ones that make the difference between intelligent use of AI and unreturnable spending.
Token consumption is a decision criterion – not an objective.
The reverse skill of tokenmaxxing
Tokenmaxxing is the exact opposite of this posture. It is the abdication of judgment in favor of consumption. And behind this abdication, there is often an organization that failed to explain to its teams why they were using AI – only that they had to do it.
What organizations need is not teams that consume more. It’s from teams that consume better. Who know when AI provides real value, when it is the wrong tool, and when not using it is the smarter decision.
I pride myself on using as few tokens as possible. Not out of cheapness. By common sense. Because for me, using AI intelligently doesn’t mean making a machine produce miles of text just because you can do it. It’s understanding how to use it. It’s knowing when it provides real value. It’s about keeping a critical eye. It means avoiding absurd consumption.
Perhaps it’s simply using AI the way you should use any tool: with discernment.
And this skill is not acquired by exhausting a subscription.
