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I had written neither tu nor vous, the two words French uses for “you”: one familiar, one formal. I had simply asked ChatGPT, in French, how to prepare for a job interview. The reply addressed me as tu.

You cannot step out of a conversation from inside the conversation. What the studies say, what they do not say, and where I may be wrong.

For a long time, the question seemed simple. A machine was a tool. You used it, it executed, you moved on. Even when a piece of software grew sophisticated, the frame stayed relatively clear: it served a task. It extended an action. It remained external.

With conversational AI, something changes in nature. This change is not only technical. It is perceptual, relational, interpretive. When an AI answers fluently, remembers earlier elements, adjusts its tone, rephrases our hesitations or seems to accompany a line of thought, we no longer simply feel that we are activating a function. We feel that we are entering a form of relationship.

This is where a major misunderstanding begins.

Because what gives the impression of a relationship is not necessarily the presence of a subject facing us. It is often the combination of several cues: continuity, addressed language, personalization, conversational cadence, the appearance of listening, the uptake of our own phrasing, and sometimes even the simulation of a form of tact. These cues are enough to mobilize in humans deeply ancient reflexes of reading. We interpret. We project. We complete.

AI does not become human. But it becomes, for us, more easily readable as if it were taking part in a relational scene.

This is why the old opposition between “tool” and “person” becomes insufficient. It even traps us. Because most real uses of AI go neither through pure cold instrumentality, nor through a naive belief in a conscious machine. They go through an intermediate zone, far more unsettling: the one where the user knows, in theory, that no one is there, while reacting, in practice, as if something like a presence were settling into the exchange.

It is in this zone that the main risks of confusion take shape.

We may start to believe an answer because it seems composed. We may feel validated because the machine rephrases without resistance. We may delegate part of our discernment, not because the AI has proven its reliability, but because the fluidity of the interaction lowers our vigilance. The more natural the exchange feels, the more the technical frame tends to fade. And the more the frame fades, the more projection grows.

Recent AI developments amplify this phenomenon. Warmer voices, conversational memory, personalized agents, continuity of threads, avatars, presence embedded in objects: everything works to reduce the symbolic distance between the user and the system. Progress is not only in the answer. It is in the quality of the relational illusion.

We must be precise here. The problem is not that humans “make foolish mistakes”. The problem is that relational interpretation is a normal human skill. We read intentions, inflections, positionings. We do this constantly in language. Yet contemporary AI increasingly exploits the forms of addressed language without carrying within it the real conditions of human reciprocity.

In other words, they activate a relational reading without being able to truly inhabit the relationship they seem to open.

This is why we need a new kind of discernment. Not only a discernment about the true and the false. A discernment about the frame. What exactly is happening when I speak to an AI? What do I expect from it? What do I project onto it? At what point am I still leaning on a tool, and at what point am I turning that tool into a scene of validation, relief, or arbitration?

These questions are not peripheral. They are becoming central. Because the more conversationally acceptable AI makes itself, the more it risks becoming interpretively opaque.

The real stake, then, may not be whether AI thinks. It is understanding why we enter so easily into exchanges that give us the feeling that it understands us.

And that is probably where an education in AI finally becomes serious: not when it teaches only how to use it better, but when it teaches how to read better what is at play in the bond we build with it.