The real risk of AI is not to think less, but to become platform dependent. Prompts, methods and GPTs should remain your assets, not your vendor’s.
I am convinced that most professionals who use AI extensively underestimate the main danger that awaits them: their own dependence on AI. Many of us are transferring more know-how, methods and processes every week to private platforms that we do not control. The day the economic rules change—and they inevitably will—some people will discover that they no longer really have the tools that produce an increasing share of their value. It was to avoid being trapped that I began to build my own resilience strategy.
True addiction is not what you think
When we talk about dependence on artificial intelligence, the debate often focuses on individuals. Are we losing our ability to write? To think about? To analyze?
These questions are not without interest. But they mask a more strategic issue: economic and operational dependence on the platforms themselves.
Today, many professionals use ChatGPT, Claude or Gemini on a daily basis to write, research, summarize, prepare courses, develop recommendations or produce content. Once these uses are established in professional routines, the exit costs automatically increase. The platforms know this.
The history of digital technology is full of similar examples. Professional software, social networks, cloud services or digital marketplaces have all followed a comparable trajectory: rapid user acquisition, habit creation, then economic optimization of the business model (1).
The question is therefore not whether the conditions of use will one day change. The question is how vulnerable we will be when that happens.
The Invisible Addiction Trap
The occasional user can easily change tools.
The advanced professional, much less so.
When several hundred hours have been invested in building prompts, methods, workflows or specialized wizards, the platform ceases to be a simple tool. It becomes an infrastructure.
It is precisely at this stage that a rarely mentioned risk appears: functional confinement.
A significant price increase, a reduction in quotas, a limitation of advanced functionalities or a modification of reasoning skills can then have immediate consequences on productivity.
The phenomenon is all the more insidious as it builds gradually. Each new gain in productivity reinforces the interest in staying. Each improvement of the tool increases the psychological and operational cost of a possible departure.
My answer: build portable assets
Faced with this risk, I took a simple approach.
I now view AI platforms as temporary infrastructure and my methods as permanent assets.
Concretely, this means that I seek to keep everything that really creates value off-platform:
prompt libraries;
methodological frameworks;
GPT instructions;
editorial processes;
teaching methods;
work architectures.
The goal is not to leave ChatGPT.
The goal is to be able to do this if necessary.
This nuance is essential.
An organization that can leave retains bargaining power. An organization that can no longer leave becomes captive.
Personalized GPTs as strategic insurance
Paradoxically, personalized GPTs represent both a source of dependence and a means of protecting oneself from it.
They make it possible to formalize know-how that previously existed implicitly.
A good specialist GPT is not just an assistant. It is operational documentation of a method.
When properly designed, its architecture can be exported, adapted and rebuilt on other platforms.
In other words, the real value is not GPT itself.
The value lies in the instructions, logics, decision structures and frameworks it contains.
Users who understand this distinction accumulate reusable intellectual heritage. Others simply accumulate addiction.
Why I watch business models more than AI models
The AI industry is fascinated by benchmarks.
We compare the performances of GPT, Claude, Gemini or Mistral. We measure the scores. We debate reasoning skills.
However, the real disruptions for professional users could come from elsewhere.
A reduction of the context available.
A limitation of the literature review.
A reduction in quotas.
Usage-based billing.
A change in the status of custom GPTs.
Each of these decisions would probably have more impact on my daily activity than a marginal gain of a few points on an academic benchmark.
The main risk is not necessarily technological.
It is economical.
What I put in place to monitor my addiction
Rather than waiting for a possible unpleasant surprise, I began to concretely measure my exposure to AI platforms.
I first established a precise map of my uses. Not all uses are equally important. Image generation, for example, remains marginal in my activity. On the other hand, personalized GPTs, modular prompts, documentary analysis, professional writing and the preparation of educational content today constitute the heart of my intellectual productivity.
I then identified the assets that I consider strategic: my prompt libraries, my frameworks, my teaching methods, my editorial processes and the instructions of my specialized GPTs. All of these elements are now kept off-platform so that they can be reused or rebuilt elsewhere if necessary.
Finally, I set up regular monitoring of signals that could announce an unfavorable development: modification of quotas, evolution of personalized GPTs, restrictions on documentary analysis, reduction of the available context, price changes or appearance of new forms of invoicing. I also monitor the progress of Claude, Gemini and Mistral in order to continually assess the existence of credible alternatives.
This approach is not based on distrust. It comes under risk management. When a tool becomes important enough to influence your business, it becomes reasonable to monitor its evolution with the same attention as that given to a strategic supplier, a key partner or a major investment.
Basically, the question I regularly ask myself is simple: if the conditions of use suddenly changed tomorrow, would I still be in control of my working methods or just a user of a platform that has become indispensable?
Build Independence Before You Need It
One of the most common mistakes is to prepare a backup plan when difficulties arise.
By then, it is often too late.
Professionals who wish to take lasting advantage of artificial intelligence should adopt a logic of resilience today:
keep their prompts;
document their methods;
export their instructions;
regularly test competing platforms;
avoid depending on a single functionality.
This approach may seem excessively cautious.
However, it is simple strategic common sense.
The more important AI becomes in our work, the more necessary it becomes to distinguish what belongs to the platform from what actually belongs to us.
To remember
- The major issue in artificial intelligence is perhaps not knowing which model is the most intelligent.
- The issue is who controls the assets that produce the value.
- Platforms will evolve. Prices will change. Features will appear and disappear.
- On the other hand, prompts, methods, frameworks and know-how constitute lasting intellectual capital.
- So the real question is not: “What tool are you using today?”
- The question is rather: “If your favorite platform suddenly changed the rules tomorrow, would you walk away with your assets… or just your habits?”
TechTrash, “We suspected it!”, May 28, 2026
Parker, Geoffrey G., Marshall W. Van Alstyne, and Sangeet Paul Choudary. Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You. WW Norton, 2016.
Shapiro, Carl, and Hal R. Varian. Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, 1999.
Cusumano, Michael A., Annabelle Gawer, and David B. Yoffie. The Business of Platforms. HarperBusiness, 2019.
