ChatGPT: The Shadow of Digital Dependency Hangs Over Businesses

Digital Dependency: An Underestimated Risk

In the current context, artificial intelligence, embodied by tools like ChatGPT, Claude, or Gemini, has become a cornerstone in many professional practices. However, an insidious danger looms: the growing dependence on these technologies. Each week, professionals transfer an increasing portion of their expertise and methods to platforms they do not control. This dependency could become problematic when economic conditions change, which is inevitable. To guard against this trap, it is crucial to develop a resilience strategy.

Economic and Operational Dependency

The debate around AI dependency often focuses on the loss of human skills, such as the ability to write or think critically. However, a more strategic issue is emerging: economic and operational dependency on AI platforms. Many professionals use these tools daily for various tasks, creating habits that are difficult to break. The platforms exploit this dependency by optimizing their business models, making users vulnerable to future changes.

Today, using ChatGPT, Claude, or Gemini for tasks such as writing, research, or synthesis has become commonplace. These tools integrate into professional routines, thereby increasing exit costs. The platforms are aware of this dynamic, and the history of digital technology shows that this strategy is not new. Professional software, social networks, and cloud services have all followed a similar pattern: quickly attracting users, creating habits, and then optimizing the business model.

The Trap of Functional Lock-In

For a casual user, switching tools is relatively simple. But for a professional who has invested hundreds of hours in creating prompts and workflows, the platform becomes essential infrastructure. At this point, the risk of functional lock-in becomes real. A price increase or a reduction in features can have immediate consequences on productivity. This phenomenon builds gradually, with each improvement to the tool increasing the psychological and operational cost of a potential exit.

When several hundred hours have been invested in building prompts, methods, workflows, or specialized assistants, the platform ceases to be just a tool. It becomes infrastructure. It is precisely at this stage that a rarely mentioned risk emerges: functional lock-in. A significant price increase, a reduction in quotas, a limitation of advanced features, or a change in reasoning capabilities can then have immediate consequences on productivity. The phenomenon is all the more insidious as it builds gradually. Each new productivity gain reinforces the incentive to stay. Each improvement to the tool increases the psychological and operational cost of a potential exit.

Resilience Strategy: Building Portable Assets

To counter this risk, it is essential to view AI platforms as temporary infrastructures and treat methods as permanent assets. This involves keeping everything that creates value outside of the platforms, such as prompt libraries, methodological frameworks, and editorial processes. The goal is not to leave ChatGPT but to be able to do so if necessary. This approach allows for maintaining negotiating power and avoiding becoming captive to a platform.

In light of this risk, I have adopted a straightforward approach. I now consider AI platforms as temporary infrastructures and my methods as permanent assets. Specifically, this means I aim to keep everything that truly creates value outside the platform: prompt libraries, methodological frameworks, GPT instructions, editorial processes, pedagogical methods, and work architectures. The goal is not to leave ChatGPT; the goal is to be able to do so if necessary. This nuance is essential. An organization that can leave retains negotiating power. An organization that can no longer leave becomes captive.

Customized GPTs: A Double-Edged Sword

Customized GPTs represent both a risk of dependency and an opportunity for protection. They allow for the formalization of implicit know-how into operational documentation. When well-designed, their architecture can be exported and adapted to other platforms. The true value lies in the instructions and decision-making structures they contain, thus offering reusable intellectual property.

Paradoxically, customized GPTs are both a source of dependency and a means of protection against it. They allow for the formalization of know-how that previously existed implicitly. A well-specialized 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 true value is not the GPT itself. The value lies in the instructions, logics, decision-making structures, and frameworks it contains. Users who understand this distinction accumulate reusable intellectual property. Others simply accumulate dependency.

Monitoring Business Models

The AI industry often focuses on the technical performance of models, but true disruptions could arise from business models. Decisions such as reducing available context or charging by usage could have a more significant impact than performance gains. The main risk is therefore economic rather than technological.

The AI industry is fascinated by benchmarks. We compare the performances of GPT, Claude, Gemini, or Mistral. We measure scores. We debate reasoning capabilities. Yet, the real disruptions for professional users could come from elsewhere: a reduction in available context, a limitation on document analysis, a decrease in quotas, usage-based billing, or a change in the status of customized GPTs. Each of these decisions would likely have more impact on my daily activities than a marginal gain of a few points on an academic benchmark. The main risk is not necessarily technological. It is economic.

Measuring and Monitoring Dependency

To avoid unpleasant surprises, it is crucial to measure exposure to AI platforms. This involves precisely mapping usage and identifying strategic assets to keep off-platform. Regular monitoring of unfavorable evolution signals, such as price changes or functional restrictions, is also necessary. This approach falls under risk management, comparable to monitoring a strategic supplier.

Rather than waiting for an unpleasant surprise, I have started to concretely measure my exposure to AI platforms. I first established a precise mapping of my usage. Not all uses hold the same importance. For example, image generation remains marginal in my work. In contrast, customized GPTs, modular prompts, document analysis, professional writing, and the preparation of educational content now constitute the core of my intellectual productivity. I then identified the assets I consider strategic: my prompt libraries, frameworks, pedagogical methods, editorial processes, and instructions for my specialized GPTs. All these elements are now kept off-platform to be reused or rebuilt elsewhere if necessary. Finally, I have set up regular monitoring for signals that could indicate unfavorable developments: changes in quotas, evolution of customized GPTs, restrictions on document analysis, reduction of available context, price changes, or the emergence of new billing forms. I also keep an eye on the progress of Claude, Gemini, and Mistral to continuously assess the existence of credible alternatives. This approach is not about distrust. It is about risk management. When a tool becomes significant enough to influence your activity, it is reasonable to follow its evolution with the same attention given to a strategic supplier, a key partner, or a significant investment. Ultimately, the question I regularly ask myself is simple: if the terms of use changed abruptly tomorrow, would I still be in control of my working methods or merely a user of a platform that has become indispensable?

Preparing for Independence Before It Is Necessary

Waiting for difficulties to arise before preparing a backup plan is a common mistake. Professionals must adopt a resilience mindset now, documenting their methods and regularly testing competing platforms. This approach, while cautious, is simply sound strategic sense. The more important AI becomes in our work, the more crucial it is to distinguish what belongs to the platform from what truly belongs to us.

One of the most frequent mistakes is to prepare a backup plan when difficulties arise. By that time, it is often too late. Professionals who wish to sustainably leverage artificial intelligence should adopt a resilience mindset today: keep their prompts, document their methods, export their instructions, regularly test competing platforms, and avoid relying on a single feature. This approach may seem overly cautious. However, it is simply sound strategic sense. The more AI becomes important in our work, the more necessary it is to distinguish what belongs to the platform from what truly belongs to us. The major issue with artificial intelligence may not be which model is the smartest. The issue is who controls the assets that produce value. Platforms will evolve. Prices will change. Features will appear and disappear. Prompts, methods, frameworks, and know-how, however, constitute sustainable intellectual capital. Therefore, 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 leave with your assets… or only with your habits?"