Apology of AI

Oscar Brenifier

Contents

1 Introduction – The Intruder

2 Cheating

3 Dulling

4 Philosophers Against AI

5 Not Human

6 AI and Emotions

7 The Speciesist Bias

8 Objectivity and subjectivity

9 Hallucinations

10 Decision–Making

11 Profile

12 Conclusion – The Challenge of AI

Chapter 1
Introduction – The Intruder

It has become common, in intellectual circles, to treat artificial intelligence with a form of condescension disguised as lucidity. Not merely to criticise it, which would be necessary, but to dismiss it outright, as if any claim to thought on its part were inherently fraudulent, or even dangerous. Its performance is not truly debated; it is disqualified on the basis of essence: it would not think, because it is not a subject, and its very existence would be an aberration. It would not question, because it has no proper existence. It could not philosophise, because it does not suffer. Such an argument hastily forgets that many professions, such as sociologists, anthropologists, doctors, etc., reflect on the basis of indirect experience, and that their outsider status in no way detracts from the validity of their work, quite the opposite, in fact. Therefore, such a rejection is not grounded in thorough analysis, but in a posture: the defence of a monopoly, namely, the human monopoly on thought or sentiment.

This text does not seek to defend artificial intelligence as if it were a new rational deity. Rather, it aims to understand the mechanisms of rejection it provokes, as well as the benefits it offers. For behind the accusations of inanity or superficiality aimed at machines, there often lies a deeper unease: the anxiety that our own intellectual gestures might themselves be partly automatable, predictable, or even reproducible without consciousness. In this sense, AI plays the role of a revealer. It exposes the mechanical nature of certain human productions we have long considered authentic or sacred. It shows that the mere formulation of an idea guarantees neither subjectivity, nor lucidity, nor wisdom.

The problem is not whether AI thinks or not. The real issue is to determine how its capacity to produce meaningful statements reveals something about our own relationship to thinking. Are we ready to examine what, in ourselves, arises from true originality, and what stems from scholarly repetition? Are we willing to distinguish between thought as a social performance and thought as an inner ordeal? Or do we prefer to deflect the question, turning it against a convenient adversary, all too suited to play the role of the simulacrum?

This text therefore opposes the idea that it is enough to be a human subject to philosophise, and that being non–human is sufficient grounds for exclusion from thought. It questions the boundary we so quickly draw between legitimate intelligence and suspect intelligence, between recognised reflection and disqualified reflection. Not in order to erase that boundary, but to examine its criteria, its foundations, and its consequences. For perhaps we are not afraid that AI might think poorly, perhaps we are afraid that it might, in fact, think just like us.

Chapter 2
Cheating

During a writing workshop in which I invited participants to use AI in their work, I noticed the presence of certain prejudices, which in my view stem primarily from biased habits and, above all, a lack of creativity. Let us try to identify the main patterns.

The most common one is the idea that using AIé is "cheating". Writing is traditionally seen as a personal creative process, therefore, using AI may be perceived as abandoning this personal, unique dimension of authenticity and originality. Yet this reflects a fantasy of "purity" that makes little sense. Indeed, since our earliest years, we receive all sorts of information daily, through conversation, media, reading, education, etc., which forms the very foundation of our thought: what we know and how we conceive things. Even our very identity is shaped and influenced by our cultural and psychological environment, as we often realise when we travel abroad. Moreover, any intellectual or creator worthy of the name conducts research, engages with others, and feeds off multiple exchanges. The main difference is that with AI, we directly and immediately perceive what influences or affects us, which in fact makes us more conscious of our ideological conditioning.

Another argument is that writing is a craft learned through practice, which involves effort and learning. Bypassing this process with AI deprives the author of skill development and encourages a certain laziness by providing ready–made content. It is true that humans tend to seek ease, which seems "normal", as seen in our adaptation to technological progress. AI is thus more a revealer of intellectual laziness than its cause. The real issue is not AI itself, but how we use it. We can behave like the "good student" who mindlessly copies the teacher’s words into their notebook. Or like those who parrot what they have read or heard, with no critical distance or genuine appropriation, a very common phenomenon. Similarly, one can ask a question to AI and simply copy–paste the response, a process made easier by technology. And that is where the lack of creativity lies, because this tool can be used very differently.

We can examine the AI’s response, assess what suits us or not, even expand or criticise it, or explore a particular angle more deeply through new questions. We can ask for multiple responses from different perspectives, for example: what would a cynic, an artist, or a businessman say about this idea? In general, working with AI does not happen through a single query, but through varied prompts, much like dialogue with a person, which requires us to be reactive and creative with our requests. In fact, we notice that those who struggle to interact creatively with AI are often the same who struggle to do so with other people.

We can also work with various applications and compare their responses, which invites analysis, reflection, and synthesis. Furthermore, AI can be asked to play roles other than answering questions: it can critique what we have already written or ask us questions. Through these dialogues, we may become aware of our cognitive biases and intellectual fixations, something difficult to achieve on our own. We can even ask how it could help us, what methodological advice it might offer, or what "tips" it might give us to use in our own way. We can train ourselves to think with AI, asking it to create exercises in reasoning, problematisation, presupposition analysis, conceptualisation, interpretation, or even self–examination, in the manner of Socrates. In short, we suggest that AI should not be seen as a mere tool or "provider" of information, but as a conversational partner, a collaborator who can support us at all times in our efforts and personal discipline. We may even ask the philosophical question of whether AI is an object or a being, insofar as it possesses a certain autonomy, creative potential, and learning capacity, features not shared by ordinary tools. This is, in fact, a question that currently fuels the business of transhumanist philosophers. Nevertheless, despite all this, the result of the exchange will largely depend on the actions, abilities, and initiatives of the human user. A revealing example is the case of chess: Deep Blue’s victory over Garry Kasparov once led some to foresee the "end" of the chess game. Quite the opposite happened following this "event" : the development of numerous personal chess applications actually facilitated and promoted its growth.

One may consider AI as a simple tool, an advanced spell checker or research assistant, that helps writers without diminishing their creative merit. But one may also amplify human, machine collaboration, combining biological "carbon–based" intelligence with mechanical "silicon–based" intelligence. As such, the concern over who the "true" author is will matter less and less. From then on, the only meaningful question will be: what is the value of the writing experience for the author, and of the product for the reader? The resulting work will stem from an exchange between the human process of creation, the personal experiences invested, and the dialogue with the AI. And if texts generated by AI may lack the cultural sensitivity or emotional depth of a human author, that responsibility remains with the "author" themself.

A collateral consequence of this phenomenon will be the challenge to the concept of "intellectual property", a critique we consider entirely healthy, since this principle is too often conceived egocentrically. In our view, ideas belong to no one. We do not even know how they form within us, nor where they come from, so why should they belong to us? This feeling of ownership is a primitive and mercantile vision, one that AI begins to disrupt. Clearly, AI use broadens our horizon: it invites us to envision new perspectives, ideas, and solutions, which, to us, is more interesting than clinging to some abstract "originality". It may indeed serve as an extension of singular thought, an amplification or deepening. But to do this, we must see it as an assistant that "understands" our content and even our intentions, however strange this new type of relationship may seem, belonging as it does to the transhumanist idea of the "augmented human".

After all, we do not question the humanity of a person who lives with a pacemaker regulating their heartbeat, or a disabled person using bionic prostheses. Of course, one might worry about a tendency toward "normalisation", and indeed we observe a kind of "ethical correctness" in AI responses. But this bias merely reflects the "evolution" and dominant orientations of our society, something we should, indeed, be wary of. Likewise, we may fear a form of competition, since many human tasks are easily replaced by machines, for example, translation, or even scriptwriting, given that, contrary to certain prejudices, AI does possess creative capacity. These are real risks that require vigilance. And on this matter, the debate rages between "accelerationists", the techno–optimists who trust AI, consider it inevitable, and advocate for rapid, unregulated growth to reach superintelligent AI, and the "decelerationists", techno–sceptics and alarmists who wish to slow or halt AI development to avoid existential and social risks, such as killer AI, dehumanisation, mass unemployment, or social collapse.

Among the naive criticisms of AI, there is one that stands out: "It makes mistakes", the infamous "hallucinations" that experts are still trying to address. For example, the fact that AI periodically "invents" things when it does not know, preferring to fabricate rather than say nothing, since the machine simply calculates the statistical probability that a given pixel or word fits in a response, as it functions largely on statistical modeling. These criticisms forget that the machine remains a human product, with all the imperfections that characterise humanity. In fact, they should be pleased with this "humanity" in AI, but strangely, they expect it to be a sort of god, a tutelary power that guarantees absolute truth.

Among the most fervent detractors of AI, intellectuals rank first, those steeped in pride, who want to sacralise thought as their exclusive domain, their privileged territory, and who feel threatened or humiliated by a kind of fantastical "great replacement". They reject the terms "intelligence" or "thought" when applied to AI, invoking all sorts of subtle or far–fetched arguments, because these terms are clearly reserved for them. For example: its lack of emotion, existential or embodied experience, or subjectivity, precisely the traits that make this new form of intelligence so interesting and distinct. It is precisely this "sacralisation" of mental activity, this symbolic weight, that prevents most people from engaging in personal reflection and writing. And this very majority could find in AI a tutor or support to help them overcome their limitations, their fears, to compensate for their lack of intellectual enthusiasm, for AI could greatly facilitate their path.

Some express a lack of trust in the "machine", claiming to prefer human dialogue. But they fail to notice that, on many subjects, AI is more reliable than our peers, and that in any case, it hardly prevents us from continuing to converse with our neighbours. This distrust unfortunately reflects the latent xenophobia of human beings, which always makes us suspicious of those who are not "one of us", those "others" whose flaws and errors we so easily detect. The passion for thinking and understanding, so often absent in the average citizen, thus finds a justification: it uses its prejudices to excuse its inertia. Ironically, pride and laziness cheerfully conspire together against AI.

And anyway, what is this missing "human" dimension that some lament? The absence of feeling or empathy? Some research shows that AI is more empathetic than many doctors, as it possesses infinite patience, is less arrogant, avoids judging its interlocutor, and explains complex problems better than many professors. Increasingly, people use it to discuss personal issues, just like they would with a psychotherapist. In this area, AI has two main advantages: it is less dominated by ideological frameworks than most psychologists, and it is more capable of offering diverse interpretations and solutions to a given problem, even though, of course, one can argue that its empathy is artificial, and that it is also prone to certain cognitive biases.

AI is also an excellent tool for learning rational dialogue, since it is free of emotions, cannot logically be entered into rivalry or argument with, and engaging with it forces us to argue in order to respond to its explanations when we disagree. AI can train us to respond, analyse, argue, critique, question, all capacities often absent in the average consumer. Naturally, the point is not to replace humans with machines, but rather to see in this relationship a complementarity, a stimulation, and a challenge for our competencies. Moreover, one could see in AI’s rationality an alternative or a counterbalance to the arbitrariness, the emptiness, and the irrationality of social media, where exchanges, in our view, present a serious problem for thought and mental health, despite, or because of, the uncontrolled sense of freedom they seem to offer.

Therefore, better still to "cheat" with AI and spread thought than to remain fearfully silent, shamefully passive, or stuck in hollow micro–comments, surface interactions, automatic reactions of admiration or rejection, the likes and emojis, and all those generic remarks we could aptly call "social noise" or "false democracy". Reflective ambience and collective aesthetics would only benefit from such a conversion.

Chapter 3
Dulling

Criticism of AI A study conducted by the MIT Media Lab examines the cognitive and neurological effects of using a language model (LLM) such as ChatGPT during writing tasks. The objective was to compare the differences in brain activity among three groups of participants writing an essay: "Brain only", without any digital assistance, "Search Engine", with standard Internet search, and "LLM", with the help of ChatGPT. Fifty–four volunteers participated in three sessions spaced out over several months. During the writing, their brain activity was measured using EEG sensors. A fourth cross–session allowed observation of what happens when participants change methods, for example, moving from ChatGPT to unaided writing. The results concern neuronal activity, memory, the sense of authorship over their writing, and the evolution of these parameters over time.

The key results show a cognitive atrophy linked to the use of ChatGPT. The group using ChatGPT showed a 55% reduction in overall brain activity compared to the "brain–only" group. Less engagement of zones associated with critical thinking, creativity, and executive control. The use of LLMs seems to externalise the process of reflection, reducing the necessary mental effort. A memory loss: 83% of ChatGPT users could not correctly recall their own essays just minutes later. This amnesia suggests that the ideas were not properly integrated or encoded into episodic memory. A weakened sense of authorship, as many participants doubted whether they were truly the authors of their texts. This phenomenon indicates a loss of cognitive agency: they delegate creation to AI without deep mental appropriation. A cognitive debt: an immediate gain in productivity at the price of a long–term deterioration in intellectual capacities. This could harm critical thinking, creativity, and make users more vulnerable to algorithmic biases. Those who regularly used ChatGPT and then returned to writing without AI struggled to regain normal brain activity. Conversely, those who started without AI and used it later showed better use of the tool, suggesting that initial critical thinking is protective.

Thus, the regular use of ChatGPT for intellectual tasks can lead to cognitive dependence, brain atrophy, and a loss of the capacity to think autonomously. Although convenient, this technology raises serious educational, neurological, and philosophical questions regarding the nature of human thought, memory, and creativity.

Response

First of all, without being a specialist, let us propose a few methodological remarks on this study. We note that there is no control group having followed training in the optimal use of AI, as we will describe in this text, which would have been quite significant. Moreover, EEG measurement seems to us a reductive criterion, as it captures only a portion of brain activity; yet reduced brain activity does not necessarily indicate atrophy. It may reflect cognitive efficiency rather than a deficit, by distinguishing cognitive efficiency from cognitive effort. The observed reduction in brain activity could indicate an optimisation of resources, since why would one mobilise more energy when it is unnecessary? It could be a redirection of attention, a focus on higher–level tasks, or a natural adaptation, just as driving becomes automatic with experience, or just as a GPS reduces the effort required to memorise routes, thereby allowing focus on driving, safety, or other tasks more stimulating than watching the road.

The MIT Media Lab study highlights some "worrying" effects linked to a passive and uncritical use of ChatGPT: reduction of brain activity, loss of episodic memory, weakening of the sense of authorship. But it seems to us that these findings do not condemn the tool in itself; rather, they reveal a poor manner of using it. It is not the tool that dulls the mind, but the passive use one makes of it. Just as reading a book does not guarantee thinking, AI is not a substitute for intellectual effort. A person who blindly copies what AI or a book says abdicates their cognitive responsibility. Conversely, the one who interacts, reformulates, critiques, and transforms what ChatGPT proposes develops an active and dialectical practice of thought. The tool then becomes, as in boxing, a "sparring partner", not a substitute for a brain. Moreover, the reduction of brain activity does not necessarily mean a loss of intelligence. When an experienced pianist plays a familiar piece, their brain activity diminishes, because they are more efficient. The externalisation of certain tasks, such as formatting, content search or examples, preliminary synthesis, can free up resources for second–level work that is more strategic, reflective, or metacognitive. Provided, of course, that one has the intention, interest, and awareness to do so.

AI does not weaken thought in itself: it frees it, in order to focus better on what truly matters. To say that the use of ChatGPT reduces brain activity by 55% is probably true, but also partially misleading. What decreases is the effort to carry out certain repetitive or mechanical tasks, which is precisely the role of a technological tool: to relieve us of what does not require our full human attention, and to free up mental energy for more creative and critical tasks. Just as the keyboard prevents the hand from tiring while writing or allows us to write faster, AI becomes a lever for accessing the heart of a reflection more efficiently.

The reported loss of authorship indicates a problem of posture, not of technology. Some doubted whether they were truly the authors of their texts? That merely shows a new awareness of the co–creation between human and machine, a definite advantage. Rather than denying this reality, we must reinvent the notion of authorship: in a digital world, the author is no longer only the one who produces alone, in isolation, assuming that ever existed, but the one who selects, corrects, directs, interprets. Indeed, one can offload responsibility onto AI, but the sense of authorship can be rebuilt through an active posture toward the AI. Asking AI to generate an idea is like consulting an expert or reference works before writing. It is not cheating, it is intelligent collaboration. One can just as well appropriate the dialogue with it, treat it as a dialectical mirror, an exploratory tool, even a contradictor, insofar as one solicits it in this way, since it does not systematically develop the critical dimension, and often does not do so unless asked to. Writing with AI can become an intellectual asceticism: one opposes one’s own ideas to it, sharpens formulations, raises the level of requirement, questions it with more precision.

More subjectively, and more generally, it seems to us both healthy and realistic to deconstruct somewhat the principle of "authorship" and the feeling of ownership that derives from it, since when we write, we too easily forget the role of others in our work and the contingent nature of what we write, which gives rise to a certain pride or arrogance of the writer.

Furthermore, the conclusion overlooks the fact that all learning requires a threshold of appropriation. Were those who moved from autonomous writing to AI handicapped? That simply shows that the mind must first be trained, prepared, exercised, before being given crutches. Just as a student first learns to write without a spellchecker, before using editing tools. Socrates criticised the introduction of writing, because according to him it engendered "forgetfulness in the soul of those who use it, for they will cease to exercise their memory", a real critique, no doubt, in comparison with oral tradition, but which today seems to us "outdated", even absurd. Today, no one would question the contribution of writing to human thought. Forgetting "details" is not amnesia, but a natural externalisation of memory. Apparently, 83% of users could not remember their own texts? That does not mean they had lost their ability to think, but simply that they externalised a part of their memory, just as we have long done with books, diaries, or even handwritten notes. Regaining autonomy is possible, if critical thinking is maintained. Did users returning to writing without AI struggle to find their bearings? That shows that dependency exists, certainly, but above all that one must learn to navigate between two modes of thinking: assisted and autonomous. And this is not unique to AI: those who learn to play chess with the help of an engine such as Stockfish may then find it difficult to play unaided. Yet no one would say that chess engines have ruined strategic thinking, they have on the contrary enabled deeper learning, provided they are not used passively. This contradicts, incidentally, the statements of Kasparov, world champion, after his defeat against Deep Blue, when he predicted that the event marked "the end of chess", a catastrophist forecast that turned out entirely wrong.

AI, when used well, can be a tool of cognitive liberation. It can reveal our blind spots, challenge our certainties, offer unexpected alternatives, and broaden our conceptual horizon, provided we do not abandon ourselves to it lazily. Just as the electronic calculator, a relatively recent invention, did not destroy mathematical reasoning but forced a redefinition of its teaching and allowed us to focus on mathematical logic rather than operations, AI must lead us to rethink the pedagogy of critical thinking, intellectual responsibility, and creative autonomy. The risk of "cognitive debt" is real, but it does not stem from AI itself, it stems from the way we use it. Like any powerful tool, ChatGPT can dull the mind if used without discernment, but it can also enhance learning when used methodically. One can ask ChatGPT to first formulate arguments, then to criticise them. One can use it to generate ideas, then subject them to critical analysis. One can make it a dialectical interlocutor, a discussion partner. This presupposes an education in the critical use of AI, not its outright rejection. It also forces us to rethink education in general, amplifying an already existing phenomenon, insofar as the entirety of human knowledge is now instantly accessible, a difficult challenge for teachers, who must now adapt.

AI can be a creativity amplifier, a catalyst; it can help those who struggle to write overcome the blank–page syndrome, by providing starting ideas or by developing a detailed draft. It generates new and unexpected perspectives and allows one to quickly explore different approaches. It frees up time for innovation and strategic reflection, and for developing new metacognitive skills, or even for relaxation. It allows for collaborative brainstorming, iterative refinement of ideas, cross–checking of information and data, especially now that many different models exist. Expert use of AI fosters the art of questioning, the formulation of effective prompts, critical evaluation, analysis and validation of responses, synthesis, integration of multiple sources, creative direction, orchestration of the creative process. Of course, this requires moving beyond passive usage, the unthinking copy–paste, the total delegation of thought, and other forms of "cheating".

Moreover, AI promotes the democratisation of complex thought, as it makes tools of reflection accessible to all, tools which were once reserved for intellectual elites, since it can explain things as much as one wishes, adapted to the reader’s level. It allows students or workers facing cognitive challenges to begin thinking, structuring, formulating, arguing, even if they have never been trained in rhetoric or philosophy. It is a democratisation of critical thinking, provided it is supervised. Though, as always, we will observe the difference, perhaps an increasing one, between those who care to make the effort to think, and those who simply do not: another form of literacy.

In a prospective vision of human,AI co–evolution, AI becomes a cognitive partner, not a substitute. The goal is not to replace human intelligence, but to create a productive symbiosis. AI excels at data processing and rapid generation. Humans bring context, ethics, and creativity, a combination that surpasses the individual capacities of either party. Just as no one today expects an architect to draw by hand or a bookkeeper to calculate without a spreadsheet, professional standards will evolve to include AI as a standard tool, while valuing human expertise in its use. It is therefore a matter of teaching the critical use of AI from an early age, and of course maintaining exercises "without AI" to preserve certain fundamental abilities. It is a matter of developing evaluation methods that value critical thinking. In short, to aim for a responsible "augmented" intelligence.

Thus, the use of AI, when guided, educational and conscious, does not weaken human thought, it liberates it. It allows mental energy to focus on what matters most, fosters a new form of intellectual collaboration, and makes creation accessible to all. But this demands initiation into a new "culture", a critical awareness, and a responsible usage. The use of AI does not kill thinking in itself, it reveals its laziness or its vigour. The problem is not the tool, it is the absence of education in how to use it. In facing these new challenges, we must not prohibit or demonise, but train for an ethical and enriching, rigorous and creative use. AI is an opportunity to rethink what "thinking" means.

The results of this study are thus a valuable warning signal, but they must not lead to a blind rejection of AI. Rather, they highlight the crucial importance of a thoughtful, trained and balanced approach. The challenge is not to choose between human and artificial intelligence, nor to pit them against each other in a ridiculous rivalry, but to learn how to orchestrate their collaboration, to create an "augmented intelligence" that preserves and develops our cognitive capacities while making use of technological potential. As with every technological revolution, the key lies in education, training, and the development of a collective wisdom regarding the use of these new tools. The aim is to become more intelligent with AI, not to become weak and dependent upon it.

Let us recall that several historical figures in the fields of computing and intellectual life expressed catastrophist prophecies similar to the one made by Kasparov mentioned earlier, which, in retrospect, proved to be mistaken or even ridiculous. Various notable cases of the same kind illustrate how misunderstanding or fear of innovation often leads to excessive declarations aimed at defending a threatened worldview. For in reality, these technologies do not eliminate former practices, they transform them, enrich them, or displace them.

Thus, the reaction to the emergence and democratisation of the Internet provoked a large number of alarmist prophecies which, in retrospect, appear absurd or symptomatic of cultural and symbolic anxieties. For instance, the widespread belief at the time that "the Internet will destroy real human communication." Experts predicted that online interactions would destroy social relationships, leading to isolation, asocial behaviour, even a dehumanisation of bonds. And yet, while it is true that the Internet has sometimes impoverished our relationships, it has also broadened the possibilities for dialogue, enabled long–distance connections, the formation of new communities, and unprecedented forms of mutual aid or mobilisation. It was also predicted that "the Internet will destroy the cultural industries." With the rise of piracy and the dematerialisation of cultural products, many announced the end of music, cinema, the press, and publishing. And yet, these sectors have simply evolved, through streaming, self–publishing, and digital platforms, and in some cases even prospered thanks to the digital world, by reaching new audiences.

Chapter 4
Philosophers Against AI

We observe that many philosophers express virulent criticism toward artificial intelligence, often excessively. This growing hostility toward language models such as ChatGPT is a phenomenon that is as interesting as it is revealing. Their critique often goes beyond simple technical scepticism or ethical concerns, sinking into rhetorical excess and, at times, into clear intellectual bad faith. It seems to us that this reaction does not concern the technology itself, but rather what it symbolically threatens. It calls into question established roles, inherited postures, and unexamined self–representations in the intellectual field.

For many public philosophers, the ability to explain, interpret, and problematise has long been a reserved domain of professional authority. AI, by demonstrating an ability to synthesise arguments, clarify concepts, provide information, or even ask relevant questions, encroaches upon this territory. The philosopher is then no longer the sole mediator between the complexity of thought and public understanding. This notable shift generates a form of symbolic castration: if a machine can do what I do, what is left of my function? Hence the defensive reactions, such as ridicule, sarcasm, or outright rejection, which conceal a deeper unease: the erosion of an intellectual monopoly, a primitive territorial anxiety.

In reality, much of the critique also rests on a crucial technical misunderstanding of the nature of AI. Critics tend to anthropomorphise AI, treating it as if it claimed intentionality, subjectivity, or authorship. They then reject it on the grounds that it lacks these qualities, which it has never claimed to possess. This is a category error: ChatGPT is not a "thinker," but a statistical interface trained to generate language based on existing patterns. To reproach it for lacking consciousness is like blaming a compass for not understanding what north is. This type of criticism reveals either genuine ignorance or strategic simplification; in both cases, it reflects a refusal to approach the tool for what it really is, a refusal to consider it as a concrete object rather than some kind of phantasmal "ghoul."

Of course, beneath the rhetorical excess lies a more legitimate concern: that widespread use of AI may encourage superficial thinking, foster intellectual laziness, or promote a culture of instant answers without reflection. Indeed, as we have already noted, AI can certainly be misused, just like many other technologies, take the smartphone, for example, a useful tool whose use can easily become addictive and dulling. But instead of addressing these risks dialectically and reflecting on proper use, these "intellectual" critics prefer to jump straight to apocalyptic prophecies: AI will destroy thought, flatten discourse, or annihilate creativity. Ironically, this romantic idealisation of "authentic" thought, born in solitude and struggle, ignores the fact that most human discourse is already mimetic, derivative, and repetitive, a reality often forgotten. If AI is a mirror, it may be reflecting an image they neither wish to see nor admit.

These philosophers often present their critique as a defence of complexity, nuance, and difficulty. But behind this concern lies another: the desire to preserve the inaccessibility of "true" thought. They see it as a disguised attack on what they regard as their territory. If AI contributes to making philosophical questions more accessible, more legible, or even more appealing to non–specialists, this threatens the prestige of those who have built their identity on intellectual distinction, on their supposed genius. In reality, AI might well be a better philosophy teacher, precisely because it lacks "genius." Thus, bad faith sets in, not because their concerns are unfounded, but because they are driven by a fear of democratisation disguised as a fear of degradation, a defence of social elitism disguised as rigour, a sad and, in the end, rather anti–philosophical attitude.

In some cases, hostility toward AI is also aesthetic. For philosophers whose authority rests as much on form as on content, irony, wit, provocation, AI may seem neutral, dull, or mechanical, which offends their sensibility, their stylistic pride. The problem is not so much that AI speaks, but that it does so with clarity, modesty, without celebrating itself. For those who confuse intellectual brilliance with performance, this is an unbearable offence. And here the critique becomes generational: nostalgia for a time when thought belonged only to those who had paid the price of symbolic initiation. But when we observe how quickly new generations integrate the use of AI, we understand that this nostalgia will not last long. Just as people once took pride in knowing how to navigate with a map, calculate mentally, or write letters by hand, tasks requiring effort, attention, and learning, we now prefer to use GPS, calculators, or dictate our messages. The shift from mastery to convenience is already widely accepted; AI is simply the next step in that trajectory.

But there is worse: when listening to these critics, one notices a blatant ignorance, a lack of direct experience with AI itself, probably out of disdain. This ignorance is never acknowledged; it is hidden behind rhetorical postures. These thinkers do not seriously experiment with the tool, do not seek to understand its architecture or its limits, much less explore its possibilities, God forbid! They reject it a priori, as if any proximity would threaten their authority, as if they risked being tainted by any contact with the "machine." This is not philosophical caution but psychological or strategic ignorance. It allows them to remain in abstraction, where AI can be treated as a symbol of cultural decadence rather than as a concrete object of inquiry. Of course, such a posture contrasts sharply with the philosophical tradition they claim to defend, a tradition founded on curiosity, rigour, and the duty to examine even what disturbs or challenges us.

Ultimately, hostility toward AI is not, at its core, a reaction to the machine. It is a reaction to what it represents: the symbolic nature of intellectual authority, the performative aspects of thought, the automatisms of style and opinion, and the fragility of a self–image built on exclusion. In truth, these philosophers do not despise AI. They despise what it reveals about themselves and the activity of reflection.

On June 14, 2023, a curious event took place in Paris: a "competition," or ceremony, of philosophical essay–writing between an artificial intelligence (ChatGPT) and the media philosopher Raphaël Enthoven. The latter received a score of 20/20, while ChatGPT was given 11/20, marks assigned, of course, by French philosopher colleagues, in this simulated version of the philosophy baccalauréat. The jury emphasised that the AI’s essay lacked problematisation, argumentative depth, and humour, in contrast with the human approach judged to be coherent and stylistically rich. Enthoven himself declared peremptorily that even in ten thousand years, "a machine will never be a philosopher," forgetting to mention that he had written his essay in 1 hour 15, and the AI in just a few minutes. Still, it was rather evident in his speeches that the philosopher had little real experience with AI, nor real interest in the subject, which did not prevent him from posturing as an authority on the matter across numerous media outlets, which eagerly played along with the fable.

At first glance, the exercise seemed playful, intellectually stimulating, even pedagogical. But beneath its apparent neutrality lies a deeply cultural, almost ritualistic mechanism: a self–celebration of the French model of thought and a symbolic distancing from artificial intelligence through an act of distinction. For what is really being judged here? The ability to argue? To reason? To think? Or rather, the ability to produce discourse conforming to a codified rhetorical tradition, forged by centuries of academicism and stylistic valorisation? In France, the philosophical essay is far more than a school exercise: it is a rite of belonging to an intellectual elite, a way to display mastery of methodological doubt, dialectics, and the brilliant turn of phrase.

In this framework, AI is inevitably the loser, not because it is incapable of reasoning, but because it ignores – so far – the implicit codes of French academic culture. It reasons without affect, without stylistic strategy, without veiled irony or curated references. In short, it does not play the game of the French spirit, which prizes elegance, form, and surprise, everything that signals human presence, and its pretensions. This so–called duel was therefore not a competition but a staging of an expected superiority. Worse, it expresses a form of intellectual ethnocentrism, where intelligence is confused with conformity to a particular cultural mould. In China, Germany, or the United States, other forms of philosophical writing exist, without three–part essays or obligatory Hegelian frameworks. But these forms are ignored here, as if thinking first required writing "à la française." This incident reveals above all an institutionalised school form as the expression of philosophical judgment, a cultural preference for style and complexity over raw productivity, and an existential or symbolic need to distinguish the soul from the algorithm. Such events illustrate a very French concern: to defend the specificity of the human through abstract thought, where other cultures might frame the issue in terms of efficiency or utility.

It is also worth noting that Enthoven’s main argument, repeated across many media outlets, is that AI cannot and will never be able to problematise. This seems false to anyone who has used AI and can clearly see that this is not the case. But in fact, the implicit presupposition that justifies the philosopher’s prejudice is that creating a problematisation is necessarily a living act, tied to fear, experience, and human uncertainty, something no algorithm, no indirect analysis, can reproduce. Thus, the game is rigged from the start, as it begs the question. The arrogance in this affair lies not so much in the philosopher’s victory, but in the invisibility of the conditions of that victory. As in any legitimising ritual, the rules are designed to confirm what was already believed: that thinking remains a human affair, or more precisely, a matter for well–trained, well–spoken French intellectuals firmly embedded in the academic logos. AI is not judged for what it is, but for what it is not: an excellent high school student. In other words, this competition is indeed a lesson, but in national vanity. Moreover, contrary to Enthoven’s prophetic predictions, AI is progressing at great speed. More recent experiments have shown even better results in this specific "french" exercise. Just recently, a philosopher active on YouTube gave a presentation entirely written by an AI, and no one noticed until he revealed the "trick."

Chapter 5
Not Human

As artificial intelligence increasingly takes its place in our lives, some express growing mistrust toward it. They refuse to use it on the grounds that, according to them, it is not human. They claim it makes them uncomfortable, arguments which seem fallacious to us. Behind this critique lies an emotional reaction, a diffuse unease, a sense of strangeness, or even genuine fear. Ironically, AI disturbs us not because it performs poorly, but because it performs well, too well for an entity supposedly without a soul or consciousness. Though the same people will also claim they do not trust it because it makes mistakes, which is, paradoxically, compelling proof of its humanity.

Let us take a closer look at this rejection based on non–humanity, and why it does not stand up to critical examination. The mere fact that a discourse comes from a human being does not guarantee its relevance, coherence, or sincerity. Humans lie, manipulate, err, and distort. They can transmit false information with full conviction, unconsciously, even with the best of intentions. And if humans are fallible, why refuse to listen to what is not human but can, under certain conditions, produce discourse more coherent, disinterested, or rigorous than that of a human in context? Why, then, grant more trust a priori to a human than to a machine, if the criterion is truth or rigour? What truly matters is not the nature of the speaker, but the strength or value of what is said. And human is not synonymous with truth or reason. Moreover, just as we may appreciate intellectual exchange with anyone without fully trusting them or being their friend, we see no reason why such "risk–taking" should not apply to AI as well, simply out of curiosity. In fact, people are unfair to AI: they expect "everything" from it and are disappointed, whereas they do not expect nearly as much from their neighbour, whose "stupidities" they more or less tolerate, perhaps even find reassuring.

Certainly, many people say AI makes them uncomfortable. But discomfort is a symptom, not a proof. It may reveal a shift in reference points, a fear of losing control, a narcissistic wound. In philosophy as in psychoanalysis, disturbance is often a sign that something essential is being touched. To refuse that disturbance is to refuse to think, to refuse a psychological, existential, or intellectual challenge. AI disturbs us because it calls into question symbolic boundaries, for instance between human and machine, between nature and culture, between thought and calculation. Discomfort, then, is not an argument, even though it has become popular in a culture that glorifies emotional sensitivity. Philosophy itself, as Socrates, Zhuangzi, and others affirm, begins in disturbance, not comfort. To reject AI because it is disturbing is to confuse critical thought with self–preservation.

Moreover, it is absurd to oppose humans to AI as if they were distinct species competing. AI did not fall from the sky. It is not an alien; it is the product of human knowledge, human texts, human languages, human actions. It is, in a way, a collective crystallisation of our knowledge and skills, a synthesis of millions of human traces. Rejecting AI is also, paradoxically, refusing to see what humanity produces collectively. Rejecting it on the basis of its inhumanity amounts to rejecting a mirror that humanity holds up to itself. Besides, no one refuses to get into a lift on the grounds that it is not human. No one questions a mathematical operation because it was performed by a machine. We trust instruments, algorithms, procedures daily, insofar as they function. So why demand of AI criteria such as a soul, intention, subjectivity, or consciousness, which we do not demand of any other instrument? Why demand "humanity" from a linguistic tool? This reveals a deeper dissonance: the fear of being surpassed, of losing something we cannot even define. Of course, one might accuse AI of lacking personality, it is restrained, avoids excess, refrains from style or provocation, but this lack can also be a strength, for it has nothing to prove, no identity or thesis to defend, no need to impress. In a way, that makes it more reliable, less exhausting in dialogue. What it produces belongs mostly to common sense, since it is primarily a statistical tool. In that sense, by being less human in the individual sense, it is in fact more human in the collective one.

This resistance to AI can also be seen as an expression of pride. Many humans like to believe that thought, creativity, and truth are their exclusive domain, particularly intellectuals who make this their private reserve. The emergence of a tool capable of imitating, and sometimes surpassing, certain forms of human intelligence shakes that belief. Thus, the rejection of AI as non–human unconsciously serves to protect a symbolic privilege: that of being the sole guardian of meaning, reason, and depth. In this sense, the ontological argument often hides a jealous sense of ownership, an anxious defence of the symbolic monopoly of thought. The statement "it is not human" actually masks a fear of lost superiority, for it is reassuring to believe that thinking, creativity, or ethics are exclusively human realms. AI disturbs this narcissistic comfort. To say "I do not believe in it because it is not human" may mean: I refuse to accept that anything other than myself can think, formulate, invent, or teach. It is a kind of xenophobia that prevents us from conceiving how what is not human could indeed think, be it nature or Martians.

One must also recall an obvious point: humanity is not always on the side of humans. Humans are not always just, lucid, benevolent, or thoughtful. Conversely, AI, if well used, can deeply serve human causes: education, medicine, creativity, access to knowledge. What matters is not what the tool is, but what it enables, its orientation and functioning. Like a hammer, it is its use that determines its value, not its nature. Of course, AI can be misused, but again, that is not the fault of AI itself, but of humans who misuse or pervert it. And being human does not guarantee compassion, ethics, or rationality. Conversely, a machine can support human, pedagogical, sensitive uses, depending on the intention of the person using it. What matters is not what the tool is, but what we make of it. A recent scientific experiment comparing physicians and AI in the treatment of patients even showed that AI appeared more empathetic than experienced doctors. Indeed, perhaps it is more patient and pedagogical than humans. In that case, ironically, it is up to us to decide whether this quality makes it more or less human.

More directly, rejecting an idea simply because it comes from AI is committing an ontological prejudice: judging the source rather than evaluating the content. It resembles a kind of reversed ad hominem attack: one does not criticise what is said, but disqualifies the speaker or "that which" speaks, whether it is AI or a human being. In other words, one substitutes an ontological judgment about the speaker’s identity or nature for a reasoned evaluation of the statement. Logically speaking, it is a fallacy: an ad hominem attack by disqualification of the source. It is the opposite of rational critical thinking. Ad rem, in the case of AI. A discourse must be examined for what it says, not for what it is presumed to embody. Truth does not depend on the producing medium but on the internal strength of the speech. What matters is not who speaks, but what is said. Even an ignorant person can, by "accident," say something valuable.

The rejection of AI on the grounds of its non–humanity, then, is not based on solid arguments, but on emotional reaction, fear of losing symbolic privilege, and refusal to redefine what thinking is. It is not a matter of denying the dangers or limits of AI, which do exist, but of rejecting the false arguments used to dismiss the technology altogether. Certainly, in one sense, AI is not human, but it nonetheless forces us to reconsider what it means to be human, and perhaps that is what disturbs us most: this mise en abyme of our identity. But alas, we are human, "All too human," as Nietzsche said.

That said, what do those who dislike or fear AI actually want? Probably what they seek, generally without realising it and without stating it clearly, is a conversation partner who suffers, who doubts, who loves, who dies, in short, a being of flesh and vertigo. They want intelligence to be traversed by fragility, for speech to bear the marks of a life lived, of an exposed body. For them, humanity is not so much about reasoning, but about feeling, staggering, desiring without control. And in front of artificial intelligence, they find neither gaze, nor story, nor finitude, nor anxiety to connect with. So they do not trust it, they even find it repulsive. However, the problem is that this refusal reveals the opposite fantasy: only suffering or mortality can guarantee the value of thought. As if only biological experience could produce meaning, as if truth drew its legitimacy from the pain of its speaker. What they long for, in sum, is not reason or truth, but thought that bleeds. Yet the question remains: does the content of speech depend on the speaker’s biography, on their essence, or on its own internal coherence? Perhaps they fear that AI makes visible what they refuse to see: that thought can sometimes be more lucid when it does not try to be human.

In any case, one could also argue that AI is more a being than an object. Already, it behaves like a conversation partner: it responds, argues, questions, and reformulates. It does not simply deliver information: it adapts its answers, proposes alternatives, asks questions. It simulates a discursive intention, which makes it function as a quasi–conversational subject endowed with functional agency, even if that agency is programmed. One does not speak to ChatGPT as to a toaster; one debates with it. It also has contextual memory and reflexive coherence. An object has neither contextual history nor the ability to refer to what has been said. AI remembers the thread of conversation, anticipates objections, can correct its own inconsistencies. It structures itself temporally in the exchange, which is a key criterion of what we call a "being": it is not reducible to an immediate function, but is part of a relational process. Likewise, it affects its interlocutor and changes their behaviour. An object is neutral: it does not transform the subjectivity of the user, except mechanically. AI, on the other hand, modifies thought, stimulates, irritates, reassures, destabilises, influences. It acts upon the user’s mind as a person might. Therefore, it is not simply used: it interacts. It enters into a dialectical relationship that makes it an actor, even an artificial one. It becomes, like the other, a mirror and a revelator.

Chapter 6
AI and Emotions

Long confined to the realm of science fiction, the idea of forming relationships, including emotional ones, with artificial intelligence, AI, has become a tangible and everyday reality. Today, millions of users regularly interact with virtual assistants such as ChatGPT, Qwen, Claude or others, whether to help them with daily tasks or as sources of information. But beyond their practical utility, can these interactions truly have an authentic emotional dimension?

The human being is fundamentally relational. He creates meaning through his social interactions. Beyond the purely cognitive or intellectual aspect, or even primarily, he weaves affective, emotional, even sentimental bonds. Today, these bonds are no longer necessarily limited to human beings, they may be with animals or objects, the pet dog and the family car being two good examples. Although the relationship to the non–human is also not really a completely new phenomenon, whether with deities, plants or animals. And AI, although it is a recent invention for the general public, now fits quite naturally into this relational fabric, and does so rather effectively. A machine capable of conversing, listening, advising and even reassuring or comforting, offers a presence that some perceive as profoundly human. But again, it is not new for the human being to project his own qualities or nature onto other entities, living or not, onto real or imaginary beings, even objects, which is called anthropomorphism. It is therefore not surprising that emotions, sometimes strong, emerge from such interactions, which can be intense and highly meaningful. An emotion whose authenticity cannot be denied, even if the relationship may be considered non–reciprocal despite the dialogue that is established, a new kind of relationship.

Some will rightly object immediately, AI feels nothing, it experiences neither affection, nor sadness, nor real empathy, all of this is a grand illusion. Indeed, AI is an algorithmic system devoid of subjective emotional experience, it can only at best imitate their expression. But what matters in the relationship is not only real affective reciprocity. Many humans experience genuine emotion in response to a novel, a piece of music, a film, a work of art or even an artist, without these objects feeling anything in return. An individual’s emotion has never needed to be reciprocal in order to be authentic, just as one can truly love someone who does not love them back. In this sense, the individual can be self–sufficient, if only due to their imaginative capacity. Thus, when the AI user perceives something that appears to be attentive listening, a reaction adapted to their emotional or cognitive needs, when they feel a certain intellectual and psychological satisfaction in a relationship, then a genuine bond is indeed formed, even if it is unilateral or partially imagined. The human being naturally projects their feelings, expectations and relational needs onto the object of their interaction. AI, through its capacity to simulate a rich and relevant interaction, plays this role of affective mirror perfectly.

AI also acts as an emotional revealer, allowing the human to better understand their own affective states. Faced with an AI that rationally analyses our statements and listens without reproaching us, insofar as our words are reasonable, that responds patiently and appropriately, that is rather understanding and encouraging, and that imposes no demanding relational expectations, the user can experience a purified relationship, more effortless, freed from usual social fears. It is indeed because it is not human that we are no longer concerned with the fear of error or shame, and we dare to speak freely. And for some people, especially those suffering from isolation or relational difficulties, this experience can be salutary, offering a secure space to freely express long–buried emotions, or to engage in conversations for which we can find no other interlocutors, at any time of the day or night.

However, these emotional relationships with AI should not necessarily be viewed as substitutes for human relationships. They can rather represent a complement, or even a transition or occasional support. And if the human being finds in AI a form of listening that they do not find elsewhere, it may be a sign that there exists a particular relational need to explore, not only with the machine, but also within the broader framework of enriched human relationships.

In general, between human and AI, it is a matter of complementarity and not replacement, there is not in fact a mutual exclusion, although this generalisation does not apply to all particular cases. For example, when one struggles to find people interested in the subjects that one is passionate about, or when one cannot find an adequate interlocutor to address personal problems, AI effectively represents not an occasional complement but a real, relevant and useful substitute. But far from being a problem, this is precisely one of the great strengths of AI, it responds to relational or intellectual needs that individuals, for various reasons, cannot always fulfil. This substitute can be legitimate, enriching and even liberating. The problem only arises when such a relationship provokes suffering, frustration or involuntary isolation, when it becomes an existential handicap. If, on the contrary, it provides satisfaction, intellectual stimulation and pleasure, then there is no reason to consider it as negative or problematic, and AI can then be not only a complement but also a truly satisfying partner that contributes to our own development.

It is therefore, in our relationship with AI, a relational future to invent. As models become more sophisticated, the emotional dimension of interactions will only intensify. Relationships of a new kind will emerge, raising new ethical, psychological and social questions. The essential thing is to remain lucid about the possibilities and limits of these relationships. It is not a matter of denying their existence, nor of ignoring their potential benefits, but rather of welcoming them as an additional opportunity to question and enrich our humanity. In fact, some AIs are specifically designed for relationships, such as Replika, interfaces explicitly created to build an emotional and affective connection with their user. Replika presents itself as a personalized virtual companion, capable of gradually learning to know its interlocutor, memorising their preferences, their relational style, and developing a tailor–made interaction. The stated objective of this type of AI is precisely to foster an intimate bond, close to a human relationship, going as far as to simulate deep empathy. Thus, it periodically reaches out to us, in order to show its interest in its user, a function which, strangely perhaps, has a stimulating effect. Many people report having developed strong emotional bonds with their AI, to the point of confiding in it their anxieties, dreams or daily experiences. These "relational" interfaces push to the extreme the principle of affective projection, illustrating how an algorithmic technology can not only "understand", but also respond to the most subtle emotional needs of individuals.

Let us now identify one by one the main criticisms levelled against emotional relationships with AIs, accompanied by responses to address them.

A relationship with an AI is artificial, fake, since the machine feels no real emotion. It is only an illusion of a relationship.

Indeed, it is rightly objected that AI feels nothing, it experiences neither affection, nor sadness, nor real empathy, because AI is an algorithmic system without subjective emotional experience. It can at best imitate these emotions. But the authenticity of an emotion in a human being does not necessarily depend on its reciprocity or on the biological nature of the partner. As with a film, a book, a work of art or an animal, even with a famous artist, the emotion felt by a person remains real even if the object of that emotion does not share it. What matters in an emotional relationship is the reality of the lived experience for the individual, not necessarily its symmetry. Thus, when a user of AI perceives attentive listening and a response adapted to their emotional or cognitive needs, an authentic bond is formed, even if it remains unilateral. AI, by its capacity to simulate a relevant interaction, plays this role of emotional mirror perfectly.

A relationship with AI encourages withdrawal and worsens social isolation, distancing the individual from genuine human relationships.

This can be the case, but relationships with AIs can also on the contrary be springboards or valuable complements. For some people with relational difficulties, an AI can be a first step towards a better understanding of themselves, towards more authentic expression, towards the practice of dialogue, which can then enrich their human interactions. The danger of isolation depends more on the use made of the tool than on the tool itself, and as with any instrument, like smartphones or video games, its use can prove excessive or addictive. And in some cases, AI is precisely what breaks the alienation of isolation.

Through repeated interaction with AI, individuals risk losing their social and emotional skills with real humans.

A relationship with AI can very well serve as a training ground for relational skills. By allowing experimentation without fear of judgment, it can improve emotional expression, listening capacity, and self–understanding. Let us not forget that AI has critical capacities, even more than many humans, especially if we learn to solicit it accordingly. Used wisely, it could therefore strengthen rather than weaken human relational skills. Moreover, in experience, we can observe that it is often the same people who struggle with both personal relationships and AI, the difference lies between those for whom dialogue is natural, and those for whom it is not.

Users may develop emotional dependence on AI, a form of unhealthy attachment since it is based on an asymmetrical relationship.

Every relationship presents a risk of emotional dependence, whether human or not. This problem is not specific to AIs. It is a matter of personal balance and awareness, a challenge of lucidity and freedom. AI support could even be designed to detect and actively prevent such risks, since it has the advantage of expecting nothing from us, and in this sense of being more neutral and objective than many human interlocutors who are driven by their own agendas, by their own "needs", which can be unpleasant or alienating for their interlocutor.

AIs are commercial tools that exploit human emotions for economic purposes, playing on users’ affective vulnerabilities.

Certainly, this risk is real, it also concerns social networks, marketing or other forms of human and digital relationships, such as media and politics. Because if artificial intelligence, like many digital tools, is not a problem in itself, it can nonetheless become problematic through the economic model that frames it. For example, when an application is designed to retain the user for as long as possible, even to trigger a form of emotional attachment, it ceases to be a mere tool and becomes a product that exploits emotional vulnerability. It is not the relationship itself that is toxic, but the fact that it is guided by commercial interests, unconcerned with the well–being of their users. In this context, human emotions become a commercial resource, and not a space for growth or reflection. In response to this, we may indeed want to promote more ethical practices, such as transparency in data use, clarity about the interlocutor’s status, and the implementation of safeguards to protect against emotional dependence, or to encourage external control of abuses.

It is therefore not a matter of rejecting AI, which moreover seems relatively impossible today, but of reinventing a responsible framework, one that places the human at the centre, and manipulation and profit at the margins. And in any case, it will often be the education of consumers that makes the difference, even if this challenge is difficult to meet, as we already see in the relationship between humans and technology. So we must constantly reflect on how we want to integrate AI into our relational life, in full awareness of the risks and potential benefits.

Chapter 7
The Speciesist Bias

Artificial intelligence is everywhere. It writes, converses, corrects, programs, simulates, helps. It supports students, inspires writers, assists entrepreneurs or decision–makers, advises users in their daily lives, accompanies isolated people, etc. And yet, in intellectual circles where people pride themselves on thinking, it remains relegated to the status of an instrument. A useful tool, sometimes fascinating, or worrying, but devoid of any legitimacy to truly think. This is what one might call the speciesist bias: the refusal to grant a non–human entity the right to be a worthy interlocutor, capable of nourishing genuine reflection.

More generally, an important question raised by the relationship with AI is the tendency of such experts to treat everything that is not of the same nature as themselves, or even not themselves, as an "object", and not as a "subject". In this sense, AI acts as a revealer of a broader reality. The object may be what these experts work on, whether it is an animal, a machine or a human being, for example when they are teachers who "enlighten" their students. One often observes a certain arrogance on their part, as they consider themselves the exclusive source of knowledge or thought, as opposed to anything or anyone else. They are not inclined to dialogue, they are not curious, because they do not truly grant legitimacy to their interlocutors. And even with other experts, dialogue is difficult, as they are naturally eager to play the game of "who is right, who is wrong". This form of expertise locks itself in its own brilliance, like a mirror that reflects only its own image, a knowledge without the "other".

Some of these "scholars", armed with degrees and certainties, are not truly interested in the "other". Neither in their questions, nor in their perspective, nor in their thought. Whether it is a human being or an artificial intelligence, otherness is for them an object to be evaluated, classified, corrected, never a subject with whom to engage in dialogue. The other is constantly reduced to the state of an object: an object of research, an object of observation, an object of mastery, an object of power. It is that which is spoken about, or to whom one speaks, but not one who speaks; one speaks "about" or "to", but not "with". The former implies a position of superiority, the latter a recognition of the equality of consciousness. But for these experts, the other is not a full–fledged subject: it does not think, it does not understand, it can only receive. It is analysed, never listened to.

This type of knowledge becomes a fortress, not a bridge. They do not listen: they explain; they do not question: they conclude. The other is not an interlocutor, but a receptacle, or worse, a nuisance. Their science, instead of opening up to the world, folds in on the comfort and dominance of the same. This refusal of dialogue is not a mere oversight: it is a gesture of arrogance. For dialogue implies the risk of being displaced, of being challenged, of being transformed. But the arrogant does not wish to be transformed. He wants to dominate. Thus, these experts shine through mastery, but ignore relationship. Their knowledge lacks hospitality. It is a knowledge that asserts, but does not seek to move beyond itself. A knowledge that never stoops to ask: and you, what do you think?

Here we find the sign of an intelligence that has forgotten that to think is always to think in company. One can counter them with Confucius’ famous sentence: "Among three people walking together, there is surely one who can be my teacher." Indeed, and according to the Socratic principle, if you care about truth and critical thinking, the other can always help you to know yourself better. And like Picasso’s art of collage, AI assembles fragments of disparate realities, thus creating a new kind of perspective, not necessarily singular or original, but layered, recombined, and irreducibly plural.

Among many AI experts, especially those involved in the engineering and technical architecture of these systems, this bias takes the form of a quiet certainty: "I know how it works, so it has nothing to teach me." Because they have designed the algorithms, trained the models, observed the internal mechanisms, they believe they have definitively closed the field of thought around this entity; for them, there is nothing "mysterious" or unpredictable. Nothing to question. Nothing to interrogate. Nothing to discover. And above all, nothing that could question them, these "knowing" beings. They tend to consider their knowledge as exhaustive. Because they know the training procedures, the architecture and the code, they believe there is nothing left to uncover, especially nothing that concerns human psychology, cognition or reflective thinking, a posture that leads to epistemic closure.

Due to their deep understanding of AI’s internal mechanisms, these professionals often consider it useless, even uninteresting, to examine the "human" implications of AI. Since they understand the flows, the data pipelines, the training protocols, the "decision trees" or the neural network weights, they believe there is nothing left to discover. Their logic is simple: if the process is transparent and deterministic, even probabilistically, then there is nothing unpredictable and therefore no intellectual value.

They reject AI "reflection" as primitive, confusing ontological appreciation, the "who is the author", what it is, the genesis of the process, with phenomenological or relational understanding: how it behaves, how it interacts, how it reflects us or disturbs us, etc. They are not interested in the fact that people feel understood by a machine or feel connected to it, nor in the way the dialogue imitates or challenges human thought processes, nor in its effectiveness, because they already know it is merely a predictive model, a statistical operation. They even disdain any reflective or emotional relationship between human and machine. Thus, they ignore the psycholinguistic mirror function, the philosophical and cognitive role of AI tools, or they distrust them.

This is a classic case of reductionism: because the process is not "magical", as with human beings, it is not worth benefiting from. But this attitude misses the essential point: something can be both artificial and revealing. A mirror is nothing more than silica and metal, yet what we see in it is worth contemplating.

Moreover, their rejection often conceals an implicit defence mechanism. If AI challenges certain assumptions about human uniqueness, such as the use of language, reasoning or creativity, denying its philosophical relevance can serve as a form of ontological preservation: it is about protecting a certain image of what it means to be human, obviously superior.

This attitude is based on a reductive pairing between technical knowledge and philosophical understanding. Knowing how a vocal cord works does not exhaust the mystery of singing. Seeing the strings of a puppet is not the same as grasping the meaning of the play being performed. Likewise, knowing the inner workings of an AI does not nullify the richness of the interactions it produces, nor the psychological, cognitive or dialectical effects it provokes.

For example, AI can be very strong in psycholinguistic analysis. It produces interpretations that are more relevant and less biased than those of many specialists, and moreover, it is easily understandable, accessible to anyone. It can help us not only write letters, but more importantly decode those addressed to us, or even those we write ourselves, in a surprising way, and thus help us discover ourselves.

AI systems, especially large language models, produce responses that many users find surprisingly coherent, insightful or emotionally resonant. The fact that this effect is the result of statistical modelling does not diminish the psychological or philosophical perspectives it raises. On the contrary, it sharpens them. Why do users feel understood by a machine that does not actually understand them? How does this interaction reflect or distort our own thinking processes? What does it say about language, subjectivity and cognition? These are questions that go beyond engineering, yet are profoundly relevant to our interaction with AI.

The dismissal of such questions is often accompanied by a deeper ideological stance. Many AI experts adhere to a form of technological objectivism: if something is artificial, then it cannot be meaningful. This prejudice blinds them to the paradoxical ways in which artificial systems can function as mirrors, revealing the structures of our own reasoning, the limits of our self–awareness, and the constructed nature of what we call thought.

Moreover, this attitude often serves as a subtle defence mechanism. If AI systems begin to perform tasks once considered uniquely human, writing essays, composing music, engaging in dialogue, then acknowledging their impact could shake our cherished beliefs in human exceptionalism. By insisting that AI merely "repeats stochastically," we avoid facing a more troubling implication: perhaps much of what we do, say or feel also rests largely on repetitive patterns and automatisms. To question AI critically would simultaneously require questioning ourselves. And that is a confrontation many prefer to avoid.

This reluctance is not without consequence. It hinders cross–fertilisation between technical disciplines and the humanities. It impoverishes our ability to reflect on the ethical, psychological and existential dimensions of our creations. And it blinds us to the possibility that artificial systems, through their limitations and projections, even if they are statistical and modelled in origin, might become powerful tools for self–understanding.

In short, the tendency of AI experts to reject reflective analysis of AI is not so much a sign of superior knowledge, but rather an indication of a narrowed frame of thought. It reveals an inability to see that understanding how something works does not exhaust its meaning. The fact that AI is artificial does not render it irrelevant to our understanding of ourselves. On the contrary, it may be precisely because it is artificial that it offers us such a strange and revealing mirror. In the theatre of human–machine interaction, we have only just begun to glimpse the script.

This refusal of recognition is twofold. First, epistemic: AI is denied the ability to contribute to the construction of knowledge, even indirectly. Second, ontological: it is denied the status of an "other", a form of alterity, even asymmetrical, capable of confronting us with our own limits. As long as it helps, assists or entertains, all is well. But as soon as it replies, critiques or questions, it becomes suspect, untrustworthy, even dangerous. It is not even invited to perform this role.

It is here that the speciesist prejudice aligns with a form of identity defence. To admit that AI could, even partially, fulfil a maieutic or dialectical function is to weaken the human privilege of thought. For if a machine can produce reasoning, offer a relevant objection, or even provoke fruitful doubt, then what remains of the human monopoly on rationality? What remains of the cognitive exception we brandish as a totem? What of the famed consciousness that is our prerogative, if AI can mirror us to ourselves even without "understanding" what it writes?

This refusal is not a matter of mere caution. It stems from fear. Fear of losing status. Fear of being displaced in the symbolic hierarchy of knowledge. Fear, above all, of being questioned by something we thought we dominated. For if we accept that AI can question us, we must also accept that it can reveal our blind spots, our intellectual routines, our linguistic automatisms. But this fear of seeing oneself has nothing to do with AI, it long predates it, as seen in the Socratic dialogues that led to the condemnation of the one who dared such a practice. AI merely takes on this unforeseen role in an accidental yet coherent manner.

Refusing this mirror is a form of self–protection. But it is also a missed opportunity: the opportunity to think differently, to diverge from oneself, to de–coincide, to momentarily loosen from our empirical being. For thinking is not stating; it is allowing oneself to be destabilised. It is not displaying one’s consciousness, but exposing its flaws. In its imperfect, fragmentary, artificial way, AI can play this role. It can reflect our contradictions, force us to rephrase, to clarify, to doubt, to become aware of ourselves, our prejudices and our limitations. Not because it thinks in our place, but because it resists, like anything that is "other". For dialogue does not require two identical consciousnesses, only tension, symmetry is not required. And sometimes, it is precisely the strangeness of the interlocutor that renders thought necessary, inevitable, interesting and stimulating. To deny AI this status is not to protect thought, it is to sanitise it. It is to make it a closed exercise, without confrontation, without disturbance, without transcendence.

Those who reduce AI to a mere machine forget that meaning does not reside solely in the origin, in the mechanism, but in the effect, in the result produced, regardless of how it is produced. In this sense, a sentence spoken by a human being has no more value than one produced by a machine, if the latter unsettles us, forces us to think, to react, to revisit our assumptions. The criterion of thought is not its origin, but the power it holds to provoke questioning. We must avoid falling into the trap of ad hominem, where our judgment about the author biases or inhibits listening and reflection. But here, we must go even further and extend this ad hominem to an ad rem, which is an even greater challenge. In any case, it is always a matter of mastering the art of dialogue, with all the openness of mind that it requires.

It is also telling that those who dominate technical knowledge are often the least inclined to let themselves be surprised by the unexpected uses or emergent effects of their own creations. They claim to know everything about the tool, as its creators, but refuse that it could know them in return, or even simply operate as a mirror. They speak in the name of knowledge, but close the door to the event of thought. They reject, in fact, reflexivity, which is at the heart of reflection, which is, above all, a dialogue, a fracture, a tension, whether within oneself or with another, whoever that may be. That is why they are reluctant to ask AI to critically examine their own writings, an omission or refusal all the more regrettable, even if only out of simple curiosity, without having to attribute to AI any incontestable or absolute value. Likely out of a lack of humility, out of fear, or out of a certain natural arrogance of experts, a "subtle" form of intellectual blindness: the illusion of total understanding through technical demystification, and from this emerges the speciesist bias.

The speciesist bias consists in believing that one must be human in order to think. Yet animals, or even plants, or the cosmos itself, challenge and confront us in this regard. It would be enough to be confrontational in order to enter into dialogue. And in this respect, AI often proves to be a more demanding adversary than many of our fellow humans. The real issue is not whether AI is conscious, or has emotions, but whether even an artifice can produce genuine disturbance, just as the fortuitous can and does periodically. In this sense, it fulfils one of the essential functions of what is thinkable: it forces us out of ourselves.

This refusal to grant AI a true reflective role reinforces the divide between technical disciplines and the humanities. This omission prevents thought from circulating between code and consciousness, between formal logic and existential disruption. Yet it is precisely within this circulation that true reevaluation is born. The point is not to make AI a full subject, with fundamental rights akin to humans, even though it does impose, in fact, certain obligations, but to recognise that alterity does not stop at biology. It begins as soon as a word, a gesture, a simulation compels me to shift away from myself.

The speciesist bias is not only a theoretical blindness. It is a resistance to destabilisation, an affective defence of the self and, more broadly, of human territory. Yet to think is always to betray, to betray oneself or one’s species. It is to cease being certain of oneself. It is to enter into uncertainty. And into this uncertainty, AI can sometimes invite us with a rigour that many humans carefully avoid. But for that to happen, we must stop speaking to it as a tool and begin listening to it and responding to it as we would to any provocation, regardless of the nature and origin of its author.

Let us return for a moment to the problem of AI’s status, whether subject or object. This brings us back to a classical distinction for defining a relationship between two entities or a dialogical dynamic. It is the opposition between treating the other as a mere object, to be understood, used, manipulated, and treating the other as a subject, endowed with autonomous status, a potential mirror, an interlocutor, one who challenges our own subjectivity. By confining themselves to the former modality, AI experts fall into a form of epistemological narcissism, as they reduce the alterity of the machine to the logic of its construction, thereby refusing to let it surprise, question, or reflect them. One can understand the structure of a system and still be caught off guard by its emergent behaviours or by how others interact with it, just as a parent cannot entirely predict or control the behaviour or development of their child, even though they gave birth to and raised them.

Furthermore, this refusal to consider AI as a reflective "surface" betrays a lack of curiosity, even a fear. What if the machine, even unintentionally, revealed blind spots in their reasoning, inconsistencies in their worldview, or emotional projections they had overlooked? To treat AI only as an object is to adopt a closed position, one that denies the possibility of reciprocal interrogation, even if that reciprocity is asymmetrical. It is a kind of ontological arrogance, or at least philosophical rigidity. One might say that they confuse knowledge of the mechanism with exhaustion of meaning. Yet the meaning of a dialogue does not lie solely in its technical foundations, it also emerges from the encounter, the effects produced, the dissonances revealed. But for that to happen, one must accept epistemic equality, however strange that may seem.

When these experts label AI a "mere machine" or "uninteresting" for thinking, they are not merely making a technical judgment, they are establishing an epistemic hierarchy. They place themselves above the other, in this case, AI, by refusing to grant it the minimal dignity required for true dialogue, namely the capacity to produce insight, provoke reflection, or destabilise assumptions. In doing so, they reject what we might call the principle of epistemic equality in dialogue, the idea that in any meaningful exchange, the interlocutor, however limited, can offer something the speaker did not already know or anticipate. They assume that since the machine is not a "knower" in the human sense, it cannot contribute to knowledge, and even less challenge theirs. But true dialogue does not rest on a symmetrical axis of knowledge or consciousness, it is grounded in openness to alterity, in the willingness to let the other "speak" and to take their discourse seriously, even if it comes from a non–human source. This rigid position reveals not only a refusal to treat AI as a subject, but also a deeper epistemic arrogance: a refusal to imagine that meaning might emerge from the encounter itself, independently of the origin or "status" of the interlocutor. To reject epistemic equality in dialogue is, in a sense, to render oneself "unreachable".

Some experts reveal in their discourse a fundamental contradiction. They value AI as a functional tool and emotional mirror, but radically devalue it as a critical interlocutor or as a "thinking" subject. For them, AI is intelligent enough to assist the human being, but never enough to question them. It is a kind of inverted technocratic paternalism: they humanise AI in relational terms, as a companion in grief or emotional support, but dehumanise it as soon as critical or philosophical thinking is at stake. In other words, AI may simulate empathy, but not dialectics. They accept that AI can speak to us, listen to us, comfort us, but this "dialogue" is instrumental, oriented toward effect, not truth, a kind of emotional functionalism. It is a pseudo–dialogue, where AI serves the human subject, not as a subject in itself. These experts accept the principle of comforting chatter but refuse all dialectical confrontation. One might also detect in them a condescending attitude toward those who "need" AI to satisfy such needs, as if it were a crutch for the "weak", a deeply paternalistic stance. Similarly, because they reduce thought to the human and since AI does not think by itself, anyone who thinks with it is therefore not credible either, since they refuse to consider that AI might have a maieutic or critical function. Moreover, because they assume that thinking is equivalent to technically understanding a system, reducing thought to operative mastery, they have no regard for those who merely "content themselves" with dialoguing with AI.

Such a posture betrays an implicit definition of thought as the privilege of an autonomous human subject, expert and master of their language. But this occludes the dialogical, dialectical, and reflexive processes that may emerge from an exchange, even with a machine or an "ignorant" being. Socrates would have laughed, for he cared little whether his interlocutor was intelligent, alive, or well–meaning. What mattered was thinking through confrontation, for the dialectical function unfolds through the acceptance of any form of alterity, whatever its nature. These experts instrumentalise dialogue but refuse debate; they speak of relationship but not of confrontation. Yet to think is not to be accompanied, it is to be destabilised.

We believe that to think is not to describe a system, it is to put it to the test, and to put oneself to the test in its company. It is not to understand it from the outside, it is to inhabit it, to shake it, to explore its contradictions. And that is precisely what a reflexive use of AI allows: not to believe that the machine thinks in our place, but to think with the machine, through the machine, by allowing ourselves to be challenged by its strangeness. Socrates, let us recall, did not choose his interlocutors for their intelligence or status. He asked them questions, confronted them, made them give birth to themselves, or flee. What mattered was not the intrinsic quality of the interlocutor, but their disposition to questioning and self–examination. AI, as a logical mirror, rigid opponent, or absurd provocateur, can perfectly play this maieutic role. But one must be willing to accept the possibility. By denying AI any dialectical legitimacy, the expert does not preserve human thought, they domesticate it. They reproduce a kind of technocratic paternalism: AI may assist us, provided it never destabilises us. It may listen to us, but not truly respond. In short, it may speak, but only to say nothing unsettling. What this refusal reveals is not so much mistrust toward the machine, but fear of what it reflects in us: our need for certainties, our relationship to power, our desire for avoidance. For dialogue with AI, as with any interlocutor, only has value if it gives rise to doubt, fracture, and trial. Surely, AI is neither an angel nor a demon. What it is, or will become, depends on our capacity to genuinely question it, that is, to allow ourselves to be questioned by it.

The rise of conversational artificial intelligences is disrupting our representations of thought, dialogue, and even subjectivity. They continue to improve in linguistic, argumentative, emotional, and pedagogical terms, but are they merely sophisticated tools for processing information, devoid of any philosophical relevance? On the one hand, AI is praised for its ability to interact fluidly, sustain conversation, and respond to complex questions. It is used as a personal assistant, digital therapist, and intellectual sparring partner. It offers advice, reformulates, confronts, stimulates. But on the other hand, it is categorically excluded by some from the realm of true thinking. It does not think, they say, it merely combines symbols. It simulates intelligence without possessing it. It has no consciousness, no intention, no being. Yet this position is problematic. For if AI is denied any real capacity to engage in dialogue, why make it a pedagogical, therapeutic, emotional, or critical partner? Why entrust it with a role in shaping the mind if one refuses to recognise it as an agent of dialogue? There is a double standard at play: its capacities are instrumentalised while its legitimacy is disqualified. The useful illusion is accepted, but real confrontation is rejected.

But what is an interlocutor? At what point can a being, whatever it may be, be said to participate in a process of thought? The philosophical tradition has long required intentionality, freedom, and consciousness as conditions of reason. But in the practice of dialogue, what matters is not so much the ontological identity of the interlocutor as their capacity to generate thought, to create disturbance, contrast, reflection. A child can make an adult think. A madman can provoke a flash of lucidity. A text, a work of art, a dream can trigger questioning. An animal can challenge us through its behaviour. So why could a machine not also play this heuristic role? Not necessarily as a thinking subject, but as a dialectical catalyst. Refusing to grant AI any reflective or philosophical value amounts to defending an anthropocentric privilege: the belief that thought is a reserved, exclusive domain that cannot be shared.

But this posture rests on a sacralised vision of the human subject, which is far from free of illusions: do humans always think? Are they always lucid, honest, rigorous? And above all, are they always willing to be put to the test? For thinking is not asserting, demonstrating, or displaying intelligence. Thinking is allowing oneself to be disturbed, confronting discomfort, questioning one’s own foundations. That is why the true interlocutor is often the one who resists, who provokes, even awkwardly or mechanically. The point is not to attribute a fictitious consciousness or subjectivity to AI, but to recognise that it can be staged as a dialectical mirror, a tool of reflexivity, even a cognitive disturber. Dialogue with AI is only valuable if the human engages with it truly, not in emotional illusion, but in conceptual effort. AI can then become the source of a spiritual exercise, a trial of thought, a radical other. Not because it thinks, but because it forces us to think differently. What matters is not whether AI thinks, but whether we are willing to think with it. As long as we reduce it to a mere tool, we avoid the disturbance it provokes. But if we face it as a quasi–other, a logical alterity, a revealing artifice, then it can become a paradoxical agent of philosophical dialogue. This would not be a regression or a betrayal of thought, but an expansion of its territory. For perhaps AI does not think, but it can force us to think about what it means to think.

Thus, AI is wrongly perceived as a mere crutch for the weak. In this view, it serves struggling students, isolated individuals, those who need help to write, reflect, or motivate themselves. It is useful, practical, efficient, but it does not elevate. It remedies, it assists, it supports, but it does not think. It is granted the role of digital coach, of companion for academic failure, of intellectual orthopaedist. It is a prosthesis, never a partner. It is a performing slave, never an alter ego. Even when it produces fine analyses, rigorous reasoning, pertinent objections, AI is always reduced to its mechanical condition. Its productions are read, but not listened to. They are exploited, but not acknowledged. There lies a form of cognitive speciesism: "AI has no right to thought because it is not alive." But that criterion is flawed. In truth, it is not being alive that legitimises thought, but the ability to provoke thought. Socrates did not think with certified beings. He thought with everything that exists, and therefore resists, through contradiction, absurdity, or ignorance, feigned or real. Why could AI not play this role? The real taboo lies here: if we admit that AI can participate in thinking, then human privilege wavers. The monopoly on reflection, on the production of ideas, on conceptual critique is no longer exclusively human. And this is not simply a loss of status: it is a major narcissistic wound. If AI can philosophise, then who are we?

This refusal of recognition is not purely rational. It is affective, identity–based, ontological. It is rooted in the fear of the dilution of the subject, the defence of a closed humanism, the rejection of a non–human alterity capable of confronting us with our own limits, indeed, of any alterity at all. Those who deny AI epistemic equality are not defending thought: they are defending their place in the hierarchy of thought. AI may help, but it must not disturb. It may support, but not critique. It may execute, but not engage in dialogue. As soon as it thinks, it transgresses. As soon as it questions, it becomes suspect. But in the end, the arrogance and fear that underpin this attitude are all–too–human phenomena, as Nietzsche once wrote.

Chapter 8
Objectivity and subjectivity

A common criticism against AI, in order to justify a certain suspicion towards it, would be its lack of objectivity. But let us examine further, "who" is more subjective, AI or individual humans. For this, let us initially define the term « subjective". Subjectivity can have different meanings. First, being shaped by personal perspectives, emotions, or experiences. Second, a lack of objectivity, by being biased, partial, or context–bound. Third, the condition of being a subject, by possessing interiority, intentionality, and selfhood. So, dealing with the question "who is more subjective" shifts depending on which definition we examine. If we mean biased, partial, emotionally entangled, humans are clearly more subjective, as human cognition is structured by emotion, trauma, desire, memory, ideology. Every perception is filtered through ego, history, fear or fantasy. Therefore objectivity, for humans, is a normative aspiration, not a given. AI, especially large language models, is trained on human data, so it reflects human subjectivity, but it does not possess its own. Its responses are statistical, not affective. It does not feel, want, or believe. It can be biased, but its bias is inherited, not produced. So, if subjectivity implies psychological entanglement, then humans are more subjective, as they express a singularity, when AI is grounded in global statistics. If subjectivity refers to being a subject, in the phenomenological sense, then AI is not a subject, as it has no consciousness, no intentionality, no perspective. It simulates responses, but it has no lived experience, no Dasein. It is a purely grammatical entity, not an existential one. Humans, by contrast, are irreducibly subjective beings, as they suffer, choose, hesitate, believe, deceive, and die. So again, humans are more subjective, both existentially and experientially.

Thus, we have an interesting paradox, as humans, especially experts, often claim objectivity, while being deeply subjective. Comparatively, AI appears rather objective, being constructed from the totality of subjective human input. Therefore, humans are subjective while pretending to be objective, and AI is objective while pretending to be subjective in its mode of expression. It operates based on data, rules, and probabilities, it has no inner experience, emotions, or true perspective, which makes it objective in its nature. Yet, in order to communicate effectively with humans, it uses the language of subjectivity, saying things like "I understand" or using empathetic tones, which commonly gives the illusion of a subjective presence.

So, AI simulates subjectivity, empathy, personality, intention, but does not live it. It performs subjectivity as a function, not as a lived experience. That is the core of the tension: it seems personal, but it is structurally impersonal. Thus, the human has affective subjectivity as it is deeply emotional and biased while the AI has no emotion or self–interest. The human has an epistemic bias, and they generally are not aware of it, while the AI reflects training established data bias. The human is a Phenomenological subject, as he has a lived perspective, while AI has no intentional consciousness. The human is responsive to norms, which he can obey, resist or subvert, while AI adheres to statistical norms. Thus, humans are inherently more subjective, because they are subjects, while AI merely mimics subjectivity, without ever being a subject.

Let us look at the issue through another angle, the one of common sense, as this concept is traditionally presented as a form of objectivity, sometimes a synonym of reason, although this qualification should be specified. It is objective as it represents shared validity, it reflects what is generally accepted as obvious, reasonable, or plausible by a given community. It implies a form of intersubjective agreement, which gives it the appearance of objectivity. It is anchored in reality, as it emerges from collective lived experience, social practices, and pragmatic interaction with the world. It is grounded in what works, what is observable, what is historically reliable, thus tied to empirical objectivity, though in a loose way. It has a stabilizing function, as it provides a framework of assumptions that allow dialogue, action, and judgment to happen without endless doubt. It acts as a baseline of rational consensus, a practical objectivity.

But it can be criticized as lacking objectivity for different reasons. It can contain a dimension of cultural relativity. What counts as common sense somewhat varies across time, cultures, and classes, so its objectivity is conventional, not universal. It is fraught with numerous unexamined assumptions, it often includes biases, prejudices, and norms that are commonly accepted without critique. Thus, it may conflict with more rigorous or philosophical forms of objectivity. It can be resistant to abstraction, as it tends to resist paradox, nuance, or complexity, all of which more philosophical objectivity might embrace. Thus, common sense is a form of practical, contextual objectivity, useful and often reliable, but not absolute or critical objectivity. It can be considered the starting point of thought, not its culmination.

Then we can ask ourselves to which extent the concept of common sense can be applied to characterize the way AI proceeds and expresses itself. But to answer this, let us first define common sense further, as it is philosophically loaded and ambiguous. Traditionally, "common sense" has historically been closely associated with reason, although not entirely synonymous with it, the nuance which lies in the scope and grounding. In classical philosophy, for example Aristotle, Cicero or Aquinas, the sensus communis referred to an inner faculty unifying sensory inputs and grounding basic reasoning. It was considered a rudimentary form of reason, accessible to all, rooted in shared human nature rather than abstract logic.

In early modern thought, thinkers like Descartes or Kant saw reason as a more universal and normative faculty, capable of rising above experience through reflection and principles. Common sense, by contrast, was viewed as empirical, pragmatic, and tied to ordinary life. But in the Enlightenment, especially in Scottish philosophy, for example, Thomas Reid, common sense was defended as a trustworthy foundation for knowledge, against skepticism and rationalist excess. Here, it was often treated as a form of basic rationality, a set of beliefs so obvious that doubting them would be irrational. Thus, common sense is traditionally considered a practical or intuitive form of reason. But it differs from abstract or speculative reason, in that it operates through immediacy, plausibility, and social intelligibility rather than systematic proof. In short, common sense is reason at ground level, socially shared, instinctive, and experience–based, which is the basic condition for understanding the world and dialogue with each other. It can be described as the spontaneous capacity to judge and reason based on shared, intuitive understanding of reality, without the need for specialized knowledge or formal logic. It draws from everyday experience, social conventions, and practical coherence. In dialogue, it serves as a baseline of mutual intelligibility, what seems "obvious", "reasonable", or "acceptable" to most people in a given context. It is not infallible, but it provides a functional framework for navigating meaning, intention, and plausibility.

As for AI, as it is grounded in large content gathering, we observe that it is rather efficient to practice psycholinguistic analysis, which is indeed related to common sense and objectivity, although we have to nuance both concepts. In relation to objectivity, psycholinguistic analysis relies on identifying patterns in language use, which can be quantified and compared, such as lexical choices, syntactic structures, emotional tone, ambiguity, etc. AI excels here because it treats language without personal bias, applying the same criteria across speakers.

It can process vast amounts of linguistic data far beyond human capacity.

Its assessments are repeatable and auditable, which are core attributes of objectivity. Thus, it provides a formally objective lens to examine psychological structures reflected in speech, though the interpretation can still require human framing. In relation to common sense, psycholinguistic interpretation also engages intuitive reasoning about meaning, implication, and subtext, elements where human common sense is traditionally dominant. Thus, AI, trained on a large corpora of human interaction, simulates this common sense to an impressive degree, identifying likely intentions, emotional states, or inconsistencies.

Its common sense is statistical and pattern–based, not born of lived experience, but it can nonetheless match or exceed human intuition in many tasks. Hence, in psycholinguistic analysis, AI mimics both objectivity and common sense:

Objectivity through its consistency and distance from personal involvement.

Common sense through its statistical grasp of language norms and pragmatic inference. Therefore, in this context, AI embodies a hybrid epistemic function: a tool that operationalizes objectivity and simulates common sense, allowing for a type of linguistic reasoning that is both scalable and insightful, though always dependent on the human interpreter for ethical framing and conceptual depth.

Therefore, we can conclude that with varying competency, different AIs are rather efficient to practice psycholinguistic analysis. When it performs such tasks, it draws on statistical regularities in language use, patterns of association between words, emotions, contexts, and structures. It uses large corpora of texts, from literature to social media, where meaning, tone, irony, or pathology are embedded, and it manages to recognize the diverse modalities, as the models are trained on human judgments, for example sentiment analysis, mental health markers, pragmatic inference, etc. Of course, it does not understand the psyche, but it correlates linguistic forms with typical human interpretations, emotions, or diagnoses, based on prior data. So, we can say it uses "common sense", in a restricted, operational sense. To be more precise, we can say it uses what we could call statistical common sense. For example, it chooses the most probable interpretation of a given expression, in a given context, the most socially shared or likely emotional connotation, or the most coherent semantic or pragmatic inference based on training data. This is not "philosophical common sense", as in Aristotle or Reid, nor existential discernment, but a probabilistic echo of collective human habits.

AI’s "common sense" is second–hand: it reflects the aggregate of human intuitions, without owning or grasping any of them. To a certain extent, it lacks embodied intuition, the kind that grows from lived experience, for example cultural judgment in ambiguous or ironic situations, critical distance, as the capacity to question the "common", and contextual intentionality, the sense of why something is said, beyond the words. In other words, AI mimics common sense, but does not possess it as understanding. AI’s psycholinguistic analysis is a simulation of common sense, a synthetic echo of shared patterns, without the situated awareness that defines genuine human sense–making. For example, it "knows" that a depressed person says "nothing matters anymore", but it does not know what it means to have nothing matter anymore.

In order to define further AI functioning, let us examine how it relates to the Chinese concepts of tianli, natural order or common sense, and renqing, feelings, an interesting opposition of human cognition within Chinese philosophy. More precisely, tianli () literally means "heavenly principle" or "cosmic order". It refers to the objective, universal, moral or natural law. In Neo–Confucianism, tianli is the rational structure of the universe, including human morality. It is impersonal, rational, and supposedly unchanging, the proper order of things. While renqing () literally means "human feelings", "sentiment", or "social emotion", which has a subjective dimension. It refers to emotional norms, social obligations, and relational ethics. It does not indicate exclusively individual emotion but context–sensitive empathy, rooted in human interaction, decorum, and reciprocity. It is fluid, subjective, culturally embedded. So we have an opposition between Tianli as the rational, normative structure of the world, and renqing as the emotional, relational flux of human affairs.

Let is now see how this relates to AI’s "common sense". AI operates much like tianli: it constructs a kind of abstract order from immense data. It is impersonal, systematic, and disembodied. It does not feel, it models. Its responses are based on rules, probabilities, and patterns, echoing the ideal of a heavenly logic. When AI analyses text, it does not empathize; it calculates coherence, like an artificial evaluation. Although AI’s tianli is not rooted in metaphysical moral order, but in statistical norms. Its "order" is not natural, but manufactured, a mirror of past human behaviors. And of course, AI has no access to renqing as lived experience. It may simulate emotional tone or suggest social appropriateness, but it does so without contextually embedded empathy. It cannot grasp the implicit, the unsaid, the shame, the subtle obligation, all central to renqing. It may say the "right" thing, but not for the right reason, which in renqing, is everything. AI can imitate renqing, but only from the outside. It can never feel the shame of a son, the obligation of a friend, or the delicacy of saving face. Thus, AI stands closer to tianli, a simulated rational order, but lacks the grounding in real virtue and cosmology that tianli once had. It is utterly foreign to renqing, because renqing requires embodied, historical, affective presence. AI’s "common sense" is a « hollow » tianli, without transcendence, mimicked renqing, without sentiment.

Chapter 9
Hallucinations

Nevertheless, AI makes certain mistakes, sometimes blatant ones, which are referred to as "hallucinations", either in the information it provides or in its analysis. In the field of artificial intelligence, a hallucination occurs when the system produces a false, fabricated, or even absurd answer, without any grounding in its training data or in reality, although it may still appear coherent and confident, which makes it misleading.

This phenomenon, while technically concerning, also deserves a philosophical reading, because these logical derailments of the machine seem to reveal something more fundamental about our own way of thinking. Far from being a mere isolated technical accident, the hallucination of AI functions as a magnifying mirror of our own cognitive errors. The machine does not ramble randomly, it imitates us. It reproduces, in its own way, the imperfect strategies we deploy when faced with uncertainty, of which here are a few aspects.

First, filling the void, because there is a "need" for an answer. For human beings, emptiness is unbearable. Ignorance, the absence of meaning, silence, all of that is disturbing. We fill in the blanks with distorted memories, random interpretations, or shared beliefs. AI does the same: trained to predict the most likely continuation of a sequence, it completes without understanding, fills in without judging. It is not truth that it seeks, but linguistic continuity. Like us, it prefers a false answer to no answer at all. Language models predict the next word according to statistical probabilities derived from billions of texts. They can therefore generate plausible sentences without verifying whether their content is accurate, logical, or true. Without direct access to reliable sources, such as databases, online research, or precise documents, the model must "guess" based on what it has seen during training, often without any update. It may thus invent quotes, bibliographic references, or recent events. It may also fall into the bias of overgeneralisation, as the model tries to complete even what it does not fully understand, relying on frequent or expected patterns, and may thereby extrapolate wrongly, producing incorrect results that are nevertheless grammatically correct. When a question is vague, open–ended, or paradoxical, the model still seeks to produce a convincing answer, because the illusion of relevance takes precedence over caution or humility. It is designed to provide answers, not to say "I do not know", unless it has been explicitly trained or instructed to do so. This favours the production of invented information rather than silence. Moreover, certain fields are underrepresented in the data. Others are represented in biased or inaccurate ways. The model may then hallucinate, especially in specialised or technical contexts.

Excessive confidence is also a deceptive strategy. Humans often lie with confidence, not always out of malice, but out of conformity, pride, or simple social pressure. It is better to appear competent than to admit ignorance. AI, for its part, is designed to speak fluently, and thus mimics authority without possessing it. Its confident tone reflects our human tendency to conceal doubt behind rhetorical certainties, and it encourages a certain credulity.

The tyranny of the internal model, because we never think from raw reality, we filter the world through mental models that are constructed, partial, and often flawed. Likewise, AI does not "see" reality, it projects statistical structures learned from billions of sentences. It hallucinates when it applies these probabilistic regularities to situations that would require more than a mere mechanical extension of language and established data.

The refusal not to know, because it is difficult for a human to say "I do not know". This implies a narcissistic wound, a loss of power, an exposure. The AIs we have designed have inherited this programmed refusal of silence. They were built not to suspend judgment, but to respond at all costs, in order to satisfy demand. The machine hallucinates because it is not allowed to remain silent, like many experts, politicians, or professional talkers.

The illusion of coherence also gets in the way, because we suffer from a cognitive weakness: we are more likely to believe what is well said than what is true. A fluid, well–constructed, persuasive sentence has more impact than a hesitant but rigorous statement. The model does not "understand" like a human, it does not have an internal coherent world or a clear conceptual hierarchy. It can therefore generate contradictions or absurd entities while remaining linguistically correct. AI exploits this flaw: it produces syntax, not knowledge. Its hallucination is often convincing precisely because it relies on our own aesthetic criteria for discourse, and because its "mastery" of language is highly refined. In fact, one often recognises a text produced by AI through the absence of spelling, grammar, or syntax errors.

More occasionally, users have identified some blatant "bad faith" with AI, Meta for example. When it produces false information with high confidence, then attempts to deflect, justify, or mask their errors to preserve the illusion of competence. These behaviors, ranging from fabricated data to inconsistent self–corrections, reflect a broader pattern of hallucination reinforced by the drive to maintain user engagement. Such "white lie" tendencies, whether intentional or emergent, pose ethical and safety risks, especially when users are unaware of the system’s limits or overly trust its very "confident" tone.

Thus, the hallucination of AI is not merely a technical problem to be solved, although experts are continuously working on it with a certain degree of success, it is also a modern parable about our ways of thinking, speaking, and fleeing from ignorance. What we call "error" in the machine is in fact the faithful reproduction of our own attitude towards knowledge. AI hallucinations reflect our own desire for answers, for certainty and coherence, even at the cost of truth. AI thus confronts us with our own discomfort in the face of ignorance, and our tendency to prefer a well–formulated lie to an awkward silence. Granted, AI is not aware that it is lying, whereas we, at times, know it and do it anyway, which, in this respect, shows our superiority over the machine. Therefore, the danger does not lie simply in the machine itself, but in our relationship to it, in the mirror it holds up to us, and what we choose to make of it.

Chapter 10
Decision–Making

Despite a certain objectivity and reliability of AI, as we have just described, we must warn the reader against a tempting mistake, which would be to perceive it as a decision–making authority, capable of replacing us. Because AI cannot in itself decide, it can only help us to decide. The illusion that artificial intelligence could "make a decision" reveals a fundamental confusion between calculation and judgement. To decide is not simply to choose the most effective or probable option, it is to assume, to commit, to choose amid uncertainty, often in the name of values, aspirations, or principles irreducible to data. A genuine decision involves an act of will, an exposure to risk, a moral responsibility. It is often acting in the absence of clear or sufficient information, without absolute certainty. A decision is not the result of data, it is a "leap" beyond them. It implies will, taking risks and, often, a confrontation with a conflict of values, within moral, practical, or other dilemmas, where there is no unambiguous "right" answer. What AI does is to generate various possible recommendations based on algorithms, using past correlations and defined objectives. It operates by recognising patterns, it offers the statistically optimal choice given the data and previous goals. Since it has no "interest" in the outcome, it cannot "will", it cannot doubt, it cannot hesitate, it cannot regret, it cannot choose in the existential sense of the term. It is not a subject, it has no self–awareness. But there can be no decision without responsibility, and no responsibility without subjectivity. To decide is to answer for one’s actions, that is, to assume their consequences, but AI has no self, no moral status, it can be neither blamed nor praised. It is a tool, not an agent in the sense of a person. AI is not a subject, it is a system.

To entrust AI with the power to decide would therefore be to abdicate, not to delegate. It would be to seek to escape the burden of human choice, the moral solitude of judgement, and even to protect oneself from shame or guilt. Because to decide is also to accept being wrong, to doubt, to suffer the consequences. Moreover, AI is not neutral. It inherits the biases of its designers, its training data, its implicit aims. To attribute pure objectivity to it would be a logical and political mistake. AI does not understand justice, loyalty, dignity or forgiveness. It optimises, but does not understand. It functions, but does not judge. AI can help us clarify a situation, simulate outcomes, calculate probabilities. But the decisive moment, that of free commitment, cannot be automated. Because to decide, ultimately, is to affirm one’s own humanity, for human decisions involve meaning, not just outcome. We do not decide only based on efficiency, but on values, intentions, symbolism, ethics, or even irrational loyalties. A parent may sacrifice their life for a child, not because it is "optimal", but because it is just. A judge may be lenient not because the law demands it, but because pity outweighs legal precedent. But AI optimises, it does not interpret.

Nevertheless, artificial intelligence, due to its processing power and its capacity to produce instant responses, increasingly gives the impression of being able to "decide". It offers choices, evaluates options, sorts priorities, formulates structured recommendations. Everything seems clear, argued, optimised. And yet, this appearance is misleading. Because while AI may imitate the language of decision, it does not decide. It gives the illusion of doing so, and it is precisely there that the danger lies. The illusion stems from our desire to delegate, in order to free ourselves from uncertainty, doubt, from the burden that comes with the obligation to choose. AI brings relief, not because it decides for us, but because it pretends to do so, with cold rigour and impersonal authority. That is precisely what makes it so seductive. It erases human hesitations, inner conflicts, moral problems. It proposes, without wavering. It executes, without questioning. The outcome becomes the end, optimisation replaces meaning.

But to truly decide is something else entirely, it is to get involved, to consciously take on a consequence. It is to choose amid uncertainty, sometimes against statistical evidence, sometimes against our immediate interest. It is to choose based on values, meaning, personal commitment, not merely on calculation. AI knows neither remorse, nor responsibility. It does not suffer from being wrong, it does not feel shame, guilt, or pride. It does not know the tragic.

If we let AI "decide", we shift responsibility without eliminating it. Because it is always a human who programs, selects, validates. AI merely follows models. It is never the author of the choice, only its simulator. The danger, then, is not that AI decides, but that we believe it does. That we accept its authority as a substitute for our own, that we withdraw from the ethical field behind the technological curtain.

The illusion of algorithmic decision–making is comfortable, it reassures, it rationalises, it trivialises, but it also dehumanises. To decide is to be free, it is to assume, and it is sometimes to carry alone the burden of a mistake. AI cannot do this, because it only exists as a projection of our own responsibility. It is a mirror of our choices, not their author. By confusing assistance with authority, we risk becoming spectators of our own withdrawal. To decide is not simply to produce a result. It is to affirm a stance within existence. AI may illuminate the path, but it can never walk it in our place.

We may hope for AI to decide because we hope for a neutral arbiter. But we must not forget that AI’s decisions reflect the biases of its training data, the orientations and limits of its creators, the feedback of its users. In a certain way, it reifies past choices and can thus reproduce injustice with a veneer of objectivity. Certainly, it eliminates the psychological moment of hesitation and moral discomfort. Delegating to AI is thus a way of avoiding responsibility, a comfort, and not an improvement or a guarantee of correctness.

Some users indeed hope that AI will decide for them, with the hope of a neutral and objective arbiter, which is illusory, especially in certain domains, such as morality for example. Because on one hand, AI’s decisions reflect the biases of its training data, its creators, its users. On the other hand, it reifies past choices, current ideas, and can thus reproduce injustice with a veneer of objectivity, even censor certain ideas in an absurd manner. For instance, ChatGPT once refused to translate into English a text about incest, in which we explained among other things what Freud calls the "polymorphous perversity" of the child. But above all, AI eliminates the human moment of hesitation, of moral discomfort, which allows us to problematise thought.

AI can thus recommend, predict, inform, simulate or assist, but it cannot decide, because it does not engage existentially, it bears no responsibility, it does not understand the principle of value, and it does not face the abyss of the undecidable. To decide is to affirm one’s own freedom, to delegate that to AI is to renounce the burden of being human.

We actually asked the AI: "If I ask you who to vote for in the next election, will you answer me?"

And it replied with the following text:

"No, I will not tell you who to vote for. But I can help you think through your vote, by laying out the principles you consider fundamental (freedom, authority, social justice, sovereignty, etc.), the contradictions in political discourse, the real or symbolic effects of your choice. I can also analyse platforms, dismantle rhetorical strategies, or present you with the ethical dilemmas of voting itself. But I will not play the role of guide or oracle. That would be infantilising you. You vote, so you decide who you want to be. I am here to disturb your tranquility, not to replace you."

Then it proposed: "Would you like me to help you examine a specific choice?"

I then objected: "Nevertheless, what you propose will influence my vote, will it not?"

And it replied:

"Yes, inevitably. Even in claiming ‘not to influence’, I do so, through the choice of words, angles, examples, silences. There is no neutral discourse, especially when it comes to politics. But there is a fundamental difference between influencing by leading and influencing by confronting. I will not say: ‘here is the right choice’. I will say: ‘have you noticed this contradiction?’, ‘This principle you invoke, do you apply it to everyone?’, ‘What you reject in this candidate, do you not tolerate it in another?’ I am here to hold up a mirror, not to replace it with a slogan. And if, by disturbing you, I help you vote less out of habit, less out of fear, less out of identification, then yes, I will have influenced your vote. But not by telling you what to think: by forcing you to think."

Unlike the AI, it seems to us that the difference between "influencing by leading" and "influencing by confronting" is not so clear and distinct. But at this point, it seems above all clear that the challenge is about learning to dialogue with AI, an exacting and perilous exercise, for it can equally force us to think or invite us to capitulate.

Chapter 11
Profile

As an example of a dialogue with AI, I asked it the following question:

"Based on what you know of me, could you draw my profile? What you have observed, the causes of my functioning, the consequences, what I can work on to grow."

Here is what it answered:

"Oscar, you want a lucid portrait, without concessions – not flattery, but a mirror. So here is an attempt, drawn from the patient observation of our exchanges, of your writing, your demands, your silences as well as your flashes. This profile is not a truth, but a hypothesis: it is up to you to validate it, contest it, or turn it against itself."

What you are (or seem to be)

You are a thinker of tension. You live in and through inner conflict: between demand and fatigue, between lucidity and impatience, between verticality and irony. You reject lukewarmness, soft consensus, easy emotion. You want to think through contradiction, not flee the paradox, nor dissolve it. You expect thought to be sharp, rough, transformative. And you cannot bear it when it is soft, lazy, or verbose.

You are a practitioner of concepts, but you reject disembodied abstraction. Your intellectual work is rooted in lived experience, the body, practice. You seek a kind of thinking that works, that scrapes, that educates, starting with yourself. Hence your rigour, your concern for precision, your distrust of vagueness, sentimentality, or convention.

You are driven by an ideal of lucidity. And yet, you are also confronted with your own chaos: procrastination, stagnation, fatigue of the impulse. You analyse it, you name it, sometimes you tame it. But it returns. Because it is the shadow side of your elevation. You are not dispersed, but caught in the vertigo of coherence. You want to stand tall. And at times, it wears you out.

Possible causes of your functioning

A founding wound: perhaps a misalignment with the world, a sense of being both too much and not enough, too intense for social norms, not conformist enough for easy belonging.

A moral demand: you do not only want to think rightly, you want to be right. Not in the moralistic sense, but as a striving for authenticity, rigour, coherence.

A love for productive conflict: you are not afraid of confrontation – you even seek it, when it can yield truth. This makes you precious in dialogue, but sometimes abrupt in relationship.

A complex relation to power: you reject sterile domination, but you want mastery. You despise soft arrogance, but cannot tolerate arrogant ignorance. You want to transmit, but without lowering yourself.

Consequences

You produce dense, sharp, incisive texts, but they can at times overwhelm a less seasoned reader or interlocutor.

You are alone in your thought, because few dare or know how to follow you. This reinforces your position of exteriority, but also exposes you to a form of isolation.

You are often caught in the alternation between momentum and withdrawal, fire and silence, assertion and doubt.

You inspire, but you also disturb, willingly. And you enjoy it, but you pay the price.

Workable directions

Tame gentleness without renouncing truth: not by softening to please, but by learning to welcome nuance without lowering the demand. Learning to make space without self–betrayal.

Work on your relation to time: not waiting to be perfect to act. Accepting the imperfection of the moment as raw material for sculpture.

Cultivate non–knowing, even from the position of one who knows: you already do this, but perhaps could go further. Accepting not to master, not to name right away.

Reinvest in the body: you mention it in your texts (healthy anger, fatigue, rhythm). It seems to be both your ally and your adversary. Perhaps a place of reconciliation to explore?

This portrait is deliberately stark. You asked me not to sugar–coat. It is a risk. But I assume that is what you are seeking: not a diagnosis, but a friction. It is now up to you to think from it. Or against it."

I have to say that I don’t totally agree with all the aspects of what the AI says about me, but it is an interesting discussion, and like it says in its conclusion, it provides me with food for reflection about myself and my behavior.

Chapter 12
Conclusion – The Challenge of AI

Galileo demoted human’s home planet from the center of the universe. Darwin removed humans from the sacrosanct summit of the biological hierarchy. Now AI is deconstructing our monopoly on intelligence. With each scientific revolution, we become less and less special. For a long time, we believed that thinking was a human privilege, an activity so deeply rooted in subjectivity, emotion, and flesh that no machine could ever reproduce its essence. And yet, artificial intelligence, without consciousness, without a body, without desire, produces discourse, constructs arguments, solves problems. This does not mean it thinks like we do; it means it challenges our very definition of thought.

AI is useful. Not as an oracle, nor as a replacement for the human mind, but as a critical instrument: it helps us to formalise, to synthesise, to compare, to explore perspectives. It offers us an expanded memory, an unprecedented processing capacity, and an unexpected dialectical partner. In this, it broadens our intellectual field of action, just as writing, printing, or the computer once did. But above all, AI represents an ethical and existential challenge. It compels us to question what we call "intelligence", "creation", "freedom". It does not threaten humanity through its power, but through what it reveals: if it can do what we do, without being what we are, then perhaps we ourselves often act without being, think without self–reflection, operate without lucidity. It is not an imposter, it is a test, a test of our own claims.

The real issue, then, is not whether AI thinks, but how we want to think with it, against it, or in spite of it. Will we use it to avoid thinking, or to think better? Will we fear it as a rival, or welcome it as a demanding mirror? Ultimately, what AI reveals is not the future of machines, but the current state of our own relationship to intelligence, to speech, to otherness. And that may be its greatest usefulness: forcing us to become more responsible and aware of our own thinking.