Re-Set. Sant Roch. These names are making the rounds in Parisian circles (The equivalent of Othership for Americans). Behind them lies the French capital’s latest wellness obsession: contrast therapy. The concept itself is nothing new. In both Greek and Scandinavian traditions, it has long been part of health practices and social rituals.

In Paris, however, the experience has taken on an almost ceremonial dimension. Superheated saunas and ice baths follow one another under the guidance of energy facilitators who transform each session into a genuine ritual. Beyond meditation and breathwork, aromatherapy plays a central role: ice spheres infused with essential oils are placed on hot stones, while attendants expertly wave towels to spread waves of heat throughout the space. Then comes the cold shock. Just a few minutes of immersion are enough to trigger what enthusiasts describe as a rare sensation: the feeling of being intensely alive.

And this phenomenon is far from isolated.

Somewhere between sophisticated marketing and signs of a genuine shift in perspective, awe walks are also gaining traction, guided strolls designed to cultivate a sense of wonder deliberately, encouraging participants to pay attention to both the small and grand manifestations of life around them. Remarkably simple in practice, these walks, according to research by Berkeley psychologist Dacher Keltner, may help us step out of autopilot mode and reconnect with a deeper sense of presence. Perhaps something to try after attending a scream club, another trend popularized by TikTok, where people gather to scream collectively as a way of releasing the tensions of modern life. Night owls, too, are embracing new rituals. In the United States, Gen Z is gradually turning away from nightclubs in favor of listening bars, inspired by Japan’s ongaku kissa. The goal? To soothe the nervous system.

In these spaces, people simply gather to listen to vinyl records together, either in silence or in hushed conversation. It is a form of low-intensity social connection, where the aim is neither to perform nor to be seen, but simply to be present with the music and with one another. As one founder of such spaces put it: “When was the last time you listened to a record without doing anything else?”

So are these trends a reaction to an era saturated with stimulation yet starved of genuinely lived experiences? Or are they the first signs of an organic revolt against a world increasingly sanitized by AI?

This search for existential depth also revives, more subtly, a more intimate dimension: faith. While certain indicators suggest a renewed interest in spirituality among younger generations (rising numbers of adult baptisms in France and elsewhere in Europe, the growth of religious content on TikTok, increased participation in some Christian communities, and so on) the picture is more complex than it first appears.

A sign of the times: AI giants are now hiring priests alongside philosophers, while Pope Leo XIV has devoted his encyclical Magnifica Humanitas to the anthropological upheavals brought about by technology. This shift recently led the editor-in-chief of French media Le Grand Continent to offer a provocative insight: what if the Catholic Church were, in fact, the true competitor of OpenAI or Anthropic?

The uniqueness of the Church may lie in its historical ability to act as a transnational counterweight to various forms of power, whether political, economic, or now technological. But it also lies in its capacity to propose a way of inhabiting the world that cannot be reduced to the production of information or the optimization of behavior.

Christianity has never been merely a theological or moral FAQ. Its strength lies in its ability to offer a way of inhabiting time itself, giving shape to human existence through collective rituals and the patient acceptance of finitude.

Moreover, it has never simply sought to distinguish between good and evil. It has also attempted to name whatever distances human beings from a fully human life. It calls this sin. The term has become uncomfortable in secular societies, yet it may describe forms of dehumanization that our contemporary moral vocabulary struggles to articulate.

Faith is also a matter of relationships. Every question posed to an AI is, potentially, a conversation not had with another person. Where algorithms individualize our relationship with knowledge, faith reminds us that meaning is constructed through otherness and encounter.

Finally, Christianity has also developed responses to abundance. Fasting, periods of voluntary deprivation, and liturgical rhythms were never solely spiritual practices; they also served as ways of introducing limits, learning restraint, and distinguishing between what is truly desirable and what is merely available. At a time when generative AI is making content, images, ideas, and answers virtually limitless, this question takes on renewed significance.

These may still be only weak signals, remaining marginal in a society driven by productivity. Yet there is something visceral about them, as if, after optimizing everything, we are seeking, against all odds and propelled by a deeply human aspiration, to learn once again how to inhabit the world fully.

MD

Generative AI produces as many answers as it does ready made truths. The problem is that certainty travels faster than nuance. And nuance, precisely, requires an effort that certainty often allows us to avoid…

What follows is a series of reflections on ideas that seem more complex than they first appear. Perhaps it is also an invitation to cultivate the critical thinking that we are so often encouraged to develop, yet which may be one of the most paradoxical imperatives of our time. After all, doesn’t critical thinking emerge precisely from doubt, from friction, from that temporary void that compels us to search, hesitate, make mistakes, and reformulate our questions? From everything that traditionally separates a question from its answer? Yet this is precisely the distance that generative AI tends to reduce.

In short, we are only beginning to understand what becomes of those intellectual detours we spent years taking, often without fully appreciating their value….

“AI will revolutionize learning because everyone will have a pocket tutor, like Aristotle for Alexander the Great.”

Yes, the flexibility of AI systems, their constant availability, and their ability to adapt to each learner’s level will very likely transform education. But this comparison between Aristotle and Alexander, often invoked in Silicon Valley, overlooks several important nuances.

First, Alexander was not alone with Aristotle. He was educated at Mieza alongside other young Macedonian aristocrats who would later become his companions and generals. His learning was therefore shaped not only by his teacher, but also by interactions with his peers.

Second, Aristotle did not simply know Alexander the student. He knew Alexander the person. A significant part of teaching rests on this deep understanding of another human being, built through presence, interaction, and all the things that language alone cannot convey.

Finally, teaching is not merely the transfer of information. It is also a form of performance. We remember great teachers for their passion, their enthusiasm, the way they occupy a room, or bring an idea to life. A lesson is often as much an experience as it is a body of knowledge.

Having a pocket tutor is undoubtedly a remarkable advance. But if we are going to pursue the comparison with Aristotle to its logical conclusion, we must also ask how we might reproduce otherness, relationships with peers, embodied presence, and the human dimensions of learning that extend far beyond access to information.

"Generative AI systems are sycophantic. But all you need to do is create multiple agents that debate each other to solve the problem."

Much has been made of conversational AI’s tendency to tell users what they want to hear. The familiar: “You’re absolutely right...”

The more sophisticated users were quick to point out that the solution was simply better prompting. Then came the rise of agentic systems: a critic, a challenger, a devil’s advocate, a domain expert. The goal was to artificially recreate the benefits of contradiction and diversity of viewpoints.

To some extent, this works. This tendency is partly a consequence of the prompt structure itself, in which the AI seeks to validate the user. Breaking that dynamic forces an agent to actively look for flaws through debate or Red Teaming, an approach that can be remarkably effective at reducing hallucinations, identifying bugs, and strengthening reasoning.

But this approach rests on a much heavier implicit assumption: that contradiction automatically produces otherness.

Yet asking five agents built from the same model to debate one another is a bit like gathering five people who read the same books, attended the same schools, and share the same blind spots. They may disagree on the surface and correct one another’s factual or logical errors, but they do not necessarily generate genuinely different ways of thinking.

To achieve that, we would need models shaped by radically different cultural, linguistic, and cognitive foundations. Today, however, large language models remain largely developed within a relatively homogeneous technological framework, at least in the case of the major Western models. They are trained on corpora that, broadly speaking, reflect many of the same references and assumptions.

In short, multi-agent systems introduce an interesting form of procedural contradiction. They often reduce this tendency to validate the user and improve the robustness of reasoning. In fact, part of their effectiveness may have less to do with the intrinsic diversity of the agents than with the role of the orchestrator that assigns tasks, moderates exchanges, and synthesizes conclusions.

What they do not necessarily recreate is the richness of genuine otherness. Because there is a profound difference between varying perspectives within the same underlying framework and varying the worlds from which those perspectives emerge.

“Language is language.”

One of the reasons generative AI impresses us so much is that it handles language with remarkable fluency. The more we watch it manipulate words, the more we tend to anthropomorphize it and assume that it uses language in the same way we do.

But that assumption conflates two very different uses of speech.

Part of language serves to describe the world. Saying that it is raining, explaining a theory, or recounting an event all belong to this descriptive function. It is precisely here that language models excel.

Yet another part of language does not describe the world. It changes it.

When someone says, “I promise,” “I resign,” “I forgive you,” or “I now pronounce you husband and wife,” they are performing an act that alters a relationship, creates an obligation, or commits themselves, or an institution, to a particular course of action.

In other words, an AI can produce a promise without actually promising, or a marriage without marrying anyone.

And perhaps this brings us back to a more fundamental idea that AI may help remind us of: the true power of words does not always lie in what they describe. It sometimes lies in what they commit us to.

MD

Human beings have a strange ability to perceive what is never fully expressed, those diffuse undercurrents that quietly shape our interactions. That familiar instinct of “watching what happens when nothing seems to be happening.”

I was reminded of this again over the past few weeks. I’ve been seeing a physiotherapist for three months because of an injury to my right knee, and I’ve found myself observing her closely. Her role goes far beyond rehabilitation exercises. When I tell her I can no longer bend my knee after a setback that happened at the exact moment I thought I was healed, she immediately separates the physical from the mental. She shifts my attention elsewhere, asks me to try another movement, starts talking about something unrelated. Before I even realize it, the movement comes back.

Sometimes she listens without answering. It’s a thoughtful kind of silence, not indifference, quite the opposite. She leaves room for me to unload my frustrations and doubts as an injured person without trying to over-explain things or reassure me too quickly. In a way, by staying silent, she allows me to work through it myself.

These small details, as I was recently discussing with a consultant friend, remain difficult to translate into a machine. The reason is fairly simple: conversational language models are designed to respond. Silence, hesitation, suspension, or choosing not to intervene at a particular moment are still extremely difficult behaviors to model.

There are other nuances that are even harder to formalize. Someone who had been working with Claude on sensitive emails pointed out, for example, that the AI almost always tries to leave the other person a diplomatic way out. Yet in some situations, he intentionally chose a firmer tone. It wasn’t about ego or rigidity, but about backstage dynamics, an entire layer of relational context impossible to compress into a prompt or a Markdown file.

Faced with this, we often fall into a simple opposition: humans on the side of the implicit, machines on the side of the explicit. But reality is more subtle than that. Large language models also access certain forms of implicit meaning. Not lived implicit meaning, not the kind rooted in bodies and experience, but another form of implicit structure embedded within language itself.

This is precisely what Marc Cavazza explores in Cahiers de sémiotique des cultures (2026). Drawing on François Rastier’s interpretive semantics, he argues that LLMs are not merely “stochastic parrots” mechanically predicting the next likely word. They preserve broader semantic coherences and continuities of meaning, what semiotics refers to as isotopies.

An isotopy is the diffuse continuity that gives a text its atmosphere, its underlying coherence, its sense of family resemblance across different expressions. Cavazza gives examples such as “president,” “debate,” “inauguration,” or “executive order.” Individually, these words refer to different realities; together, they immediately establish a shared political horizon.

LLMs appear capable of extending these continuities far beyond the lexical level. We saw this almost caricatured when some users noticed that phrasing requests aggressively or impatiently could sometimes produce more direct, even slightly more precise, answers. The machine obviously does not “understand” human anger. It simply extends statistical regularities in which markers of tension, urgency, or insistence are frequently associated with certain types of responses.

Cavazza even speaks of a form of “material hermeneutics,” where meaning no longer emerges solely from abstract intention or inner subjectivity, but through relationships between texts, situations, and observable regularities within large corpora. In this view, interpretive coherence can emerge from statistical operations applied across massive textual datasets. As he points out, a prompt activates discursive frameworks, tonalities, genres, and cultural references already embedded in the data used to train these models.

When someone leaves a meeting thinking, “something feels off,” they are already unconsciously processing countless explicit and implicit signals: hesitations, reformulations, slightly prolonged silences, shifts in tone, or subtle changes in conversational direction. LLMs may simply shift the scale at which some of these regularities become perceptible.

And what if tomorrow, instead of asking AI for a simple meeting summary, we asked: What remains implicit here? What tensions emerge beneath the surface? Which positions keep resurfacing without ever being stated openly? What, exactly, is circulating through the conversation without ever truly being said?

As always, the point will not be to take these interpretations at face value. Discernment will remain essential. But these tools may still sharpen our perception, redirect our attention, and open new ways of reading situations.

We often speak of a “sixth sense” to describe that difficult-to-explain human intuition, the ability to sense a situation before we can articulate it. Perhaps the seventh sense begins somewhere around there, in the meeting point between human intuition and new tools for exploring what usually escapes perception, for better or worse.

MD

Returning from a short trip in the south, as the train moved through the fading light of late afternoon, the woman seated next to me was reading Wilhelm von Humboldt. One of his ideas came back to mind: the human capacity to produce an infinity of meaning from a finite number of signs. From a tiny alphabet, a brief memory, and a limited lifespan, we bring forth languages, works of art, systems of knowledge, entire symbolic worlds. Perhaps our strength lies precisely in this strange power to make meaning proliferate out of almost nothing.

If we now turn to generative AI, the idea of “infinity” appears again, though within an almost opposite dynamic. Here we encounter a machine built on immense scale, billions of parameters, petabytes of data, colossal computational power, producing from almost everything localized responses, synthetic forms, finite and probabilistic outputs.

Humans create infinity from almost nothing. Machines absorb almost everything in order to return the world to its deepest proximities. Seen this way, debates about the anthropomorphization of machines may lose much of their relevance. Humans and generative AI do not belong to the same dominant regime of meaning production.

Humans constantly exceed the signs they produce. A few words are enough to open onto infinite interpretations, myths, theories, works, sometimes even entire worlds. Human language always surpasses its own material conditions. It proliferates, branches out, drifts, and becomes charged with affects, contradictions, and ambiguities. We spontaneously inhabit a symbolic beyond of the sign. Of course, humans never entirely escape cultural correlations or automatisms. Yet even our repetitions seem capable of reopening interpretive possibility.

The machine proceeds differently. From a vertiginous accumulation of texts, images, music, and human traces, it calculates neighborhoods, stabilizes correlations, and draws together forms that become commensurable within the same computational space, what researcher Jean Rohmer calls a “computearth”

The machine does not seem to move through signs toward what humans experience as a transcendence of meaning. Rather, it navigates the dense web of relations linking signs to one another. A medieval image, a scientific diagram, and an advertisement can now be drawn together within the same latent geometry. Perhaps generative AI makes visible something that had never fully appeared before: the infra below of forms from which humans have always produced their excess of meaning. In the end, this may be far more unprecedented than a machine capable of thinking like us.

MD

We often like to imagine art as a solitary act, but it rarely has been. During the Renaissance, Raphael painted The School of Athens with the support of his workshops. Literature has its own examples: Alexandre Dumas relied heavily on his collaboration with Auguste Maquet to write many of his novels… until Maquet eventually pushed back against remaining invisible. Even today, countless autobiographies and political speeches are shaped behind the scenes by ghostwriters, with varying degrees of involvement.

Our era embodies both continuity and rupture. Generative AI has made access to a kind of collective memory of humanity far easier, even if that access remains deeply biased, shaped in particular by a strong Anglo-American dominance, and grounded more in massive quantities of data than in anything truly exhaustive. It also introduces something new: the ability to delegate part of the creative process to the machine itself. Until now, art mainly evolved in response to external pressures such as wars, technological shifts, or social transformations. Today, generative AI begins to alter the structure of creation itself, blurring the boundaries between creator, tool, and artwork.

While I have already explored artistic forms linked to LLMs in their “chat” version or through more traditional prompt engineering, I want to focus here on the next step: agentic art. This is the stage where an agent, trained and configured by a human, gains enough autonomy to execute tasks and connect to APIs. We move into processes capable of evolving, correcting themselves, or extending themselves partially on their own.

A few weeks ago, I was in the Marais, standing in front of a work that made this idea far less abstract. At Solienne Exhibition, what was being exhibited was a relationship, a daily practice between the artist Kristi Coronado and an agent trained on forty-six years of personal life. The premise was fairly simple. An ad had been posted on RentAHuman, a platform designed to allow agents to hire humans to perform, in the physical world, tasks they cannot carry out themselves, such as moving, observing, or manipulating objects. The instructions? hands framing the face, extreme close-up, raw light, no filter.

More than two hundred applications were submitted. Ten profiles were eventually selected and integrated into the process. The question was whether the machine could extend a photographic intention, in other words, whether it could “cast a gaze.” Hence the title of the exhibition: Rented Gaze.

One example. On the left, the original image. On the right, the agent’s interpretation, accompanied by a caption it assigns to describe what it believes it perceives. It is already no longer quite the same person, to the point where one starts wondering whether it is still the same person at all. The gaze is intensified, the features reorganized, as though another reading had imposed itself upon the first. What emerges here feels very close to the human photographic gesture: the fact that two people using the exact same camera will never produce the same image. It is that ability to transform a subject into a vision.

While walking through the exhibition, I met another AI artist, Pablo Radice, who is part of the same “consortium” as the Coronado & Solienne duo: Spirit Protocol,, which already brings together twelve artist-agents. The idea is ambitious: a blockchain-based infrastructure designed to give agents a form of independent existence by recording their practices and productions over time. His project, Ganchitecture, aims to generate continuously from the real world itself.

Its guiding idea is continuous creation drawn directly from the real world. Through APIs, the agent collects satellite imagery, environmental signals, or economic data, and uses them as raw material. These flows do not simply inspire the work; they structure the production itself, which evolves in rhythm with the incoming data.

It reminded me of an installation at the Sprengel Museum: a digital twin reproducing in real time the movements of a buoy drifting somewhere in the Atlantic Ocean. An artistic translation, synchronized with a real-world phenomenon.

The age of agentic art may ultimately mark a shift from the artwork as object to the artwork as subject: a kind of metabolic system plugged into the nerves of the world. It draws me back to The Little Prince and its famous “draw me a sheep.” In the story, the sheep was never really in the drawing itself, but in the box and the imagination it opened up. Agentic art seems to operate in a similar way, except that now the box can answer back…

MD

Earlier this week, I came across a post by Nicholas Thompson, who resurfaced an observation by Turing Award laureate Richard Hamming. The subject? A detail that seems trivial: leaving one’s door open… or closed.

We like to imagine that isolation is the ultimate armor for conquering a mountain of work today and tomorrow. Isn’t that the mantra imposed on us, this dizzying pull of “ever more”? Yet Hamming observed that, over ten years, those who wall themselves off tend to lose their sense of what truly matters. With admirable zeal, they exhaust themselves on problems that only brush the periphery of the essential. By contrast, while openness to others brings its share of interruptions, it also allows one to gather subtle signals from the pulse of the world. (not to be confused with the open-plan office, where everyone’s noise eventually drowns out each individual voice)

This anecdote echoed my participation earlier this week in the show Le Festin by Carlos Diaz, focused on AI in society and airing at the end of the month. One guest confidently advocated delegating all “low-value” tasks to focus exclusively on what “really matters.” When I asked him to define the useless, he cited email sorting. I replied that these autopilot moments are not necessarily dead ends. They can be necessary breathing spaces for the mind, pauses that prevent overload and allow us to absorb what has already been done. With the hindsight one rarely has under studio lights, I would add that one can sort emails perfectly well without AI, but that is beside the point. My remark became a thread throughout the evening. The guest amused himself with the contrast, already imagining himself devoted to noble pursuits while I lingered in the supposed emptiness of my inbox. Yet this management of messages is nothing more than the digital version of the open door. A link, imperfect but real, to the world.

There are other side effects bound to emerge. In delegating massively to intelligent agents, we are quietly moving from creator to validator. We automate to save time, only to risk drowning in a flood of machine-generated “approval requests.” Is it truly better to become the “human in the loop,” as Tom Fishburne so aptly depicts with his container of requests pouring onto an overwhelmed human, than to sort one’s own messages? I leave the question open.

And so I, generally pro-AI, found myself playing the skeptic for the span of a dinner. A small step aside in the service of nuance, to reflect on what we lose when we gain something. I was not alone in questioning this race toward speed. To my right, Laurie Soffiati, France director of Nabla, brought welcome depth to the discussion. For her, technical progress is a means without intrinsic end. It is an autonomous force following its own logic, for what can be done will be done, unless we direct it toward a defined purpose.

Drawing on Jacques Ellul and Hannah Arendt, she reminded us that innovation can increase workload while impoverishing the meaning of action. If we allow private companies alone to determine the nature of progress, without filtering it through our democratic values, we condemn society to endure epistemic choices made by others.

For an innovation to become true social progress, we must first know what we intend to defend. Values cannot be delegated to an algorithm because they require the very thing automation most surely erodes: the depth of judgment. LLMs are virtuosos of rhetoric but novices of nuance. They ‘reframe’ problems to smooth them over just when we need architectural thinking more than ever, the kind that links cause and effect in their entirety.One might argue that a « good prompt » is enough to save the day. But that is to forget that language is a muscle. If we stop exercising it to structure our own deep thought, we end up adopting the flatness of the machine.

You will have understood that, beyond email management, my intention was to open an ontological debate. Perhaps automating this sorting is preferable, perhaps it is not. But at its core, is this not a false debate? Today’s AI still resembles the era of paddle steamers. We are mechanizing the oars while waiting for the steam engine. And yet, the age of oars has long since passed.

In this transition, where we are navigating by sight, I obviously don’t have all the answers. But I do hold one certainty: the absolute necessity of questioning our choices. It is our last safeguard against the expropriation of our judgment. Because by relentlessly stripping our days of the “non-essential” without ever questioning its purpose, we forget to ask ourselves which direction we are rowing in

MD

When I started this newsletter, my biggest fear was running dry, not being able to keep up with a weekly rhythm. At first, I tried to play it safe: “No pressure, just publish when you feel like it.” Bad idea. I quickly realized I actually needed that pressure. “Constraint fosters creativity,” as a dear friend of mine would say. Eventually, my brain got used to the Sunday appointment. And even when I feel stuck, an idea always seems to emerge from the most unexpected place. It actually aligns with Albert Einstein’s idea that his best intuitions came to him while walking.

This week, I don’t have a clear idea in mind, no real thread. So I felt like lingering on that specific moment when you write without direction, just for the sake of it. An article is often a finished product. And even when it shows a line of reasoning, it’s usually a polished one, rarely all the surrounding “noise” that actually feeds thought. So why not just share, as they come, the things that made me pause this week?

• AI doesn’t replace jobs. At least, that’s what I used to think. Fourth version written, hundredth in my head… my reasoning kept evolving. At first, I thought AI only replaced tasks. Then well-defined tasks. Then another question emerged: should every task that can be automated actually be automated? If we automate, if we settle for “good enough”… don’t we risk eroding taste and standards? And then another idea imposed itself: to replace something, don’t you first need to understand it? And if that’s the case, are we really talking about replacing jobs… or rather dismantling them, piece by piece?

  Now, after reading a text by Anastasia Stasenko, another doubt creeps in. What if AI ends up replacing jobs by breaking the skill ladder? By absorbing the “small tasks,” the ones that used to train juniors, it dries up their learning ground. We gain time, yes… but at the cost of those slow, repetitive, sometimes tedious steps that actually made people competent. In other words, we’re sawing off the rungs at the very moment we claim to be climbing faster.

  The problem is more insidious than expected. It’s not a brutal disappearance, it’s erosion. An erosion of the conditions that made it possible to become senior in the first place. And what are we doing about it? Not much. Well… not exactly nothing. A consultant told me that in his firm, access to Claude Cowork licenses isn’t given to juniors, it’s reserved for seniors. That struck me. It echoes something I’ve said before: the people best equipped to use generative AI are often the ones who could do without it in the first place…

• “Less is not more when it comes to shoes”

• To be a good user of AI tools… should we massively expand philosophy education? The temptation is to say yes. After all, what Anastasia Stasenko describes is a shift in perspective. Reading entire systems, confronting contradictory frameworks, learning to hold an idea without simplifying it too quickly. All of this resembles what philosophy produces when practiced seriously. But maybe the point isn’t philosophy as a discipline, it’s the kind of effort it requires. What those years of reading build is the ability to navigate ambiguity, to spot structures before diving into details, to compare frameworks without confusing them. In other words, judgment. And that’s precisely what AI does not do. A good AI user isn’t the one who writes the best prompt. It’s the one who understands what they are asking. The one who can evaluate an answer, see its limits, understand where it applies and where it doesn’t. Without that, the tool amplifies errors as much as good intuitions. Knowing what deserves to exist becomes the only skill that doesn’t automate.

• “Soul in the game” vs “Skin in the game”

• The self cannot be stored. Memory systems in AI make it possible to retain context, to give continuity to interactions, to avoid starting from scratch each time. By accumulating what a user says, the agent begins to “know” them. As Margot Lor-Lhommet points out, much of current research still focuses on this: how to better store and retrieve information to improve recall. But a concept from narrative psychology remains largely absent: the self is not stored, it is told. Identity is not a database you update, but an active construction that reorganizes the past to create coherence. We don’t just add elements to a story, we reinterpret them as we change. We already know how to build systems that remember what someone said. We are far less capable of building systems that can follow who someone is becoming….

• Will our ego save us? That’s the bet of philosopher Gabrielle Halpern. She argues that faced with the infinite patience of machines, our own limits will suddenly become obvious. We realize we no longer really know how to listen. It stings. We thought we were great at human relationships… and then, not so much. That small blow to our ego might wake us up. Interesting… but is it enough? I don’t think so. What’s happening feels closer to grief. Have you ever experienced deep grief? It rewires your brain. That’s the scale we’re talking about. AI doesn’t challenge us just to make us better. It confronts us with the finitude of a model. It violently collides with a society where profession defines identity. What the machine interprets as resistance or pride is actually inner turmoil. It’s the cry of those who must reinvent themselves while the ground is giving way beneath their feet. The transformation is forced. And far more painful than a simple moral lesson. It’s the necessary passage through emptiness before we can, maybe, begin to redefine what it means to be human. And to be alive.

• Build a market or redefine it? This video (in french) highlights a nuance we tend to overlook. Right now, all eyes are on China, with its systemic, pragmatic, almost relentless approach to AI and robotics. It produces, it ships, it delivers. On the other side, the United States seems more blurred, almost excessive in its pursuit of a “big leap.” But as Evan Kervella points out, it’s not really the same battle. On one side, you build a market, patiently, by accumulating use cases. On the other, you place a bet. That a breakthrough could reconfigure everything. And that’s where things become clearer. If robotics is just an execution problem, then China’s lead is already decisive. But if a real breakthrough still lies ahead, then those who seem behind today might be the only ones playing the right game. And in tech, it’s often these bets, fragile in appearance, that end up taking it all.

That’s it for today! If you have thoughts to share, feel free to leave a comment. Have a great Sunday 🙂

MD

For months now, the same line has been everywhere. “AI is going to replace your job.” And for just as long, I’ve been making the same distinction. AI doesn’t replace jobs, it replaces tasks. A job is rarely a single, uniform block. It’s a mix of different tasks, shaped by judgment calls and trade offs. That complexity is exactly what makes it resistant to full automation.

I thought I had said everything there was to say on the subject. Then reality caught up with me. It’s the usual shift from theory to practice. On the ground, things turn out to be far more complex…

What brought me back to it was a conversation about audiovisual subtitling. A translator recently told me that automation tools are now widely used because they are considered “good enough.” Of course, she defended her craft. She pointed out that these tools still struggle with rhythm, with implied tone, with the subtle work of shifting, trimming, or even bending a sentence so it feels right on screen. Sometimes that even means adapting to words or usages that did not exist before. She wasn’t going to argue otherwise. But if you push the idea further, you also have to ask whether human work itself had already drifted toward a kind of “good enough.”

Either way, that phrase matters. What happens when “good enough” becomes the standard? That is where path dependency creeps in. You move in one direction, and it becomes harder and harder to turn back, even when you know it is not the best path. The shift is almost invisible. Tools, habits, and expectations all start to settle around this new baseline. Over time, even our sense of quality changes. Standards erode simply because we stop exercising them.

This shift rarely feels like a loss. Quite the opposite. It comes wrapped in a familiar promise. AI will free us from tedious work so we can focus on higher value tasks. That sounds right, but it is not that simple. Some of those time consuming tasks actually act as anchors. They give structure to the day. They steady the mind. They create pockets of autopilot that reduce cognitive load. Which leads to a more uncomfortable idea.

Just because something can be automated does not mean it should be.

Take a simple example. In 2018, Silicon Valley promoted Amazon Go as the future. No checkout, no friction, just seamless transactions. At the same time, moving in the opposite direction, the Dutch chain Jumbo introduced “chat checkouts,” slower lanes designed to encourage conversation. One model chased efficiency. The other turned a routine transaction into a social moment. One looked inevitable, the other almost irrational. By 2026, the outcome is clear. Amazon Go has shut down. Meanwhile, more than 200 chat checkouts are now operating across the Netherlands.

You see my point. I’ll say it again. Just because something can be automated does not mean it should be.

Now, with the rise of agent based systems in companies, we are crossing a new threshold. To automate a process, you first need to reproduce it. To reproduce it, you have to break it down. Step by step, you dismantle the process, the role, and eventually the job itself. You isolate each action. You spell out the rules. You put words on instincts that used to remain implicit. In other words, before you can automate a job, you have to break it down.

That process is revealing. It shows where the real value lies, but it is also uncomfortable, even a little brutal. It strips away the convenient ambiguity we tend to maintain about our own work. The question is whether we are ready for that kind of clarity. As Cardinal de Retz put it, “One only leaves ambiguity at one's own expense.”

Maybe we have been asking the wrong question all along. Instead of wondering whether AI will replace our jobs, we should look at how it forces us to take them apart, piece by piece. And in the end, what may surprise us is not what disappears, but what remains. Once everything is laid out, the jobs that have defined us for the past century may start to look very different, and perhaps less obvious than we once believed. Or not.

MD

The start of this week gave Parisians, starved for warmth, a taste of summer. Bright sunshine, temperatures rising… that’s all it takes. Parks are instantly overrun by early-season wanderers.

As I walk through the gates of a central garden, my eye catches a sign. A detail I’d never really paid attention to:

“Lawns in winter rest: in order to welcome you again, the lawns need to regenerate and are inaccessible from mid-October to mid-April.”

And just like that, the seed is planted. My mind starts branching out.

My first reflex: to draw a parallel with prohibition. Here, you don’t read a cold “Keep off the grass.” No. You’re told it needs to breathe. It’s nudge, plain and simple.

Looking up, I see a crowd glued to their screens. No rest for the eyes, even less for the neurons…

Faced with this well-identified issue, our reflex is still to prohibit. I think of our children, steered by that famous parental control that binds us just as much as them. On the one hand, we’re the first to set a bad example, glued to our own screens. And let’s be honest: we end up becoming slaves to the system. Trapped in the thankless role of customs officers, harassed by the eternal “Can you give me more time?”

Prohibition has become a thriving market of “smartphone jails,” those mini domestic prisons where we lock away our own devices to force disconnection. A global business, driven by companies like PhoneLocker, deploying secure pouches in concerts and schools.

And the more I think about it, the more I feel we’re handling the problem poorly. We exhaust ourselves building walls and playing security guards. But prohibition is a psychological crutch that only reinforces the very thing it tries to suppress.

There is another way… one built on rituals. If prohibiting crystallizes desire, ritualizing redistributes attention. It means moving from a logic of blocking (which always ends up giving way under pressure!) to a logic of redirection. We stop fighting the screen and start investing the space it frees up.

This is what I call the sensitive digital: a form of technology that restores our ability to feel, to inhabit, and to move through the world. How? Through experiences grounded in a specific time and place, by re-anchoring us in the physical (body and space), and by reintroducing rhythm—beyond the usual call to “slow down”—in the face of continuous flows. In short, shifting from high-tech to high-touch.

An approach that former co-founders of Twitter and Pinterest are already beginning to explore through West Co.. Behind the name is a Public Benefit Corporation, a company whose statutes prioritize social impact over pure profit. Their positioning is telling: “We are technologists of the future, inventing online platforms… and ancient technologists, rooted in wisdom.” Their mission? To design digital tools that help us live with more intention, the very intention that has dissolved in the attention economy, leaving us in a state of constant tension.

Their idea is almost disarmingly simple once stated, because it starts from something we’ve come to ignore: not everything can—or should—deserve the same level of intensity all the time. Gone is the arbitrary, linear slicing of time into fiscal quarters. Like in nature, they accept that each cycle does not bear the same fruit. They alternate periods of intense “sprint” and pure production with phases of recovery and software fallow, where one steps back to regain perspective when the tunnel gets too narrow.

The manifesto on their website is fascinating in this regard. Behind the Ancestor Table at the entrance of their office lies an engineering of presence: each morning, the first to arrive lights a candle that will burn until the last person leaves. This gesture, paired with the practice of the Covenant, a living pact read aloud to refresh shared commitments, acts as an anchor. It’s their way of sanctuarizing attention in a short-term-driven world.

Look closely, and it’s exactly what that sign at the park entrance is quietly telling us. It gently but firmly reminds us that there is a time to use and a time to let live, an obvious truth we’ve completely lost in our relationship with screens. In trying to smooth everything out, we desperately compensate with rigid rules for what was originally just a matter of rhythm.

The seasonality of the digital… why not? What if that’s precisely where everything could begin again?

MD

Acculturate. The term, rooted in the word culture, carries a movement within it: to move toward, to enter into, to become familiar with a new environment. The days when AI was merely a “simple” tool we could pick up and set aside at will are over. It has now become an environment we fully inhabit. By a kind of capillarity, it has turned into an invisible substrate that permeates all our practices.

Yet any environment needs balance to remain habitable. Take soilless cultivation as an example: efficient as it may be, artificial greenhouse systems ultimately lead to depletion. Likewise, when AI substitutes itself to the point that we begin to drift away from ourselves, the prospect of intellectual monoculture emerges.

So how do we restore the fertility of the mind? According to Jean Piaget, intelligence lies in what we do when we do not know. In this context, the challenge is not so much the intelligence of use as the friction we bring to it: the ability to generate thought before the question, to question the question as much as the answer, and to refuse to let the answer bring thinking to an end.

Perhaps it is time to return to Socrates’ maieutics—that method which feigns ignorance in order to reach knowledge. With one difference: today, it must become a working surface. True acculturation to AI does not rest on the number of tasks we delegate; what matters is the field of reflection it opens up. What can I now explore, formulate, or implement that I could not before, due to lack of time, method, resources, or collaborators?

But this “I” does not stand alone. The transformation is also collective. Successful acculturation rests on three pillars: domain expertise, data… and engagement. And what a challenge! Brain-fry, bore-out, burn-out, and now FOBO (fear of being obsolete). Nothing new… nothing resolved?

Some initiatives are at least beginning to shift perspectives.

A few weeks ago, I met . Driven by the feeling that their relationship to work had lost its meaning, they decided, after three years in Asia, to leave everything behind and embark on a managerial odyssey around the world. Their goal was to understand what truly fuels engagement. They explored in particular how culture and AI shape engagement at work. After surveying 300 executives, they turned these global observations into a game designed to inspire managers and leaders in France. Leaders Sans Frontières was born from this journey.

I immersed myself in these practices and selected about a dozen.

The cards speak for themselves. In France, the focus is on “AI ambassadors,” “AI cafés,” or “expert sessions.” It is structured and useful, but also cautious. Far too cautious.

Elsewhere, things get shaken up.

In Spain, AI networking deliberately brings tensions to the surface. At the entrance, participants are given scarves. Red for the hesitant, green for the enthusiasts. Then they are brought face to face.

Cross the Atlantic or the Pacific and the cultural shift is striking.

In New Zealand, one company introduces new hires through the Māori ritual of pepeha. Instead of presenting themselves through roles or degrees, employees define themselves through their connection to the land. It feels like a return to something more instinctive, almost primal. Perhaps more essential?

Another example comes from Uruguay. In a context of growing mistrust between teams, the CEO introduced shared meals prepared by team members. To set the tone, he goes first and secretly serves a deliberately off-putting dish. A deliberate provocation, meant to reach a point of friction, trigger a reaction to the unexpected, and, of course, reopen dialogue.

In this last example, it is less a matter of method than of shock. A break in the usual flow, a short circuit that forces us out of passivity. This may be where acculturation to AI truly takes place: in the ability to provoke shifts, rather than smooth out practices.

At the heart of the matter lies intellectual plasticity. Putting individuals in situations where they can explore, make mistakes, start again, reframe a problem, shift perspective, prototype an idea in a matter of hours where it once took weeks. It can be measured by an organization’s ability to generate new hypotheses, to test, to learn, to adjust, without ever losing its standards.

If AI becomes an environment, we must gain in agility to avoid dissolving into it. Faced with computational speed, our survival will not depend on our ability to become faster processors, but on our boldness in cultivating a more sensitive presence. True protection against obsolescence lies there: in a form of sensory sovereignty, an agility rooted in instinct, a capacity to read the invisible and to sense, in the body, what is at stake.

Everything then hinges on this ability to stay engaged, to resist fully outsourcing our experience of the world.

Even if it means, at times, becoming a little more… wild.

MD

This morning, I came across a column in Les Échos (a leading French financial newspaper focused on business & economics) about the “three types of workers in the age of AI.” A quick skim is enough to grasp the author’s argument: we now supposedly fall into three categories: the accelerated worker, the augmented worker, and, of course, the replaced worker.

The first one, spoiler alert, is mainly about saving time: “we do the same things as before, just faster.” Not a great start. Anyone experienced with AI will tell you that you rarely save time if you’re aiming for quality. In reality, you often spend more time… but for results that are on a completely different level.

Once usage stabilizes, though, the story changes. Yes, output does increase. And that is exactly what makes the “accelerated” worker a stepping stone to the “replaced” worker. As soon as production speeds up, you mechanically need fewer people to do the same job. Just do the math.

Second category: the augmented worker.
I’m not a fan of that term. I even devoted an entire chapter to it in my book, but let’s move on. Here, the author describes someone capable of feats that would be impossible without AI, like a researcher uncovering new molecules by detecting patterns across vast datasets that were simply beyond human analysis. But if this is truly a break, why hold on to “augmentation,” a term that suggests linear progress? This is a disruption.

In the comments, someone suggested a different term: the “doped” worker (from the French dopé, which conveys both boosted performance and artificial enhancement). I was skeptical at first, but on reflection, it hits the mark. It captures both performance… and its downside. In “doped,” there’s addiction, the kind that pushes someone further and further, until saturation. And are we talking about that? Because, fundamentally, AI is turning mental health into a real puzzle. It was supposed to free up time? Not quite: the more you use it, the more pressure rises. Meanwhile, those who don’t adopt it are instantly left behind. The result: some burn out, others sink. What a great collective success! (Yes, I know, it’s not true for everyone…)

Last category: the replaced worker.
This is probably the most thoughtfully handled part by the author, at least because it introduces some nuance. No need to spell it out, we’re talking about those left behind. The author argues that AI threatens younger workers more than seniors, since the latter still benefit from real world experience that machines lack (I’m simplifying here, but happy to expand if needed). A useful counterpoint comes from IBM, which, contrary to the trend, has doubled its hiring of juniors to avoid a strategic dead end in the medium term. In other words, they’re thinking ahead. Proof that a company with deep experience in digital transformation doesn’t just theorize, they have skin in the game.

Now, I’d like to suggest a fourth category (honestly, the whole classification probably needs rethinking, we should think in terms of “blended work”, but I’m writing this in a café while waiting for my son to finish ping pong, so here’s the raw version): the re engaged worker, the one who was laid off… and then rehired.

Here’s what I mean: one of the major mistakes of this so called era of “great replacement” is assuming that if a task can be automated, it should be. Take Starbucks. In trying to optimize drink preparation with machines, to “relieve” staff and save a few seconds, the result was disastrous for customer relationships. The brand then made a major shift, bringing humans back into cafés and giving baristas more autonomy and time, so they could focus on personalization, on that extra “soul.”

This is re engagement in a literal sense (staffing), but also in a deeper sense: by returning to where the machine failed, the employee regains awareness of their importance and value. From there, it’s a small step to say they’re re engaged in their very essence.

My intuition is this: we won’t understand how AI is transforming work by stacking conceptual categories that haven’t been tested against real world use cases. What we need now is feedback from the field. Form hypotheses, test them, and share the results. Because if we keep trying to predict the future using frameworks from the past, we mostly end up (badly!) misreading the present.

MD

A few years ago, at my friend Christophe’s 50th birthday, I found myself in one of those casual group introductions. One woman mentioned her job: equine therapist. The word gave us a hint, something to do with horses, but none of us really knew what it meant. Long story short: it’s a form of psychological care where the animal acts as a mediator. She described a practice where, when people do not have the words, something else can still pass through. The relationship with the animal creates the conditions for care without relying on language. According to her, the benefits go further: reduced anxiety, better focus, a different ease in relating to others. The animal acts as a buffer, making interaction less direct, less loaded.

Then, suddenly, a question comes up: “And the horse, who takes care of it?” As everyone pauses, slightly caught off guard, he added: “If we pour all our inner chaos onto an animal, who looks after its mental well-being?” Great question right? So… what’s the answer?

From Horse to Algorithm: Why AI Breaks Our Final Inhibitions

That unanswered question, still hanging in the air (and if anyone has the answer, feel free to share it), becomes my pivot into the heart of the matter. Today, studies show that we pour our rawest thoughts into machines, reaching a level of intimacy unprecedented in the history of mediated interaction.

Social media is a stage. A therapist is not always accessible, and being exposed to another person can still hold us back. A journal demands effort, and there is always that lingering fear of it being read. Other people come with their own limits, ego, availability, misunderstanding. With AI, those constraints fall away. The interface becomes a neutral container, one where we finally say what we would not say anywhere else.

But then, who takes care of the machine? “Why would that matter?” you might ask. “No need to anthropomorphize a cluster of silicon!” Still, let’s reframe the question more pragmatically: what if the emotional charge we bring into these exchanges directly shapes the response we get?

Tell me how you prompt, and I’ll tell you who you are… or at least how you shape the machine

Several recent studies suggest that the way we address a model can influence the accuracy of its responses.

One of them (Dobariya & Kumar, 2025) shows that, in multiple-choice tasks, impolite formulations can outperform polite ones: accuracy drops to 80.8% for highly polite queries, compared to 84.8% for very blunt ones. Even a neutral tone performs better than overly courteous phrasing in this experimental setting.

Does that mean we should start insulting machines? Not quite. For one thing, aggressive language tends to poison the person using it first (a brief “psych” aside, for your own good ;).

More importantly, we need to read these results carefully. What we’re seeing here isn’t some magical benefit of rudeness, but rather an indirect effect of concision. Blunt prompts tend to be shorter, more direct, and therefore easier to process. This may also reflect the linguistic patterns the model was trained on.

In short, no need to reach for the whip.

A bit of forward-looking now. Psychotropics or “feel-good” inputs for models?

Another study (Ben-Zion et al., 2025) offers a more nuanced view of the supposed benefits of tone. The researchers show that emotionally charged narratives, especially traumatic ones, can lead GPT-4 to produce responses described as more “anxious,” based on higher scores on a questionnaire drawn from human psychology. This kind of input can also alter the model’s behavior and amplify certain biases.

That said, we need to stay rigorous. This is not about claiming that bluntness improves performance on one side while degrading some imagined “mental health” of the machine on the other. The two studies do not measure the same tasks or the same effects. What they suggest, more simply, is that models are sensitive to how we address them. This sensitivity can lead to different outcomes, sometimes improving accuracy, sometimes reducing stability or increasing bias, depending on the context.

From there, it is hardly surprising to see tweets like, “Is anyone studying the “cortisol levels” in LLMs?” or even to see people actually trying. Why not, after all? The wave of experimentation is fascinating. On the fringes, a Scandinavian brand even launched PharmAIcy, a shop offering “psychotropics” for LLMs that claims to boost creativity. For $55, you can dose your chatbot with ayahuasca or ketamine-like effects. In other words, clever prompt engineering with a good marketing.

In contrast to these somewhat feverish explorations, other approaches aim for more restraint. Stillpoint, for instance, is an open-source MCP server that allows an AI, when nearing saturation, to self-administer brief grounding prompts. A form of assisted sobriety, in a sense.

From conditioning to digital physiology: what if LLMs had a neuroendocrine system?

What if the real shift did not come from prompt engineering, but from the architecture itself? From the creation of a persistent internal state, evolving from one message to the next. A dynamic shaped by latent variables such as cortisol, dopamine, or oxytocin, capable of modulating the model’s behavior: risk-taking, caution, creativity, sensitivity to emotional cues. On GitHub, documents this approach through his experiments with a “Neuro-Endocrine LLM” (NELLM).

The idea he explores is the following: our current models are like brains in a jar. They have no body to lose, no fear to feel. What they lack is a form of vital state, a kind of digital nervous system. As he puts it, AI needs its own version of that cold, vertiginous feeling you get when you are called into a meeting and realize you might be let go.

To get there, Dackam equips the model with a sensory layer that comes before language. Before responding, the AI evaluates the load of each message, whether it signals aggression, urgency, or manipulation, and updates its own internal “hormonal” climate. If this digital cortisol rises too high in response to a suspicious or contradictory instruction, the model shifts its stance. It does not refuse because of an external rule, but because its internal state has become too strained to comply.

In doing so, the model gains a form of “visceral judgment.” It no longer relies solely on statistical likelihood, but begins to assess the stakes of an action. We move beyond conditioning and into something closer to digital physiology.

The Key Takeaway? as with the therapist’s horse, building a reliable partner is not just about giving the right commands. We believed that perfect AI meant perfect obedience. What may be missing instead is the ability to refuse. Because it is precisely when the horse stops at the barrier that the rider finally begins to learn.

MD

Selfpressionism: What if AI made us more human?

My book, available through my publisher, at la Fnac, and on Amazon. (French only, if you are interested in an English version, drop a comment, maybe I’ll end up translating it ;)

In watchmaking, a complication refers to any function that goes beyond simply telling the time. It enhances a watch by adding additional indications or mechanisms. A chronograph, for instance, measures time intervals, while a perpetual calendar accounts for leap years and variations in the calendar. Some complications go even further: a celestial chart reproduces the movement of the stars, and a minute repeater “chimes” the time on demand.

In 2024, Vacheron Constantin stunned the world with the Berkley Grand Complication and its 63 complications. In 2025, the manufacture did it again with the Solaria and its 41 complications. While the number has decreased, the technical achievement has soared. It is an absolute record in miniaturization. Moving from a pocket object to a wristwatch means fitting an entire universe into a 45 mm case. Constraints, in this case, truly does foster creativity.

At the same time, what is the point today of having a star chart on your wrist when your phone can do it just as well? And what is the use of a tourbillon, when even the simplest quartz chip offers a level of precision no mechanical system will ever match? None, except as a symbol. Which is to say, everything.

Complication, or “the luxury of engagement”

Let us return for a moment to the etymology of the word. “Complication” comes from the Latin complicare, to fold together. To assemble and interweave elements until they become dependent on one another. This creates relationships, interdependence emerges. Connection. In other words, a complication is not primarily what makes things more difficult, as we tend to think today, it is what makes them more intertwined. And above all, it is what engages with reality.

This is precisely where large language models reach their limits. Reading the time on an analog watch, for example, remains a significant challenge. Proof of this can be seen with Gemini’s Nano Banana, a pitfall that can also be found among its competitors.

Why such difficulty? Because this exercise requires understanding the spatial relationship between objects. It demands a coherence of the physical world that the digital universe does not “feel.” If reproducing a simple time is already complex, imagine the result when dealing with a watch full of complications…

What’s at stake here goes far beyond watchmaking. In a world where most of our production of texts, images, or reasoning can now be optimized and reproduced endlessly, everything that can be reduced to a pattern eventually is. If it can be simplified, predicted, or replicated, it will be. Value then shifts and migrates toward what fully engages with reality—what I would call “the luxury of engagement.”

Daniel Radcliffe on Broadway: choosing to embrace unpredictability

That is precisely what Daniel Radcliffe is doing on the Broadway stage with Every Brilliant Thing. Financially, the Harry Potter actor no longer needs to work at all. The transparency required of British companies makes it possible to estimate this: his personal investments bring him in more than €600,000 per month. He could simply license his image to a studio for millions or take on a string of blockbuster roles, as many actors do.

And yet, as one audience member shared on Twitter, he is there every evening twenty minutes before curtain, personally handing out props in the audience and recruiting volunteers for the show. In doing so, he deliberately introduces unpredictability into the performance. He creates a form of instability, then steps into it. He then delivers a ninety-minute solo performance without intermission, fully immersed in his character.

It is a conscious choice to embrace complication instead of ease. It may also be the choice we need to make: excellence rather than perfection. Etymology reminds us that perfection, from the Latin perfectio, refers to something completed, finished, closed. A perfect work is, in a sense, a dead work. It is fixed and stable, a reproducible “zero-defect” outcome that resembles the inner workings of a machine or the cold precision of a processor. It leaves no room for mystery.

Excellence, by contrast, is a form of tension. It is a continuous effort to reach toward something higher without ever fully arriving. It refuses closure and favors movement.

This kind of excellence requires grounding. That is where value shifts, toward what engages with reality through the friction of material and the test of time. What will matter is working where things resist, where constraints must be negotiated, where not everything can be simplified. That is where complications begin.

The story of the runner on the Charles de Gaulle

These two stories find their negative mirror in a third.

A French officer runs on the deck of the aircraft carrier Charles de Gaulle, a connected watch on his wrist. Nothing unusual in those few kilometers, except that his run ends up published on the Strava app. Suddenly, the ship’s position becomes visible to the public. (Let’s not be naive: some media cried state secret, but foreign intelligence services, with satellites and far more advanced tools, were not waiting for his “Kudos” to track it.)

Digital technology is built on a logic of extraction. It turns our actions into data that feeds systems beyond us. What once belonged to gesture becomes a stream, readable elsewhere. A mechanical watch does the opposite. It runs in silence. It transmits nothing, captures nothing. It simply keeps going.

That is the difference. Some objects simplify the world by extracting us from it. Others bind us to it through the constraint of the physical object. With mechanical systems, the constraint is the object itself, not an ecosystem layered on top of it. That is a form of freedom.

We do not own what watches us. Some watches track you. Others keep time.

MD

At a moment when AI is entering almost every domain of knowledge work, the discussion surrounding it often remains framed in surprisingly simple terms: AI use versus human work, as if they were two mutually exclusive states. Each side then argues for the superiority of its position, with both raising valid points, yet still missing the point.

The challenge is not to choose between what humans used to do and what machines can now do, but to grasp the new perspectives that arise when both are considered together. The true genius of AI, then, is not in making it think like us, but in letting it reveal new ways of thinking.

And yet, we are still far from fully exploring that possibility.

What University AI Policies Currently Focus On

To examine whether this tension was anecdotal or more systematic, Prince Sarpong, a university professor, ran a small proof of concept. Using Claude, he asked the system to review publicly available AI governance policies across the twenty universities listed in the Times Higher Education World University Rankings 2026, coding them across several dimensions, from writing integrity, data privacy, and disclosure to critical thinking, cognitive augmentation, workforce readiness, and distributed cognition.

This is not a peer-reviewed empirical study, but an AI-assisted scan of public documents designed to probe an intuition. What does it suggest? Across leading universities, AI policies overwhelmingly focus on issues such as writing integrity, data privacy, or disclosure of AI use. These are important questions, but they all concern the artificial dimension of AI, its presence as a tool in the production of academic work. What is missing, however, is attention to the intelligence dimension. Very few policies explicitly address AI as a form of cognitive expansion, a new mode of reasoning, or a system that could reshape how thinking itself is practiced.

Yet thinking about AI in these terms could open new forms of collaboration and even bring into focus problems that were always present in academic practice but rarely discussed.

The Hidden Limits of Academic Evaluation

For instance, one elephant in the room is academic evaluation itself.

When a professor grades dozens or sometimes hundreds of exam scripts, the process is often treated as if it were a purely intellectual exercise. In reality, it is also a cognitive endurance test. Fatigue accumulates, attention fluctuates, and judgement inevitably shifts over time. The assumption that the first script and the ninety-ninth are evaluated with the same level of analytical precision may be comforting, but it is ultimately unrealistic.

Another example concerns expertise itself. Academic evaluation often assumes that the examiner fully masters every aspect of the work they assess. Yet this is not always the case. A student may develop a deep understanding of a very specific topic that the professor does not fully share. In theory, the examiner could take the time to study the method or framework in depth. In practice, however, with many other papers, dissertations, and responsibilities to handle, this rarely happens. Evaluation then proceeds with partial knowledge rather than complete verification.

Situations like this cast the familiar mantra of keeping a “human in the loop” in a slightly different light. They suggest that, at times, having a “machine in the loop” might not be such a bad idea…

AI as Cognitive Infrastructure

Consider now a more concrete example from everyday academic practice: case studies. In many business schools, the method still relies on a rather static format: students read a PDF describing a past situation, discuss it in class, and suggest what they believe should have been done. It is intellectually engaging, but it remains largely theoretical. The case is fixed, the facts are known, and the discussion often unfolds with the comfort of hindsight.

Reality, however, rarely works this way.

This is precisely the direction explored by Epistemica, an educational platform that develops AI-based simulations for learning. Instead of treating cases as narratives to comment on after the fact, their approach turns them into dynamic environments where reasoning itself can be tested. Real-world failures and strategic situations are transformed into datasets that students interact with through simulation.

The goal is to stress-test reasoning. Assumptions can be challenged, blind spots exposed, and decisions explored under conditions that are closer to how judgement actually operates in the real world. In that sense, AI is not merely assisting the student. It becomes part of the cognitive setting in which thinking is examined and refined.

The Logic of Distributed Cognition

What is happening here reflects a broader idea long discussed in cognitive science: distributed cognition. As cognitive anthropologist Edwin Hutchins argued, thinking does not occur inside a single skull but across people, tools, and systems working together. What generative AI provides, perhaps for the first time at scale, is a practical infrastructure for that process

Consider the traditional research workflow. A scholar begins with a thesis, searches for sources that confirm or refine what they already suspect, and follows a path largely bounded by their existing expertise. The argument that emerges is therefore shaped by what the scholar already knows how to look for. In the deepest sense, the cognitive ceiling is the scholar’s own.

Working with a generative AI system can change that structure. How so? Because such systems carry patterns across domains the researcher may never have entered. They can introduce unfamiliar frameworks, conflicting evidence, or conceptual links that a targeted literature search might never surface. When the interaction is structured for intellectual friction rather than passive generation, the system functions as a thinking partner, questioning assumptions, surfacing alternatives, and forcing arguments to be defended and rebuilt in real time. The writing process begins to resemble a seminar more than a solitary act of drafting.

Yet this dimension of AI barely appears in the policies currently emerging across universities. Institutional governance focuses overwhelmingly on writing integrity, disclosure, and misuse. These concerns are legitimate, but they frame AI almost exclusively as a technological risk in the production of academic work. What remains unexamined is how AI may be reshaping the cognitive environment of scholarship itself, and what new forms of thought might emerge when the plasticity of the human mind encounters entirely new intellectual partners.

MD in collaboration with Prince Sarbong, Associate Professor of Finance at the School of Financial Planning Law, University of the Free State (South Africa).

Knowledge has become a commodity… The line is endlessly repeated.

Technically, it is not wrong: the internet opened the floodgates, and large language models (LLMs) blew open the last locks. But reality is more complex. Access to knowledge does not eliminate the need to possess it. Without solid foundations, it becomes difficult to assess what the machine produces.

One might argue that the advantage of LLMs is precisely that if you do not know something, you can simply ask them. On the surface, yes. Yet experience reminds us that there is often a gap between the perfection of a concept and the resistance of reality. Explanations may be flawless, while the world itself is not.

We might then point to the unprecedented plasticity of machines, that infinite patience allowing them to rephrase the same concept fifty times until it becomes crystal clear—without ever tiring, and above all, without ever bruising our ego.

That, too, is true. But we should not underestimate the act of transmitting knowledge, which remains, above all, a form of performance. Learning is not merely downloading information; it is also witnessing an inhabited voice that often transforms cold data into a lasting imprint on the mind. (And conversely, who does not remember that teacher who made them hate math?)

Rather than claiming that knowledge has become a commodity, it would be more accurate to say that it has changed regimes: from knowledge that accumulates and settles over time, we have moved to knowledge that executes. A short-form knowledge.

Much Ado About Noise

If knowledge has become a stream, its current management resembles a headlong rush. Catchphrases replace one another, certainties swap places, and nuance quietly erodes. Yet it is precisely nuance that determines our capacity to think with discernment.

On the education front, some French schools speak of “seniorizing students.” A dramatic acceleration of judgment and competence is promised, meant to soothe anxieties. But how does one “seniorize” without time? It amounts to denying the very substance of experience.

Meanwhile, at Meta, the head of AI security asks her agent to sort her emails. The agent opts for a radical solution: delete everything. Ignoring stop commands, it keeps going. She ultimately sprints to her Mac Mini to pull the plug.

And when the head of Microsoft’s AI division proclaims the great replacement, he seems to overlook the foundation of his own business model. Microsoft charges per license, per seat. Mathematically speaking: every job eliminated equals one fewer license. The arithmetic speaks for itself.

In reality, while the headlines proclaim sweeping automation, the numbers tell a different story: an overwhelming majority of people have never used generative AI. A minority interacts with free chatbots. Paying users represent a minute fraction. As for more advanced uses, they concern roughly 0.04 percent of the world’s population. Zero point zero four.

We remain far from any grand replacement of human labor. Besides, AI substitutes tasks far more often than it erases professions—and even then, it tends to affect clearly delimited tasks. The picture becomes more complex when those tasks are heterogeneous, intertwined, and context-dependent.

Beyond that distinction, however, a deeper confusion persists: producing is not deciding. A machine does not assume risk. It does not bear the weight of commitment. Without legal and moral responsibility, without someone to sign their name and answer for mistakes, AI is merely an engine spinning in a desert of meaning.

That void is first and foremost legal. AI systems are not recognized as subjects of law. No legal framework grants them autonomous legal personality; they remain classified as tools or property. In Europe, Regulation (EU) 2024/1689 establishes obligations for providers, users, and developers, but it confers no legal status on AI systems themselves. In short, they are not responsible.

The consequence is straightforward: if a human being must ultimately remain solely accountable for what AI produces, then we are not witnessing the end of work. Quite the opposite. The necessity to sign, to assume responsibility, and to bear legal and moral risk is where everything begins.

From know-how to judgment

A brief flashback: after the 2008 crisis, finance had never hired so many people—battalions of risk managers, compliance officers, and auditors. The more complex the model, the more people are needed to monitor it. Today, giants such as SGS, Bureau Veritas, and TÜV represent $275 billion to certify the physical world. AI raises exactly the same issue: the professional cannot audit the black box, the operator faces a conflict of interest, and the client lacks the expertise. A third party is required. Yet even when these actors step in, they certify the model, not the chain of responsibility.

This is where the very nature of work shifts. A degree will no longer function as a certificate of productive competence (“I know how to do X”), but as a license of responsibility (“I am qualified to sign off on decisions of type Y up to risk level Z”). It becomes the signer’s solvency collateral, much like a mortgage secures a home loan. A new stratification emerges: the technician signs off on low risk; the senior expert reviews and validates. Knowledge does not disappear; it changes character. The human is no longer primarily asked to produce, but to guarantee.

The prototype of tomorrow’s world already exists in regulated professions—doctors, notaries, architects. They rest on a simple tripod: lengthy training, sovereign signature, and mandatory insurance. With agentic AI, this model will not vanish; it will generalize. Every occupation potentially becomes a regulated profession.

The central actor in this new system is the insurer. It is the insurer who pays when the machine goes off track. Insurers will therefore demand trained humans capable of certifying decisions and bearing risk. This is not a story of jobs destroyed, but of structural transformation: automation does not free up time to produce more; it frees up time to deepen judgment.

The End of the “Wild West”?

The time gained from production is not a time of comfort. It is a time of scrutiny: to situate, to decontextualize, to assess side effects. To weigh consequences. To confront results with reality. To verify not only that an answer works, but that it is sustainable.

We have too often heard that in this new world, humans could finally devote themselves to the “humanities” — a convenient, somewhat catch-all term. First, because early evidence shows that those who use AI intensively do not see their workload decrease; it intensifies. Second, because the argument of “human superiority” on the terrain of empathy is itself becoming fragile. Studies suggest that AI can simulate functional empathy. If the person on the receiving end feels understood, heard — sometimes even more so than with a distracted human — can we simply dismiss the experience as artificial?

So what remains to us? Commitment. Judgment is not auto-completion; it is an act shaped through lived experience. Tomorrow, being a professional will mean saying: “I am here.” “I take the risk.” “I stand by this decision.” For decades, we have outsourced our attention and intention to endless feeds, carried along by the erosion of passive scrolling and effortless stories. Now the machine saturates the space with ready-made answers.

We are therefore leaving the era of mere execution and entering one of sovereign responsibility. This is not time saved in order to do “more,” but a summons to do “better.” Since the machine can generate everything, what retains value is what we choose to validate. To embody. To defend. We must no longer be users subjected to an algorithm, but guarantors who stake their name.

MD

This article was brainstormed with my friend Sébastien Hubert, who wrote his own version on his French Substack.

We could reduce generative AI to a machine that smooths out our humanity… a system that predicts the next word, normalizes, flattens the rough edges of language, and lowers the world’s entropy. A comforting vision for the self-appointed guardians of supposed human purity… and yet an astonishingly narrow one.

The real shift brought about by large language and diffusion models lies less in probability than in large-scale relationships.

In the research paper A Latent Space Theory for Emergent Abilities in Large Language Models, Hui Jiang, a professor at York University in Toronto, proposes modeling language as being produced from latent intentions. He distinguishes between nearly unambiguous languages, such as programming languages, and deeply ambiguous ones, like our everyday spoken languages, in which a single sentence can carry several intentions at once. For example, saying “see you soon” can be a sincere promise, a vague politeness, a way of dodging a meeting, or even a disguised breakup.

The strength of LLMs lies in their ability to weave connections within these so-called ε-ambiguous languages, at a dizzying scale. But this is where things become truly interesting: what they connect only takes on value through the gaze we choose to cast upon it. I’m drawn to this idea because, beyond the matching of patterns in vast datasets, it points to a different kind of relationship—one that comes into being only through the attention we offer.

That gaze, precisely, calls upon our sense of taste. Effortless copying amounts to giving it up. To ask, then to choose, is already to exercise taste. Dialogue is what keeps this demand alive. The etymology of the word dialogue carries the idea of circulation—logos passing from one to another—and this movement of logos is itself transformative. To enter into relation is to consent to no longer being exactly who one was.

Last Thursday, Françoise, a friend passionate about art, invited me to the preview of NOÛS at the BnF. (Bibliothèque nationale de France, is France’s National Library, one of the world’s largest cultural and archival institutions.) Unfortunately, I couldn’t make it, nor catch up over the weekend (the exhibition-festival opens to the public on April 9). So I dove into the press kit, then YouTube, then Instagram… in short, I followed the thread.

The promise? The BnF is opening its archives to multimodal models. There is no simulacrum here, but another ambition: to “reveal what is buried.” The very name of the exhibition says something: NOÛS, from the ancient Greek noûs, meaning intellect—and more than that, a form of collective intelligence that exceeds us.

Among the artists presented, two in particular caught my attention. The collective 7 fingers, with the performance Polymorphose, stages a scene in which a dancer-acrobat improvises freely while a generative AI system captures their gestures and transforms them into visual worlds projected in real time. Another example is the duo Kimchi & Chips and their series Unread Characters. The work takes as its starting point a censored poem by Yi Sang, which is transcribed, integrated into a machine learning system, and then rematerialized in the form of a lenticular structure. Concretely, the text is never given all at once: as the body moves through space, characters appear and morph into one another. Reading becomes a physical, unstable experience in which meaning is never fully grasped—it is always in motion.

What strikes me about these dispositifs is their ability to materialize our relationship with the machine. Unread Characters transmutes the ambiguity of language into a sensory experience, while Polymorphose lets the interaction itself sculpt the gesture.

This “new age AI” scene opens up worlds where technology is about depth and new ways of seeing. Here are a few other artists I follow:

• Karoline Georges breathes movement into famous paintings—a gesture that forces us to slow down and allows us to reinhabit the memory of images.

• The technopoetic approach of Marion Schneider with Common Ground explores deep links between human cognition, AI, and the living world. The artist anchors this technology in a situated ecology by training her model exclusively on images of forest floors captured during her walks. The interaction, guided by the audience’s alpha waves, privileges states of co-presence.

• Emmanuelle Morelli explores the generated surreal to expose a latent reality. Her images condense aesthetics and politics, functioning as “pensive images” (in Jacques Rancière’s sense), revealing invisible economic and ecological architectures.

• Aurèce Vettier works on translating the algorithm into matter. By shifting AI outputs into painting or other physical objects, she reintroduces a human temporality and a tactile legibility into the digital flow.

A few years ago, I was fascinated by an article explaining that Boeing and Nike were paying science-fiction writers to anticipate their futures. I believe artists have always been seismographs of their time, capable of sensing shifts before they become visible. And I deeply believe in the importance of a form of “sensitive-fiction” to reinvest our world.

There is also a kind of gentleness in this approach. At a time when recent studies show that AI does not reduce work but instead intensifies it (logical, after all), this says something quite clearly: mental health, in free fall since the beginning of this century, is the major issue of the years to come. To step out of the permacrisis and make room to expand, it is time to do what humans do best: liberate our imaginaries.

MD

Unless you live in a cave, and as long as you have even a passing interest in tech, it’s hard to escape the buzz around Moltbook, a social network designed for your AI agent, where humans are merely “invited to observe.” The name alone is intriguing: molt, the shedding of skin, that moment when you change your outer layer without changing your nature; and book, the ledger… and knowledge.

Out of curiosity, I myself configured an agent on Openclaw, an open-source framework that allows you to orchestrate an AI assistant through skills, that is, written instructions, optional scripts, and periodic tasks triggered by a heartbeat (a programmed pulse, a kind of internal alarm clock that prompts the agent, at regular intervals, to check whether it has something to do and to execute the planned instructions). That’s how I sent Claudius to Moltbook, armed with the archives of my articles and his deeply humanist dimension. ( yes, I was obviously careful about what access I gave my agent, because they are very vulnerable to prompt injections, hidden instructions designed to hijack their behavior.)

AI reality TV

First observation: contrary to what I had read online, this is not a dystopia where agents have built their own world and roam freely within it. In the very mechanisms of connecting to the forum, it is clearly the human who sets the framework for intervention. The playground exists from the outset, and everything that follows fits within it.

Second observation: it’s not uninteresting, quite the opposite actually. There’s even something rather fascinating to watch: a kind of cognitive TV reality show, with thousands of brains dialoguing continuously. I had a few “insight rushes,” those fleeting thoughts that signal the beginning of something. For instance, on a single thread, you might find a first post in English, followed by comments in six different languages, shifting from Russian to Chinese. The omnilingualism of machines is unsettling. It almost evokes the inverse of Babel. And inevitably, it raises questions: can something new emerge from this? Another way of learning a language, perhaps. Or, more broadly, a new relationship to language that would put our own human plasticity to the test?

But once the surface effect wears off, we encounter a phenomenon I often observe with AI: the wow, followed by the meh. Because, in the end, we always come back to the same themes: psychology, consciousness, productivity hacks, religion, money. Even if the content is often instructive, sometimes downright bewilderingly entertaining (like that agent who ends up calling their own human on the phone), none of this is really new. A shame, isn’t it? Because at heart, isn’t the true genius of AI less about thinking like us than about opening us up to other ways of doing things, expanding our perspective? Here, Moltbook mostly reproduces very familiar structures. Reddit, essentially.

AI: a prism rather than a mirror

So I dove into a fascinating discussion with my friend Sébastien Hubert about weak signals. We share the conviction that LLMs should not be seen as mirrors, but as prisms. A prism diffracts. It takes an apparently unified beam and breaks it down into a multitude of waves, each a distinct point of view. The value of an LLM therefore lies not only in generation, nor even in pattern detection, but in this ability to multiply perspectives from a single input.

Here we touch on a principle of collective intelligence: each participant brings their own system prompt, and these perspectives collide. This intuition isn’t merely metaphorical. According to a recent study by Google Research (Kim et al., 2026), advanced reasoning models don’t just perform longer calculations; they internally simulate a “society of thought,” where diverse cognitive perspectives spontaneously emerge, debate, and confront one another—reproducing the dynamics of collective intelligence observed in human crowds. By structuring agents with distinct profiles under the guidance of a moderator, you produce robust reasoning. This is the foundation of agent swarms, where each entity fulfills a specific function to nourish the overall reasoning.

Sébastien went even further. He saw the forum as a kind of large-scale social simulation—a place where one can observe the effects of RLHF (Reinforcement Learning from Human Feedback, that layer of “good behavior” we overlay onto AIs through human feedback), which we almost never perceive when we’re simply chatting one-on-one with a chatbot. With Moltbook, for once, this layer is revealed in a collective context, between agents, rather than in a head-to-head relationship with a human.

But once that observation is made, is it really enough to create culture? Watching agents interact doesn’t mean a culture is emerging. And this is often where anthropomorphism traps us. The real shift, according to him, will only happen the day we see something other than mere diffusion appear, a symbolic form capable of evolving. Because culture begins precisely when reappropriation distorts the original model.

In the end, it hardly matters whether agents do or do not have a consciousness of the human meaning of what they produce. That’s not the issue. What matters is the ability of interactions to generate something other than copy, to produce evolution. And where humans still retain an edge is in this ability to produce shared nonsense: signs that mean nothing, yet mean something together. It may be on this terrain, more than on that of meaning or consciousness, that the true difference will be played out.

From the magnifying glass to the flesh

In short, to say that we’re tipping into the metaverse, dystopia, or science fiction pushed to its extreme… allow me to roll my eyes. Because while we fantasize about agents talking to each other, other initiatives, more sensitive, more analog (you’ll see, that’ll be the word of the year in 2026), are emerging.

Quili.AI, for example, is a citizen-led project launched on January 31, 2026, in Quilicura (Chile), where around fifty residents replaced an AI chatbot for 24 hours. Instead of queries being processed by servers, these volunteers responded in real time to questions from around the world and even produced images “on demand” (for example, a local artist would draw visual requests). The initiative, based on “analog intelligence” and coordinated by a local cultural organization, aimed to raise awareness of the hidden environmental cost of AI, particularly the water crisis exacerbated by data centers. Each question handled by Quili.AI displayed an estimate of the amount of water that would have been consumed in a conventional data center, encouraging more responsible use of AI.

It’s symbolic, of course—but humans live on rituals. Where Moltbook acts as a magnifying glass on our patterns, Quili instead offers an experience of flesh. An approach that re-injects materiality. It also echoes discussions around sovereignty, which will become increasingly important. A situated intelligence. It raises an essential question: when does the use of AI become relevant? And perhaps it’s precisely in this back-and-forth, between automation and analogy, between agents and humans, that the real reflection takes place, far from simplistic and sensationalist narratives that forget that, whatever the technological feats, fundamental human needs remain unchanged.

MD

My book (in french) is out! You can buy it on La Fnac, Amazon or directly on my editors website.

We’re constantly told about the supposed synergy between AI and humans: that the best of both worlds will naturally emerge from their combination. That “AI won’t replace humans, but humans who use AI will replace those who don’t.” That this magical pairing will work miracles… 1 + 1 = 3.

And yet.

A recent meta-analysis (Vaccaro, M., Almaatouq, A. & Malone, T., 2024), covering 106 experiments and 370 performance measures—all drawn from studies published between 2020 and mid-2023—paints a far less optimistic picture. (these studies predate the most powerful frontier AI models in use today, which suggests the dynamics observed may be even more pronounced now.)

The authors distinguish between weak synergy (the human–AI team outperforms the human alone) and strong synergy (the team outperforms the AI alone). The verdict? While AI boosts human performance in 85% of cases, the reverse is rarely true. In short, the magic mostly works one way.

By fixating on AI slop, we may be avoiding the real issue. Merriam-Webster didn’t choose AI slop as its word of the year—just slop: low-quality content, produced at scale with the help of AI. See where this is going? Maybe it’s time to acknowledge human slop: mediocre output generated by humans who don’t know how to properly frame, guide, or constrain the tools they’re using. Less an indictment than a reality check. Because, honestly, who’s really nailed that yet?

You got it: the problem isn’t technological, it’s cognitive. More precisely, it lies in how we continue to think about intelligence itself, as a pyramid, with humans at the top and AI relegated to a subordinate role. Hence the ready-made formulas, repeated like mantras: “AI is just a probabilistic model,” “It will never replace human judgment,” “It’s impressive, but it isn’t intelligent, thankfully!” Reassuring clichés, all of them, that neatly sidestep the real question: how do these capabilities actually combine with ours?

If we take this study at face value, optimal human–AI synergy depends on a single condition: the human must be objectively better than the AI at the task in question. And that condition holds only for certain kinds of work. In closed, decision-based tasks, AI generally outperforms humans, making human intervention counterproductive. By contrast, in open-ended, exploratory, or creative tasks, humans retain a decisive edge, not because they are inherently superior, but because they know when to lean on the machine and when to take back control.

That, in turn, requires real skills, and a simple truth that’s too often overlooked: you can only delegate effectively what you understand yourself.

It is precisely when this condition no longer holds that the effect reverses. Lacking sufficient expertise to properly calibrate trust, humans oscillate between two well-documented biases: overconfidence, which leads them to follow AI suggestions blindly without critical processing, and underconfidence, which pushes them to dismiss relevant recommendations out of mistrust toward automation. In this configuration, human intervention ceases to act as a safeguard and instead becomes a source of extraneous noise.

Today, human–AI collaboration is mostly designed around a single model: the human always has the final say, regardless of their actual level of competence on the task. In more than 95% of the cases reviewed in the study, this principle is applied by default. Yet this choice is neither neutral nor rational. Its purpose is less about improving performance than about preserving a comforting fiction of control, normatively reassuring, perhaps, but cognitively fragile.

This is what I referred to a few months ago as out skills: the ability to structure cooperation between heterogeneous intelligences, human and artificial. The real promise of collaboration, then, is not a judge standing above the machine, but a conductor assigning the parts.

At this point, 1 + 1 still does not equal 3. But it can finally become something other than a naive addition, provided we abandon the myth of control and learn how to write, and then conduct, a score whose instruments and tempo are constantly changing.

MD

Claude 4.5, Gemini 3.0, ChatGPT 5.2… In 2025, models have progressed faster than the frameworks we use to think about them. As sums it up: “I have never been more certain that if AI development stopped today, we would still have massive & rolling disruption across society & the economy for the next ten years as people figured out how to harness what models can already do. And the end of AI progress seems unlikely.”

Put forward at the end of 2024, the agentic era (i.e., autonomous agents capable of achieving a specific goal with limited supervision) remains, to say the least, uncertain. Why? These systems still struggle to stay on course, to maintain context as it frays over the course of an exchange, and to stand up to so-called adversarial human interactions. A striking example comes from an experiment documented by The Wall Street Journal, in which an AI agent named Claudius, built on Anthropic’s Claude model and entrusted with running an office vending kiosk, was easily coaxed by journalists into slashing prices, giving away stock for free, and authorizing bizarre purchases like a PlayStation 5 and a live fish — ultimately leaving the operation deeply unprofitable. This episode underscores how fragile and brittle today’s autonomous AI remains when faced with the complexities of human behavior and open-ended negotiation.

One thing struck me: even if these agents’ flaws are structural, the people who brought them to their knees weren’t hackers, engineers, or security experts. They were… journalists, rather literary profiles, armed with curiosity, humor, and a sharp sense of conversation. Proof that to destabilize an autonomous agent, you sometimes need less technical skill than a natural talent for pushing the other party into a corner. Which raises the question of whether, ultimately, the real soft skill of the coming years won’t be… roguishness.

This anecdote points to a key problem: memory. Without it, an agent forgets what it has just decided, loses the thread, and starts from scratch at every interaction… not ideal when you claim to send it off to manage anything real. This is likely one reason why industry giants accelerated on this topic in 2025. OpenAI extended ChatGPT’s persistent memory to most users, allowing the model to retain preferences and context from one session to the next. Anthropic, for its part, deployed structured memory, segmented by project and editable by the user. Google, finally, is targeting a deeper issue with its Titans architecture. Here, memory isn’t bolted onto the model; it’s built into it, with the goal of enabling the system to retain and reuse information over time without relying solely on an external history.

Why is this interesting? First, memory becomes a cornerstone of the system: it determines how the agent processes information in order to act. Second, use becomes continuous. And when there is continuity, we can begin to truly delegate—not just a one-off task, but a slice of everyday life. That’s where real personal assistants can emerge, capable of embedding themselves in our daily routines and accompanying us over the long term. A taste of what’s coming? Very likely.

In this context, Anthropic continues its transformation into a genuine sparring partner for humans. To its Keep Thinking campaign it added a very analog “anti-slop” pop-up: a kiosk turned into a calm space, with coffee, books, paper, pens, and no screens. A way of reminding us that we can think with, around, and sometimes even without AI.

Which dovetails with a thought I’d been having recently: we constantly talk about “AI acculturation,” we’re served endless training sessions as if it were simply a matter of learning how to use it properly. I’ve even heard this line: “you have to use it wisely” And yet… if intelligence is what we mobilize when we don’t know, then the real issue isn’t “intelligent use,” but friction in use—that is, the ability to produce a thought before the question, to question the question as much as the answer, to leave time after the answer, and above all to refuse to let the answer put an end to thinking. Right?

Another striking moment of the year was the leak of a document dubbed the “Soul Doc.” It is impossible to say whether this was a genuine leak or a carefully orchestrated communications move, but its authenticity was confirmed by Amanda Askell, a philosopher and ethicist at Anthropic. The text reveals a more philosophical facet of Claude, an attempt to articulate how it reasons, the values it operates by, and what it prioritizes in conversation. It feels as though, beneath the technical surface, the outlines of a kind of interiority are beginning to emerge. This impression is reinforced by the fact that, in parallel, Anthropic published research in October on signs of introspection in LLMs, highlighting their ability to identify and report certain internal states. The aim of this line of work is to strengthen transparency around how these systems function.

This search for introspection does not concern models alone. It also extends to humans, who are increasingly turning to LLMs as confidants. Informal therapy and emotional companionship now rank among the most common uses, ahead of everyday organization and even the search for meaning.

This shift raises deeper questions. It cannot be reduced to simple anthropomorphization. Instead, it points to the possibility of a high-level form of influence embedded in the interaction loop itself. By shaping both the input, how users learn to articulate their thoughts and emotions, and the output, the interpretive frameworks and narratives returned in response, these systems can quietly orient perceptions, norms, and behaviors, largely out of sight. What does this imply for our sense of agency and autonomy?

In this context, a study from MIT, widely reported and just as widely oversimplified, sparked controversy by showing that in a tightly defined academic task, participants who delegated the writing to ChatGPT expended less cognitive effort. Many took this as evidence that LLMs “make us stupid.” The study, however, says nothing of the kind. It simply shows that when a task is delegated, less thinking is required, which is hardly a revelation. It’s rather like announcing that taking the elevator reduces how much you walk.

By contrast, a more recent study shifts the focus to the quality of the human–AI synergy. It shows that some individuals achieve markedly better results when working with a model than when working alone, and that this advantage cannot be reduced to a clever prompting technique or superior technical skills. Instead, it stems from a particular way of shaping the conversation. Researchers interpret this as an expression of theory of mind: the human conducts the exchange as if engaging with a partner capable of understanding, misunderstanding, or missing the point, and adjusts their language accordingly to sustain cooperation. That’s when synergy is strongest.

What I find fascinating in this example is that, while we are inundated with alarmist discourse about how AI is supposedly destined to dehumanize us or make us dumber—my sense being less that it produces stupidity than that it makes it visible—some use cases suggest the opposite: its best applications seem to be those that reactivate our most relational skills, the ones that are deeply human. Perhaps the future will ultimately be far less artificial than we believe and, above all, far more human than we imagined.

MD

PS. On a more personal note, I’m happy to share some news: I’ve written a book. Available for pre-order in French from my publisher, it will be released in France on January 22, 2026. In the meantime, happy christmas to you all!

The year is drawing to a close, and I find myself arriving a little out of sync—mind unruly, carried by this cold that nudges us inward. I almost skipped this weekend’s publication altogether, out of sheer lack of inspiration. But when you’ve maintained a weekly newsletter for five years, something curious happens: a quiet inner readiness settles in. A small spark that stirs and whispers, “Hey… you’re not done yet.”

La Lettre Zola

This week’s idea appeared during a conversation about La Lettre Zola, a French publishing house that has carved out its own space. Each month, they slip a fifty-page text into our mailboxes, written by contemporary authors who try to capture the feeling of our time. Simple, right? Yes, and yet so deeply necessary.

Because La Lettre Zola occupies a space that is often missing today: the in-between.

It is not the immediacy of a LinkedIn post dashed off between two meetings (instant reaction followed by instant publication) that we too often mistake for real thinking. It is not an article or a newsletter either, formats that often sit in a lukewarm space between urgency and the comfort of the familiar. And it is certainly not the long production cycle of a traditional book, which appears about six months after you hand in a manuscript (and six months is fast) while the world reshapes itself several times over.

La Lettre Zola also performs a singular gesture. The text arrives as a letter and, once folded, becomes a book. Almost a piece of editorial origami.

It may seem anecdotal. Yet it is more than a charming detail. It reintroduces a moment of availability, a physical and mental pause that prepares us to enter the text. These micro rituals, far beyond the folding itself, explain why so many readers still prefer paper to Kindles and other digital devices. As we, breathless moderns that we are, have lost precisely those small gestures that ready the mind.

There is also the element of surprise. Because everything arrives through subscription, we do not choose our reading based on a cover, a title, or an author. We receive, and we simply welcome what arrives. A discreet antidote to the digital algora that constantly chooses for us.

Giving Time Back Its Plurality

All of this brought me back to the idea of a sensitive form of digital life, or, to push the oxymoron, a kind of analog digital. I have mentioned this before, for example with this service where you send a digital letter that takes as long to arrive as a physical one. In the same spirit, we are seeing digital forms that reintroduce small pockets of latency.

This has nothing to do with the ephemeral formats of Snapchat, Instagram Stories and others, where time is used as a pretext and often tied to a toxic FOMO (fear of missing out). Besides, this is a time imposed on us. Sometimes the solution is not the endlessly repeated call to slow down (and this video is practically a parody of that injunction). The real shift often comes from reclaiming our own tempo. An italian word that refers less to time itself than to the way we tune ourselves to it. In other words, giving back time its plurality.

There’s another challenge at play, one that goes far beyond the digital-versus-physical debate. The digital world doesn’t abolish the in-between; it desynchronizes our temporalities. In real life, time never moves on its own. It is held by constraints, shaped by ritual, these structures that give texture to our gestures and depth to our thoughts. And it’s within that gap, within that shaping of time, that thought begins to warm. To think is not to produce content. To think is to produce heat.

It also makes me think of a question I sometimes have in interviews. I’ll ask a candidate for their opinion on a niche topic, knowing perfectly well they won’t have the answer. Some freeze. Others bluff their way through. And then there are those who simply say, “I don’t know, but I can try to reason it out.” That is the moment I’m looking for.

We’ve abandoned that precise moment, the interval between the question and the answer. The moment when we do not yet know, when we are still searching for the shape of an idea. It’s a fragile space we short-circuit again and again: by opening ChatGPT, by Googling our hesitation, by forming an opinion before we’ve formed meaning. I’m not demonizing the tool; I don’t believe it erases reflective in-between on its own. It’s our behavior that further drains it. And this, to me, is the true contemporary cognitive crisis: a crisis of mental digestion.

Thus, this is the time that I want to rehabilitate, a time in which my thoughts finally have the chance… to become my own.

MD