Mastering AI: A Must to Avoid Digital Alienation

The era of artificial intelligence (AI) is well underway, but a widespread lack of understanding of its mechanisms persists. This ignorance, far from being trivial, can turn a powerful tool into a constraint, limiting its competitive potential. According to Gilbert Simondon, understanding the nature of machines is essential to avoid alienation. Today, this reflection is more relevant than ever.

Generative AI is ubiquitous across various sectors, yet many of its users are unaware of its foundations. This lack of knowledge can affect the quality of use and the added value. Using a language model without understanding its principles, such as conditional probabilities, context windows, or inference modes, is akin to driving a car without knowing the mechanics.

Key Skills for Optimal Use

Choosing the Right Model for the Right Task

Not all AI models are created equal. Deep reasoning models, such as those currently under accelerated development, leverage multi-step reasoning capabilities known as chain-of-thought. They are suited for complex tasks such as strategic analysis, contract review, or organizational diagnosis. In contrast, for quick rephrasing or classification tasks, a lighter model is preferable. Understanding this distinction is crucial to avoid wasting computational and energy resources.

Orchestrating Multiple AIs

The value lies in the ability to create intelligent workflows. According to the McKinsey State of AI 2025 report, 23% of organizations are already using agentic systems at scale in at least one function. This involves connecting a document analysis model, a synthesis agent, and a fact-checking tool through protocols like the Model Context Protocol (MCP) or the Agent-to-Agent (A2A) standard. These protocols enable effective coordination among specialized agents, avoiding reliance on monolithic systems.

Configuring for Greater Control

Knowing how to configure a model is essential to avoid verbose and costly responses. An unconfigured model can produce unnecessary iterations and adopt a time-consuming conversational mode. Tools like Claude Code allow for effective interaction with the development environment, thereby reducing token and energy consumption. Setting format constraints and defining a precise tone transforms AI into a rigorous work tool.

Adopting a Critical Stance

AI models can generate errors, such as hallucinations, confirmation bias, or factual approximations. Nearly one-third of companies surveyed by McKinsey, in a study involving 1,993 companies across 105 countries, have experienced negative consequences due to AI inaccuracies. Therefore, evaluating and confronting the results is essential. The AI ecosystem is evolving rapidly, and understanding the fundamentals will enable professionals to adapt to upcoming technological disruptions.

An Educational Responsibility

Simondon advocated for a reconciliation between humanistic culture and technology. Training professionals capable of understanding AI is crucial for their autonomy. According to a survey by the CGE on AI in grandes écoles, which gathered responses from 5,069 participants, 82% of students want more training on AI. Meeting this demand is essential to prepare for the future. Training professionals who understand the nature of AI—its real capabilities, limitations, costs, and internal logics—empowers them with true autonomy. It is, to borrow the philosopher's words, a way to protect them from alienation.