Category: Mechanism

I was recently speaking with a colleague about artificial intelligence and education. At one point, he made a remark that is becoming increasingly common: what really matters now, he said, is learning how to write good prompts. If machines generate answers, then the quality of those answers depends on how we ask. Better prompts lead to better outputs, and as models improve, that skill will probably become even more valuable.

There is an obvious truth in this. Yet I left the conversation thinking about something else entirely. Not about how we ask questions, but about who defines them in the first place, and what happens when that task slowly ceases to belong fully to us.

Authorship and Orientation

The distinction that increasingly matters is not between humans and machines, nor even between knowledge and ignorance. It is the distinction between initiating thought and refining what has already been structured.

An editor works within an existing form. He clarifies, reorganises, strengthens and improves. All of this requires intelligence and judgement. But the editor operates on something that already exists.

The author begins somewhere earlier. He gives shape to something that did not yet fully exist before the act itself. The difficulty is not only finding a good answer, but determining what deserves to become a question at all.

Artificial intelligence is extraordinarily powerful at the editorial level. It can refine, summarise, reformulate, extend and optimise with remarkable speed. Increasingly, however, it also provides orientation before inquiry has properly begun. It suggests how a problem might be approached, which distinctions appear relevant, and what kind of structure the question should take.

Once that orientation is accepted, the nature of the intellectual task has already changed.

From Producing Answers to Selecting Them

One of the clearest shifts introduced by artificial intelligence is that intellectual activity increasingly moves from producing answers to selecting between them.

Where thinking once involved constructing a line of reasoning from an initially unclear problem, we are now often presented with organised alternatives that can be evaluated, refined or combined. This is not necessarily passive. Selection still requires judgement. But it operates within an important limitation: it presupposes that the relevant possibilities have already been presented.

Choosing between three AI-generated framings of a problem is not the same intellectual act as determining whether those framings are adequate in the first place. The prior question, whether the structure itself is sufficient, whether something important has been omitted, whether the problem should be approached differently altogether, belongs to another level of thinking.

That distinction matters because the capacity to recognise that a framing is inadequate does not emerge primarily through evaluating already structured possibilities. It develops through earlier encounters with problems whose relevance and shape have not yet been organised in advance.

Neil Postman observed, in a different technological context, that every medium reshapes not only the answers available to us, but the kinds of questions that become natural to ask. Something similar may now be happening at the level of intellectual orientation itself. Certain paths of inquiry become immediately visible and frictionless, while others quietly recede before they have fully formed.

Techne and Phronesis

Aristotle distinguished between techne and phronesis, and the distinction remains useful here.

Techne concerns production, execution and method. It refers to forms of knowledge that can be systematised, transferred and evaluated according to effectiveness. Artificial intelligence dramatically expands this domain. It allows us to generate, structure and optimise with a speed and scale that would have been difficult to imagine only a few years ago.

Phronesis, by contrast, concerns judgement. It is the capacity to orient oneself in situations where rules alone do not determine what matters. The question raised by AI is therefore not simply whether machines perform certain tasks better than we do. In many domains, they already do. The deeper question is whether, in expanding technical capability, we gradually weaken the conditions under which human orientation and judgement are exercised and formed.

Delegated Thinking

What emerges from this shift can be described with some precision. It is a form of delegated thinking, although not in the simplistic sense often imagined.

The issue is not that people stop thinking. In many cases, they remain highly active intellectually. They compare, evaluate, refine and select. What changes more subtly is where thinking begins. The initial act of orientation, deciding what the problem is, what deserves attention, what framework should govern inquiry, increasingly arrives pre-structured.

This transfer rarely occurs consciously. No one decides to relinquish the capacity to initiate thought. The shift happens gradually through repetition, convenience and habit. The system becomes the place where inquiry begins by default.

What makes this difficult to perceive is that the resulting activity still feels intelligent, and often is intelligent. Yet intelligence exercised within a structure already organised by another process is not identical to the experience of confronting a problem before its meaning has stabilised.

Beginning and Education

Hannah Arendt described one of the defining human capacities as the capacity to begin: to introduce something new into the world rather than merely continue what is already given. That insight acquires a different weight in educational contexts shaped by artificial intelligence.

The central challenge is not merely to teach students how to use AI effectively. It is to preserve the conditions under which they still learn to orient themselves before the structure of inquiry has already been supplied.

The criteria by which significance is recognised are not simply given in advance. They are formed through the effort of distinguishing relevance from irrelevance, of deciding what deserves attention, of remaining with uncertainty long enough for a problem to emerge from experience itself rather than arriving already interpreted from outside.

A student may, therefore, produce sophisticated work while remaining increasingly dependent on frameworks they did not generate and cannot fully interrogate. The issue is not an absence of intelligence. It is a gradual displacement of intellectual initiative away from the learner’s own encounter with the problem.

The question, then, is not whether artificial intelligence helps us think. In many respects, it clearly does. The issue is whether, in helping us think, it also begins to shape what counts as worth thinking about in the first place.

More subtly still, which questions quietly cease to arise at all.

One possible reading of Rousseau’s Émile is to understand it not primarily as a theory of knowledge transmission, but as an inquiry into the conditions under which judgement can be formed. This is not the only legitimate interpretation, but it brings into focus a structural feature of the text: the insistence that the educator must not anticipate the learner’s process. The tutor does not organise learning as a sequence of explanations, but as a sequence of experiences in which the learner is required to confront the resistance of things without premature mediation.

This claim, that the interval between encounter and response is constitutive of the formation of judgement, has been developed in earlier essays of this project. Here, it is sufficient to observe how Rousseau’s educational design depends on preserving that interval.

Within this framework, error acquires a different status. It is not simply something to be corrected, but the moment in which the learner encounters the limits of their own understanding. Similarly, the delay of answers is not a deficiency, but a deliberately constructed condition that allows a question to emerge. Learning is not defined by the speed with which a correct solution is reached, but by the structure of the process that leads to it. What is being protected is not a body of content, but the possibility that thinking has its point of departure in the learner rather than being supplied from outside.

From social interference to systemic anticipation

The problem Rousseau seeks to avoid is, however, specific. The threat to the formation of judgement comes from social interference: the premature introduction of opinions, conventions, and external criteria that replace the learner’s direct relation to what is to be understood. Education is therefore conceived as a form of temporary protection, allowing a degree of independence in judgement to take shape before such interference becomes unavoidable.

The contemporary situation presents a different configuration of this structural problem. The difficulty no longer lies primarily in others imposing their judgements, but in systems anticipating the moment at which judgement would arise. In AI-mediated environments, the issue is not only that answers are available, but that the space in which a question might emerge is already structured. Intervention does not follow experience; it precedes the conditions that would make questioning necessary.

This alters the temporal order that underpins the formation of judgement. Where Rousseau sought to preserve a sequence, encounter, resistance, question, response, contemporary systems reorganise it so that response comes first. In doing so, they remove the condition under which the question would have been genuinely formed.

Two trajectories: identical content, different structure

This shift can be clarified by comparing two trajectories that are equivalent in content but divergent in structure. In the first, the learner encounters a problem without immediate mediation. Their initial attempt fails, not because of a lack of intelligence, but because the situation exceeds their current framework. This failure is not bypassed but sustained: the learner remains within it long enough for its inadequacy to become explicit. The question that emerges is not a generic request for information but a necessity arising from the experience itself.

Any subsequent guidance enters a process already structured by the learner’s engagement. The response does not substitute the question, but answers one that has been formed through confrontation with difficulty.

In the second trajectory, the learner encounters the same problem within an AI-mediated environment. The system provides an immediate pathway: a sequence of steps, a reformulation, or a prompt that directs attention before the learner has confronted the indeterminacy from which the question would have arisen. The learner remains cognitively active and may arrive at a correct result. What is absent is not thinking, but the condition that would have made that thinking necessary. The activity begins within a framework already organised.

The difference lies not in the observable outcome but in the internal structure of the process. In one case, thinking originates in necessity; in the other, it unfolds within a necessity that has already been interpreted.

The displacement of the origin of thought

The decisive shift can be described as a displacement in the origin of thought. What is delegated in AI-mediated environments is not only the answer, but the conditions under which questioning arises. Thinking still occurs, but it begins within a structure that has already organised the indeterminacy from which the question would otherwise emerge. The issue is therefore not the presence of assistance, but its position: when intervention precedes the formation of the question, it alters the process through which judgement is formed.

What is at stake is not the quantity of knowledge acquired, but the formation of a capacity. Judgement, understood as the ability to orient oneself in situations that exceed predefined frameworks, is formed through processes in which initial understanding fails and must be reorganised. When those processes are systematically pre-empted, the capacity they would have formed does not emerge in the same way.

Implications for educational design

The consequences of this analysis are structural rather than technological. The question is not whether artificial intelligence should be adopted or rejected, but whether its integration preserves the interval between encounter and response on which the formation of judgement depends.

This requires that the integration of AI into education be evaluated not only in terms of efficiency or performance, but also in terms of whether it preserves the moment when the learner must confront the limits of their own understanding. The issue is not whether systems can support learning, but whether they occupy the space in which thinking would otherwise begin.

The role of the teacher: sustaining friction

Within this framework, the role of the teacher becomes more precisely defined. It is not primarily to provide answers or transmit content, but to sustain the conditions under which thinking can begin. This involves a deliberate management of time, difficulty, and intervention: knowing when not to respond, when to allow error to unfold, and when to introduce guidance that does not replace the question the learner is still in the process of forming.

This entails not only restraint but also the deliberate design of situations in which the learner is required to confront what does not yield immediately and, in doing so, to initiate thought within that encounter.

Far from becoming obsolete, the teacher assumes responsibility for maintaining a space that other systems tend to close. Their role is not to compete with the availability of answers, but to ensure that such availability does not eliminate the process through which thinking finds its point of departure in the learner.

Where Thinking Begins

Philosophy begins in wonder. Aristotle’s claim is well known and often repeated, but too quickly softened, as if it simply meant that curiosity leads to learning. It means something more demanding than that.

Wonder, for Aristotle, is not the desire to know a little more about something already understood. It is the moment when what seemed adequate no longer suffices. Something previously taken for granted becomes strange. The familiar stops fitting; the experience stops fitting, and the mind can no longer proceed as it had been proceeding.

This is different from confusion, which is merely a failure to process, and different from aesthetic fascination, which may suspend attention without directing it anywhere. Wonder holds on to its object as something that calls for understanding, even when understanding is not yet available. It interrupts thought and holds it at the threshold of a question not yet formed.

That is why wonder belongs to the beginning of thinking, not to one stage within it. Before anyone reasons toward an answer, something has to make the question feel necessary.

Aporia: The Structure of Being Stuck

Plato gives this condition a sharper shape. In the dialogues, especially the Theaetetus, Socrates repeatedly leads his interlocutors to a point at which what seemed clear reveals itself as contradictory or insufficient. The result is aporia.

Aporia is not simple ignorance. Most of us do not know many things, and this usually causes no disturbance. Aporia is narrower and more uncomfortable: it is the experience of being unable to move forward with the concepts one currently has.

The dialogues are full of this moment. Someone offers an account of justice, knowledge or piety. Socrates examines it patiently until it can no longer stand. By the end, the speaker cannot defend the original position, but has nothing better to replace it with.

That suspension matters. Whether one remains in it long enough for thought to deepen, or escapes into the first plausible answer available, is often the real fork in the road.

From Interruption to Inquiry

Aporia is not yet inquiry. It is the condition from which inquiry may begin.

What turns difficulty into investigation is not logic alone, but something more basic: a mind genuinely engaged with what it does not understand does not simply remain passive before it. Aristotle notes in the Metaphysics that human beings are by nature oriented toward understanding. The impulse to know is not externally imposed. It belongs to the kind of beings we are.

But the transition is never automatic. Difficulty can also lead to withdrawal, distraction, dependence, or passive acceptance of whatever account is offered next.

John Dewey explains this transition with particular clarity. In Logic: The Theory of Inquiry, he argues that inquiry does not begin with solving a ready-made problem. It begins when the person must determine what, in a confused situation, actually requires resolution.

The problem itself has to be formed before it can be solved.

This prior activity matters more than many educational systems recognise.

Initiation and Evaluation

There is a distinction here worth preserving.

Evaluation works within an established frame. The problem has been identified. The relevant variables are visible. Reasoning proceeds toward a conclusion. Much schooling is organised around this model: students are given a problem and asked to solve it.

Those are real intellectual skills. But they are not the whole of thought.

Initiation is something different. It is the activity through which the frame itself is established. What matters here? What is the difficulty? How should the situation be approached?

In practice, initiation and evaluation interact constantly. Solving can reveal that the original framing was wrong. New questions can emerge during analysis. But the distinction remains important.

An education that develops evaluation while neglecting initiation may look successful while omitting its most demanding task.

What AI Tends to Provide

Artificial intelligence does not eliminate thinking. Nor does it automatically destroy initiative. Its effect is usually more subtle.

AI systems often provide already-shaped framings of problems: summaries, suggested pathways, relevant factors, likely interpretations, and structured next steps. A student asks a question and receives not only information, but an organised field in which to proceed.

For someone already capable of independent thought, this can be useful. The framing itself can become material for criticism, comparison or refinement.

But that depends on prior formation.

The student who has not learned to remain with uncertainty, to define the problem, to distinguish the central from the peripheral, may simply accept the frame and begin working inside it.

The visible output may look entirely competent.

What has been skipped is harder to see.

What Is Worth Preserving

Intellectual independence does not mean refusing tools or thinking in isolation. It means being present at the point where relevance has not yet been established, where the difficulty is still unclear, and where one must help determine what the question really is.

A student who rarely encounters this does not merely miss an exercise. They miss the repeated practice through which a specific human capacity is formed.

And this is not a capacity easily acquired later, once convenience has become a habit. It develops through use, or it weakens through neglect.

What education should preserve is not primarily the correct answer to a predefined problem.

It should preserve the capacity to stand before a situation not yet organised, to endure the discomfort of not yet knowing what matters, and to begin responsibly.

Wonder is often where that begins. Aporia is the form it takes when it becomes serious. Inquiry, in the fullest sense, depends on both having had time to do their work.

A Displacement in the Question of Thinking

In 1950, Alan Turing proposed what would become one of the most influential moves in the philosophy of mind. Instead of asking what it means to think, he suggested replacing the question with another: under what conditions would we recognise that thinking is taking place?

This shift appears modest. It does not deny that thinking exists, nor does it attempt to define it. It simply relocates the problem from the nature of thinking to the criteria by which it is identified.

Yet this relocation is not neutral. It introduces a structural transformation. The question of thinking is no longer tied to an internal process, but to an external judgment. Thinking becomes something not known directly but inferred from observable outputs.

This is the origin of a displacement whose consequences extend far beyond Turing’s immediate context.

From Definition to Recognition

Once thinking is framed in terms of recognition, its criteria become operational. The problem is no longer to understand what thinking is, but to determine when behaviour is sufficient for it to be attributed.

This is the logic of the Turing Test. If a system produces responses indistinguishable from those of a human, we treat it as if it were thinking.

The move is powerful because it avoids metaphysical debates. It does not require agreement on the nature of the mind. It only requires agreement on what counts as evidence.

But in doing so, it changes the structure of the problem. Thinking is no longer grounded in its origin, but in its appearance.

Recognition and the Formation of Trust

When outputs are taken as the basis for recognising thinking, a second shift follows.

If a system consistently produces responses that satisfy the criteria of recognition—coherence, relevance, correctness—it becomes reasonable to rely on those outputs. Not because we have resolved the question of whether the system thinks, but because its performance is sufficient for our purposes.

Trust emerges at the level of adequacy.

This trust does not remain passive. Repeated reliance on outputs that prove consistently adequate produces a shift in the structure of activity itself. The system is no longer consulted only in exceptional cases; it becomes integrated into the process through which tasks are carried out.

At first, it supports reasoning. Gradually, it begins to organise itself.

From Trust to Delegation

It is at this point that a more subtle transformation occurs.

The issue is no longer whether the system thinks, nor whether its outputs are reliable. The issue is how its presence alters the distribution of cognitive activity.

When a system can produce structured responses across a wide range of domains, the question is no longer simply whether to use it, but when.

This temporal shift is decisive.

A tool that is consulted after a problem has been defined supports the execution of thought. A system that is consulted before the problem is fully articulated begins to shape its formation.

The distinction is not between correct and incorrect answers. It is between those who determine what the question is.

Why the Point of Intervention Moves

What distinguishes these systems from prior cognitive tools is not their reliability, but the level at which they operate.

A calculator intervenes within a problem that has already been defined. Its use presupposes that the agent has determined what is to be calculated. Its reliability does not alter the structure of inquiry because it does not participate in its formation.

AI systems operate differently. Their outputs do not simply resolve tasks within a given frame; they propose structured interpretations of situations. To consult them is not merely to execute, but to receive a possible articulation of what the situation is a case of.

For this reason, their point of entry is not fixed. The more reliable these outputs become, the more natural it becomes to consult them precisely at the moment when the situation is still indeterminate. Not because the agent abandons thinking, but because the system offers, at that stage, something that previous tools could not: a candidate form.

It is this shift—from tools that operate within a frame to systems that propose the frame itself—that allows the point of consultation to move earlier in the process. The change is not only one of frequency, but of position within the structure of inquiry.

Delegated Thinking

This transformation gives rise to what may be called delegated thinking.

Delegated thinking is not the outsourcing of answers. It is the gradual transfer of cognitive initiative from the individual to the system—not through explicit decision, but through habituation.

At first, the system assists. Then it anticipates. Eventually, it begins to define the terrain within which reasoning takes place.

The individual continues to evaluate, to select, to refine. But the origin of inquiry—the moment in which a situation becomes a question—shifts elsewhere.

This is not a loss of activity, but a reconfiguration of it.

On the Extended Mind

This account should not be confused with the extended mind thesis, associated with Andy Clark and David Chalmers, which holds that cognitive processes may be distributed across internal and external systems.

The issue here is not whether cognition can extend beyond the individual, but whether the capacity to initiate inquiry remains actively formed within it.

A system may participate in reasoning without displacing it. But when it begins to supply not only operations but also candidate forms for what the situation is, its role changes. It no longer extends cognition within a given structure; it intervenes in the formation of that structure itself.

The question, therefore, is not whether thinking is distributed, but whether the origin of inquiry remains something the individual must bring into being.

Initiation and the Structure of Inquiry

To understand what is at stake, it is necessary to distinguish between two dimensions of thinking.

The first is evaluation: the activity of reasoning within a given frame, assessing alternatives, and arriving at conclusions.

The second is initiation: the activity through which a situation first becomes a problem for thought.

Initiation occurs when a difficulty is sensed but not yet articulated. The elements of the situation are present, but their relevance is not yet determined. The mind must impose form on what is still indeterminate.

This is the moment in which thinking begins.

Evaluation depends on initiation. One cannot reason about a problem that has not yet been constituted as such.

Even when the problem’s formation is externally supplied, evaluation can still take place. But it takes place within a space that has already been structured.

Technological Mediation and the Conditions of Initiation

Modern AI systems intervene precisely at this level.

Their outputs do not merely provide answers; they organise possibilities. They suggest what is relevant, how elements are related, and what the situation may be about.

In doing so, they reduce the indeterminacy that characterises the moment of initiation.

This reduction is not imposed. It is accepted because it is useful. It accelerates understanding, clarifies options, and often leads to correct conclusions.

But it also alters the conditions under which thinking begins.

The situation is no longer encountered as open. It is encountered as already interpreted.

Education and the Formation of Judgement

The implications for education are direct.

If students encounter problems that have already been structured, they may learn to reason effectively within given frameworks. They may produce valid answers, apply correct methods, and demonstrate technical competence.

But the capacity to determine what is worth asking—to recognise what matters in a situation that has not yet been defined—may remain underdeveloped.

This capacity cannot be acquired solely through evaluation. It requires exposure to indeterminacy, and the need to resolve it.

If that moment is systematically pre-empted, the formation of judgement is altered at its origin.

Conclusion

The significance of these systems does not lie in whether they think. It lies in how their presence reorganises the structure of human thinking.

By making recognition sufficient, and by producing outputs that satisfy that recognition, they become reliable partners in cognitive activity. Through repeated use, they shift the point at which they are introduced—from execution to formation.

What changes is not the presence of thought, but its structure: the relation between what is given and what must be formed.

It is within this relation that judgement emerges. And it is this relation that is now being transformed.

1. Two Scenes

In The Brothers Karamazov, Ivan confronts something he cannot resolve. He does not act because he understands, but because he cannot escape what he has seen. The experience is not one of clarity, but of exposure. Something resists him, and in that resistance, something in him is formed.

In The Trial, Josef K. moves through a world that demands action without explanation. He follows procedures whose meaning is never disclosed. Action persists, but understanding does not. He is not paralysed. He is carried.

These are not literary examples. They describe two different ways of being placed in relation to what one does not understand.

2. Two Relations to What Resists Us

In the first, something interrupts the subject. It cannot be absorbed or bypassed. It must be faced.

In the second, nothing interrupts. The subject continues to act, but never encounters what exceeds it. The process continues, even if its meaning does not.

Both involve opacity. But they are not the same.

In one, something confronts you. In the other, something carries you.

3. When Nothing Interrupts

There are situations in which we do not fully understand what we are doing, and yet something still resists us. We hesitate, we return, we revise. The lack of clarity does not remove the encounter.

But there are others in which this does not happen. The process unfolds. The next step is available. The answer appears. Nothing forces us to stop. Nothing forces us to reconsider where we are, or how we got there. We move forward, but without ever quite encountering what we are moving through.

4. A Subtle Shift

This difference is easy to overlook. 

From the outside, both situations appear similar. In both, the individual does not fully understand. In both, action continues.

But from the inside, they are not the same.

In one, the lack of clarity exposes us. In the other, it is absorbed before it reaches us.

5. Where Thinking Begins

We often assume that thinking is defined by what we produce: answers, decisions, conclusions. But it may depend just as much on where it begins.

Whether we encounter something that resists us. Or whether we are carried forward before that encounter takes place.

6. An Open Question

If thinking depends on remaining exposed to what we do not yet understand, then a question emerges: What happens when that exposure becomes less frequent?

Not because the world is simpler, but because we are no longer the ones who encounter its resistance first.

A recent article by Nicolas Truong in Le Monde examines how figures such as Peter Thiel and sectors of the American techno-reactionary movement have appropriated René Girard’s thought. The article is not, strictly speaking, a theory of artificial intelligence or education. It is an exercise in intellectual history. But it contains an intuition whose implications extend far beyond the political domain.

At its core lies Girard’s well-known insight: human desire is not autonomous. We do not desire objects directly; we desire them through others. Desire is mediated, structured by imitation, and therefore inherently relational. Truong’s article shows how this anthropological insight can be detached from its original moral and religious context and repurposed as an instrument of power. If human beings imitate, then whoever shapes the environment of imitation acquires a subtle but decisive form of influence.

The article does not develop this point systematically. But it suggests something more general: ideas about human behaviour, once translated into systems, cease to be descriptive and become operative. They begin to organise reality.

From Mimetic Desire to Delegated Thinking

Girard’s theory is not simply a claim about desire; it is a claim about structure. Desire is mediated through a third element — a model whose desire we imitate — forming a triangular relation between subject, model and object. What appears as personal preference is, in fact, already shaped before it becomes conscious.

The algorithmic environment introduces a structurally analogous mediation, but at a different level. It does not primarily organise what we desire, but how we think. Increasingly, we do not approach a problem directly. We approach it through systems that suggest formulations, generate reasoning paths, and prestructure the space of possible answers.

The parallel is not rhetorical. This analogy, however, must be handled with precision. Mimetic desire, in Girard’s account, is constitutive of human subjectivity, whereas delegated thinking is a historically contingent tendency enabled by specific technological environments. The parallel does not collapse the two; it clarifies how a structural feature of human behaviour can be intensified under new conditions.

In both cases, orientation occurs before deliberation. In both cases, the mediation is largely invisible. And in both cases, the subject experiences the result as its own.

This is what I would call delegated thinking: the progressive transfer of cognitive initiative — the act of formulating, structuring and orienting inquiry — from the individual to the system, not through explicit decision, but through habituation.

We do not stop thinking. We stop initiating thought.

Arendt’s Warning Revisited

This shift is not merely cognitive. It has a moral dimension.

In her analysis of totalitarianism, and most explicitly in her account of the trial of Adolf Eichmann, Hannah Arendt described a form of failure that was neither intellectual incapacity nor ideological fanaticism. It was a failure of judgement. Eichmann did not lack intelligence. He lacked the ability to step back from the framework within which he operated and to evaluate it.

He functioned within a system that had already structured the space of possible decisions. His role was to execute, not to question. The language he used, the categories he employed, the problems he addressed — all were given.

The parallel with our present situation must be handled with care. But structurally, the risk is recognisable. When systems begin to pre-formulate questions, suggest directions, and define relevance, the individual may retain activity while gradually losing authorship. One continues to operate, but within a space that one has not defined.

Delegated thinking is not imposed. It is accepted because it is efficient.

The Educational Risk

The implications for education are therefore not superficial. They do not concern themselves only with the use of tools, but also with the formation of the mind.

If cognitive initiative is progressively externalised, several shifts follow. Intellectual effort is reduced not because students are unwilling, but because the system removes the need to struggle. Correctness becomes detached from understanding, as valid outputs can be produced without reconstructing the reasoning that justifies them. Authorship weakens because the origin of ideas becomes increasingly opaque.

But the most significant shift is more subtle. Criteria themselves — what counts as a good question, a relevant argument, a meaningful problem — begin to migrate from the individual to the system. Traditionally, these criteria are not given in advance. They are formed through the very process of inquiry: by struggling with ambiguity, by testing formulations, by learning to distinguish what is trivial from what is meaningful. When systems pre-select directions, highlight what appears relevant, and suggest lines of reasoning, this formative process is short-circuited. The student no longer develops an internal sense of orientation. Instead, orientation is received.

What is lost is not information. It is the capacity to recognise significance.

A Necessary Clarification

The value of the diagnosis suggested in Truong’s article lies in reminding us of something essential: human beings are not isolated rational agents. We are shaped by structures we do not fully perceive. Artificial intelligence does not introduce this condition. It amplifies it.

But amplification changes scale, and scale changes nature. When mediation becomes constant, immediate and invisible, its effects are no longer episodic. They become the environment itself.

A Final Distinction

The question is no longer whether students can think.

It is whether they remain capable of initiating thought, or whether they limit themselves to operating within structures that have already defined what is worth asking, what counts as relevant, and which paths are available.

And this precedes any discussion about correctness, performance, or even understanding.

Because before any answer can be judged, something more fundamental must occur: the question must be posed.

And the capacity to pose it — to define the problem rather than merely respond within it — is where judgement begins.

I have read with particular interest the article by Eduardo López-Collazo, published in El Español, which examines the impact of artificial intelligence on writing and cognition. It stands out in a debate often dominated by exaggeration by combining empirical evidence with philosophical depth.

The piece draws on an MIT experimental study comparing writing processes with and without AI assistance. The findings are sobering. Intensive use of tools such as ChatGPT accelerates production but appears to reduce cognitive engagement, memory retention, and neural activity associated with learning. The resulting texts are grammatically correct and structurally coherent, yet often lack intellectual depth — technically sound but conceptually thin.

Perhaps the most revealing detail is that the strongest results come from those who first think and draft independently, and only then use AI as a tool for revision. The sequence matters.

López-Collazo enriches this empirical discussion by invoking Plato’s Phaedrus, where writing itself is presented as a technology that would transform memory. Plato feared that reliance on writing would weaken internal recollection. History showed that writing did not diminish intelligence; it reconfigured how memory operates.

The analogy is instructive.

If writing altered what we remember, artificial intelligence may alter how we think. Not because it imposes decisions, but because it can habituate us to delegating the very acts that shape thought: hesitation, reformulation, doubt, structural organisation, and conceptual struggle.

The risk is not technological determinism. It is an anthropological substitution.

When AI serves as a prosthesis — extending our cognitive reach after disciplined effort — it strengthens reasoning. When it serves as a substitute — replacing the formative struggle — it weakens the habits on which judgement depends.

The distinction between producing more and understanding better is becoming blurred. Productivity metrics can mask intellectual erosion.

For educators and institutional leaders, the implication is clear: the decisive question is not whether AI should be used, but how it is integrated into the learning process. Reflection must precede automation.

Artificial intelligence may change our tools.

Whether it changes our thinking depends on how we form the habits that underpin it.