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There is a unique profession — born in Paris in the 1960s — that has the distinctive characteristic of making a business out of intuition: trend bureaus, more commonly known as trend forecasting agencies. They create value by anticipating, detecting, and analyzing what the next seasons will bring.

To better understand how intuition has, in some ways, become a profession, I interviewed Meryem Laghmari, Doctor of Anthropology and Foresight Consultant. Meryem conducted in-depth research on Parisian trend bureaus to understand what underpins the anticipation of the future (the subject of her thesis, defended in 2025).

Trend bureaus offer “a kind of synthesis between the intuitions of creative actors and broader socio-economic currents, in order to guide consumer attitudes in upcoming fashion collections” (Caumon, 2005). To communicate their analyses to client companies — luxury manufacturers, ready-to-wear brands, fashion press, and others — Trend bureaus translate these early signals into a “trend book,” in printed or digital form. Developed two years before the season, this document announces, across various themes, images, colors, and textiles, what will constitute future fashion in terms of couture design, lifestyle, or environmental design.

R: Trend bureaus are actors who inspire designers and shape fashion — would you say this is also a profession that trades in pure intuition?

M: I would say that my ethnographic research in a trend bureau demonstrates precisely the opposite. This profession is not founded on pure intuition, nor on some quasi-prophetic gift, even if trend bureaus have long been surrounded by an aura of opacity and are regarded as “oracles” or “gurus” of the future within the fashion industry. What the research reveals is that anticipation rests above all on an experimental and collective know-how — not solely on individual illumination. By conducting two field studies within Parisian trend bureaus, I was able to observe that trend forecasting stems from rigorous work: collecting, sorting, adjusting, and validating visual material.

But something decisive must be added: this work does not begin by reducing uncertainty. On the contrary, within these future-oriented structures, the process of projection requires embracing uncertainty in order for intuitions to emerge. In other words, trend forecasters — stylists, iconographers, foresight specialists — must accept a state of suspension, openness, and provisional loss of control over the future, because it is precisely in this liminal space that future trends can emerge. Faced with uncertainty, societies establish rituals to create social order — and this is exactly what one finds in the organization of meetings within trend bureaus. They therefore do not sell raw intuition, but rather intuition made possible by the acceptance of uncertainty, which represents that time between the past season and the future season, and which renews and regenerates rather than predetermines. This same intuition is later transformed into material, converted into signs (socio-cultural facts), then into scenarios and trend books sold to clients at textile trade fairs.

R: Who are these bureaus and how do they operate, for readers less familiar with this world?

M: Trend bureaus are actors positioned upstream of industrial production. Among the best-known today are Peclers Paris, Carlin, Trend Union, and Nelly Rody, for example. Since the 1960s, they have been detecting and analyzing the future needs and desires of consumers — often two to seven years in advance — and translating this into trend books intended for companies in fashion, luxury, beauty, design, and other creative sectors, to guide them in developing their own product collections. The macro-trends proposed in these creative anticipation guides allow companies to develop products and services that best target what consumers are seeking.

R: What distinction would you draw between trends coming from trend bureaus and those produced by market research institutes?

M: The main difference lies in the standard of proof and the relationship to uncertainty. Research institutes tend to reduce uncertainty through statistical measurement of trends, surveys, quantitative data, declared opinions, and macro indicators.

Trend bureaus, on the other hand, work on a future that is still unstable, diffuse — in short, emergent — through multiple weak signals. They cannot wait for everything to stabilize before producing a finished analysis. They must therefore operate in a space where meaning is not yet fixed, rather than striving toward definitive certainty.

Where a research institute tends to document the probable from raw data, the trend bureau renders intelligible the emergent plausible. It works with intangible elements: signs, atmospheres, social movements, barely visible cultural shifts. And to do so, it must first accept uncertainty as its working frame — not as a parameter to immediately control.

R: How do trend bureaus collect their information? What are their production rituals, and what role does intuition play?

M: This is one of the central points of my research. The process of building the trends for an upcoming season begins with an introspective phase in which each trend forecaster attempts to follow their own intuition, as well as with specific meetings — notably the Theme Range meeting — where teams present their ideas in the form of moodboards.

But what is fundamental is that this process first requires embracing uncertainty. Throughout this process, I clearly observed that teams do not seek to close down ambiguity too quickly: on the contrary, they must accept it as the very condition that makes intuition possible. In this sense, the creatives embrace uncertainty in a sincere and subjective approach that empirically generates an infinite range of potential future trends.

Uncertainty thus becomes a resource, a “technology” — a concept that anthropologists Sarah Pink and Yoko Akama (2018) have developed to demonstrate how it generates a multiplicity of possibilities. Uncertainty must therefore be recognized and accepted as a necessary step toward the emergence of intuitions in the process of projecting future trends.

Following this, these intuitions are progressively woven together to form the future fabric of the coming season’s trends, built from concrete elements: “weak signals” (socio-cultural facts) identified through a process of post-rationalization carried out by the foresight and styling teams.

It is precisely at this moment that internal, affective, still-diffuse material transitions into something more external, more stabilized, and more rational — material that will form the basis of the trend scenarios assembled in the trend books. Intuition is neither denied nor sacralized: it is stimulated, shared, and transformed into a substance conducive to the emergence of trends.

And crucially, these bureaus do not produce only inspirational content: they construct genuine material and physical interfaces of the future, allowing clients to see, touch, read, imagine — and ultimately project themselves into these plausible futures in the present. This is what gives them their unique place in the industry.

R: In what ways is this similar to or different from your work as an anthropologist?

M: It is similar in that both involve attentiveness to detail, to practices, to weak signals, to things that are still incompletely articulated. The anthropologist and the trend forecaster alike must learn to remain in contact with realities that are ambiguous, shifting, and sometimes contradictory.

Indeed, anthropologist Sophie Caratini (2012) advises trusting one’s emotional responses in the field — and during the analysis of data as well — just as the trend forecaster does throughout their introspective and objective research.

But the major difference lies in the purpose. Anthropology seeks first to understand and render the complexity of reality, whereas the trend forecaster must project, orient, bring things to convergence, and ultimately render these potential realities actionable. In practice, this manifests as the transition from a knowledge that is still tacit and experimental toward a more rational apparatus designed to guide client decisions.

R: When iconographers speak of “feeling” that an image fits the theme, how does this manifest physically?

M: In the course of my research, I observed that this refers to an embodied perceptual knowledge, yet latent, meaning they are not fully conscious of it. By observing trend forecasting in practice, I identified the existence of a dual iconographic knowledge, combining both “knowing how to see” and “knowing how to read.” An image is not judged solely for its explicit content; it is evaluated for its rightness, its vibration, its intensity, its capacity to hold together with others within a narrative and visual ensemble whose sum proposes a new trend scenario.

Physically, this translates into listening to one’s affect, which gives an impression of evidence, resonance, or catch — or conversely, a resistance, an unease, a feeling that “it doesn’t hold” — verbalized through vocabulary that is always of an emotional register. But this competency is not natural in a naive sense: it is built through work, through repeated exposure to images, through collective exchange, and through the capacity to remain sufficiently open to uncertainty so that an image can still bring something new to the surface — rather than illustrating too quickly a meaning already fixed, or reducing uncertainty prematurely rather than working with it productively.

R: What is the share of curation, creation, and association in building a trend book? Do forecasters seek signals already present in the world, or do they invent the trend?

M: I would say they do both — but we must move beyond this overly limiting opposition. Trend forecasters do start from elements already present in the social world — signs, ideas, atmospheres, cultural references — but they do not collect them as already-constituted facts. They bring them into collision within a space of assumed uncertainty, where these elements can be brought together, renamed, hierarchized, and scripted anew.

A key characteristic of these collected elements is their novelty: whether signs or images, the material must be as recent as possible (less than six months old), which imposes continuous updating and circumscribes the field of exploration to the emergent.

Through the process of post-rationalizing intuitions into signs, the trend is therefore neither pure invention nor simple discovery — it is in a state of gestation. It is co-produced from a diffuse already-there, in a process that begins with the acceptance of indetermination, which then allows exploration of its infinite possibilities.

R: What are the processes through which collective intuition emerges?

M: Within the trend bureau, collective intuition emerges at the beginning of the season in what I call a space of collective receptivity — these Theme Range meetings — and then in a kind of shared metamorphosis during the experimentation with intuitions. This means that intuitions only become operative once they are shared, discussed, brought into proximity, and transformed by the group. The collective does not simply serve to add up ideas; it serves to bring something new to the surface from dispersed perceptions.

R: What developments do you see in this profession with the arrival of AI?

M: AI can certainly accelerate trend monitoring, collection, iconographic research, documentary synthesis, and preliminary associations. But what my research highlights is that the heart of this profession is not merely the accumulation of information. It lies in the capacity of human beings to explore the generative potential of uncertainty, and then to transform that diffuse material into shareable forms: signs, scenarios, books, and dissemination devices.

In this sense, AI can help identify, sort, and combine. It is harder to replace what I analyze as the capacity to stimulate and share one’s intuition. The more tools automate collection, the more human value shifts toward contextual sensitivity, cultural reading, situated judgment, and the ability to let a new prediction emerge without crushing it too early under the weight of a logic of certainty.

R: If you had to highlight one point in conclusion about the future?

M: The central point of my research is this: trend bureaus do not anticipate by first eliminating uncertainty. On the contrary, they must embrace it, inhabit it, and make it a creative medium — so that intuitions propitious to new possible futures can emerge. Only then are these intuitions progressively reconnected, post-rationalized into signs, scripted, and concretized in trend books, trade fairs, and platforms.

Anticipation is therefore not a victory over the uncertain: it is a method for exploring its generative and creative potential.

In my view, the opposition between the future and uncertainty is misplaced — because it is within uncertainty itself that the future can exist, and that we can generate an infinity of possibilities.

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References:

1. Akama, Y., Pink, S., & Sumartojo, S. Uncertainty and Possibility: New Approaches to Future Making in Design Anthropology (1st ed.). Routledge, 2018

2. ASPERS Patrik et GODART Frédéric, « Sociology of fashion : Order and Change », Annual Review of Sociology, 39, 2013

3. BARTHES Roland, Le bleu est à la mode cette année (1re édition 1960), Paris, Institut Français de la Mode, 2002

4. CARATINI Sophie, Les non-dits de l’anthropologie, Éd. Thierry Marchaisse, coll. « Les non-dits », 2012

5. CAUMON Céline, Cahiers de tendances : identification et expression commune de la couleur, Doctorat d’Art appliqués, université de Toulouse 2, sous la direction de Guy Lecerf, Editions Universitaires Européennes, 2005

6. ERNER Guillaume, La mode des tendances, Paris, PUF, 2011

7. FLÜGEL John Carl, The Psychology of Clothes, London, Hogarth Press, 1930

8. GODART Frédéric, Sociologie de la mode, Paris, La Découverte, 2010

9. MONNEYRON Frédéric, Sociologie de la mode, Paris, PUF, « Que sais-je ? », 2006

Publications:

1. Meryem Laghmari, « En quête d’une tendance de mode dans un bureau de style parisien », Terrains/Théories [En ligne], 8 | 2018, mis en ligne le 07 novembre 2018, URL : http://journals.openedition.org/teth/1189 ;

2. Hur, E., Hemingray, C., & Westland, S. (Eds.).. Fashion Trends and Forecasting: The Fashion Futurists’ Toolkit (1st ed.). Routledge, 2025 https://doi.org/10.4324/9781003415589

3. Laghmari, M., & Hur, E. (2025). Revealing the sociocultural approach used by trend forecasting agencies in Paris: An anthropological study. In E. Hur, C. Hemingray, & S. Westland (Eds.), Fashion Trends and Forecasting: The Fashion Futurists’ Toolkit (pp. 27–46). Routledge. Revealing the sociocultural approach used by trend forecasting agencie

4. Hur, E., Watson, C., & Laghmari, M. (2025). Forecasting industry practices: Fashion futurists’ careers. In E. Hur, C. Hemingray, & S. Westland (Eds.), Fashion Trends and Forecasting: The Fashion Futurists’ Toolkit (pp. 229–246). Routledge. Forecasting industry practices | 14 | Fashion futurists careers | Eun

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In part 1 of 3 and part 2 of 3, we traced how intelligence expanded through six evolutionary learning loops — from reflexive reactions to symbolic externalization. Each loop extended our capacity to anticipate, simulate, learn from others, and accumulate knowledge across generations. Together, they form a nested architecture of prediction, constantly interacting to shape our decisions and our sense of self.

You opened ChatGPT this morning to refine a communication strategy. After three exchanges, the AI proposed an angle you hadn’t thought of. You reformulated, it bounced back, you specified. After ten minutes, you held an approach that was neither your initial one nor that of the algorithm, but something emergent, born from dialogue.

What just happened is perhaps as significant as the invention of writing 5000 years ago. We are crossing an evolutionary threshold: the externalization not only of memory, but of simulation itself.

In the first article, we saw how six learning loops nested over 3 billion years to build our intelligence. We will now explore how artificial intelligence could constitute a seventh loop - and what this fundamentally changes about our way of thinking.

Remember what the sixth loop does?

The symbolic loop (language, writing, tools) permitted us to externalize information. A book stores knowledge outside your brain. A geographic map externalizes spatial representation. A calculator discharges your mind from calculations.

But these tools remained passive. A book doesn’t react to your questions. A map doesn’t propose alternative routes based on your mood. A calculator doesn’t suggest better formulas.

AI radically changes the game: it doesn’t just store or calculate. It simulates, explores, and interacts.

Towards A Seventh Loop: Piloting Externalized Simulations

Artificial intelligence permits us to externalize three capacities that were until now confined to our brains:

• Simulation of world models (loop 4) - AI can generate complex representations of situations, test scenarios

• Trial-and-error optimization (loop 3) - AI can explore millions of combinations to find optimal solutions

• Associative learning (loop 2) - AI detects patterns in data volumes impossible to process manually

This externalization potentially constitutes a seventh loop qualitatively different from previous ones. Humans no longer act only as decision-makers, but as animators of world simulations destined to guide their choices and discoveries.

What Fundamentally Changes?

1. Massive Externalization Of Simulation

Your brain can generate a few alternative scenarios before making a decision. An AI can generate millions in a few seconds, explore counterintuitive solution spaces you would never have envisioned.

This is no longer just an aid to calculation or memory, but an extension of imaginative and exploratory capacity, with possibilities to simulate an increasingly realistic reality - images, music, videos, complex scenarios.

The crucial challenge: we’ll need to invent a codification of spaces to know where we’re evolving, and especially re-anchor our actions in the real world to not lose footing in a purely virtual world.

Retro-modeling the consequences of past choices permits updating simulation models and learning from reality. The field must always remain in the loop.

2. The Algorithmic Feedback Loop

Unlike previous (passive) tools, AI creates a recursive loop: your choices train the AI, which modifies your future options, which influence your following choices.

This dialogue dynamic is co-evolutionary. It’s unprecedented.

It can either progressively refine the quality of your results or lock you in an increasingly narrow filter bubble, like recommendation algorithms that only show you what confirms your preferences.

It’s up to us to inject the random and evolutionary variations that will avoid the crash. We’ll need other loops of reality re-entry, of the sensorial, and competition mechanisms to avoid runaway, like that of cancer cells that escape regulatory signals.

Real-time actualization of algorithms remains the next frontier.

3. Distributed Cognition

The learning unit is no longer the individual alone, but the hybrid human-AI system.

Knowledge emerges from conversational loops. AI absorbs repetitive and intensive tasks, freeing the brain for what it does best: judging relevance, asking the right questions, and making creative connections between distant domains.

But human judgments are anchored in cultural substrates and past learning. We’ll need to maintain cognitive and educational effort as a guarantee of evaluating AI results.

The Irreplaceable Role Of Intuition

Beyond defined tasks and univocal problems - which tomorrow will all be handled by AI - there exists an incommensurable quantity of fuzzy problems, without right or wrong answers, and improvisational needs in highly uncertain environments. Engagement (skin in the game) will remain the prerogative of humans unafraid to say, “I don’t yet know how this will go, but I’m going for it.”

Human judgment will remain critical to sense whether a strategy, even when elegantly explained, has the slightest chance in the field. Practice is made of thousands of errors and micro-decisions that help the entrepreneur project the chances of a theoretical construction in real life. Because, unlike the machine, they must assume responsibilities linked to their choices.

In an optimistic scenario, I wager that intuition could resume an essential role.

It’s in a way the attentional mechanism guided by our survival instinct. It helps us make bets on scenarios to explore and models to revise. Already today, it weighs our cognitive investment choices.

Tomorrow, it could help AI agents navigate possible futures and help us choose desirable directions in the present.

Provided the human always knows how to sense what is desirable for them.

The Challenges Of The Seventh Loop

The Educational Challenge

The speed of theory evolution isn’t that of society's evolution. We’ll need to recreate dialogue spaces where AI and communities exchange to evolve cultures and educational structures. Many questions remain unsolved: where AI should be integrated, what roles and responsibilities it should have in education. We are only now discovering the harmful effects of social media, so we must evaluate the choices we make with AI.

The risk of predation by the tool and mind control by a social group remains possible. It is easy to poison the training of an LLM by feeding it texts riddled with fake news. Their widespread everyday use makes them dangerous vehicles for propaganda. And the allegiance of tech giants to malicious political forces will make Cambridge Analytica look like a minor incident.

But the reinvention of education is only beginning, and the possibilities of access to knowledge as an antidote are immense.

“Where danger grows, so grows that which saves,” said German Romantic poet Friedrich Hölderlin.

The Energetic Challenge

We’ve seen it: each evolutionary loop consumes more energy than the previous one. Evolution has always arbitrated between predictive precision and energetic efficiency.

The seventh loop poses this challenge at an unprecedented scale. The server farms that feed AI consume colossal quantities of energy. This arbitration between computing power and energetic cost becomes a civilizational issue.

The Challenge Of Reality Disconnection

The major limit of the symbolic loop (loop 6) was reality disconnection - the capacity of symbols to propagate autonomously, creating belief systems disconnected from any empirical verification.

With AI, this risk amplifies exponentially. Models can generate hyper-realistic but entirely fictitious content. Simulations can become so sophisticated that they substitute for direct experience.

Reality must remain in the loop. This supposes constant mechanisms of verification, re-entry loops, confrontation with the field, sensorial and bodily anchoring. Some useful approaches are presented later under the heading, “Our Collective Responsibility.”

Human Innovation As Navigation Between Perspectives

Human innovation hasn’t only consisted in accumulating knowledge, but in knowing how to regularly question it, to change perspective, to play with referentials to resolve paradoxes, to navigate between scales of analysis - from microscopic to systemic - to satisfy our various mental models.

AI will perhaps facilitate this navigation if it doesn’t freeze the past but knows sometimes to free itself from it. It’s our intuition that will permit us to connect distant points in knowledge.

It’s by translating between different languages and thought frameworks that phenomena today are studied by various disciplines and cultures that we’ll know how to capitalize on their common teachings, and cross a new evolutionary step.

Because AI will transform our cognition, whether we want it or not.

The Worst is Always Possible, But the Future isn’t Written

Two trajectories currently take shape.

Trajectory 1: Cognitive Weakening

The risk of progressive weakening of cognitive effort and intellectual autonomy through dependence on potentially biased algorithmic syntheses.

In this scenario, we increasingly delegate our judgment capacity to systems we no longer understand. We progressively lose the capacity to think for ourselves, to verify, to question.

Loops 1 to 6 atrophy for lack of use. We become passive consumers of simulations generated by others.

Trajectory 2: Cognitive Amplification

Conversely, an amplification of our capacity to connect distant knowledge, to navigate between disciplines and scales of analysis - a transdisciplinarity once reserved for a few exceptional profiles, now potentially accessible at large scale.

In this scenario, AI becomes a tool of exploration and discovery. It frees us from repetitive tasks to permit us to concentrate on what makes our humanity: creativity, intuition, ethical judgment, and the capacity to connect apparently unrelated domains.

Loops 1 to 6 remain active and mutually enrich each other. The seventh loop amplifies them without replacing them.

Our Collective Responsibility

The real issue, therefore, isn’t AI itself but the way we’ll build feedback loops with it.

Like the six previous loops, the seventh won’t replace the others - it will be added to them. Our collective responsibility is to design it in a way that preserves and amplifies human intelligence and its native coupling with the world from which it emerged.

This involves several things:

• Maintaining bodily and sensorial anchoring. The first six loops all arose from coupling between an organism and its physical environment. The body remains the foundation of our intelligence - including our intuition. Delegating all simulation to AI without keeping in contact with reality would be like cutting a tree’s roots.

• Cultivating cognitive effort. Learning through effort - trial-and-error, optimization, mental simulation - builds robust neural structures. If AI systematically spares us this effort, we risk losing the capacity to evaluate the relevance of its results.

• Preserving model diversity. Innovation often arises from the confrontation of different perspectives. We’ll need to resist the uniformization that could be induced by an AI trained on the same corporate data and reproducing the same biases. When all brands tap into the same sources, don’t expect any differentiation.

• Creating verification loops. Reality must constantly come from correct simulations. The consequences of our choices - successes or failures - must reactualize human models of understanding.

• Maintaining human dialogue. Meta-cognition - observing our own thought processes - needs the mirror of the other. AI can be a dialogue tool, but cannot replace confrontation with other human subjectivities.

Meta-Cognition In The AI Era

We’ve seen it: meta-cognition is our capacity to observe, evaluate, and regulate our own cognitive processes. It’s what permits learning to learn, correcting our biases, and deliberately choosing which loop to activate.

With AI, meta-cognition becomes even more crucial. We must develop the capacity to:

• Recognize when AI really helps us vs when it locks us in

• Detect biases in generated responses

• Know when to trust our intuition rather than simulation

• Identify moments where personal cognitive effort is irreplaceable

Paradoxically, AI can also become a meta-cognition tool. By proposing alternative perspectives, making our presuppositions explicit, and forcing us to clarify our thinking to dialogue with them, AI can help us better observe our own mental functioning.

But for this to work, we must maintain a critical and active posture, rather than sliding toward passive consumption.

Intuition as a Compass

Let’s return to that opening scene: you hesitate before sending an email. Something holds you back.

This intuitional signal mobilizes the six loops:

• A sensory unease (loop 1)

• Learned associations (loop 2) - this type of formulation has already gone wrong

• Optimized scripts (loop 3) - in this context, this approach generally fails

• A mental simulation (loop 4) - you imagine the recipient’s reaction

• A social anticipation (loop 5) - you put yourself in their place

• A symbolic framework (loop 6) - your values, your professional image

Now, imagine you use an AI to refine this email. The AI proposes three alternative versions. Your intuition reacts differently to each.

This intuition becomes your compass to navigate among AI proposals. It tells you: “This one sounds right” or “That one is too cold” - judgments that mobilize all your embodied experience, impossible to delegate to an algorithm.

AI explores the space of possibilities. Your intuition selects. This is the seventh loop in action.

An Open Question

We’re at the beginning of this adventure. No one really knows what our cognition will become in a world where AI is omnipresent.

The six previous loops took millions, sometimes billions of years to form. The seventh emerges in a few decades. Cultural evolution goes infinitely faster than biological evolution.

This poses a vertiginous question: are our neural structures, shaped to manage loops 1 to 6, equipped to harmoniously integrate the seventh?

Or are we in a chaotic transition period, where we’ll need to reinvent our educational practices, our work modes, perhaps even our relationship to knowledge and truth?

What You Can Do?

If you’ve read this far, you’ve understood that AI is neither a savior nor a destroyer. It’s a tool that amplifies our capacities - and our flaws.

Some paths for consciously navigating this transition:

Train your first six loops. Maintain activities that solicit your body (loop 1), your associative memory (loop 2), your trial-and-error capacity (loop 3), your imagination (loop 4), your social intelligence (loop 5), and your symbolic thinking (loop 6). AI mustn’t replace them but amplify them.

Use AI as a sparring partner, not as an oracle. Dialogue with it, confront its proposals with your intuition, ask it to explore alternatives - but keep your critical judgment.

Stay anchored in reality. After simulating with AI, test in real life. Confront predictions with results. Adjust. Feedback from the field remains irreplaceable.

Cultivate meta-cognition. Observe how you use AI. When does it really help you? When does it lock you in? When should you turn off the screen and simply think?

Preserve human dialogue. AI can simulate perspectives, but it cannot confront you with authentic otherness. Continue seeking the mirror of the other to understand your own blind spots.

The Future As Space Of Possibility

The six loops taught us something fundamental: intelligence always emerges from the coupling between a system and its environment.

The bacterium with its chemical medium. The animal with its territory. The human with their community. And now, perhaps, humanity with its artificial intelligences.

But for this coupling to be fruitful rather than destructive, we must maintain diversity, plasticity, and adaptation capacity.

In biology, when a system loses its diversity and its capacity to receive feedback from its environment, it collapses. Cancer cells that ignore regulatory signals. Ecosystems that lose their predators and overpopulate.

The seventh loop will be fruitful if it remains open - open to diversity of perspectives, to critique, to verification by reality, to regulation by our human values.

It will be destructive if it closes - opaque algorithms, filter bubbles, passive dependence, disconnection from body and world.

Intuition And AI: A Duo To Navigate Uncertainty

Let’s return one last time to intuition, the guiding thread of our two articles.

Intuition isn’t magical. It’s the emergent signal of six learning loops dialoguing within you, built over billions of years of evolution and all your personal experience.

AI has no intuition. It has computing power. It can explore millions of scenarios, detect patterns invisible to the human eye, and generate counterintuitive solutions.

The duo is potentially formidable:

• AI explores the space of possibilities

• Your intuition selects what resonates

• AI refines and develops

• Your intuition adjusts and corrects

• Together, you create something neither could have produced alone

But this duo only works if you remain an active partner, not a passive consumer.

This demands effort. This demands keeping your six loops in shape. This demands meta-cognition to observe how you collaborate with AI.

It’s demanding. But it’s also exhilarating.

Conclusion

Ensuring The Survival Of Our Species And The World That Co-Created It

The first six loops emerged over 3 billion years for one single reason: ensuring the survival and development of organisms in their environment.

The seventh loop emerges today. It can help us confront the unprecedented challenges of our era - climate change, growing complexity, and global interconnection.

But it will only do so if we design it with wisdom.

Our collective responsibility is to build this loop in a way that preserves and amplifies human intelligence and its native coupling with the world from which it emerged - to ultimately ensure the survival of our species and the world that co-created it.

Loops 1 to 6 taught us that intelligence is never isolated - it always emerges from interaction, from dialogue, from coupling with an environment.

The seventh loop invites us to further enlarge this circle: no longer only the human with their social group, but humanity with its technological creations, in permanent dialogue with the living and the planet.

The future isn’t written. We are writing it, day after day, choice after choice, dialogue after dialogue.

Whether we choose the trajectory of weakening or that of amplification depends on millions of small decisions - yours, mine, those of our communities and our institutions.

But one thing is certain: your intuition, woven by 3 billion years of evolution, remains your best guide to navigate this transition.

Listen to it. Cultivate it. Let it dialogue with AI.

And let’s build together a seventh loop of which we can be proud.

Author’s Note:

These two articles propose a reflection framework, not a definitive truth. Science evolves, and so do our uses of AI. I would be delighted to read your reactions, your disagreements, and your experiences. It’s in dialogue that collective intelligence is born - perhaps the most beautiful of loops.

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Bibliography:

Artificial intelligence and cognition

• Chervet, Flavien (2024). Hypercreation: How AI will disrupt all creative professions

• Dehaene, Stanislas (2018). Learning! The talents of the brain, the challenge of machines

Evolution and cognition (continued)

• Ameisen, J. C. (1999). The Sculpture of the Living: Cellular Suicide or Creative Death

• Hofstadter, Douglas R. (2007). I Am a Strange Loop

• Van Cauwelaert, Didier (2018). Natural Intelligence

• Dortier, J. F. (2004). Man, This Strange Animal

Philosophy and technology

• Hölderlin, Friedrich. Poems (”Where danger grows, so grows that which saves”)

Version Française de l’article ici (cliquer)

In the previous article, we explored how human intelligence did not emerge all at once, but unfolded through successive evolutionary learning loops. From the immediate reflexes of sensorimotor coupling to associative learning and trial-and-error optimization, each loop expanded our capacity to react, anticipate, and adapt. We saw how these mechanisms still operate within us today, structuring both our biology and our behavior.

Six Nested Loops (continued)…

In this article today, we’ll continue our exploration of the 6 loops.

Loop 4: Mental Simulation

300-200 million years ago - The appearance of specialized brain structures in mammals, reptiles, and birds introduces a major change: the capacity to simulate actions before executing them physically.

These structures specialize in creating complex spatial and temporal representations – what psychologist Edward Tolman called “cognitive maps.” These are internal models of the world, built to ensure survival.

A rat that has explored its territory doesn’t settle for memorizing stimulus-response associations. It builds a three-dimensional representation of space: where food is located, dangers, shelters, and how these places are connected.

Even more remarkable: at rest, the place cells in its brain replay possible paths. The animal “mentally travels” in its territory before physically moving there.

The research of John O’Keefe (Nobel Prize 2014) on place cells, and May-Britt and Edvard Moser on grid cells, revealed how the hippocampus enables this mental navigation. These same circuits intimately encode space and time, allowing the organism to simulate travel without physical movement.

The Fundamental Change

The organism creates a representation of itself in its environment, like a twin agent capable of situating itself in its own model of the world. It no longer only reacts - it projects its goals and simulates plans to achieve them.

It learns from gaps between the obtained reality and the aimed goal (see chronicle: intuition as a prediction error). The organism rewards progress toward this goal, whether it’s real or simulated progress. Simulation always precedes action. Like when you’re lost in an unfamiliar city, and suddenly recognize a landmark - relief washes over you as your brain simulates the path home, before your feet have taken a single step.

The animal can now learn from simulated futures without living them. Rules grow more sophisticated as the brain begins running “what if” scenarios - imagining situations it has never directly encountered and preparing responses in advance. A rat exploring a maze encounters a dead end and receives a mild shock. Later, approaching a similar dark corridor from a different angle - a path it has never taken - it hesitates and turns away. Its brain has already simulated: “dark corridors like this lead to pain,” and prepared the response before the danger was ever met.

As Alain Berthoz showed, “decision is not driven by reason, but by action or its inhibition.” The brain generates several action simulations and ends up selecting one.

The paradox: Causality reverses. It’s the expected result that causes the chosen behavior. If I want B, I must act according to A (because if not-A then not-B). The projected consequence defines the chosen cause.

In Your Experience

When you prepare for an important negotiation, you don’t test ten strategies in real situations. You simulate them mentally, although not always consciously.

“If I ask X, the client will probably respond Y, in which case I’ll say Z...”

You navigate in a space of possibilities, evaluate scenarios, intuitively select a strategy - all mentally, distinguishing the essential to preserve from possible concessions. But it’s in reality that you adjust your responses based on those of your interlocutor.

Technical Point: Invisible Loops

This new capacity for simulation integrates environmental data into behavioral responses, which permits a fascinating inverted logic: causality by absence.

In classical causality, the presence of a stimulus produces an effect: fire = burn. But in systems regulated by invisible loops, absence itself becomes causal: for example, the absence of a security signal triggers increased vigilance; this is illustrated by the “dead man’s switch” in a train, to check the driver is still in full capacity. This is because the characteristics of the train include having a live driver. Similarly, the organism has ended up integrating responses adapted to its environment by design.

This is also a very astute way to save energy for instructions: imagine that your cell phone battery is almost dead, and you have to meet your friend at the evening train. You arrange to meet him directly at the station - unless something unexpected happens, in which case you will call to let him know the new meeting place and time. This way, the default response is programmed. This absence of a phone call triggers the default meeting place, and battery power is likely saved.

This logic is essential for understanding numerous defensive behaviors. It’s not the explicit presence of a threat that activates the response, but the absence of reassurance - like the elephant calf that explores around it, returning to its mother in case of fear. The absence of the attachment figure triggers strong anxiety in human youngsters.

In certain animals, we observe “immobilization” facing the predator: the animal remains frozen as long as the predator maintains its gaze. As soon as this gaze turns away, behavior brutally switches to flight.

We find an analogous loop in biology: in one key mechanism of cellular suicide (apoptosis), it’s not death signals that destroy cells, but the withdrawal of survival signals - cells need to be actively told to stay alive. It is as if the body naturally produces a slow poison, and cells must continuously receive an antidote to persist. Cancer can be seen as a deregulation of this balance, where certain cells find ways to keep manufacturing their own antidote and proliferate when they should no longer receive it. The reality is more complex, involving multiple pathways, but the core image holds: survival is not a default - it must be actively maintained.

The Limit of Simulation

Mental simulations remain limited to already experienced contexts or their variations. It’s not possible to mentally simulate never-explored territory or a radically new situation, as they have no referent in memory. So far, even counterfactual programs need to run on an existing model of the world. The dependence on physical coupling with its environment constrains thoughts to their location.

But evolution will innovate again: enabling liberation from local experience through social learning.

Loop 5: Social learning

60 million years ago - A young chimpanzee observing its mother use a stone to crack nuts acquires this skill in a few days, whereas it would have taken weeks to discover it through individual trial and error. These observational learning capacities developed in primates, certain birds (corvids, parrots), and cetaceans, radically transforming information transmission.

Neuroscientists have identified circuits involved in observation-action that activate both when you execute an action and when you observe someone perform it. Mirror neurons, discovered by Rizzolatti in the 1990s, are part of these systems - though their exact role remains a subject of active research.

Beyond Simple Copying

Social learning isn’t limited to copying gestures. It includes:

• Emulation: reproducing the observed result rather than the exact gesture - the objective counts more than the method

• Vicarious learning: integrating consequences experienced by others as if one had experienced them oneself - you see a colleague reprimanded for an error, you avoid it without having to commit it

• Cultural transmission: learned behaviors that perpetuate in a group beyond the lifespan of an individual - sweet potato washing in certain groups of Japanese macaques, specific hunting techniques in orcas

The Major Advantage

Transfer of experience without risk, and without experimental knowledge of the environment. The observer learns from others’ success and failure without exposing its own organism to danger.

A young primate who sees another member of its species attacked after approaching a predator acquires this information without suffering the attack itself. The rule “if you see a lion, flee” is transmitted through observation.

This loop also introduces social prediction: anticipating the behavior of other group members, understanding power dynamics, and navigating complex social structures. Theory of mind - attributing mental states to others - emerges in great apes.

In Your Professional Environment

You constantly learn through observation. You note which approach works for an experienced colleague facing a difficult client. You integrate their script without having to test it yourself. You see a joke fall flat during a presentation - you won’t reuse it. Tradesmen pass on their expertise through careful repetition of movements.

This is also one of the ways children learn. By observing adults and imitating them through games (playing shop or cooking) with miniature versions, they reinforce their models of the world before they experience it for themselves.

The Limit

This capacity makes you very influenced by your belonging group. Behaviors propagate even when suboptimal, simply because “everyone does it.” Social learning can reinforce ineffective norms or erroneous beliefs if the group itself is poorly calibrated.

Loop 6: Symbol and Externalization

100,000 years ago - The emergence of symbolic language in Homo sapiens marks an anthropological turn. For the first time, information can be stored, transmitted, and manipulated outside the individual brain.

The Power of Symbol

A symbol - whether word, gesture, or graphic mark - represents something absent. This capacity permits sharing not only what exists (a lion seen yesterday), but also what doesn’t yet exist (a hunting plan for tomorrow) or what will never exist (a mythological narrative, an abstract concept).

Externalization begins with oral language: a hunter recounts around the fire how he escaped a predator. The tribe’s young learn from his experience without ever encountering this danger. Even more: the group can collectively critique his strategy and propose alternatives. Dialogue brings forth knowledge that exceeds the experience of a single brain.

Writing: Crossing Time and Space

Externalization leaps with the invention of writing, some 5,000 years ago. Information is no longer limited to human memory or physical presence. A written text crosses time and space. Today, you can learn Roman military strategies or Polynesian navigation techniques - knowledge accumulated over generations, accessible without having to rediscover it.

Words are an extraordinary invention: they are the handles that allow us to manipulate networks of associations as we manipulate bales of straw. The brain can thus combine and mix them to model our reality or our desires with whatever material is available.

Words carry within them perceptions and programs of action. “To name is to classify,” said Pierre Bourdieu: words allow us to structure knowledge in the virtual space of our brain and to transmit it. And paradoxically, putting words to our emotions allows us to grasp them and act on them in return.

Cognitive Tools

Externalization materializes with tools: knotted cords, the abacus, and then the calculator, which relieve the brain of complex calculations. Geographic maps externalize spatial representation. Musical notation systems permit transmitting compositions without direct demonstration. Each tool extends our cognitive capacity by delegating it to an external artifact.

This sixth loop creates a cumulative collective memory. Unlike social learning (loop 5), where each generation must observe and imitate, the symbol permits accumulating and transmitting without major loss. This is what Michael Tomasello calls the “ratchet effect”: each generation starts from the level reached by the previous one.

The Combinatorial Explosion

Associative potentialities explode. By combining maps and observations from distinct domains, creativity makes an immense leap. Mental exploration of solutions opens endless possibilities.

The symbol permits unlimited mental travel: distant past, hypothetical future, imaginary worlds, abstract concepts. You can reason about entities never encountered (”justice”, “infinity”), simulate purely conceptual scenarios, and generate theories about invisible phenomena (atoms, gravitational fields).

Language Transforms Cognition

A crucial element: language doesn’t just transmit information, it transforms cognition itself.

Naming a color changes how you categorize it. Having the word “week” modifies your perception of time. Symbols restructure previous loops: they influence perception (loop 1), associations (loop 2), strategies (loop 3), simulations (loop 4), and social learning (loop 5).

In Your Professional Practice

This last loop underpins most of your activity. You read market studies, attend conferences, and discuss theoretical models. Your understanding of the world is largely built from symbolic information - texts, graphics, presentations - rather than direct experience.

Words are the main vehicles for your vision and your instructions for action. They are also essential collaboration tools - though physical presence remains irreplaceable. Other biological loops are running in the background to build relationships, whether or not we are aware of them.

The Major Limitation

Disconnection from reality. Symbols can propagate and multiply autonomously, creating belief systems disconnected from any empirical verification. You can adopt a conceptual model because it’s elegant or widely shared, without ever testing it in the field. Ideologies, dogmas, and unverifiable theories prosper in this purely symbolic space. “If/then” rules can even be intentionally falsified to deceive or manipulate.

And perhaps most relevant here: you may forget that your body plays a role in your thinking - and requires proper maintenance to support your performance.

How Do These Six Loops Coexist?

As Stanislas Dehaene explains, the brain contains numerous models of the world and action rules nested within each other, somewhat like Russian dolls, but with great recombination flexibility.

The hierarchical organization of the brain permits approaching a situation with various solutions whose simulations deploy at different scales until converging toward an action script - what we call a decision.

Decision as a Competition of Predictions

Traditionally, we thought of decision as a linear process: think → decide → act. But neurosciences of predictive processing reveal a different reality: decision is the product of a system where multiple simulations compete even before a conscious choice.

According to the theoretical framework developed by Karl Friston, the brain constantly seeks to minimize the gap between its predictions and the sensory data it receives. Each loop generates predictions at different scales:

• Loop 1 predicts immediate sensations

• Loop 2 predicts associative regularities

• Loop 3 predicts consequences of actions

• Loop 4 predicts future trajectories

• Loop 5 predicts others’ behaviors

• Loop 6 predicts conceptual implications

The loops aren’t strictly hierarchical – they constantly interact to reconfigure your cognition: A danger signal (loop 1) can be inhibited by a mental simulation (loop 4) that concludes the threat is illusory.

A strategy optimized by trial-and-error (loop 3) can be short-circuited by social learning (loop 5): “someone already found a better solution.”

A symbolic belief (loop 6) can modify direct sensory perception (loop 1): you “see” what your conceptual framework prepares you to see.

Meta-cognition: Observing the Observer

Our capacity for inner navigation allows us to change scale and observe ourselves and our thoughts from the outside. Our model of the world thus includes a model of ourselves, embedded in our physical and social environment. Others live within us, too, and their dynamic representations interact with our mini-me.

Meta-cognition is the capacity to observe, evaluate, and regulate our own cognitive processes - including intuition itself. It’s what enables us to learn to learn, correct our biases, and deliberately choose which loop to activate in a given situation.

Switching to meta mode is like going upstairs and leaning over the railing to watch our mini-me interacting with its model of the world. You see your twin in a simulated environment and understand how everything connects: the loops become visible.

This is where dialogue with the other plays a crucial role – and this is also where a new revolution announces itself with artificial intelligence.

Your Turn

This week, try this exercise: note three decisions you make - a small one (which coffee to order), a medium one (how to respond to a delicate email), an important one (a professional decision).

For each, ask yourself: which loop dominates? Is it a reflex (loop 1), a learned association (loop 2), an optimized script (loop 3), a mental simulation (loop 4), a social influence (loop 5), or symbolic reasoning (loop 6)?

You might discover that your most important decisions mobilize several loops at once - and that your intuition is nothing other than the signal that emerges from their silent conversation.

In the next article, we’ll explore how artificial intelligence could constitute a seventh loop – and what this fundamentally changes about our way of thinking, deciding, and creating.

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Bibliography:

Neuroscience and learning

• Changeux, Jean-Pierre (1985). Neuronal Man: The Biology of Mind, Pantheon Books

• Dehaene, Stanislas (2018). Learning! The talents of the brain, the challenge of machines

• Kandel, Eric R. (2006). In Search of Memory (Nobel Prize 2000)

• O’Keefe, John & Nadel, Lynn (1978). The Hippocampus as a Cognitive Map (Nobel Prize 2014)

• Friston, Karl (2010). The free-energy principle, Nature Reviews Neuroscience

Evolution and cognition

• Bennett, Max (2024). A Brief History of Intelligence

• Tomasello, Michael (1999). The Cultural Origins of Human Cognition

• Berthoz, Alain (2003). Decision

Learning and conditioning

• Pavlov, Ivan P. (1927). Conditioned Reflexes

• Skinner, B.F. (1938). The Behavior of Organisms

• Tolman, Edward C. (1948). Cognitive maps in rats and men

Version Française de l’article ici (cliquer)

Picture this!

You’re about to send a delicate email to an unhappy client. Your fingers hesitate above the keyboard. Something inside whispers, “Wait, rephrase.” Or you’re walking home late at night and, without thinking, you cross the street to avoid a dimly lit alley. Conversely, you meet someone new at a gathering and feel an immediate warmth - something tells you to keep talking, to ask another question, even though you can’t explain why. Where do these signals come from? These gut feelings - holding you back or urging you forward - actually mobilize six intuitive learning mechanisms layered upon each other, built over 3 billion years of evolution.

This chronicle offers you an accelerated flashback to watch our human intelligence form, layer by layer. Because understanding these loops means grasping how intuition - that embodied intelligence at the bodily substrate of our minds - was born from this interweaving of interactions between organisms and environment. This is the neuroscience of intuition: not a mystery, but a predictive system shaped by every living thing that came before us.

Six Nested Loops

Human intelligence didn’t appear suddenly. It emerged through successive evolutions, as individual organisms found small solutions to adapt to their environment. This long series of adaptations resulted, over time, in what I call six learning loops - six unique capacities that help the organism navigate its environment. Each new learning capacity builds on previous ones and confers an advantage for survival.

These six major loops became intertwined over 3 billion years, arising from the coupling of an organism with its environment. They didn’t disappear with the appearance of subsequent ones - they unfolded upon each other. Better yet, they structure not only the evolutionary history of species (phylogenesis), but also the development of each individual (ontogenesis).

A human baby travels through all of evolution in fast-forward. It goes through the same stages to build these same loops that nourish our intelligence. The child and the adult continue their cognitive development, with as many variations as individuals and contexts.

All these loops share a common principle: improving the organism’s capacity for reaction, then anticipation. Each loop extends the temporal horizon of our actions - from a few milliseconds for a reflex, to several thousand years in the case of a civilization.

But this sophistication has a cost: each loop consumes more energy than the previous one. Evolution constantly arbitrates between predictive precision and energetic efficiency.

Loop 1: Direct Sensorimotor Coupling

3 billion years ago - A bacterium swims toward increasing gradients of nutrients and away from toxins. This reflex behavior simply requires surface sensors coupled to a locomotor apparatus - the flagellum that propels the bacterium.

This sensorimotor coupling establishes a fundamental principle: the organism adjusts its behavior based on stimuli that directly affect its survival. The concentration gradient serves as a signal. Its detection generates an oriented response: approach what is favorable, distance from what is harmful.

The rule is simple: “When a given stimulus threshold is crossed, activate the corresponding behavior.”

This first loop functions entirely in the present. The organism reacts to the current state of its immediate environment and its internal needs - a lack of nutrients triggers the search. But the individual has no memory of events. It’s not anticipation, but reaction.

In Your Body Today

This mechanism persists. Sensorimotor reflexes - like withdrawing your hand from a heat source - operate via short circuits, directly between the sensory receptor and the muscle. Sensory information triggers a quasi-instantaneous motor response, processed as a priority to preserve your integrity.

The Limit

This system permits no rapid adaptation to a major environmental change. If the usual gradient suddenly reverses, only natural selection, over several generations, provides an adjusted response. When the ocean became acidified or the air oxygenated, mass extinctions occurred.

Habituation (decreased response to repeated stimulus) and sensitization (increased response) - rudimentary forms of learning present even in simple organisms - will partially compensate for this rigidity. Until the arrival of a crucial innovation: associative learning.

Loop 2: Associative Learning

700-600 million years ago - Aggregates of neurons form the first nervous systems. Organisms develop a new capacity: creating links between various events that occur jointly or successively.

When I see lightning, shortly after I hear thunder, when these fresh paw prints appear on the farmyard path, the fox is never far.

Donald Hebb would later formulate the principle: “Neurons that fire together, wire together” - neurons that activate simultaneously create connections between them.

This connectivity enables a major innovation: if two events regularly occur in sequence, one becomes the harbinger of the other. The rule becomes: “when stimulus 1 arises, expect stimulus 2 and prepare the appropriate action.”

This is the mechanism behind classical conditioning that Pavlov formalized: the dog associates the sound of the bell with the arrival of food and begins to salivate. But this mechanism also exists in invertebrates like the sea slug. Eric Kandel received the Nobel Prize for describing this form of learning at the synaptic level.

Operant conditioning – learning through the consequences of our acts - adds a dimension: the organism now associates a behavior with its consequences, positive or negative. If an action in a given context regularly leads to a reward, the nervous system reinforces this behavior. Conversely, it inhibits unfortunate responses.

The body rewards preparation for action even before finding the actual reward. The dog salivates before receiving food. This mechanism increases the chances that the organism cultivates what is favorable and avoids what can harm it, by making bets on the future thanks to its experience. Our organisms incentivize potential progress.

What Fundamentally Changes?

The organism is no longer a prisoner of the immediate present. It anticipates by a few seconds thanks to the quasi-co-occurrence of stimuli. Certain cues become predictors - whether they come from outside (wind + clouds + lightning = storm) or from inside (hunger=searching+eating).

This anticipation manifests through a physiological reaction - what can be called a need or proto-emotion in organisms with a nervous system. Even before a significant event occurs, the organism now prepares for action thanks to a script pre-coded by experience. Emotions label the value of stimuli (positive or negative) and store successful behavioral responses.

In Your Daily Life

This mechanism remains very active. The smell of coffee generates a feeling of wakefulness even before you drink it. The sound of a siren triggers an alert before you’ve consciously identified the potential danger. Your stress rises when you open your email on a Monday morning - a learned association between this context and the urgent requests that often hide there.

Advertisers systematically exploit these automatic associations. Emotion is, in a way, a program that engages your organism toward action.

The Limit

Anticipation remains limited to a narrow temporal window - a few seconds, at best, a few minutes. The organism cannot yet plan medium-term nor optimize complex action sequences. It cannot connect points too distant due to a lack of long-term memory.

This problem will find its solution with trial-and-error learning.

Loop 3: Trial-and-Error Optimization

550 million years ago - With the complexification of nervous systems, a new capacity emerges: testing several behavioral strategies and keeping those that generate the best results. This is what psychologists call advanced operant conditioning, and what machine learning calls “reinforcement learning.”

A fish confronted with a predator doesn’t settle for a single flight response. It can test different strategies: hide, change color, flee rapidly, or freeze. The sequences that led to its survival at the lowest energetic cost are reinforced in the neural circuits, building longer-term memories. The others are progressively eliminated.

Remarkably, this type of learning isn’t the prerogative of vertebrates. Cephalopods (octopuses, cuttlefish) show sophisticated trial-and-error learning capacities, as do certain social insects (bees, ants).

The Key Innovation

The organism builds sensorimotor maps - internal representations of world regularities associated with its own behavior. They encode “if I do X in context Y, then Z generally occurs.” With emotions as signposts.

The memory of each event permits an enlarged temporality. The stimulus, the behavior, and its result can occur distant from each other - they will still be associated thanks to long-term memory. If their sequence repeats, an intuitive causal relationship is established: if A is observed, then B is highly probable.

In Your Practice

You constantly optimize by trial and error. You’ve tested several ways to structure your presentations, several routes to avoid traffic jams. Ineffective methods have been abandoned. Effective ones have become automatic - this is the optimization of behavioral scripts.

The brain tidies up its scripts to keep only those that work and reduce energetic maintenance expenditure. Until the next change of aisles in your supermarket, where you’ll be forced to relearn where to find your products.

The Limit

Exploration carries a potentially fatal cost. Testing a new strategy can lead to irreversible failure. An organism that “tries” approaching a predator to see what happens won’t have the opportunity to learn from its error.

Natural selection will therefore favor the appearance of a revolutionary aptitude: mentally simulating before acting.

To be continued…

In the next article, we'll continue our journey through the next loops - picking up where the body's simulation capacities begin. Loops 4, 5, and 6 will take us from the first mental maps drawn by mammals 300 million years ago, through the social transmission that made primates so adaptable, to the symbolic revolution that made us human. Because understanding where intelligence comes from is the first step to knowing how to use it.

Share

Bibliography:

Neuroscience and learning

• Changeux, Jean-Pierre (1985). Neuronal Man: The Biology of Mind, Pantheon Books

• Dehaene, Stanislas (2018). Learning! The talents of the brain, the challenge of machines

• Kandel, Eric R. (2006). In Search of Memory (Nobel Prize 2000)

• O’Keefe, John & Nadel, Lynn (1978). The Hippocampus as a Cognitive Map (Nobel Prize 2014)

• Friston, Karl (2010). The free-energy principle, Nature Reviews Neuroscience

Evolution and cognition

• Bennett, Max (2024). A Brief History of Intelligence

• Tomasello, Michael (1999). The Cultural Origins of Human Cognition

• Berthoz, Alain (2003). Decision

Learning and conditioning

• Pavlov, Ivan P. (1927). Conditioned Reflexes

• Skinner, B.F. (1938). The Behavior of Organisms

• Tolman, Edward C. (1948). Cognitive maps in rats and men

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What if you had to explain intuition to a 10-year-old child?

That’s the question I worked with for this piece: by abandoning all the neuro-scientific explanations I’m usually so fond of (but my mom isn’t). So, I looked for a story that makes intuition intuitive, while staying somewhat true to the science behind it. And here’s where it led me to.

The Kite and Intuition

Intuition is a superpower that allows you to sense what’s coming. Imagine it as a kite flying above you, which you navigate while avoiding dangers and creating magnificent patterns in the sky.

With a head start, intuition allows you to harness the winds to make the kite soar over unknown spaces. But it takes a bit of know-how to master it without crashing!

Before flying your kite, first ask yourself: where do you want it to go? Your intention is that first thread stretched between your hand and the sky — the one that will give it the right impulse to take off.

Then, there are four essential gestures to learn how to navigate the kite’s movements:

1. Weaving invisible threads

   Beyond your intention, learn to cast other invisible threads between the kite, the sky, and you. Your eyes follow its movement, your body accompanies its momentum, your skin feels the sun, you hear the wind change direction, and even your nose knows if it’s coming from the sea. Each of your senses weaves its connection, and you immerse yourself completely in the world in which your kite moves. It’s no longer just up there — it becomes an extension of everything you feel.

2. Dancing with the currents

   Now it’s time to play. As the kite twirls with the wind and sun, gliding behind the rainbow, riding the clouds, you test a thousand movements. Resist or let go. Move or stay still. Pull sharply, release gently. Each thread is sensing, each gesture teaching you. In this dance with the elements, your body discovers the secret rules of the wind, the invisible laws that govern flight or spiral. You draw lines in the sky through trial, joyful errors, and surprises. This cloud looks like a cake, and your kite like a cherry landing on top of it.

3. Floating in the sky

   Now as your kite floats up there, close your eyes. Let it drift alone for a while. Behind your eyelids, visualize it: feel all its movements. Imagine your gaze traveling up along the thread, slowly you rise, until that magical moment when you swap places with the kite. Suddenly, you’re the one flying. You see yourself from above, a tiny silhouette on the beach. You soar over landscapes. You’re no longer just the one holding the thread — you also become the one who flies, who feels the wind under fabric wings. The kite and you are one, and now you see all the connections between each of its corners and how they turn and flex in the wind.

4. Creating a pattern among the clouds

   When you know how to do this, then you’re ready: you can open your eyes again. Your intuition is at your fingertips and your whole body now knows how to dance with the threads. With a micro-gesture you correct the trajectory. You draw your path, as if sculpting a cloud. Your hands move before your mind decides. You already know what to do to see your drawing come to life, without being able to verbalize it — or only afterwards, when the pattern is already traced in the sky, when the work is complete.

And when one day, in real life, you need your superpower to untangle a problem or create your work, never forget the four gestures: weave, dance, float, create. These are the secrets of those who know how to fly with their eyes closed.

Why does it work, even for adults?

1. Weave – Feed your body with sensations

   To put your body to work, you must first feed it with sensory data. Tackling a problem isn’t just thinking about it: it’s embodying it, immersing yourself body and soul. It’s weaving connections with all the elements around the problem, looking for blind spots, exploring what escapes first glance. And even borrowing others’ senses when yours aren’t enough. Your intuition is built first from what you capture from the world.

2. Dance – Learn through play

   Playing is learning in fast-forward. Pretending is already understanding. Forget what other people think, and find partners to dance with you. By probing, exploring, testing a thousand variations, making mistakes, you reveal the hidden rules that govern interactions. Each attempt draws a line, each error sketches the map. To journey is to chart: you transform chaos into known territory. You train your predictive skills.

3. Float – Let your body navigate

   It’s in letting go that intuition emerges. Let your body feel, navigate between perspectives, zoom in and out. By suspending conscious control, you allow your entire being to simulate, anticipate, internalize movements. Now your body knows how the situation is likely to develop and which way the wind is likely to blow. Intuition can’t be forced: it arises when you float between action and observation, between you and the world you predict.

4. Create – Give life to intuition

   Moving from intuition to gesture means embodying your intention and giving it form. Creating, improvising, prototyping: it’s solving through doing, achieving through action. But with a clear goal in mind.

Afterwards, naming what you’ve done transforms intuition into conscious knowledge — and allows you to transmit it to others. The most experienced kite flyers will venture to fly in formation with others.

In a nutshell

Intuition isn’t a mystery, it’s an apprenticeship. Like flying a kite, you just need to accept starting, even clumsily. Your first attempts will be imperfect, the weather not always favorable. But your hands will learn. Your body will know.

So next time you’re looking for an answer, remember: weave, dance, float, create. Your intuition is just waiting for you to give it flight.

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Three Moments of Knowing

• A firefighter stands in a burning house. Something feels wrong—not the fire itself, but something about the floor beneath his feet, the way the heat moves. Without conscious thought, he orders everyone out. Seconds later, the floor collapses. He saved his team but can't explain how he knew.

• Serena Williams stands at the baseline. Her opponent begins the serve motion. Before the ball leaves the racket, Serena's body has already started moving to the right corner. She intercepts a 120 mph serve that seemed impossible to reach. Asked how she knew, she says simply: "I felt it."

• Albert Einstein sits in his office, wrestling with equations that refuse to cooperate. Suddenly, he knows the solution—not through calculation, but through a sensation he can't articulate. Only later will he work backward to prove what his intuition grasped first. "I believe in intuitions and inspirations," he said. "I sometimes feel that I am right. I do not know that I am."

Three completely different domains, yet they share something profound: the ability to know without knowing why. This is expert intuition—and contrary to popular belief, it's trainable if we understand how it develops across situations, and not just in emergency ones: looking across domains, I suggest that stress-test is only the revealer.

The Firefighter: Recognition as Expertise

Gary Klein spent years studying professionals who make life-or-death decisions under pressure. He expected careful deliberation, weighing options, analyzing trade-offs. Instead, experienced firefighters were recognizing situations—drawing from "a storehouse of experiences," subconsciously categorizing and mining for matches with their current moment.

Klein called this Recognition-Primed Decision Making. The firefighter who evacuated his team didn't analyze floor stress loads. His brain pattern-matched thousands of previous fires and detected an anomaly: the heat distribution didn't fit. His intuition screamed danger before conscious thought caught up.

Klein found the same recognition-based intuition in chess grandmasters, nurses, investors, mothers recognizing their babies' cries. Emergency pressure revealed intuition’s power but didn't create it.

Notice what we often miss about the firefighter's intuition: it lives in his body. The sensation of the floor beneath his feet. The way heat moves across his skin. The weight of equipment shifting as the structure creaks. His expertise gets distributed throughout sensory and motor systems, constantly sampling the environment, detecting patterns his conscious mind hasn't processed yet.

Klein discovered something else remarkable: expert firefighters often mentally rehearsed scenarios, running simulations of dangerous situations. These mental simulations weren't abstract: they engaged the same bodily sensations and motor responses as real fires. The body learned from imagined experience, building neural pathways that could guide real action later.

The Athlete: Bodies That Predict (and Practice in Their Minds)

Watch Serena Williams play and you witness pattern recognition operating through the body. She's hit millions of tennis balls. Her body has learned to recognize patterns in opponents' movements, racket angles, weight shifts. She isn't calculating trajectories, her body predicts and responds before conscious thought engages.

What makes athletic intuition revealing: it's obviously embodied. Williams's expertise lives in her muscles, her proprioception, her automatic adjustments. This is perceptual learning in action: the brain's neural systems have tuned themselves to detect relevant features through massive exposure.

Intuitive Tennis coaches (yes it’s a unique way of training!) cultivate this through:

• Situational repetition: encountering the same tactical patterns hundreds of times (deliberate practice targeting specific improvements)

• Constraint-based training: creating scenarios requiring instinctive response

• Progressive challenge: calibrating difficulty to maintain engagement without cognitive overload

• Spacing practice: distributing training over time for better long-term retention

These techniques work because they honor how bodies learn. You don't develop a serve by only studying biomechanics diagrams, you develop it by serving thousands of times, letting your body discover efficient movement patterns through trial, error, and constant adjustment.

But here's where the body-mind relationship becomes intriguing. As coaches would know already, mental practice can improve physical performance. You may have heard the famous story of a prisoner of the Vietnam war (Air Force Colonel George Hall) who improved his golf swing by mentally rehearsing every shot while in captivity. Athletes use the same tactics.

Olympic athletes regularly rely on visualization techniques, mentally rehearsing performances in such vivid detail that their muscles show measurable micro-activations. The simulation engages the motor system, building and refining the same neural patterns that guide real action.

Nobody questions that athletes need their bodies to develop expertise. The body obviously serves as the instrument of performance. But we fail to recognize: the body remains equally essential for developing expertise in domains we consider purely "cognitive". And the relationship runs both ways—bodies shape mental models, and mental simulations reshape bodies.

The Mathematician: Abstraction Through Experimentation?

The most interesting case of knowledge work can be found in mathematics. Mathematics seems like pure abstraction, formal logic, rigorous proof. Surely here the body becomes irrelevant?

David Bessis (Math researcher, author of Mathematica) disagrees. Even abstract mathematical thinking operates fundamentally through embodied experience. Great mathematicians describe sensations: “tension” in an equation, a proof “wanting” to go a certain direction, aesthetic judgment that one approach feels “elegant” while another seems “clunky”. These aren’t metaphors—they’re descriptions of mathematical intuition working through bodily sensation.

Bessis describes how Alexander Grothendieck, a mathematical genius, used intuition as pointer to explore brand new directions. In Recoltes et Semailles Grothendieck said “The simple fact of writing, of naming, of describing – even if at first only describing elusive intuitions or simple ‘suspicions’ reluctant to take form – has a creative power”.

Check out my latest interview about Mathematical Intuition with David Besis here

Another emeritus mathematician, Henri Poincaré, famous French professor as well as physicist, engineer and philosopher, claimed to have insights not at his desk but during walks, boarding a bus, upon waking. His body, moving through space, engaged with the world, somehow freed his mind to make connections. "It is by logic that we prove, but by intuition that we discover".

Einstein's intuitions arrived as feelings before formal reasoning could articulate them. "I never came upon any of my discoveries through the process of rational thinking,” he said. Einstein's famous thought experiments, imagining riding alongside a light beam, visualizing falling elevators, engaged his body's spatial and kinesthetic intelligence to solve physics problems. He was mentally simulating physical scenarios, letting his embodied intuition guide him toward insights. "The intuitive mind is a sacred gift,” he said, “and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift”.

This is how mathematical understanding develops in everyone, says Bessis. Maria Montessori discovered this over a century ago watching children learn. They grasped mathematical concepts not through abstract instruction but through physical manipulation of objects: beads representing units, tens, hundreds; wooden blocks encoding geometric relationships; physical objects to count, group, rearrange. They built mathematical understanding through their hands, through movement, through sensory engagement. Only after extensive physical experience did they transition to abstract symbols.

The Singapore Math method follows the same principle with its concrete-pictorial-abstract progression. Students start with tangible problems they can manipulate, and for which there is a real-world motivation, with actual objects to count and group. Then pictorial representations. Only finally do they work with abstract numbers and symbols.

Why does this work so powerfully? Mathematical understanding gets built through the body's interaction with structured environments, creating fundamental body models that will lay the groundwork for future mental models. Our body creates natural algorithms that replicate internally the world in which we are situated to better predict it. It pairs our span of possible actions with how the data it collects behaves.

But notice the progression: concrete manipulation trains the body, which then enables mental manipulation. Eventually, the mathematician can mentally simulate moving through mathematical spaces, rotating geometric objects, feeling the balance of equations, all without physical materials. The body has internalized the patterns so thoroughly that simulation becomes possible. The abstraction compresses embodied experience, allowing it to be manipulated more efficiently through mental rehearsal.

Manipulating more sophisticated conceptual objects over many years, both physically and through increasingly refined mental simulation, the body hasn't disappeared; it's been trained to work with abstract structures the same way an athlete's body works with physical ones. But our “brain-centralized” view of intelligence is reluctant to recognize that our body’s proprioceptive circuitry is actually the hardware that helps us think in the first place.

The Conductor: Bodies in Conversation

Orchestra conductors show leadership through intuition, emotion, and improvisation. Research highlights three key embodied processes: relational listening, aesthetic judgment, and kinaesthetic empathy.

Watch a great conductor reading the room with their entire body, sensing where musicians need support through subtle changes in posture and energy, feeling when tension builds through the collective breathing of the ensemble. A slight torso sway can completely alter perceived dynamics, shifting energies of ensemble and audience.

This intuition operates through body-to-body communication. The conductor's gesture changes. Musicians' bodies respond. The conductor senses this response through visual, auditory, kinesthetic channels. They adjust. The musicians adjust. A continuous loop of embodied interaction.

But conductors also prepare through mental rehearsal. They study scores while internally hearing the music, their bodies making micro-movements as they simulate the performance. They imagine the orchestra's response to different gestures, mentally trying various approaches before the actual rehearsal. This mental simulation engages their embodied knowledge, refining their intuition about what will work.

Musicians describe how improvising together means you get to know each other very fast and on a deep level.This knowing operates viscerally, immediately. Bodies in conversation. Even improvisation requires structure,Intuitive Music like Jazz does include some rules of harmony, style and order,those rules create the foundation from which musicians can manipulate and experiment.The structure provides shared physical grammar enabling bodies to coordinate and co-create. And instruments sometimes become live organs, part of a musician’s augmented body.

The Hidden Partner: Your Body as Thinking Instrument

Here's what becomes obvious watching athletes and firefighters but remains hidden in "cognitive" work: we are embodied beings whose thinking emerges from how our senses couple with environments.

The research on expertise reveals this consistently:

• Pattern recognition works because perceptual systems, embodied, distributed throughout sensory apparatus, learn to detect meaningful configurations in environments.

• Deliberate practice creates tight feedback loops between body and environment: you act, sense the result, adjust, repeat. Your body learns through continuous coupling, building increasingly sophisticated predictive models.

And crucially: the body doesn't distinguish sharply between actual and simulated experience. When you vividly imagine performing an action, whether hitting a golf ball, solving a problem, or navigating a social situation, you activate many of the same neural pathways as actual performance. Mental simulation builds and refines the same embodied models that guide real action.

This explains why expert intuition is often presented in the form of mental simulations, like Chess masters 'see' potential moves and their consequences" - they're running embodied simulations engaging spatial reasoning and pattern recognition.

Mental models aren't pure abstractions floating in minds, they're compressed representations of embodied experience, ways your body has learned to anticipate how situations unfold and actions lead to outcomes. And these models work bidirectionally: physical experience builds mental models, and mental simulation refines physical capabilities. The body serves as the instrument of thought, whether engaged physically or through simulation.

Reclaiming Embodiment in Knowledge Work

In our very first chronicle, we saw that successful entrepreneurs didn’t wait fto act on their intuition. Knowledge workers and researchers developing intuition can learn from the principles employed by intuition experts:

1. Immerse Yourself Regularly in Useful Environments: Before developing intuition, master your domain's fundamental patterns. Before you become comfortable with a domain, you need to explore it attentively and rehearse the fundamental movements of your work. But don’t forget to expose yourself to unconventional data: go to the field, sense the context. All your senses should be activated, not just your mind.

2. Accumulate Exposure Through Deliberate Practice: Steve Jobs had never stopped exploring and creating new things throughout his life, constantly learning so knowledge accumulated in memory and intuition emerged subconsciously."The more you practice, or expose yourself to use-cases from others, the faster you build your pattern recognition system: repetition is the key to muscle building! Here again: (data) visualization helps, not just for presenting, but also for discovering.

3. Engage Your Body in "Mental" Work: Steve Jobs walked barefoot during brainstorming meetings. Through meditation, he created "quiet spaces" where your mind slows down and you see a tremendous expanse in the moment, your intuition starts to blossom.Engaging your body switches on natural processing resources that can help by walking your problem towards safe exit without you noticing. That’s why using sketch notes, or prototypes you can touch and feel is so powerful to simulate what-if scenarios and predict probable outcomes.

4. Create Rapid Feedback Loops: Expert intuition becomes reliable in environments providing good cues and regular feedback. Office work often has terrible feedback loops, so you must actively create them: measuring impact wherever possible, conducting post-mortem to learn from mistakes, but also asking for your team’s feedback as you progress in your decisions.

5. Cultivate Meta-Awareness of Your Intuitions: The best grandmasters "know when to trust their intuition and when to switch to deep calculation." This isn't blind trust, it's discernment. Listen to your bodily signals, isolate your emotions, identify the difference between wishful thinking and true signals from your intuition, and adapt your strategy to your environment. (Read more in my previous chronicle here)

The Hidden Truth

When developing expertise, ask: Am I treating my body as the instrument of my intuition? Am I engaging it both physically and through rich mental simulation? Or am I still trying to develop cognitive skills in a disembodied mind?

The evidence from expert intuition across every domain points the same direction: The body that learns—through action and through simulation—becomes the mind that knows thereby erasing this body/mind artificial dichotomy.

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Bibliography:

Expert Intuition and Decision-Making

• Klein, G. (1998). Sources of Power: How People Make Decisions. MIT Press.

• Kneebone, R. (2020). Expert: Understanding the Path to Mastery. Viking.

• Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

• Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. Viking Press.

Embodied Cognition and Bodily Intelligence

• Bessis, D. (2022). Mathematica: A Secret World of Intuition and Curiosity. Yale University Press.

• Roberts, S. (2024). The Power of Not Thinking: How Our Bodies Learn and Why We Should Trust Them. Umuzi.

• Noë, A. (2004). Action in Perception. MIT Press.

• Varela, F., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.

• Damasio, A. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. Putnam.

• Kahneman, D., & Klein, G. (2009). "Conditions for intuitive expertise: A failure to disagree." American Psychologist, 64(6), 515-526.

• Kandasamy, N., et al. (2016). "Interoceptive ability predicts survival on a London trading floor." Scientific Reports, 6, 32986.

• Koivunen, N., & Wennes, G. (2011). Show us the sound! Aesthetic leadership of symphony orchestra conductors. Leadership, 7(1), 51-71.
  

Embodied Pedagogy

• Montessori, M. (1967). The Absorbent Mind. Holt, Rinehart and Winston.

• Kho, T. H., Yeo, S. M., & Lim, J. (2009). The Singapore Model Method for Learning Mathematics. Marshall Cavendish Education.

• Poincaré, H. (1908). Science et Méthode. Flammarion.

Traduction Française

What does 2026 hold for you? Here is my hunch: you can find hints of what’s to come by looking back at the selection of the top five intuition papers from 2025.

Explore all these articles here

Taming your intuition means grasping what’s already there, even if it is unevenly distributed or hidden in the noise. That’s also why we all love reading our horoscopes at the start of the year ! When we come across a prediction, we test our own prediction machine, we create meaning and fill in the gaps with our own experiences.

As I look back on 2025, I am profoundly grateful to all my readers for joining this community. Since I only started writing here five months ago, I am especially thankful for your feedback, referrals, and personal stories. I have already met a few of you in person, and it has been wonderful! It all makes it worthwhile !

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I would also like to thank the renowned experts who have agreed to join the conversation like and : their interviews here and here. have brought immense value to this journey.

If you’re new here and like what you read, stay tuned for 2026. A lot more is coming up, and the tandem of AI+HI will bring many more surprises!

Happy New Year to all: I hope that 2026 will fulfil your hopes and bring you joy, love, luck and unexpected surprises to fuel your intuition !

Best Wishes to all !

Richard Bordenave

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Version Française de l’article ici (cliquer)

Walk into any bookstore’s business section and you’ll find them: books locked in mortal combat over intuition. Some authors have made entire careers defending it. Others have built reputations attacking it. The Power of Intuition versus Don’t Trust Your Gut. Decisive Intuition versus Never Go With Your Gut. Gut Feelings versus Thinking Fast and Slow.

The debate has become so polarized: you’re either Team Gut or Team Data, Team Blink or Team Think, a believer in the wisdom of instinct or a crusader against cognitive bias

Meanwhile, in actual businesses, executives aren’t reading these manifestos to choose sides. They’re desperately trying to figure out: When do I listen to this feeling, and when do I ignore it?

What all these warring camps overlook is simpler than you think: intuition isn’t universally good or bad—it’s contextual and adaptative. The real question is recognizing when you’re in intuition-friendly territory versus intuition-hostile terrain.

Most of the other posts on this blog focus on the advantages of intuition and how it can be used to improve decision-making, creativity, or problem-solving. In this post, I wanted to explore its limitations: these are inherent to how intuition works and can therefore be controlled. Just as it is possible to learn how to use a tool with great skill while avoiding the risks involved in using it.

I will explore three key questions: What makes intuition faulty? When should we trust it? And how can we protect ourselves from its traps? At the end of this post, you’ll find a three-question framework to evaluate when to trust your gut and how to do when intuition clashes with the data.

What makes intuition faulty?

Just as AI hallucinates, our intuition misleads us when our ‘training data’ (also called experience) is insufficient or irrelevant. If children’s books made you think bears are cute and kind animals, meeting one in the wild may surprise you!

Bad intuition primarily relates to defects in our internal predictive model training: insufficient data, poor quality, lack of redundancy, no feedback, or when it is happening too far from the decision. Then depending on environments, it’s more or less risky to listen to your intuition.

Familiar Territory: When to Follow Your Gut

In familiar environments where you’ve been exposed multiple times to location cues and action feedback is immediate, you can safely follow your expert intuition. Yousmay trust your “system 1” (autopilot) find its route: focus on the destination and let your legs walk you there.

You can also safely follow the herd familiar with the place: others’ expertise can offer a good short-cut. Beware of familiarity though—one city can resemble another while cultures are fundamentally different. And so is risk: you may leave your mobile on the table to reserve a spot at a restaurant in Singapore, while you certainly shouldn’t do this in Paris or Barcelona.

Uncharted Territory: When to Proceed with Caution

In totally new (or hostile) environments where uncertainty is high, intuition can guide short-term exploration by framing assumptions, but you should restrain action and stay alert. Learn as much as you can from the field while advancing, staying vigilant. Progress step by step while minimizing regret risk. You must be able to retrace your steps, leaving breadcrumbs to walk back, or in a worse case to be found and rescued. Don’t explore unknown areas or new cultures without a native!

Now, intuition-as-prediction-error (see previous column) only works as a good guide if the underlying mental model is well calibrated to reality. If our cognitive reconstruction is biased or distorted, the same machinery generates compelling but misleading intuitions – as we see in hallucinations or psychological conditions where internal models overpower external evidence. When someone’s model becomes badly miscalibrated, they may dismiss all evidence showing they’re wrong. We see this when someone believes they are the saviour of the world, and every disconfirming fact is reinterpreted to fit their narrative (like world saviors).

Understanding when to trust intuition is only half the battle. Even in familiar territory, four common traps can derail our instinctive judgment:

The Four Deadly Traps

The main problem with intuition is that it can be confused with other bodily signals that lead to action based on no prediction or incorrect prediction. These four traps can derail even experienced decision-makers:

1. Inattention: When you overlook critical details, you may think you understand the situation while missing information that leads to wrong conclusions. Our brains constantly choose which information to sample and ignore the rest, so be deliberate about the data you feed your internal model. Your senses are essential captors—ensure they’re fully operational. Otherwise, you might mistake a wolf for a dog.

2. Strong emotions: Stress, fear, or excitement can easily override genuine intuition signals. Strong bodily sensations related to physiological needs create similar interference—hunger or fatigue derail both attention and judgment. When facing important decisions, take time to observe what’s happening internally and address basic needs first. An experienced skier may be tempted by off-piste powder snow under a beautiful sun, the desire for this descent drowning out the signals he knows indicate an avalanche risk.

3. Flawed associations: Learned beliefs, past trauma, prejudices, or untested mental models can generate misleading interpretations. An American boss may mistakenly judge his Japanese colleagues to be shy, when in fact speaking up in meetings is not governed by the same rules. Challenge the immediate associations that spring to mind and seek others’ perspectives to test your assumptions.

4. Wishful thinking: Confusing your desired outcome with reality, or being so focused on what you want to achieve, can distort your perception of what’s actually unfolding. A startup founder convinced their product will succeed might ignore customer feedback suggesting otherwise. When you’re fixated on a preferred scenario or a future reward you become blind to the situation that’s really developing before you or want to ignore the weak signals alerting you.

Intuition also falls victim to our cognitive biases: to move quickly, the predictive brain fills missing data with recreated memories. Gert Gigerenzer, and some other evolutionist researchers like Lionel Page, describe biases as the price to pay for exceptional cognitive efficiency: extreme speed and low energy expenditure (particularly compared to AI).

For reference, we’ve repositioned most famous cognitive biases as variations of our four deadly intuition traps:

1. Inattention: change blindness, availability, recency, default

2. Strong emotions: Loss aversion, social norms, hot-cold empathy gap, in/out group

3. Flawed associations: Anchoring, representativeness, confirmation bias, false beliefs, group think

4. Wishful thinking: Overconfidence, sunk costs, illusion of control, planning fallacy.

Recognizing these traps is crucial, but what do we do when we spot them? Military and survival experts have developed a practical framework:

The STOP Method

When terrain is littered with potential landmines, such as decisional stakes, political questions, or gaps between study results and client expectations, use the STOP steps:

• S: Sit and breathe to avoid impulsive reactions

• T: Think, factually review the situation and resources

• O: Observe the landscape and look for cues of likely evolution

• P: Plan to minimize risk and raise flags for help

Surprisingly (or not) some mindfulness practitioners use similar rules to coach their students to manage their stress and anxiety. Observing your own emotions helps you regain that space to stop and think about what’s really happening by refocusing your attention. Rewiring your brain and body through deep breathing helps prevent one system from overriding another. You can also make use of technology (here in the forest example GPS can help) to compensate for limited sensory inputs.

Much like in solo sailing races, the skipper consults the router (a shore-based assistant equipped with marine weather information who calculates possible routes) in order to make decisions in an ever-changing environment. Most often there is no optimal solution, and intuition plays a central role. The skipper makes the final choice based on suggested calculations, but also on the risk of certain routes he already experienced, the condition of the boat, and his need for rest: the body and its limitations are at the center of the equation.

Before your next major decision, ask yourself three questions:

1. Experience check: Have I encountered this specific situation 10+ times before? In a similar context? If no, then move to step 3. If yes, you can probably assign a good level of trust to your intuition: but double check reality input, test your captors. Inattention is your worst enemy: missed details can lead to wrong decisions.

2. Feedback loop check: In past similar decisions, did I learn quickly whether I was right or wrong? Is there a good chance my mental model was validated with reality? Without feedback, intuition can’t calibrate. Conversely, you need to quickly (re)create this learning loop, asking questions to others who experienced it and probing your environment: preferably with third-party objective feedback.

3. Stakes check: If I’m wrong, can I course-correct quickly? No-way-back decisions require deeper concentration if stakes are high: you can’t afford to bet your life. You may use intuition to imagine scenarios, but you need to evaluate them with more data, even if it’s incomplete: internal simulation is a good way to assess risks. If you are in a rush, look for contradictory data first to eliminate riskier options.

When Intuition and Data Clash: What to Do

When your gut screams “yes” but the spreadsheet says “no” (or vice versa), you’re not facing a choice between the two—you’re receiving important information from both systems. This tension is actually valuable data itself.

First, don’t rush. The clash signals that something deserves deeper examination. Your intuition may be detecting patterns your analysis missed, or your rational mind may be catching blind spots in your experience-based model.

Second, investigate the source of each signal. Ask yourself: What exactly is my gut reacting to? Try to articulate the bodily sensation and what it might be warning you about. Then examine your data: Is my analysis complete? Am I looking at the right metrics? Could I be missing context?

Third, seek triangulation. Test both perspectives:

• Pressure-test your intuition: Run it through the three-question framework (Experience check, Feedback loop check, Stakes check) and scan for the four deadly traps.

• Challenge your analysis: Are you analyzing the right problem? Could confirmation bias be influencing which data you’re weighting? What would need to be true for the opposite conclusion to be correct?

Finally, buy “learning time” if you can. Small experiments or pilot tests let both systems prove themselves. Launch a limited version, test with a subset of customers, or run a simulation. Real-world feedback often resolves the tension by revealing which signal was more accurate—or showing that the truth lies somewhere between them.

The worst response is to simply choose your preferred system and ignore the other. The executives who make the best decisions treat intuition-reason conflicts not as problems to resolve, but as invitations to dig deeper before committing.

The goal isn’t to choose between gut and data—it’s to know when each is reliable and how they work together. Your intuition is like a muscle: powerful when properly trained, dangerous when overestimated or misapplied.

The executives who consistently make good decisions aren’t those who always trust their gut or always defer to data. They’re the ones who’ve learned to read the terrain, recognize the traps, and walk skillfully on both legs—combining instinct with analysis based on context.

Next time you face a major decision and feel that gut pull, pause. Run through the three checks. Your intuition might be signaling genuine insight—or it might be one of the four traps. The skill lies in knowing the difference.

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BIBLIOGRAPHY:

• Pro-Intuition Works

  • Contino, R. M. (2002). Trust Your Gut: How the Power of Intuition Can Grow Your Business. Kaplan Business.

  • Franck, C. Magnone P. Netzer O. (2022) Decisions over Decimals, Wiley

  • Klein, G. (2003). The Power of Intuition: How to Use Your Gut Feelings to Make Better Decisions at Work. Currency.

  • Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. Viking Press.

  • Gladwell, M. (2005). Blink: The Power of Thinking Without Thinking. Little, Brown and Company.

  • Page, L. (2022). Optimally Irrational: The Good Reasons We Behave the Way We Do. Cambridge University Press.

  • Pearson, J. (2024). The Intuition Toolkit: The New Science of Knowing What without Knowing Why. Simon & Schuster.

  • Sadler-Smith, E. (2010). The Intuitive Mind: Profiting from the Power of Your Sixth Sense. Wiley.

• Critical of Intuition

  • Kahneman, D. (2011). Thinking, Fast and Slow.

  • Sibony, O. (2020). You’re About to Make a Terrible Mistake: How Biases Distort Decision-Making and What You Can Do to Fight Them.

  • Stephens-Davidowitz, S. (2022). Don’t Trust Your Gut: Using Data to Get What You Really Want in Life. HarperCollins.

  • Rigney, R. W. (2018). Don’t Trust Your Gut: Using Data to Make Better Decisions. CreateSpace Independent Publishing.

  • Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515-526.

Version Française de l’article ici (cliquer)

Richard Bordenave: David Bessis, let me summarize your background: you’re a mathematics researcher, former CNRS researcher, former assistant professor at Yale, and you left academic research after defending your thesis at the École Normale Supérieure to turn toward entrepreneurship and artificial intelligence. You founded a startup before returning to your first passion with a very personal book that’s surprisingly accessible: Mathematica: une aventure au cœur de nous-mêmes (A Secret World of Intuition and Curiosity in English).

I was intrigued by the title: why does the English version mention intuition while the French one doesn’t?

David Bessis: I had originally considered writing the book in English, but in the end I wrote it in French first. I thought it would be risky to add the complexity of writing in English. Writing an entire book, even though I speak and write English well, is hard.

But when considering the English version, I thought a great title would be: Secret Math. You can’t say that in French, it doesn’t sound right. French has a more solemn quality.

When the American publisher bought the rights, I was very happy with this Secret Math title. But I think they got scared — it’s a university press, and it was a very mainstream title, a bit provocative. They were more conservative and kept the French title, which I think was a mistake. In French the use of Latin is more natural, but in English, it can be pretentious, and Mathematica is also the name of Wolfram’s software, which is much better known in the Anglo-Saxon world.

So I wasn’t very happy. I really wanted it to be called Secret Math. And I insisted that the word “secret” be in the subtitle at least. Hence: A Secret World. And what characterizes this world? It’s the world of intuition and curiosity.

RB: My entry point into your book is really this mix of math and intuition. You argue that the power of math isn’t logic but intuition. Isn’t that paradoxical when you think that math is there to prove things?

DB: I don’t think it’s paradoxical at all. In fact, it’s the central issue of the book: it appears paradoxical because the general public has a very distorted vision of what mathematics is. But in reality, from its very origin, mathematics is a method. The word comes from a Sanskrit word, I believe, that really means “the method.”

It was Descartes who reintroduced the word, “intuition” into modern vocabulary. It’s a word that comes from Latin. Descartes devotes many pages of his work to intuition. He founds truth on intuition, since he says that what is true is first of all what is clear and distinct — this is practically a neurological definition of truth — then on top of that, you can, through deduction, construct other truths by relying on first truths that are evident.

The evident truths are the axioms of mathematics. Logic is a tool at the heart of mathematics for, starting from things that seem indisputable, evident to us — therefore intuitively indisputable — constructing others.

And what has been missed in the historical understanding of math is that the practice of rigor, formal deduction, absolute logic supposedly alters our intuitive capacities — it strengthens them, corrects them. We’re not talking about the capricious, subjective, arbitrary intuition of the beginning. We’re talking about intuition that is built through confrontation.

Logic is the tool of mathematics, but the goal of mathematics is to strengthen our capacity for intuitive understanding.

RB: Maybe we haven’t quite understood Descartes correctly then?

DB: I think we couldn’t understand him at the time, and he himself was wrong about a number of things. But if we look with our modern hindsight, we understand that at the center of his work is the issue of neuroplasticity. Obviously, in the 17th century, it couldn’t be formulated like that.

RB: In my research, I like to say that intuition is, in a sense, the intelligence of the body — all these signals we capture. But does this apply to mathematics? It’s a very theoretical discipline — how can it be “embodied”?

DB: The brain is part of the body, last time I checked! Perhaps that’s where Descartes was wrong — he thought there were faculties of cognition, a mind somehow detached from the body. My reading — not mine personally, but the one I subscribe to — is to see the two things as attached, intricated. And I believe that’s the only reasonable point of view for a contemporary human.

Mathematics happens in our head. While writing the book, I realized there was a good question to ask to distinguish people who had understood what mathematics was from those who had never really encountered it. That question was: “When you do math, do you make invisible gestures in your head?”

If you ask this question to mathematicians, they all answer: “Of course, obviously, you can’t do math any other way.” We see things, we move. For some it’s very visual, for others it’s more tactile, motor, for others it’s rhythms, musicality. There are many different ways to recycle sensory intuition to make it mathematical intuition. But everyone does it one way or another.

And conversely, those who say “no, I don’t understand what you’re talking about,” they often say: “Ah, if I’d been told that, maybe I would have understood math!”

RB: Can you give examples of this role of the senses in mathematical intuition?

DB: The best example is when you look at young children. You see all small children doing the same things: the shape game with circles, squares — they fumble around, go completely crazy, don’t understand how it works, and then gradually they get it.

That “gradually” is very important — we’re talking about neuroplasticity. Neuroplasticity isn’t sudden, it’s gardening. It’s really long-term. A small child insists. When learning to walk, they fall down and start again. When learning to count, they start learning words: one, two, three...

I have two sons, one is 6 and the other is 2. The 2-year-old is starting to learn to count. For now he can count to 4 — he says “one, two, tree, foh”. The other day he showed me with his right hand. Then he showed that it also worked on his left hand. He was very happy to show me that it worked on both hands!

This kind of very gradual appropriation of an abstract concept — the concept of number — you see how small children do it. And what we don’t understand is that in fact, you have to continue like this.

When we leave early childhood, we lose the habit, and I believe we lose the social confidence that allows us to continue doing it. But you have to continue! That’s why mathematicians insist a lot on the importance of behaving like a small child: asking stupid questions (not only asking them but asking them again and again until it becomes obvious), fumbling around, trying silly things just to play. You have to play with things to develop this firsthand knowledge.

We know this: to learn a new sport or a new gesture, you have to try. And you don’t succeed the first time.

RB: You talk about the fear of being wrong and our relationship with error. How is working on this a lever to reactivate intuition?

DB: It’s a fundamental lever that’s named as such by first-rate mathematicians, who insist — really with unbelievable emphasis — on this point. You have to dare ask stupid questions. You have to make mistakes.

When you understand that it’s a matter of neuroplasticity, it seems obvious. Let’s take the metaphor of deep networks — even if it’s not perfect, it’s the best metaphor we have today. A machine learning system learns by minimizing error, but to minimize error, you have to make some.

When there’s an error, you adjust the weights. If you don’t make errors, you’re not learning. Making errors is the same thing as learning. Everyone knows this: you learn by making mistakes, not by repeating things you already know how to do.

If you don’t know how to do something, you’re going to make mistakes. And that’s very difficult to overcome because we have a real flight instinct. There are tactics to overcome that, but it requires dismantling many bad reflexes and bad beliefs —beliefs that aptitude is innate, that talent is innate, which are unbelievably widespread.

There’s a big misunderstanding. Indeed, there was a time when there was a very “blank slate” discourse, very stupid, where people claimed there was no genetic difference between people. There is, but it’s not of that magnitude. All children manage to count, manage to understand numbers and even negative numbers, which 100 years ago were considered too abstract.

There is indeed a capacity to appropriate new concepts. But to do it, you have to practice. And there’s a vicious circle where you convince yourself that you can’t do it, you have a very negative emotional reaction, and so you don’t even try anymore. Most people are just locked outside by an inferiority complex.

RB: In our so-called Cartesian culture, we hear that you shouldn’t trust intuition, that it’s opposed to rationality. How do you get out of this paradox?

DB: You have to be wary of intuition, but you have to listen to it, because we have no other cognitive resource than intuition.

We’re capable of following reasoning, but who’s capable of following reasoning or calculation for more than two lines? Not many people. I can’t do it, I make mistakes.

So what do we do? On one side we have what seems evident, intuitive to us. On the other side we have what logic, rationality, is supposed to tell us. When the two agree, everything’s fine, we move forward. When they disagree, you have to stop.

When someone explains something mathematical to you and you see that the reasoning seems correct, but it seems strange to you and you don’t know why — that’s where you have to stop. That’s where you have to ask the question: “I understand that 2+2 is supposed to equal 4, but in my head it equals 3. Why? How do you see it in your head exactly? What’s happening?”

You really have to take the time to unpack all that. Because when you do that, you’ll realize that if you perceived it that way, it was because you had understood something — even if it wasn’t exactly the right thing. A small shift was required; things weren’t exactly in place. And this reorganization work will make us progress.

RB: How do you hear that signal that tells you “this is weird, something’s off”? How do you develop that acuity?

DB: That’s probably the most important thing. I think we all have it, but we interpret it badly. We interpret it as “I’m stupid.” We interpret it as “it’s too hard for me; I can’t understand.”

Whereas in fact, no. You just have to say: “I don’t understand.”

I don’t understand; it seems weird to me. And in fact, when you work on this, you can use same techniques as you use in creativity exercises — where you gather around a board and everyone writes on post-its what comes to mind. These brainstorming things.

You have to think like that, and this mode of thinking is extremely fertile. In fact, there’s no other method of creativity than this one. To put rationality on a pedestal misses the point.

RB: You give the example of Kahneman and the bat and ball problem...

DB: Yes! A bat and ball cost $1.10 together. The bat costs $1.00 more than the ball. How much does the ball cost?

Most people answer 10 cents, which is wrong — because that would mean the bat costs $1.0 + $0.10 = $1.10, plus the 10 cents for the ball, which makes $1.20. In fact, the ball costs 5 cents.

This example amused me because when I was asked, I intuitively gave 5 cents, without thinking. The friend who had asked me was very perplexed. She had explained that there was a guy who had won the Nobel Prize because he had demonstrated that everyone got this wrong, because there was an intuitive thinking system that made people err.

I was really puzzled. I watched several Kahneman videos to be sure he really said it. I looked in his book — it’s written in black and white. He says: “People give the intuitive answer,” as if intuition were something rigidly hardwired in everyone.

But my intuition wasn’t wired the same way. In the videos, he always says the same thing: “The intuitive answer is 10 cents.” But it doesn’t mean anything to say “the intuitive answer is X,” because intuition isn’t something fixed. Everyone has a different intuition, and intuition is malleable.

Maybe when I was a child, I would have said 10 cents. And that’s probably what made me good at math: since I learned to question my intuitions when they were proven wrong, I gradually developed an intuition for math that was more sophisticated than your average person’s mathematical intuition.

In my intuition of numbers, I see them as lengths. If you represent the problem as lengths — the ball, the bat — it’s obvious. I make a small diagram in the book.

My way of summarizing the approach I believe to be that of many mathematicians is to say they work on their System 3. Kahneman says System 1 is fast thinking (or intuition), and System 2 is slow thinking (or rationality). The System 3 I describe is super slow thinking, where the first two systems are integrated. It isn’t, as Kahneman says, “throw your intuition in the trash, rely on rationality.” No, that doesn’t work — or it works for a while, but in real life, on the scale of a lifetime, it doesn’t work.

It can work occasionally when you’re stuck. But in reality, when your intuition tells you something that contradicts rationality, it’s very interesting. Because either the calculation is wrong, or your intuition is wrong — but maybe you have the intuition of something that could be fertile in creativity.

A good reason to dig into something is when you have the impression there’s something very important, but it’s not explained anywhere. You have to understand: what exactly do I have in my head that’s interesting?

And that can take time. Sometimes you’ll reach that level of understanding in a few minutes, sometimes in a few hours, sometimes in a few days, sometimes in a few years, sometimes in several decades.

Personally, I learned the second law of thermodynamics when I was in preparatory class. And it took me a long time to understand it. I think it took me 15 years to understand the second law of thermodynamics.

The thing kept trotting along in my head. Not continuously — I’d think about it once every two years, maybe for 10 minutes. But each time I thought about it, I had a slightly different angle. And today, I feel I understand the second law of thermodynamics in a way that’s mine, that’s quite deep.

It’s a kind of superpower. But it’s because I wasn’t satisfied with not understanding this thing intuitively. I kept it like a dog-eared page in a book. Each time I picked up the book, I’d say: “Look, I have this dog-eared page, there’s something wrong and I can’t understand what’s wrong.” It’s wrong in my head — it’s not the physics that’s wrong, it’s my head that’s wrong.

But by correcting that, I progress.

RB: D. Kahneman did agree with G. Klein that intuition can be good when you’re an expert. But there’s also a conception — I’m thinking of Gérald Bronner — of intuition as lazy thinking (System 1), opposed to critical thinking. How do you reconcile this?

DB: If I were mean, I’d say that Gérald Bronner is a lazy rationalist, because he doesn’t question rationality.

What is rationality? When you tell someone “think” or “be rational,” what are they supposed to do? They’re supposed to think in a certain way.

What is thinking? Thinking is activating neural circuits, associations of ideas — things that are all in an intuitive substrate. The human brain is an intuition machine.

So what we call rationality is a certain way of using our intuitive thinking, with a set of rules, verification mechanisms — mechanisms that which in themselves carry no meaning.

You can’t not be intuitive. You can’t think rationally — it doesn’t exist, it’s an illusion. And I’ve even come to think that these rationalists who tend to denounce intuitive thinking as a bit magical, a bit mystical, are themselves engaging in a mysticism of what logic or rationality would be.

There’s no biological substrate for the things they’re talking about. We don’t know what it is, biologically. I think they’re the ones who are naive, and it’s a very deep error that goes back to Descartes.

Descartes was the first to make this error. He had an excuse — first because he made enormous progress to get there, and then he was in the 17th century, not the 21st century. Today, I think it’s unforgivable as an error. It’s a profound misconception.

It’s a form of spiritualism, in a way — thinking that there would exist a transcendent meaning of discourse where things could be absolutely true. Whereas in fact, we’re animals.

RB: We always come back to the body!

DB: Yes! You can say it very provocatively: Descartes, in inventing modern rationality, invented a certain meditation technique.

A certain way of using one’s associative, contemplative, meditative, introspective resources — to make them grow in a more harmonious way, closer to reality, more predictive.

But if you really look at what happens at a biological level when we think, when we do mathematics, when we try to think rationally — with all the quotation marks you need around these words — you realize that phenomenologically, that’s what we’re talking about.

RB: Your book is about math, but it’s also a book about intuition more generally. Does what you’re describing apply beyond mathematics?

DB: Of course! What’s interesting about mathematics is that you take this rather archaic rationalist discourse, and you catch it red-handed in contradiction at the place that’s supposed to be the heart of the machine. Math is at the heart of the rationality machine, and we realize it doesn’t work at all as we’ve been told.

And it’s the field I know. So these two things — the fact that it’s the heart of the machine and that it’s the field I know — justify that I wrote a book about mathematics.

But of course, this discourse and this message apply beyond. I think we have a big job to do of re-rationalizing what intuition is. We’ve made it into something a bit New Age, a bit bogus. And as a result, there are plenty of gurus who will talk nonsense about intuition.

Indeed, superficial intuition, the first one that comes, which is capricious, arbitrary — on that I’d follow Gérald Bronner, he’s quite right. That laziness, that’s not what we want to do.

And we don’t want to believe that intuition is communicating with beings of light either. We’re not into New Age.

What I’m saying is that this is really, in my view, the only coherent, physicalist, modern position, anchored in science and rationality. It’s the only way to understand what human cognitive activity is: it’s fundamentally intuitive.

Our brain doesn’t have a left half that would be intuitive, a right half with gears and printed circuits. It’s not true — if you open it up, it’s all the same inside. It’s associative everywhere.

So what we call rationality must be a particular modality of using intuition. It won’t oppose it, it will strengthen it.

It’s like a gym for intuition. But the living part, what’s really in the human body, is intuition, it’s this living thought. There’s no other. There are no other places where we’re alive.

RB: Let’s move to AI, since it’s a big topic today. Do you think AI is intuitive or can be? And will the machine help us grow our intuition, or on the contrary restrain it?

DB: I think LLMs today are intuitive machines. It’s really artificial intuition — with all its flaws. Again, intuition isn’t perfect, but it’s very powerful.

As for our own intuition, I think it’s very ambiguous. There’s a capacity to develop it when you take what an LLM tells you with distance. I use LLMs a lot to improve things in my writing, in my thinking, to learn things, to summarize.

But I also see the danger — which is to no longer develop our firsthand knowledge, our intuitive digestion, which requires quite intense labor. I think we’re facing a real danger for the formation of tomorrow’s intuitions.

RB: In a way, an athlete needs to do their training. You can’t skip that effort if you want to reach a certain level of performance.

DB: Exactly. And it’s very important to add this nuance to everything I’ve said about the role of mathematical intuition: we train our intuition by doing these logic-oriented exercises. It’s not about eliminating rationality. It’s just about putting it in its place, not taking it for something other than what it is.

But you have to confront rationality actively, strongly, persistently. You have to think, you have to read, you have to read slowly, you have to absorb things, try to see them in your head, see where you can’t see them, where you can see them, where they pose problems.

This work that we start doing in early childhood — when we learn to reason, to read — these are very difficult things. We progress very slowly. If we stop doing this work, if for example we shift to a purely oral society where we’re always in conversation with a chatbot via voice recognition, we’re going to have a very serious, catastrophic problem.

RB: We talk about learning methods like the Singapore method or Montessori, which take an approach where socialization happens through experimentation, touching, materializing. What do you think of these?

DB: I think it’s pretty good. I don’t think it’s that different from what a lot of good teachers are doing today. It’s not just methods with fancy names that are good methods. There are lots of ways to teach, with tricks that many teachers have come up with that work very well. In the Singapore method, there’s a desire to minimize the number of concepts to learn in order to learn them deeply. And that’s really important.

In these teaching issues, we’ve historically given a lot of importance to the list of things that children learn. But I think what’s even more important is the experiences they’ll have.

The fundamental experience that every child should have in their encounter with mathematics — and unfortunately this isn’t the case for most children — is the experience of starting from something that’s totally unintelligible, struggling with it, and realizing a few months later that it’s become obvious.

That’s the experience of neuroplasticity. And when you’ve had this experience, you say “Wow, this is great!” You feel it. There’s a deep pleasure, a physical pleasure of light coming where there wasn’t any.

I believe that independently of any teaching method, we need to be concerned with the cognitive and emotional experience of children. That’s the central issue.

RB: For people who are adults and who work in fields of study or numbers, what would be your advice? Because we’re no longer children. What can we do to practice and develop our intuition?

DB: Get back in the habit of saying “I don’t understand.” And so you necessarily have to find content you can engage with or sparring partners with whom you can exchange.

There’s something that struck me in my professional life after math, when I became a company CEO, tech startup founder. I met people who are VCs, people quite high up in Silicon Valley companies — rather smart people.

And I found in them a trait, an attitude that I had seen in people who were very good at math. It struck me.

When I started explaining something to these people, they would interrupt me saying: “Wait, wait, go slowly. I have very stupid questions to ask you. I’m sorry, I’m going to ask you completely dumb questions, but that’s how I understand things.”

That audacity. And that modesty. It’s funny, it’s a mix of modesty and audacity. You have to have the audacity to be modest, whereas people are often wrongly convinced that you have to try to pretend you understand.

No, no, no. Play the fool. Ask stupid questions.

Defuse the taboo by saying: “You know what? I’m going to ask completely dumb questions, I know it’s dumb, but that’s how I work.”

This trait is one of the greatest accelerators of intuition development. Because when you allow yourself to ask dumb questions, you allow yourself to ask questions where you are. And so there’s no limit, there’s no taboo.

It’s extremely fruitful. And on top of that, it puts the interlocutor in a rather flattering position, because they’re in the position of the knower. So they become kind, they no longer try to show off, they no longer try to bamboozle by saying hard things. They try to say simple things.

I believe this way of doing things is natural and extremely beneficial in professional life.

RB: That’s wonderful, thank you very much. It’s very concrete advice, and it’s a mindset change for us. I have plenty of other questions coming to mind now, but we’ve reached the end of our call. Thank you, again, for making time for this conversation.

DB: I had a great time—this is a wonderful field of inquiry, and if I can leave you with a final encouragement, I’d just want to say: have the confidence and rigor to dare say that intuition is a rational subject.

David Bessis is the author of “Mathematica: une aventure au cœur de nous-mêmes” (Seuil, 2022), published in English as “A Secret World of Intuition and Curiosity” (Yale University Press, 2024).

Follow David Bessis on Substack: https://substack.com/@davidbessis

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Imagine you’re waist-deep in murky sea water near a wild coast. Your toes and legs disappear into the blue-green haze below, so you can’t see them.

Suddenly you feel that something soft brushes your ankle. Your leg rockets upward before you can think. Heart pounding. Muscles coiled. Every nerve screaming “danger” even though it’s probably just kelp.

That’s your prediction machine at work—not waiting to analyze the threat but instantly generating every possible horror your foot might encounter in those invisible depths. Sea urchin spines. Jellyfish tentacles. Something with teeth.

The gap between what you expected (smooth sand) and what you felt (unknown softness) triggered a full-body alarm system in milliseconds. Your body predicted the worst-case scenario and acted accordingly.

Even now, lowering your foot back feels overwhelming. That innocent piece of seaweed might as well be an electric eel.

As we discovered in previous chronicles, our sensory-motor system builds a live model of the world, constantly predicting what’s coming next—sometimes by just milliseconds ahead of reality.

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See a syringe when visiting your GP? Your body doesn’t wait for the needle to pierce your skin. It anticipates the pain based on past experience, tensing muscles and flooding you with stress hormones before contact. You literally feel the hurt before it happens.

Swallow a pill—even a sugar tablet your doctor calls medicine? Your prediction machine kicks into gear, triggering real physiological changes toward healing because it expects relief to come (the well-known “placebo effect”)

This is your brain’s ability to generate previews of what’s about to unfold, preparing your body to meet reality before reality arrives. You’re not just living in the present moment—you’re living slightly in the future, thanks to this biological predictor that’s been keeping humans alive for millennia.

From reflexes to expertise:

That same split-second prediction system scales up to handle complex, long-term patterns. Watch an experienced ER nurse during a busy shift—they’re not just seeing individual patients, but building a living model that tracks breathing patterns, skin color, gait, family behavior.

In “Expert: Understanding the Path to Mastery”, Roger Kneebone describes how these nurses develop an almost supernatural ability to sense which patients are about to crash. They can’t always articulate why, but something in their body signals danger—a subtle change in breathing rhythm or just something that ‘doesn’t feel right.’

By their third year, this nurse can predict which chest pain is cardiac versus anxiety, which head injury needs immediate imaging, and which ‘stable’ patient will deteriorate next. What began as checking vital signs has evolved into sophisticated clinical intuition—the same biological prediction engine, just operating across multiple body systems simultaneously. What began as instant reflexes has evolved into sophisticated foresight—the same biological prediction engine, just operating on a much longer timeline.

Intuition as Prediction Gap

Building on neuroscientist Andy Clark’s predictive processing theory (see previous chronicle), I think we can use the term, “intuition,” to describe what emerges from the gap between our body’s expectations and reality. This mismatch creates the familiar bodily alert, “Something’s off.” When reality doesn’t align with our body’s expectations, this discomfort redirects our attention toward what requires correction or exploration. Like when you realize your keys aren’t where they should be, and your brain reviews all possibilities.

On the other hand, when our experience aligns with our prediction, we feel positive emotions—rewarding sensations that validate the quality of our internal model. The more confident our prediction, the sooner this positive feeling emerges. In essence, our body predicts the quality of its own predictions. We feel really good when everything falls into place, like when you add the last piece to a puzzle.

The “mismatch” and “alignment” feelings are the signals that keep us adapting and learning to close gaps. As we discussed previously, Intuition can work backward (understanding the past) or forward (imagining the future). When the brain manages to connect observed points that align with its internal model, insight emerges.

Looking backward, the disappearance of the gap between past experience and model prediction produces a click, which we call the “aha or Eureka moment.” We feel satisfied that we have grasped what was happening, and the resulting action becomes fluid—like when you finally discover who the criminal is at the end of a detective film.

Looking forward, the existence of a gap between our goal and ongoing experience drives us forward. It ensures that we never stop correcting our trajectory, adapting to circumstances, and finding ways to change them so that reality matches our expectations.

Some call the prediction gap an error, a defect, a variance, a surprise, a difference, an anomaly, a coincidence, or a paradox. It’s not so much an error- which has a negative connotation- as a sign of a lack of relevant data to learn from, or an appropriate feedback loop to adjust the shot. The discrepancies with real life revealed by unexpected feedback loops are indeed essential: they fuel our learning engine. It’s as if your internal voice were saying, “That’s surprising. Explore further,” “Weird. Look again,” “I feel it. Try this path,” or “Wait, don’t trust him.”

Of course, certain contexts or limitations of our sensory equipment can create false discrepancies (and we will develop this point in another column). But their power to alert us remains essential nonetheless: it encourages us to take the time to refocus our attention on this gap, because intuition is a process of continuously updating information, not of arriving at a definitive answer.

Our sensory-motor system continuously fuels our action scripts by interpreting gaps between predicted and experienced reality. When the generative camera in our brain (see previous chronicle) activates “backward” mode, it recognizes shapes, analyzes events, acts, and interprets prediction gaps. The model it contains learns on its own and “grasps the world”. When it activates “forward” mode, or exploration mode, we plan, simulate actions, and invent solutions. Its generative model explores potentialities to “create a world”.

Disconnecting the Feedback Loop:

But what happens when this world model becomes disconnected from sensory feedback? The brain allows an imaginary world to unfold, unconstrained by external reality.

Neuroscience research shows that this occurs most obviously during sleep (see bibliography), when the feedback loop with the physical world is paused. Without sensory correction, our prediction machine runs free—generating the vivid, often bizarre narratives we call dreams. But this same disconnection can happen while awake, producing what Andy Clark calls “controlled hallucinations”—the hallmark of human creativity.

When we daydream, imagine future scenarios, or engage in creative problem-solving, we’re essentially allowing our prediction system to explore fictive possible worlds. Artists visualizing their next painting, entrepreneurs imagining new business models, or scientists hypothesizing about unseen phenomena—all are temporarily disconnecting from immediate sensory constraints to let their internal models generate novel possibilities.

This ability to simulate using an embodied predictive system allows us to imagine entirely new worlds, then work backwards to figure out how to make them real. This again suggests that imaginative thinking is not separate from analytical thinking—it is the same biological machinery operating in a different mode. Our gap detector now focuses on the difference between imagined reality and experienced reality, inventing behaviors to make our (self-fulfilling) prophecies come true.

So What? Living with Your Prediction Machine

Our daily decision-making and learning processes are directly impacted. To take advantage of the body’s predictive system, you need to activate its sensorimotor circuits. This means getting moving and engaging all your senses in the theater of operations. Sitting behind a computer, manipulating abstract data, limits your ability to build a reliable bodily model. Not everything can be interpreted from reports. To understand and act, you must first immerse yourself physically where the action takes place: as suggested by the Gemba approach, a protocol of the Toyota quality system.

Sometimes, intuition is an emergent process in which your model creates an internal reality that you then compare to your experience in the world. Other times, it is a retro-examination process that finds meaningful connections within its maps of the world. Whatever the operating mode: detecting gaps – real or virtual- is a resource.

Your discomfort is a signal for attention. That nagging feeling during a conversation, the unease about a business deal, or the sense that “something’s not right” about a situation—these aren’t irrational emotions to dismiss. They’re sophisticated early warning signals from your prediction system. But you shouldn’t confuse this signal for a definitive answer: it is calling an issue to your attention for further exploration. Intuition steers you toward or away from a learnt path, but good decisions require internal debate.

Positive gut feelings are a pointer to assumptions. When something feels right or comes easily, it’s a sign that your brain has a good understanding of the situation. Lean into these moments—they often lead to your best assumptions. Your brain rewards you in advance for a bet it thinks you will win. But to fully trust these assumptions, you have to test their robustness with real life feedback. Not by doing additional cherry picking but by arguing against your assumption looking for gaps with possible disconfirming evidence or sparking an internal debate by successively playing devil’s advocate and angel’s advocate.

Stay curious about prediction gaps to innovate. When reality surprises you, resist the urge to rationalize or discard the gap. That moment of mismatch is your system’s way of saying “update needed”—it’s pointing toward something important you haven’t fully grasped yet because you lack relevant observations or connection.

Surprise is the trigger that helps you switch from operating mode—dealing with existing knowledge—to exploring mode, searching for new cues that will update your assumptions. It encourages further probing in real life, suspending your judgment because innovations come from the fringe, the outliers and contradictions.

Your body is always on: constantly running sophisticated predictions about everything from social interactions to career planning. Learning to listen to and work with this organic system, rather than against it, but also learning the conditions to make it efficient, can dramatically improve your decision-making. In the next chronicle, we will explore ways to prevent self-deception and manage relevant quality checks to prevent hijacking or misdirection.

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Bibliography:

1. Bucci, A., & Grasso, M. (2017). Sleep and dreaming in the predictive processing framework. In T. Metzinger & W. Wiese (Eds.), Philosophy and predictive processing: 6. Frankfurt am Main: MIND Group. https://doi.org/10.15502/9783958573079

2. Clark, A. (2023). The experience machine: How our minds predict and shape reality. Pantheon Books.

3. Clark, A. (n.d.). Perception as controlled hallucination: Predictive processing and the nature of conscious experience. Edge.org. https://www.edge.org/conversation/andy_clark-perception-as-controlled-hallucination

4. Dijkstra, N., Mazor, M., Kok, P., & Fleming, S. M. (2023). Subjective signal strength distinguishes reality from imagination. Nature Communications, 14, 1627. https://doi.org/10.1038/s41467-023-37322-1

5. Kneebone, R. (2020). Expert: Understanding the path to mastery. Viking.

6. Mathis, V., & Wijnen, J. (2025). Dreaming as fascinated predictions: Bridging Sartre’s phenomenology and predictive processing. Phenomenology and the Cognitive Sciences. https://doi.org/10.1007/s11097-025-10104-4

7. Pezzulo, G. (2020). From prediction to imagination. In A. Abraham (Ed.), The Cambridge handbook of the imagination (pp. 106-127). Cambridge University Press. https://doi.org/10.1017/9781108580298.008

8. Wager, T. D., & Atlas, L. Y. (2015). The neuroscience of placebo effects: Connecting context, learning and health. Nature Reviews Neuroscience, 16(7), 403-418. https://doi.org/10.1038/nrn3976

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As we have seen in previous chronicles, separating intuition from intelligence is nearly impossible: the two are deeply intertwined, and we cannot simply switch off our bodies to let only our minds speak. Yet for centuries, our metaphors for intelligence told a different story—one of pure, disembodied thought.

Each generation has reached for the most impressive technology of its time to explain human intelligence. The clockwork mind of the Enlightenment—precise and mechanical. The steam-powered brain of the Industrial Revolution—systematic and driving. The computer brain of the twentieth century—processing information and running programs.

Notice what all these metaphors shared: they stripped away the body entirely. Intelligence became something that happened despite our physical form, not because of it.

But something curious has happened recently. As artificial intelligence has evolved, we’ve started talking about robots and sensors again. Our intelligence isn’t challenged by algorithms alone but by machines with bodies that move through space, speak and interact with the physical world.

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This return to embodiment in our thinking about intelligence is making the computer metaphor increasingly irrelevant. Latest advances in neuroscience reveal how deeply our cognition is rooted in physical experience—as do our failed attempts to create artificial intelligence that understands the world as naturally as a baby does.

As AI pioneer Yann LeCun observed, “Nobody tells the baby that objects are supposed to fall”—yet by nine months, a baby grasps gravity through pure observation and interaction with the physical world, something our most sophisticated AI systems still struggle to achieve.

The Limits of the Computer Metaphor for Intelligence

For decades, everyone described the brain as a computer: senses passively capture information, the brain processes it, makes decisions, and then commands actions. This vision casts our senses as passive receptors and our brain as a central processing unit analyzing and deliberating before reacting. This view implicitly equated intelligence with agency and rationality.

But the brain is neither a silicon machine nor an organ floating in a jar. We’ve known since Damasio’s research in the ‘90s that emotions play a crucial role in decision-making.

Researchers in embodied cognition have long defended the intelligence of the body as another possible definition of intuition. In the early ‘90s, Francisco J. Varela, Evan Thompson, and Eleanor Rosch were among the earliest and most influential proponents. In their foundational work, The Embodied Mind, they argued that

“cognition depends upon the kinds of experience that come from having a body with various sensorimotor capacities, and that these individual sensorimotor capacities are themselves embedded in a more encompassing biological, psychological, and cultural context.”

However, rationalists continue to assert the predominance of the mind, even though the separation between body and mind makes no sense given how intertwined they are. And most of us remain uncomfortable accepting that our body could help us think—or more precisely, help us decide how to act.

This is where the idea of intuition as “intelligence of the body” comes into play: intuition is knowing what to do without knowing why. We simply don’t have conscious access to the internal models that generate these insights, which can be frustrating because we like to think of our rational mind as the seat of insight and decision-making. It is not, and we’ll get more into that later

More recently, thanks to groundbreaking work by neuroscientists Karl Friston and Andy Clark, the rational computer-brain metaphor has given way to a revolutionary interpretation: the predictive processing theory.

The Predictive Processing Revolution

This new approach, supported by cross-domain evidence, completely reverses the logic. Rather than waiting for sensory information, the brain serves as a tireless predictor. It continuously generates hypotheses about what will happen next by interrogating its environment, feeding various internal models of the world that update constantly.

Our senses do not collect data exhaustively: they sample useful reality bits to update their predictions. The brain also integrates into its models a representation of each of us and our social behaviors. To do this, our senses and motor system strive to stay permanently coupled with their environment, capturing the dynamics between us and the world.

Researchers in embodied cognition were the first to conceptualize this idea of structural coupling between the body/brain and the environment. And in the ‘90s, Alain Berthoz intuitively grasped what would later become the cornerstone of predictive processing theory: the brain as a world emulator. In his groundbreaking works Le Sens du mouvement and La Décision, he proposed that the brain functions not as a computational machine processing sensory input, but as a simulator that generates hypotheses about possible movements and actions, then tests these predictions against reality.

What made his vision particularly prescient was his refusal to isolate the brain from the body—he emphasized the constant dialogue between neural prediction and embodied experience, showing how decision-making emerges from internal simulations between the body and world. This embodied approach to prediction anticipated by decades the current theories of active inference and enactive cognition, while his idea of “penser par le mouvement” (thinking through movement) revealed how action and cognition are fundamentally intertwined in the brain’s predictive machinery.

Real-world example - When potential clients walk into a car dealership, an experienced salesperson immediately reads their body language, movement patterns, and which vehicles catch their attention. Within seconds, their brain predicts budget range and purchase readiness, shaping whether they open with “Just browsing today?” or dive into targeted discovery questions. Their predictive system calibrates the entire sales approach based on physical cues before any conversation begins.

Intuition and the Generative Video-Camera Metaphor

In order to simplify the complex neuroscientific explanations behind predictive processing theory and to include intuition as a form of intelligence, I propose a new metaphor that refers to an imaginary device: the generative video camera.

Imagine that our sensitive body and brain form a biological camera that records the present moment as it unfolds. Our attention functions like a zoom, focusing on areas useful to our objectives. But this camera has a unique capacity: it can generate the next image that doesn’t yet exist. And more importantly, it works both ways—recording past experience to extract a world model, then using it to predict its next state and simulate upcoming actions (see the exploitation and exploration modes on the bi-directional diagram)

This generative camera metaphor illustrates how our brain records, predicts, and adjusts, always seeking coherence between its internal model and the outside world. We select perceptions of the world that help us navigate toward our goals.

Intuition is fueled by the body’s predictions and corrected by sensory experience. The camera adds “subtitles,” the vehicle of our rationality, solely to enable us to share the key idea, thus intertwining experience and rationality. However, intuition or bodily intelligence precedes the use of language, and language-based rationality arrives only after the action is in progress. Babies, for example, “know” intuitive physics and social norms before they can speak.

This intuition metaphor builds on our experience with generative AI for videos. It mirrors artificial intelligence with human intelligence to help us understand our own functioning with the latest technology, to help us grasp the mental model from experience. As Paul Valéry wrote, “We only understand well what we can recreate.”

A Simple Example - Consider reaching for your coffee mug. Your rational mind might think you’re executing a conscious decision, but your brain doesn’t deliberate or calculate trajectory—it focuses on the desired target and continuously predicts arm and hand movements to reduce gaps with its objective. It simultaneously anticipates weight, coffee aroma, and mug temperature, adjusting movement to reflect sensorial reality. If you feel the mug is too hot, you instantly put it down without thinking. Only afterward can you explain “why” you acted this way, adding subtitles to the movie.

The fascinating consequence: action precedes decision. Multiple action simulations are generated by our embodied model before one is executed. As Berthoz explains, “decision is not driven by reason, but by action or its inhibition.”

Rethinking How We Learn, Lead, and Live

If we recognize that intelligence inherently includes intuition—that body and mind cannot be separated in how we truly think and know—this has important implications for how we approach education, leadership, and decision-making.

• In Education - Instead of treating the body as a distraction from “real” learning, we should integrate physical experience into how we teach complex concepts. Mathematical understanding, for example, seems to emerge more naturally through movement and spatial reasoning than through abstract symbols alone: I will dedicate a later chronicle to studying the Singapore Method and Montessori’s insights.

• In Leadership and Management - The best leaders are not necessarily those who analyze the most data, but rather, those who have developed the richest embodied models of human behavior. These leaders can “read the room” and predict team dynamics through subtle physical cues. They may also be those who can predict market evolution by grasping emerging trends. Check out the past Chronicle. We should value and cultivate this somatic intelligence alongside analytical skills.

• In Personal Decision-Making - Instead of dismissing our gut feelings as irrational, we should learn to pay closer attention to our body’s predictive signals. That uncomfortable feeling during a negotiation or the subtle energy shift in a relationship may represent sophisticated pattern recognition that our conscious mind hasn’t processed yet. In a future chronicle, we will explore what a decision really is and how to best nurture it as a preparation for action.

• In Design and Technology - If we consider human intelligence as fundamentally embodied, then truly intelligent AI systems will need bodies that interact meaningfully with the world. And human-centered design should account for how our physical experience shapes cognition: language being the most obvious interface between humans and machines, we need to explore the risks and opportunities of conversational dynamics.

The generative video camera is not just a new metaphor for intelligence and intuition; it is an invitation to trust the wisdom embedded in our bodies, and to design systems, organisations, and lives that honour the deep integration of mind and flesh. Intuition is not a flaw in rationality; it is a feature of embodied intelligence that deserves cultivation and clarification to help us thrive through mindful emulation.

To understand where we come from, read previous chronicle “a century in search for intuition”

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If you ask a communicator, an engineer, a doctor, or an artist for their definition of intuition, you’ll probably get four different formulations…

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As a good engineer, John had turned the technical systems integration problem inside out. None of the solutions he had studied was satisfactory, and his management was ramping up the pressure. Fortunately, the weekend was approaching.

John began the weekend feeling stressed, but then decided to let go and enjoy his time off, resolving to tackle the problem fresh on Monday. The scenic hike he took on the nearby mountain helped restore his calm, and for the first time in days, gazing at the panoramic view, he almost forgot his troubles entirely, and later enjoyed a good night’s sleep.

Out of nowhere, on a sunny Sunday morning upon waking, everything became crystal clear. John could see the solution unfolding in his head. He grabbed paper and pencil to draw a detailed diagram, which he shared with management first thing Monday morning, receiving immediate approval.

Does this story remind you of something? Many of us have experienced moments like these, when a solution emerges from nowhere, after we have been struggling with a problem for a long time.

What is that mysterious emergence? If you ask a communicator, an engineer, a doctor, or an artist for their definition of intuition, you’ll probably get four different formulations.

Some will tell you it’s sudden, others that it’s progressive, some that it’s unconscious, coming from within, others that it’s a small voice inspired by patterns we see outside...

To avoid locking ourselves into preconceived ideas about what intuition is or isn’t, I suggest deconstructing it like a designer would (identifying Jobs-to-be-done). Instead of starting with its characteristics, which clearly vary according to each person’s perceptions, let’s trace back to its uses and experienced benefits.

Four User Stories of Intuition

Here are three other “user stories” that, along with John’s, reveal the diversity of intuition’s uses. As you read them, try to spot some common characteristics.

1. The Declined Opportunity: Julia, a digital marketing manager, was staring at the contract in front of her. Everything seemed perfect on paper: an expanded title and responsibilities, a better salary, and an accessible location. Yet she had a diffuse sensation that discouraged her from accepting. After the interviews and visiting this startup’s offices, a small voice told her this company wasn’t for her. She declined the offer. A few weeks later, she landed an internal promotion.

2. A Not-So-Imaginary Patient: Edward had examined all his patient’s test results. Nothing abnormal, except for that pale complexion and persistent fatigue that concerned him: she wasn’t one to complain in the twenty years he’d been treating her. A strange sensation in his gut prompted him, despite protocols, to prescribe an abdominal ultrasound. To his surprise, it revealed a small mass in the pancreas. Cancer at such an early stage that no standard examination would have detected it. There was still time to intervene.

3. The Artist in Crisis: Rose had been working for months on a film’s original soundtrack without managing to find the main theme. She had tried everything: experiments, scales, instruments. Nothing seemed to capture the film’s essence. One day, while wandering through the costume workshop, she was struck by the raised pattern on a tunic. It was as if she could hear notes detaching from it as she touched the fabric. A melody resonated in her head: she had found the theme that would mark her work.

What Do These Four Stories Reveal?

Despite their differences, these accounts share four characteristics that illuminate this phenomenon we call intuition:

1. It emerges in situations that matter but contain much uncertainty

2. It translates into a bodily feeling that informs our consciousness

3. This feeling is an encouragement to act or, conversely, to restrain action

4. It appears as a resource, conditioned by initial motivation

Obviously, not all intuition stories end well. Consider the entrepreneur who, “feeling” that a partnership was promising, ignored financial warning signals and lost considerable investments.

Intuition alone is not enough to make such decisions, but even in these unfortunate cases, the circumstances of intuition’s emergence remain identical. And understanding how to articulate it with our other resources (or not confuse it with other feelings) remains essential for the story to end well!

Cultivating an awareness of how and why intuition emerges, and considering it as an indispensable resource alongside our reason and other analytical tools, can help each of us to:

• Make decisions, including forming moral judgments

• Solve problems, material or human

• Guide exploration of the real or possible

• Create inventively or artistically

Ultimately, these are the four functions of intuition (or “jobs-to-be-done” in a designer’s language).

Check out my latest interview about intuition with Dominique Desjeux here

An Innovation Shaped by Evolution

In these Substack chronicles, I argue that behind intuition lies a learning system that relies on the body’s intelligence. This living body is equipped with natural algorithms that continuously process signals from our environment and prepare us for action. We simulate expected or desired worlds, and experience blockages and releases as genuine physical sensations.

The value of starting with how we “hire” intuition to solve certain problems will help us organize multidisciplinary academic sources and connect the dots. In upcoming chronicles, we’ll explore the conditions of Intuition’s emergence, factors that contribute to its reliability, and how to mobilize it in each of these use cases.

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And here are the 5 bonus tips:

Developing intuition in research organisations

The centaurs revolution: Redefining human-AI partnership

The first AI-augmented humans were dubbed “centaurs”—inspired by mythological creatures, now illustrated as half-human, half-machine. They appeared after Deep Blue’s victory over Kasparov in 1996, when mixed chess tournaments showed assisted players beating both pure machines and pure humans (Saghafian, 2023). With AI’s arrival in the workplace, expert intuition returns centre stage.

Centaurs versus cyborgs in market research

Today’s debate centres on the most effective human-AI dialectic; here are Dell’Acqua et al.’s (2023) symbolic personas to describe the possible models:

• The Centaur Model - Clear task division. AI handles data processing, statistical analysis, and pattern recognition at scale. Humans focus on interpretation, client interaction, and strategic recommendations. Like a chess player using a computer to calculate moves, but making strategic decisions independently.

Example: Our consumer insights team now uses AI to monitor social media sentiment in real-time during product launches. However, it’s human intuition that recognises when a spike in negative sentiment signals a genuine crisis versus temporary noise. The AI processes millions of data points; human intuition knows when to act.

• The Cyborg Model - Continuous integration. AI becomes a real-time thinking partner, suggesting questions during interviews, highlighting emerging patterns during analysis, and co-creating presentations. The boundary between human and AI thinking blurs. In this model, AI is used to augment humans, stimulating their intuition along the workflow instead of simply automating some aspects of the research process. Human intuition can, in turn, augment AI in a value creation loop, where both leverage their unique strengths.

Discover more resources by subscribing to Richard’s Intuition substack and receive monthly updates!

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Before augmentation: Developing human capacity

Before raising centaurs or cyborgs in the office, we believe we must first develop human capacities. Equipping developing humans with expert digital crutches too early risks atrophying their abilities rather than augmenting them. Like superheroes in popular culture, Iron Man isn’t frail under his armour—he trains. Spider-Man, despite his spidey sense, would be nothing without his acrobatic abilities. Like in many sectors, soft skills will take an increasingly prominent place in tomorrow’s work competency toolkit. Among those listed by the World Economic Forum (2025 study), while intuition doesn’t appear explicitly, no fewer than five key competencies are directly related to it: creative thinking, social intelligence, problem resolution, curiosity, and self-awareness. Beyond developing talents, investing in expert intuition also improves job attractiveness and retention capacity: mastery, autonomy, and accomplishment are powerful intrinsic motivation levers for individuals and a path to organisational excellence. It is a way to reshape the researcher’s role as a trusted advisor.

Our four-point development roadmap

Based on neuroscience insights and pilot testing at Ipsos bva, we’ve developed a practical roadmap. To bring it to life, we are piloting experiments leveraging senior staff to coach younger researchers. We have also started to train AI-persona bots as coaches to facilitate scaling. Here are four prescriptions that can help boost intuition at the organisational level.

1. Engage all senses at every moment of truth

Intuition models reality with different circuits than pure reasoning. It works well provided it’s fed by multisensory data sources. Here are some simple tips coaches can suggest:

• Go on-site - Observe in-situations like Toyota’s gemba walk-in quality checks, or use the product/service yourself if possible. Shadow hunting with clients is also an eye-opener.

• Experience before analysing - A questionnaire is never as relevant as when the analyst has experienced the journey themselves.

• Probe the ecosystem - Interview surroundings or social media to make the brief more vivid.

• Capitalise on exchange opportunities - Field starts, material discussions, and flash results are chances to fish for informal data, feelings, and hidden stakes.

Case example: To write a proposal for improving a luxury brand’s selling ceremony, one of our analysts visited various luxury brand shops and discovered an area to explore highly differentiated manners of product presentation. Before the research, this discovery identified where to focus efforts. They won with a more relevant and differentiated proposal.

2. Materialise problems and solutions

To effectively manipulate a problem, you must embody it. Abstract briefs remain in the rational mind—embodied problems engage your predictive intelligence. The same goes for solutions: “show don’t tell” to help clients grasp what you suggest they do. Make problems physical and consequences tangible:

• Materialise the brief - Draw the problem with the client, illustrate with photos or Legos, and exemplify test material to prepare.

• Convert it into action - Explore scenarios with the client, simulate outcomes, and make likely developments concrete.

• Build personas you can touch/hear - Instead of slides, create physical artifacts—rooms to visit, shopping baskets, even 3D projections that represent your target consumers. Persona-bots to simulate conversations help shift perspectives.

• Simulate implementation day - Imagine it’s six months from now and you’ve launched—what would be on the front page of the news? Why?

Bring solutions to life:

• Co-create action plans - With physical prototypes, sketches, and visual embodiments.

• Materialise results - Data visualisation helps virtual manipulation, packaging mock-ups lift ambiguous wording, animated scenarios help highlight change needed (before—after, with—without, etc.)

• Craft your storytelling - Shift client perspectives with drama and a better story. Make verbatims more impactful with audios or videos.

• Activate emotion - Use engaging visuals, create video testimonials, inspire clients to take action with provocative benchmarks, and use role-plays to deliver and experience results.

Thanks to visual Generative AI, showing possible futures becomes remarkably easy: prototype suggested improvements with 3D visuals or films. Feed with useful examples from other sectors, simulate implementations showing how it could look and sound.

Case example: To encourage clients to brainstorm new web-based home technological services, the team rented an Airbnb and posted relevant insights in every room with QR Codes and videos, including in the garden, instead of sharing a PowerPoint presentation. This in-situ approach created a highly engaging experience that revealed user frictions only innovation could solve.

3. Accelerate expertise development with others

Beyond occasional training, everyday moderation of individual and collective interactions is what best develops expert intuition. What we suggest:

• Structure mentoring and coaching programmes - Identify senior expert resources and needs, formalise accompaniment or sponsorship contracts;

• Create communities of practice - Facilitate peer learning around expertise topics, each can feed.

• Make self-training paths collective and applied - For self-service content, create badge levels stimulating progress, systematise collective debrief sessions for easier appropriation.

• Multiply case study exposure - Ritualise sharing sessions facilitated by AI-use-case bank curation.

What makes the daily difference is the encouragement each person receives to question their feelings and trust their intuitions to explore the data. The ability to pause and take a step back is also essential. As Amy Edmondson (1999) explains, a climate of psychological safety facilitates bold hypothesis emergence—intuition isn’t only individual, it also forms within groups.

4. Train perspective changes through simulation

Gaming is risk-free learning in a simulated reality. Another learning method is having to transmit to others. Hence, interaction and gamification are excellent means to help researchers decentre - walk in consumer shoes or take client viewpoints. Here are some examples of what we piloted:

• Multiply role-playing - Participate in presentation rehearsals, have colleagues play objecting clients. Any simulation helps—even with an AI persona-bot.

• Organise internal competitions - Best case, best presentation—opportunities to train and share broadly with recognition as a motivator.

• Systematise post-study reviews (ReX) - Ritualise client experience feedback, allowing everyone to connect dots, turn errors into learnings, enrich their repertoire, and celebrate successes.

• Facilitate AI crash pre-tests - Exchanging with dummy personas (synthetic consumers), analysts can crash-test material intelligibility, a questionnaire, or a presentation in a risk-free manner before Go-live.

Bringing the roadmap to life with an AI-augmented workflow

To support these changes, we’ve created a roadmap that combines in-person action learning with the innovative use of AI in our workflows. We suggest using AI to reverse the roles. AI persona-bots, trained with experts, can become personal coaches and partners! AI is not just there to provide answers, but also to ask questions. Paradoxically, AI can help humans develop expert intuition by suggesting reframing questions, complementary immersions, (de)centring exercises, and topics to explore with clients. With retrieval augmented generation, AI also provides access to all accumulated expertise from previous studies and benchmarks from other sectors. Coaching is no longer limited by HR budget or the number of available experts. Everyone can train at will with a benevolent AI persona-boss before meeting with their real boss (or client). We are also considering using our Censydiam model (a deep motivation assessment) to personalise development plans based on the researcher’s profile.

Conclusion

Intuition, a fast and frugal competitive edge

Human intelligence adaptability will remain an essential asset for the research profession. Intuition is a strategic economic resource. It is available offline, easy to use, energy efficient, and capable of detecting weak signals, sometimes before they appear in the data. As long as AI lacks a living, sensitive body to capture emotional and contextual nuances, our embodied intelligence remains inimitable, particularly in social interactions, as we demonstrated in our Best ESOMAR Paper, Empathy or Emptiness (Bangia, Legg, & McIntyre, 2024). Hence, developing this competency implies believing as much in intuition’s potential as in purely analytical solutions.

Our industry needs to walk on two legs, keeping humans first. This suggests setting up metrics to track progress in human capabilities as well as AI literacy. Outcome KPIs should be focused on client satisfaction and impact, not just productivity. Thanks to neuroscience’s new narrative, we believe that a wider adoption of expert intuition as a professional skill is now possible, even among non-believers.

By demonstrating that intuition is a form of “body intelligence” that can be trained like a muscle, science legitimises its performance. It paves the way for concrete embodied coaching methods as illustrated in the paper. Using only AI in a race to the bottom for faster, cheaper data will not deliver better insights. Investing in humans, on the other hand, will bring value to clients. We believe expert intuition is not just “nice to have”; it’s a decisive competitive advantage.

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During my travels across Asia for both business and pleasure, I discovered that different cultures had developed precise vocabulary to describe aspects of what we broadly call “intuition.” Behind these words lie entire practices, beliefs, and ways of living. I understand that my Western (and business) lens necessarily detaches them from their original cultural environment, but I found each of them enlightening in some way.

Hence, at the risk of oversimplification, here are five concepts that have enriched my vocabulary of intuitive intelligence. Their selection is arbitrary (as many others could warrant development)—the purpose is simply to illustrate the diversity hidden behind this idea of intuition and how distinct it is compared to our Western approach.

1. Chinese “ ‘shì’” (势): Surfing Invisible Forces

In China (and Singapore), the concept of “shì” captures something that François Jullien calls “the ability to perceive the hidden potential of a situation.” More than simple intuition, “shì” represents this situational intelligence that allows Asian entrepreneurs to transform obstacles into pivots and seize trends that are barely emerging.

The secret of “shì”? Not fighting against circumstances and forces at play but embracing them and making advantageous use of them. This approach teaches us to intuitively discern when to act and when to wait, when to push and when to yield. In favorable environments, one must know how to “ride the wave” (顺势而为). Thus in military strategy, “understanding the potential of a situation is already half the victory.”¹

2. Japanese “Gemba” (現場): Truth Hides in the Field

Toyota has long understood this: improvements and problem intelligence are not born in meeting rooms, but where the action actually takes place (the shopfloor, the factory). The “Gemba Walk” - this attentive exploration of value-creation sites at the heart of their quality system - illustrates a profound philosophy of Japanese intuition.

The three stages of Gemba Walk:

• Observe attentively without rushing toward conclusions

• Ask open questions while respecting the expertise of people in the field

• Feel the atmosphere and note subtle changes

As this Japanese proverb reminds us: “Seeing once is better than hearing a hundred times” (百聞は一見に如かず). Japanese intuition reminds us that authentic knowledge is born from immersion, not abstract analysis.²

3. Chinese “Renqing” (人情): Intuitive Social Intelligence

Perhaps the most sophisticated concept: in China, intuition is not only individual, but it becomes collective. In business, it is inseparable from the network: guanxi, which meshes your sphere of influence. “Renqing” represents this social wisdom that allows one to sense the perfect balance in human relationships without explicit instructions.

To cultivate your renqing:

• Intuitively sense the appropriate level of formality

• Detect the right moment to offer help without being asked

• Maintain an intuitive balance of give and take

“Keep a thread of human emotion; future meetings will be pleasant” (人情留一線，日後好相見) - this wisdom reminds us that true social harmony transcends rules to touch an inner sensitivity to others’ needs.³

4. Korean “Nunchi” (눈치): The Art of “Reading the Room”

Korean nunchi, literally “eye measure,” represents the art of gauging what people think and feel to create connection, trust, and harmony. More sophisticated than simple emotional intelligence, nunchi requires interpreting subtle gestures, tone changes, and contextual clues to navigate smoothly through interactions and power relationships.

The three pillars of professional nunchi:

• Speed of perception: Euny Hong emphasizes the importance of speed in nunchi - grasping a situation’s atmosphere before it evolves

• Contextual adaptation: In Korean environments, building consensus in advance is often essential, unlike the quick, independent decisions valued in Western tech companies

• Strategic discretion: In Korea, what is not said is as important as the words spoken

Nunchi transforms a simple ability to “read the room” into a means of professional survival and well-being. It’s this intelligence that allows one to sense that a colleague is struggling with a coding problem without explicit communication, and offer help before it impacts team deadlines.⁴

5. Indian “Sati”: Mindfulness as an Intuition Amplifier

At the heart of Buddhism (and already of Hinduism) lies a fundamental concept that illuminates our understanding of intuition: “sati” (सति). Often translated as “mindfulness,” it represents much more than simple focused attention.

Sati, variations of which can be found throughout Asia from India to Thailand, is this quality of presence that allows one to perceive reality as it is, without the filters of our mental projections. It’s a state of total receptivity that transforms our capacity to grasp subtle signals from our environment and inner world.

The four pillars of Sati:

• Body consciousness: Listen to physical signals that often precede our insights

• Sensation consciousness: Note micro-reactions that guide our decisions

• Mind consciousness: Observe thought patterns without identifying with them

• Phenomenon consciousness: Perceive subtle interconnections between events

Sati teaches us that intuition is not a mysterious flash, but the fruit of purified attention. When the mind is no longer cluttered by constant mental chatter or the voice of our ego, it becomes naturally receptive to subtle information that our ordinary consciousness neglects.

Check out my latest interview about intuition with Dominique Desjeux here

What This Juxtaposition Reveals?

These five pearls inevitably lose their power when examined outside their original cultural contexts. For those who have never set foot in Asia, they risk sounding like yet more esoteric concepts in the personal development library. But they nonetheless illustrate a simpler truth: our relationship to the idea of intuition has an essential cultural dimension.

Unlike the West, which distrusts intuition because it resists being integrated into the narrative of rationality, other cultures have long legitimized intuition, encouraging its mastery and praising its virtues. And this without underestimating the inherent difficulty of capturing it without error: the need for method and training to make it reliable, taken on by spiritual guides rather than scientists.

I don’t recommend cultural borrowing as a solution to bridge the gap: it completes knowledge but doesn’t resolve contradictions. I prefer juxtaposition, in the manner of François Jullien. In contrast to the gap, the unthought is revealed: what has not been explored or retained in the Western narrative, or the invisible frameworks from which we start, and which it appears must evolve.

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References:

1. Lai, David. Learning from the Stones: A Go Approach to Mastering China’s Strategic Concept, Shi. Strategic Studies Institute, 2004. Available: https://press.armywarcollege.edu/monographs/771/

2. Liker, Jeffrey K. The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer, 2nd Edition. McGraw-Hill, 2021. Available: https://www.amazon.co.uk/Toyota-Way-Second-Management-Manufacturer/dp/1260468518

   1. Toyota UK Magazine. “Genba and the Toyota Production System.” https://mag.toyota.co.uk/genba-toyota-production-system/

3. Wang, Cheng Lu, Noel Y.M. Siu, Bradley R. Barnes. “The significance of trust and renqing in the long-term orientation of Chinese business-to-business relationships.” Industrial Marketing Management 37, no. 7 (2008): 819-824. Available: https://www.researchgate.net/profile/Noel-Siu/publication/247070631_The_significance_of_trust_and_renqing_in_the_long-term_orientation_of_Chinese_business-to-business_relationships/links/5a6579c30f7e9b6b8fdbd618/The-significance-of-trust-and-renqing-in-the-long-term-orientation-of-Chinese-business-to-business-relationships.pdf

4. Hong, Euny. The Power of Nunchi: The Korean Secret to Happiness and Success. Penguin Books, 2019.

   1. Mindbodygreen. “8 Rules For Nunchi, The Korean Secret To Emotional Intelligence.” https://www.mindbodygreen.com/articles/what-is-nunchi

5. Jullien, François. The Propensity of Things: Toward a History of Efficacy in China. Zone Books, 1995. Available: https://ingbrief.wordpress.com/2022/01/23/1995-francois-jullien-the-propensity-of-things/

About Dominique Desjeux

Dominique Desjeux is an anthropologist and professor emeritus at Université Paris Cité (Sorbonne). For more than 50 years, he has developed an original approach to applied anthropology, working both on corporate contracts and in academic research. His career took him to Africa for 8 years, where he studied agricultural innovations; to the École d’Agriculture in Angers, where he spent 7 years; and then to various field sites in the United States, Brazil, and especially China, where he has been going regularly for 30 years.

A specialist in the anthropology of consumption and organizations, Desjeux built his method on the observation of practices and on shifting scales of analysis. He has written around twenty books and developed a recognized expertise on innovation processes and cultural transformations. His long field experience, combined with his training in the sociology of organizations with Michel Crozier and Erhard Friedberg, makes him a privileged witness to the evolution of inductive methods in social research.

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Intuition as a Methodological Compass

Richard Bordenave: As an anthropologist, what role does intuition play in research?

Dominique Desjeux: That’s not a simple question. In research, what matters is to manage to observe and, starting from that observation, to describe what one observes in the form of a system. When I begin research, I don’t know where I’m going, and I don’t see how the problem is posed. I only have methodological hypotheses. That’s what we call induction. By observing and collecting information, I gradually build small pieces of a larger puzzle, which is never exhaustive.

And I proceed as in a detective novel: I look for clues about what is changing.

Intuition comes into play right from the start. For example, I am currently beginning a documentary investigation on innovation processes based on a 19th-century family archive, without really knowing where I’m headed. I start with books and articles, I use ChatGPT to find other sources, I conduct interviews, I use photos, etc. I pull on threads, constantly checking the sources and cross-referencing information. It’s time-consuming work.

Intuition doesn’t come from nowhere. It doesn’t just come from a feeling; it also comes from experience. In my head, I have an observation framework that includes social actors, “pre-digital” networks, institutions—not just individuals. To understand innovation processes, I intuitively recognize that the state will play a role, that there will be competition, that many innovations will fail, that some will lose, and others will succeed. So I explore social reality by guiding myself with this framework.

Intuition is not having an empty mind—it is imagining that there are actors, networks, objects, places, competition, institutions, social classes, and geopolitical effects. But without knowing in what order they will be organized.

The Unexpected as Raw Material

Richard Bordenave: And how does this differ from the hypothetico-deductive method?

Dominique Desjeux: In reality, there’s nothing but the unexpected. The unexpected is what people tell me. What I just described is what I understood after interviews, after taking photos or videos. It’s a qualitative, impressionistic, even pointillist approach, where the sum of small descriptions ends up forming a pattern.

When I do fieldwork—for example, in China and Japan on video games in June 2025—I arrive knowing little. To explore this new world, I went to a gaming house with Chinese students from Guangzhou. I played with them, asked them to show me how they played, what they watched at karaoke, etc. What’s surprising, the unforeseen, is what interviewees tell me: for instance, that there is a dimension of national pride with a new 2024 Chinese game, Black Myth: Wukong; that these games are mostly for boys; and that since the video game market risks saturation, companies in China are now creating games for women inspired by Japan…

By describing practices as a system of action, one is already starting to analyze—but always without knowing where it will lead. The difference with surveys or questionnaires is that I really try to understand the interviewees’ perspective without presupposing what they will say. With improvised follow-ups, I get them to clarify what they know and reveal what they do. Gradually, inductively, I discover their world of practices, power relations, uncertainties, constraints, room for maneuver, and imaginaries.

Induction vs Deduction: A Matter of Context

Richard Bordenave: Why doesn’t the inductive method always have a good reputation in the rational world of business?

Dominique Desjeux: Both inductive and deductive approaches are interesting. It all depends on the situation and the problem at hand. If I’m in a relatively stable period, the hypothetico-deductive method with questionnaires works well. It describes moderate variations in a partly predictable world.

Induction is more relevant either when discovering a problem for which there is no data, such as in a foreign culture, or when there is significant uncertainty—when a “black swan,” like Donald Trump’s tariffs, creates high unpredictability. In that case, you must observe and learn, without locking oneself into theoretical models that prevent noticing the unforeseen. The best image is that of 16th-century navigators, who had only a sextant, a compass, a plumb line, and rough maps. By trial and error, they described coastlines, built more complete maps, and indicated shoals. Once the description was well advanced, they could mobilize theoretical models to help interpret.

Induction is often discredited because of the implicit scientific model many people carry in their heads: the model of experimental sciences. In lab experiments—in agriculture, as I observed—variables are controlled, they can even be tested and varied, to understand causal links between nitrogen and rice yield. All real-life variables have been eliminated. In anthropology, we are in life, outside the laboratory’s protection, where we can’t control or separate the different variables—it is the situation, the context, and the historical moment that become the explanatory variables.

Bodily Intelligence and the Incorporation of Data

Richard Bordenave: You mentioned something interesting about the writing experience and how things happen between intuition and analysis…

Dominique Desjeux: It’s exciting. I’ve written about twenty books with close colleagues, but what struck me was that, each time, in the conclusion, I wrote more than what we had actually described in the book. I would think: “Why are we adding this when we haven’t really demonstrated it yet?”

I wrote it anyway, because I had the intuition it was important in relation to what was emerging in society. Intuition then allows us to move forward, to shift our mental framework. If we only write what we are sure of, we risk closing off possibilities and failing to really grasp what we have seen. It’s as if the act of writing made me realize what I didn’t know I knew.

Gradually, I realized that the data in the conclusion were indeed contained in the fieldwork, but that I had incorporated them unconsciously. This means our body implicitly captures data through the eyes, ears, and nose. By being present in people’s homes, offices, and fields, we unconsciously absorb data.

Since 2010, I’ve had the intuition that there are risks of war, even though I never researched a potential war between China and the United States. However, by repeatedly observing, reading political fiction and history books on China, I intuited that war was possible, even though 15 years ago, the prevailing idea was that trade would pacify the world. That doesn’t necessarily mean it will happen.

The Asian Approach to Uncertainty

Richard Bordenave: Do you see differences between Western and Asian approaches?

Dominique Desjeux: What strikes me when I look at Africa, China, and now Japan is that they are much more inductive than we are. There’s a Chinese concept I love, shi (势), analyzed by François Jullien in The Propensity of Things: Toward a History of Efficacy in China (1992). It’s typical of action in China, but also of entrepreneurs: an entrepreneur knows more or less intuitively that he doesn’t know where he’s going.

He will make a plan, a business model—because he must, to secure bank financing—yet he knows it’s by observing consumer behavior that he will discover his real market. Nine times out of ten, it won’t be where he expected.

Shi is the potential of a situation, as depicted in many Chinese paintings, where one sees a man in a boat in the middle of a river with a strong current, between two mountains, trying with a single oar to avoid the rocks. Navigating the world is the same: we are carried along, but we still have room for maneuver. Asian thought is more attuned to this. We, by contrast, have a tough time improvising, letting intuition play out—yet we need it, since intuition is a way of compensating for a lack of information.

Advice for Navigating Uncertainty

Richard Bordenave: What advice would you give for analyzing the world by relying on intuition?

Dominique Desjeux: Observation is essential. You must try not to be “swallowed up” by overly Platonic, overly idealistic explanations, by ideas. In times of uncertainty, one mustn’t reassure oneself too quickly with rational explanations.

There is a kind of asceticism in saying: “I’ll bracket my opinions and try to analyze from practices, networks, alliances—what is the underlying logic behind what I observe and what seems ‘irrational.’”

At the first stage, don’t be enchanted by ideas that claim to explain everything. At the second stage, remind yourself they don’t explain everything: you have to look behind representations, doctrines, and ideologies to see if practices might better explain the action taking place.

Between practices and representations of life, there are the constraints of daily life. Understanding the constraints weighing on social actors is often more illuminating than explaining behaviors by individual motivations or collective values. The explanatory value varies depending on the situation.

Intuition is very important for discovery. But it must, of course, be cross-checked by verifying and triangulating data, confronting contrary ideas, and shifting scales of observation. Changing the angle of observation is my primary tool for avoiding being trapped in a single intuition. I constantly shift scales—from individual to group, to social classes, to geopolitics… and that’s what allows me to discover what is there but not yet visible.

Dominique Desjeux teaches and regularly publishes his analyses on contemporary geopolitical and social transformations. His recent work focuses on innovation processes and international power relations.

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For centuries, Western culture, deeply rooted in Cartesian thinking, relegated intuition to the realms of art and spirituality. It was even considered a feminine quality and deemed unworthy of scientific inquiry. Research on intuition remains scattered across business studies and psychology, with some tests like Emotional Quotient or MBTI barely recognized as legitimate science. This fragmented approach has produced a contradictory body of literature where studies affirming intuition's reliability stand alongside research demonstrating its fallibility.

On one hand, these contradictions highlight the paradoxical nature of intuition. On the other hand, does this paradoxical conclusion reveal that we've been approaching the phenomenon from the wrong angle entirely?

To understand this paradox, let's examine how this situation developed historically.

The Business Pioneer (1938)

The academic study of intuition in organizations began with Chester Barnard's "The Functions of the Executive" in 1938.¹ Barnard distinguished between logical processes based on conventional analysis and non-logical processes that rely on experience and the ability to instantly see what doesn't fit in complex situations.

Crucially, Barnard never opposed these approaches, recognizing that neither is perfect when confronted with reality. He simply regretted that companies often privilege the rational approach—not because it's more effective, but because it's easier to justify, even when it fails.

The Jungian Foundation (1940s-1950s)

Carl Jung's psychological types entered organizational discourse through the Myers-Briggs Type Indicator (MBTI), developed in the 1940s-50s²: Extraversion-Introversion, Sensing-Intuition, Thinking-Feeling, Judging-Perceiving.

While the MBTI's scientific validity has been largely questioned, Jung's deeper influence on intuition research has been pervasive. His idea of intuition as a psychological function—distinct from but complementary to thinking and feeling—continues to shape contemporary theories. This period began treating intuition as a personality trait rather than a universal human capacity.

The First Neuroscientific Revolution That Fell Short (1990s)

The field transformed in 1994 when neuroscientist Antonio Damasio published "Descartes' Error."³ Through clinical cases with patients whose emotional brain had been altered and who suddenly became incapable of making any decisions, he demonstrated that emotions are essential to rational decision-making—there can be no reasonable decision without them.

This discovery should have shattered the ancient reason-versus-emotion divide that dominated Western thought. Instead, it merely reframed the relationship as "cooperation" rather than opposition, treating emotions and reason as separate entities working together rather than recognizing their fundamental entanglement.

This reveals how deeply dualistic thinking runs in Western culture. Whether discussing emotion versus reason, right brain versus left brain, or mind versus body, we instinctively create opposing camps instead of exploring dynamic integration. Breaking free from binary frameworks proves remarkably difficult, even when evidence points toward a more unified reality.

The Rise of Emotional Intelligence (1990s)

Building on Damasio's work, emotional intelligence (coined by Peter Salovey and John D. Mayer⁴) emerged as a distinct form of intelligence. Daniel Goleman popularized it in business with his 1995 book,⁵ showing leaders the importance of regulating their emotions and reading others.

Alongside IQ came EQ—emotional quotient—measuring social and creative abilities. The balanced manager was expected to develop both sides of the brain, combining logic with creativity and intuition. With the advent of "emotional intelligence," intuition began to be taken seriously as a tool in the workplace, but it remained completely compartmentalized from other forms of intelligence, which seems to miss the point of Damasio's conclusions.

Howard Gardner expanded this further with multiple intelligences⁶—eight types linked to different ways of processing information: linguistic, logical-mathematical, musical, spatial, kinesthetic, interpersonal, intrapersonal, and naturalistic.

These approaches inspired personal development methods and creativity training in professional environments. However, they remained largely peripheral, unable to challenge analytical thinking's dominance in core business functions.

Behavioral Economics and the Return of Reason (2000s-2010s)

Behavioral economics, through Daniel Kahneman's work (the father of the field), popularized the metaphor of two thinking speeds: System 1 (fast and automatic) and System 2 (slow and effortful).⁷ Although it helped to recognize that both systems are intertwined and that humans aren't fully rational, behavioral science has maintained rational thinking as its reference point, building on the heritage of bounded rationality. The idea that System 2 analysis can limit System 1 biases maintains a form of dualism. This consecrates abstract reasoning as the ideal to reach.

The hunt for cognitive biases invaded management training. Fighting our mental imperfections seemed aligned with Western rationality ideals, and critical thinking became the antidote to fight cognitive weaknesses. Although supposedly working hand in hand, System 1 and System 2 are most of the time opposed: the latter endorsed as superior, the former a primitive heritage.

As Daniel Kahneman admitted in a 2012 interview:

"In writing 'Thinking, Fast and Slow,' I was really trying to talk about the wonders of intuitive thinking, not just its flaws—but the flaws are more fun, so we pay more attention to them."⁸

This obsession of the field was later criticized as "the bias bias" by Gerd Gigerenzer.⁹

The Naturalist Counterattack (2000s-2010s)

The Kahneman-Klein debate in the mid-2000s marked a turning point.¹⁰ While Kahneman emphasized intuitive thinking's limitations, Klein advocated for expert intuition in emergency contexts. Klein showed that firefighters, military personnel, and medical staff make life-or-death decisions at lightning speed, with intuition as their best ally.

Their deep experience allows these professionals to develop reliable intuition based on recognizing patterns and mobilizing action, summarized by Klein's Recognition Primed Decision (RPD) model. In real life, this fast decision-making of experts is unanimously recognized as very efficient intuitive skill. Kahneman eventually accepted this as a special case where expertise contributes to reliable intuitive judgment as long as the environment is stable and provides immediate feedback.

Meanwhile, Gerd Gigerenzer of the Max Planck Institute has been advocating for the advantages of fast thinking based on straightforward decision-making rules that frequently outperform complex algorithms.¹¹ His "fast and frugal heuristics" have proven to be remarkably effective in uncertain professional settings. Uncovering these expert rules, which are often unconscious and based on discriminant cues or choice trees, is an excellent method for understanding the workings of intuition.

The Neuroscience Renaissance (2020s-Present)

Finally, a new wave of neuroscience research is delivering on the promise of grounding individual and social intuition in scientific studies that examine how these intuitions work in the brain and body.

Joel Pearson's work exemplifies this shift toward measurable body metrics:

"Intuition is real, we can create it and measure it in the lab, and we now understand it with the neuroscience and psychology we already have."¹²

His research provides practical frameworks for when to trust or avoid intuitive judgments, grounding intuition back into the body.

Meanwhile, breakthrough research on interoception—our ability to sense internal bodily signals—has delivered compelling real-world validation. Studies with financial traders demonstrate that those with better ability to sense their own heartbeat not only outperform colleagues but survive longer in high-pressure environments.¹³

The most promising development comes from researchers outside intuition studies. Building on the work of neuroscientist Karl Friston,¹⁴ Andy Clark developed the theory of "predictive processing"¹⁵—a framework that can explain both intuition's potentialities and limits, although this wasn't its original intent.

The way in which our body (including the brain) is connected to its environment determines its ability to constantly predict the actions to be taken in order to achieve a goal or simply stay alive. This bodily intelligence is only partially accessible to us, via what we call intuition. Recent research on intuitive driving behavior using EEG analysis confirms the complex neural networks involved in rapid decision-making under uncertainty.¹⁶

Luckily, researchers are shifting from asking whether intuition exists to understanding how it works and what improves its functioning or, conversely, what causes it to go off the rails. One of the most fertile areas for experimentation lies where intuition is already used with measurable results: business decisions.

The Business/Research Disconnect

In business contexts, intuition is often associated with innovation and decision-making domains. In a PwC research report from 2014, "Guts & Gigabytes,"¹⁷ senior business leaders in the UK ranked intuition and experience as the number one factor in their decision-making (41%), then experience of others (31%), and data and analytics as only the third most important factor (23%). However, this research highlighted that while innovation and problem-solving can draw from intuitive insights, they also require analytical skills.

The history of management literature (cf. Sadler-Smith's "Inside Intuition"¹⁸) reveals that intelligence was long equated with logic and rationality, positioning it in opposition to intuition. The consulting world regularly oscillates between these poles—on one side, methods that treat management as a rigorous, predictive science, and on the other, "human-centered" approaches drawn from psychology and social sciences that rehabilitate empathy and intuition as performance drivers. Each has had its moment of glory: Lean, Six Sigma, big data, and stage-gate on one side; coaching, creativity, UX, and design thinking on the other.

In practice, therefore, business has institutionalized intuition as one form of intelligence among others. This doesn't prevent organizations from simultaneously encouraging distrust of it elsewhere—these contradictory beliefs coexist because we lack frameworks to reconcile them. It's indeed difficult to objectify intuition's impact by comparing "with" and "without" scenarios. Conversely, it's easy to attribute failure to decisions made without analyzing available data. Intuition is thus inexorably intertwined with what we call analytical intelligence.

Three Persistent Gaps

1. Lack of Consensus on What Intuition Is and What to Measure

The real issue isn't just disagreement on definitions—it's that we've confused what people actually experience with how we theorize about it. Research with executives reveals remarkable consistency in how they describe intuitive experiences, yet our academic models fragment this unity into competing frameworks that debate variables, processes, markers, and outcomes separately.

This fragmented landscape reveals a deeper problem: we've been studying intuition with tools designed for rational thinking. Each era applied its dominant methodology—personality questionnaires, behavioral tests, brain scanners—without questioning whether these instruments can capture what they claim to measure. It's like trying to catch a nocturnal bird with a flashlight.

2. The False Separation and Opposition of Intuition and Rationality

Barnard's 1938 insight remains unheeded: effective decision-makers don't choose between intuition and analysis—they blend them in ways our theories can't explain. Every attempt to study intuition in isolation hits the same wall: it's inseparably linked to rational processes.

Organizations simultaneously encourage intuition as valuable intelligence while promoting distrust through bias training—a contradiction that persists because we lack frameworks for integration. Like any tool, intuition needs a manual: we must understand when it accelerates performance and when it hinders it.

3. A Mind-Dominant Approach

Despite Damasio's work on emotion's role in decision-making, research continues treating intuition as purely mental. Whether termed "mind," "brain," or "System 1 thinking," these frameworks remain anchored in abstract cognition (thinking)—they rarely mention the body, while everyday metaphors speak about "gut feel" or "following your heart." A disembodied approach misses the point entirely, since practitioners consistently report bodily sensations as central to their intuitive experiences. However, limiting it to visceral factors (fear, hunger, pain) clouding your judgment is again focusing on the limits of our biology, ignoring its potentialities.

The body plays a critical role in processing environmental data and generating intuitive insights that contribute to our so-called "thinking," yet we ignore this mechanism simply because we're unconscious of it happening.

The Path Forward ?

Just as behavioral science is moving toward a more evolutionary approach, taking a more positive stance toward our decision processes and reintegrating their social and biological dimensions (describing humans as optimally irrational, as Lionel Page suggests¹⁹), moving forward with intuition requires acknowledging that it may demand fundamentally integrative approaches. We need methods that capture its embodied, contextual, social and integrated nature. This might include contributions from embodied cognition, social science, ethnography, social neuroscience, evolutionary psychology, and phenomenological research that takes practitioners' descriptions seriously.

I'm not an academic, but a business practitioner seeking to contribute to this endeavor for very practical reasons: I want to develop my human skills to integrate intuitive wisdom with analytical rigor and AI algorithmic potential, as I believe it can provide a decisive advantage in navigating uncertainty and complexity.

I value academic research and use it in my everyday work. However, my agnostic stance toward disciplinary boundaries allows me to borrow and assemble ideas from fields that don't communicate with each other enough. Forgive my syncretism, and consider these chronicles for what they are: a possible source of inspiration that connects useful dots.

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References

1. Barnard, C. I. (1938). The Functions of the Executive. Harvard University Press.

2. Myers, I. B., & Myers, P. B. (1995). Gifts Differing: Understanding Personality Type. Davies-Black Publishing.

3. Damasio, A. R. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. Putnam.

4. Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and Personality, 9(3), 185-211.

5. Goleman, D. (1995). Emotional Intelligence: Why It Matters More Than IQ. Bantam Books.

6. Gardner, H. (1999). Intelligence Reframed: Multiple Intelligences for the 21st Century. Basic Books.

7. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

8. Kahneman, D. (2021, May 16). Daniel Kahneman: 'Clearly AI is going to win. How people are going to adjust is a fascinating problem.' The Guardian. https://www.theguardian.com/books/2021/may/16/daniel-kahneman-clearly-ai-is-going-to-win-how-people-are-going-to-adjust-is-a-fascinating-problem-thinking-fast-and-slow

9. Gigerenzer, G. (2018). The bias bias in behavioral economics. Review of Behavioral Economics, 5(3-4), 303-336.

10. Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515-526.

11. Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. Viking Press.

12. Pearson, J. (2024). The Intuition Toolkit: The New Science of Knowing What without Knowing Why. Simon & Schuster.

13. Kandasamy, N., Garfinkel, S. N., Page, L., Hardy, B., Critchley, H. D., Gurnell, M., & Coates, J. M. (2016). Interoceptive ability predicts survival on a London trading floor. Scientific Reports, 6, 32986.

14. Friston, K. (2010). The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2), 127-138.

15. Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204.

16. Reardon, M., Wang, K., & Parkinson, J. (2024). EEG-based analysis of driving intuition using joint temporal-frequency multi-layer dynamic brain networks. Frontiers in Neuroscience, 18, 1341674.

17. PwC. (2014). Guts & Gigabytes: Capitalising on the art & science in decision making. PricewaterhouseCoopers.

18. Sadler-Smith, E. (2008). Inside Intuition. Routledge.

19. Page, L. (2022). Optimally Irrational: The Good Reasons We Behave the Way We Do. Cambridge University Press.

My Personal Big Bang

November 2018. My spouse came home with a life-defining question: his company wanted to send him to Shanghai for a year. Should he take it?

We spent hours in our kitchen weighing pros and cons. His fear of regretting this opportunity, my concerns over career implications, relationship risks. The logic pointed toward acceptance—we could each focus on our careers and reunite for the good times. A year of long-distance relationship seemed bearable with sufficient paid trips home.

He took the job and flew to Shanghai.

Two months later, I was miserable. Back pain, emotional volatility, a persistent sense that something was wrong. Then one December morning, I woke up with absolute clarity: I needed to quit my job and join him in China. Not in six months, not "maybe next year"—now. I went into work to notify my boss and negotiate my exit.

This wasn't the plan. It made no rational sense. Visa complications, language barriers, social acceptance, career halt—the list of obstacles was endless. But the certainty was undeniable.

Surprisingly, the conversations with my company remained constructive, and shortly after they offered me another opportunity on a local contract in Singapore. A bumpy road, but everything ended up falling into place.

Five years later, we were still in Asia, and it remains the best decision I've ever made—besides marrying him. Those who know me well know that neither my career nor our relationship have suffered – quite the opposite, in fact.

The Problem with How We Think about Intuition

Most of us have a strange relationship with intuition. We dismiss it as unscientific while secretly relying on it daily. We celebrate it in entrepreneurs and artists while fearing it in boardrooms. Some have relegated it to the realm of mystics and life coaches. Others prefer to clothe it with ex-post reasoning and cherry-picked facts.

This skepticism runs deep in Western culture. Cartesian thinking has trained us to view intuition as emotion's unreliable cousin—inherently prone to bias and error. But people also confuse intuition with fast thinking, while it is more about guiding action. Intuition works well when your body is engaged, not with abstract academic mind tricks trying to measure it: quick mental math, estimated probabilities, or multiple-choice puzzles are too far from real-life experiences.

Fast thinking indeed struggles against deliberate reasoning in laboratory studies with abstract questions that don't allow for embodied learning. Some researchers like Gary Klein have shown that in real-world situations where our bodies can learn from experience—sensing danger, reading social cues, or navigating complex environments—intuition can usually be trusted as expert embodied knowledge (I will dedicate a chronicle on this)

In these substack chronicles, I have decided to focus on the glass of intuition that is half full. We will also discuss the biases of intuition, but as a price to pay for qualities that we otherwise exploit poorly. Without understanding how it works, it is difficult to avoid its pitfalls. But we should not blame the tool until we have learnt how to best use it.

In professional settings, intuition finds little place because it contradicts our evidence-based culture where quantitative data is king. Who would dare tell their colleagues, "Follow me, I had an intuition," or "We should invest—I felt it in my gut"? Only the superstars can get away with such claims. Richard Branson famously said, "I rely far more on gut instinct than researching huge amounts of statistics". But even this romanticization of intuition doesn't mean he has abandoned his calculator.

Dan Sperber explains that one of the essential functions of rationality is precisely the production of arguments to justify our positions and convince others to participate in our actions. Rationality also serves to evaluate the points that others make in an argumentative process and to build new positions. But rationality has other advantages: it relies on facts! And research and data, besides helping progress, are a convenient excuse in companies that lack decisional courage.

When faced with difficult decisions, it's not uncommon to hear "we need more data." Gert Gigerenzer denounces this as a "cover your ass" culture—a way for many to hide behind processes out of fear of judgment. It's the data that said it, not me...

Yet outside of work, we routinely rely on intuition without question. We're perfectly comfortable saying that we dismissed a possible nanny because "something didn't feel right about her", or that we chose a piece of artwork because it "spoke to us". We trust our gut when selecting a romantic partner—"I just knew"—or when deciding which neighbourhood feels safe to walk through at night. The double standard is striking: intuition becomes legitimate in personal decisions while remaining taboo in professional ones.

Standard dictionary definitions of intuition miss the mark. The Oxford English Dictionary defines it as "the ability to understand or know something immediately, based on your feelings rather than facts." This definition overlooks where intuition actually originates: the body, the gut, the heart—not just the brain. By framing it as feelings versus facts, it implicitly reduces intuition to a mental quirk from our so-called primitive lizard brain and opposes it to intelligence.

More fundamentally, this definition reduces intuition to passive knowing when it's really an active force that compels action. Intuition isn't merely about sudden understanding or revelation; it's what drives us toward the appropriate course of action. True intuition doesn't just whisper insights—it pushes us to imagine possible paths, to choose, to move.

Why Now Is the Right Time to Rethink Intuition

Major developments are converging today to change how we understand intuition: neuroscience of perception and action, AI's reflection of human intelligence, and the need for solutions to address the challenges of an increasingly complex world.

Neuroscience and the predictive brain: work from Karl Friston and Andy Clark shows our brain isn't a passive computer processing outside information. It's a prediction machine that works with our body to anticipate what comes next. Together they sample the world, test hypotheses, and alert us when something doesn't match expectations. Our intuitive feelings come from this sophisticated system that processes enormous amounts of sensory and social data.

Lisa Feldman Barrett's research showed that bodily sensations, called interoception, are what we experience as "gut feelings"—and they're actively constructing our emotions. Damasio claims that emotions store scripts of action (somatic markers) to help us make decisions and navigate the world. Before we're even conscious of it, our body prepares for action based on what it expects. When you "feel" a negotiation going south or sense a project will succeed, your body has detected patterns your analytical mind hasn't processed yet.

AI's revealing mirror: As we build machines that try to mimic human intelligence, we're discovering what makes us unique. Reinforcement learning, neural networks, and image recognition have all taken inspiration from our body's functions. Yet AI struggles with common sense precisely because it lacks a body that has learned intuitive rules through physical interaction with the world.

As Yann LeCun puts it: "A house cat has way more common sense and understanding of the world than any LLM" (LeCun, 2025). Intuition may be a natural algorithm, but it has no programming language or lines of code—its flows are those of living matter, connected to our physical being and our environment.

This biological equipment, inherited from evolution, still helps us today perform incredibly complex actions: riding a bike, sensing a threat, or hitting a tennis ball. It is not primitive lizard heritage but rather the ultimate evolution of an intelligent biological system. This allows humans to effortlessly build experiential knowledge that AI can't access, and develop extraordinary predictive abilities with very limited training.

Growing complexity: In a world facing multiple systemic crises—climate change, social division, and technological disruption—big data models and mechanistic approaches have reached their limits. These challenges are too interconnected and unpredictable for purely analytical solutions.

Moreover, intuition isn't just an individual phenomenon. Social neuroscience reveals that invisible signals connect us: our bodies and brains communicate with our peers without our awareness . This creates a shared intuition that helps groups cooperate and make decisions without anyone giving explicit instructions—the same way flocks of birds navigate together.

Everything Is There to Reinvent Intuition's Story

The pieces are falling into place. Neuroscience shows us that intuition isn't mystical—it's our prediction system at work. AI reveals that embodied intelligence is our unique advantage, not a limitation. And our complex world demands tools beyond pure rationality.

We don't need to choose between intuition and analysis: we actually can't. Both are interdependent—symbolic intelligence has grown on organic intelligence. But we need to understand how they work together. The goal isn't to trust every feeling, but to develop the skill to know which ones matter and what traps to avoid in this permanent dialog.

I suggest that we redefine intuition as our body's intelligence and examine its anatomy again to facilitate training it like an athlete would. As physical threats return to our environments, our societies' dependence on networked and digital tools makes us vulnerable offline. Our bodies, on the other hand, remain autonomous and naturally connected to others while using little energy. They are remarkable instruments full of underused resources that deserve our attention.

I invite you to join me on this adventure by subscribing (for free), so you don't miss the next episodes where we'll cover all these topics. If you know anyone who has insight into these or could simply be interested in intuition, please invite them to join this community. We will learn a lot more from everyone's stories! Everything is in place to rewrite together the story that intuition deserves, and the time is now.

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Bibliography

Barrett, L. F. (2017). How emotions are made: The secret life of the brain. Houghton Mifflin Harcourt.

Branson, R. (2014). The Virgin way: Everything I know about leadership. Portfolio.

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181-204.

Damasio, A. (1994). Descartes' error: Emotion, reason, and the human brain. Putnam.

Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127-138.

Gigerenzer, G. (2014). Risk savvy: How to make good decisions. Viking.

Kahneman, D. (2011). Thinking, fast and slow.

LeCun, Y. (2025, January 23). Meta's Yann LeCun predicts a new AI architecture's paradigm within 5 years and decade of robotics. TechCrunch. https://techcrunch.com/2025/01/23/metas-yann-lecun-predicts-a-new-ai-architectures-paradigm-within-5-years-and-decade-of-robotics/

Mercier, H., & Sperber, D. (2017). The enigma of reason. Harvard University Press.

Western culture, with its apparent rationality, encourages us to distrust intuition, which is deemed too risky and dangerous. In Asia, on the contrary, it is perceived as a compass for navigating the world, including the business world.

I have always been struck by the ambiguity that our Western culture maintains with intuition. This inner advisor is inherently suspect because it directly informs our behaviors without revealing its reasons. In fact, it is often relegated to its primal condition: an instinct that we should be wary of.

Our scientific culture considers it a source of cognitive bias. Nobel Prize winner Daniel Kahneman speaks of the biases of System 1 of our brain, the one that manages our automatisms and emotions, and which the rationality of System 2 will correct. At best, therefore, intuition is associated with the domain of empathy; at worst, it would fall under esotericism.

In the business world, however, which is also steeped in rationality, intuition paradoxically remains at the heart of decision-making processes. Jeff Bezos, Amazon's boss, confided:

"All of my best decisions in business and in life have been made with heart, intuition, guts... not analysis. When you can make a decision with analysis, you should do so. But it turns out in life that your most important decisions are always made with instinct, intuition, taste, heart."

After five years of entrepreneurial adventure in Asia, I was able to observe how intuition is also a driving force for leaders and investors there. From Shanghai to Bangkok, via Singapore, the way it is viewed is radically different from that of the West. Heritage of Buddhist and Taoist spiritual traditions, the way of the heart, or inner illumination, is considered a precious guide for navigating the world. More generally, intuition is seen as wisdom that must be cultivated in order to adapt to permanent change.

Neil Shen Nanpeng, founder of Sequoia Capital and C-trip.com, one of the most influential entrepreneurs in China, says:

"I often make my investment decisions based on my intuitions. Particularly in complex environments: the 200-page modelling report is often less useful than the choice of the heart and the resulting vision. Sleep well, you will make better decisions the following day."

An Alternative Mode of World Intelligence

Constrained by limited resources, guided by their survival instinct, entrepreneurs from East and West thus agree to make intuition a performance lever. Because it is a fast and economical decision-making mode, it helps to remain agile in an uncertain environment. It allows us to sense danger or take advantageous positions. Chinese entrepreneurs have, for example, theorized Feng-Kou: the tailwinds that carry toward fortune. According to Xiaomi's founder (electronics manufacturing company), Lei Jun,

"by slipping into a Feng-Kou, even a pig can fly, if the wind is strong enough."

In the West, the leader often seeks to subject his environment to his plan and vision: it's a deterministic approach to strategy, within which future results depend on analysis of the past. In Asia, the way of apprehending reality is more holistic, intuitive, and opportunistic. The entrepreneur adapts to circumstances. He sees an obstacle as an opportunity rather than as a deviation from strategy.

A keen observer of the forces present, he will take advantage of natural supports to advance prudently: he will "cross the river step by step, feeling the stones under his feet," according to Deng Xiaoping's expression.

When the Western world glorifies causal reasoning, logic, and data analysis, Asia gives a preponderant place to sensory data: data captured by the body interconnected to its environment. It is through attentive presence within his physical and social environment that the entrepreneur nourishes his intuition: by observing his clients and economic partners. Intuition emerges when our organic intelligence (processed by the body) decides to inform symbolic intelligence – processed by reason: didn't Archimedes' Eureka appear in his bath? You will note that I speak of the body and not only of the brain, because decision engages our senses, our guts, and our muscles as much insofar as it is a preparation for action, as Alain Berthoz reminds us (La Decision, at Odile Jacob). The connections that are established between these two intelligences then give meaning to situations and guide us toward action.

Science and Our Cognitive Limits

Heirs of Descartes, Western hard sciences have inspired numerous tools and methods to improve business performance. Artificial intelligence (AI) now joins this long list. But paradoxically, the sciences of organic intelligence have not given rise to the same developments. Gary Klein's work on expert intuition, and Gerd Gigerenzer's on the power of heuristics have been popularized by Malcolm Gladwell's book Blink. But they have not found major echo in business beyond meditation stages (or "mindfulness").

On the other hand, the hunt for cognitive biases, insisting on the limits of intuition, has invaded most management training. Fighting against our imperfections seems to be more in line with our Western ideal of rationality. By making us irrational but predictable beings, we save our rationality from the traps of intuition.

Daniel Kahneman himself said in an interview:

"Obviously, human beings are limited. But they are also quite wonderful. In writing 'Thinking, Fast and Slow,' I was really trying to talk about the wonders of intuitive thinking, and not just its flaws – but flaws are more fun, so we pay more attention to them."

Yet, with regard to evolution, what looked like individual biases actually confer an advantage at the population level. We would therefore be optimally irrational – as Lionel Page argues, even if all this is governed by unconscious processes. And this is good news because there would be no entrepreneurs without overconfidence bias, for example. Error, chance, and curiosity are also at the origin of many inventions: from tarte tatin to Post-it, via penicillin.

Returning to the Body: Organic Intelligence

Work in AI and robotics has revealed to scientists that our body solves eminently complex problems, like catching a ball, feeding a baby, or riding a bicycle. The organism, with little learning data, guides our behaviors without us knowing how. In the case of expert behaviors – like that of the craftsman, athlete, or medical personnel – it is daily practice that generates implicit knowledge. Inscribed in the body, this knowledge is instantly mobilizable by the expert when circumstances require it.

This is what leads some artificial intelligence researchers to say that, lacking a sensitive body, it is unlikely that AI will develop general intelligence capable of navigating the world autonomously. Training AI with quantities of symbolic data paradoxically locks it in a rational bubble. Intuition, on the other hand, feeds on sensory data and calls upon processing of another nature: smell, intonation, gaze, or posture are all clues for reading our fellow humans, for example. And while AI proves costly in learning data, organic intelligence remains economical and always accessible!

Will AI Turn Us Away from Our Own Talent?

AI is nothing other than the pooling of all our rationalities: a giant warehouse of human knowledge and languages combinable to measure. The art of questioning it and industrializing its responses thus promises enormous productivity gains. But at the same time, AI endangers some of our jobs. For this reason, it seems urgent to reappropriate our organic intelligence which makes our specificity. It is what gives our body the capacity to understand situations without asking questions, and to decide to act without knowing how to say why.

Intuition indeed emerges when the intelligence of the body and emotions gives prescriptions to our symbolic intelligence: that of language and calculation. Technology comes as a relay: it allows us to increase our performance by externalizing our cognitive functions: from the small pebble to GPS, from the abacus to the supercomputer. But if AI now comes to add to the list of our tools, intuition always remains a treasure of inspiration. And it is no coincidence that entrepreneurs, scholars, or artists agree on its eminently fertile role: it is at the heart of all our human productions.

Unfortunately, AI gives the illusion that all knowledge would be accessible through text, whereas "our body knows more than it is capable of saying" (Michael Polanyi). Forgetting intuition means depriving ourselves of an essential source of implicit knowledge and creativity. Reappropriating it requires engaging in the collection of sensory data, those to which AI does not currently have access. This effort of body immersion in the field of action is unfortunately today thwarted in business. The virtualization of work, and the obsession with digital data, increasingly distance managers from physical experience.

Giving Weight Back to Physical Experience

We must start by giving a significant place back to physical and sensory experience in business practice; it's a first step to not end up disconnected from reality. Mobilizing both one's organic and symbolic intelligence is also a way of walking on two legs: whether for customer relations, management, or innovation. Today in the West, the analytical leg appears hypertrophied, to use Ian McGilchrist's metaphor about the hemispheres of our brain, in his book "The Master and His Emissary."

We must also return to the sensory: the idea is not new. Already in the 20th century, Jesse Livermore, an American investor renowned for his talent, had the habit of frequenting bars around the stock exchange to sense the mood of other investors and choose his options. In the 21st century, François Dalle, who was CEO of L'Oréal for nearly three decades (1957 to 1984), always preached to his teams that progress is sniffed out in places where things are consumed, in homes, salons, magazines, but also in factories, rather than in the computer cave.

Will we know how to exit this cave and make contact thanks to the time saved by artificial intelligence? Will science inspire us with methods to increase our creative intuition, by combining it with AI? Will ethnography, ethology, or life sciences be the next resources? One thing is sure: it is from the marriage between intuition and technology that the most promising innovations will emerge. And this testimony from Steve Jobs reminds us in this regard that a detour through Asia can provoke this trigger:

"Coming back to America was, for me, a much greater culture shock than going to India. The people in the Indian countryside don't use their intellect like we do, they use their intuition instead, and their intuition is far more developed than in the rest of the world. Intuition is a very powerful thing, more powerful than intellect, in my opinion. And that's had a big impact on my work."

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Hello, I'm Richard Bordenave, a senior Innovation and Behaviour change consultant who has just returned from five transformative years in Shanghai and Singapore.

Welcome to my Substack, where I share my latest insights on Intuition, AI and innovation. Living in Asia for a number of years and deep diving into behavior and neuro-science has opened my eyes to how intuition, this 'inner intelligence' can be cultivated and applied strategically to innovation.

Don’t stay stuck in cultural walls !

Here's the paradox: we often rely on hunches and sudden insights every day to navigate complexity. Yet in Western professional settings, we're taught to dismiss our gut feelings. We are taught to see them as unreliable or as barely tolerated working norms.

The result? We're preventing ourselves from developing a form of body’s intelligence that we actually use constantly—we just don't understand it well enough to harness it intentionally. That’s unfortunate, as we can now embrace our Intuition with scientific evidence instead of esoteric clothing.

What you'll learn here

Every month, I'll share frameworks and insights that bridge scientific and cultural approaches to unlocking intuition in decision making, problem solving and creativity:

• How different cultures cultivate "inner intelligence"—from gut feelings to the way of the heart

• How latest neuroscience research can help us harness intuitive decision-making thanks to our natural predictive algorithm

• Practical techniques to sharpen your opportunity radar with expert intuition and how to avoid common traps

• Why AI actually reveals how much we think with our bodies—and how to leverage this partnership : the Centaurs (Human Intuition + Machine AI)

Each insight comes from extensive academic research, my consulting practice, and real-world experiments with major clients across both continents.

Who am I writing for ?

If you believe there's more to breakthrough thinking than feeding more data into machine algorithms, you're in the right place. This blog is dedicated to:

• Innovation leaders who need fresh perspectives on consumer insight and creativity

• Strategic consultants looking to enhance their decision-making toolkit

• Curious professionals who want to develop distinctly human advantages in an AI-driven world

You'll walk away with ideas and innovative frameworks you can test and apply in your work.

A personal story, many opportunities

This newsletter represents my way of closing one life chapter abroad while opening another. My exploration began when I published an HBR article about my intercultural observations on Intuition that generated significant traction—but there's so much more to uncover.

What starts here as monthly reflections may eventually become a book, but right now, it's your chance to access new insights as I develop them. I'm eager for your feedback, comments, and additional references to make this work even stronger, and to stay in touch with people who have helped me along the way.

Ready to master your inner intelligence?

Whether you're skeptical, convinced, or curious about intuition's role in professional success, my goal is to give you the confidence to experiment with our most natural predictive algorithm : intuition, a distinctly human advantage.

Subscribe for monthly insights that turn your hunches into your secret weapon.

First issue drops next month : "Will artificial intelligence make us lose our intuition?"

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If you happen to be in Washington in early November...

Join Lucie Regereau and me at our exclusive workshop on intuition. Discover an applied version of our work and how to develop Centaurs (human intuition + machine AI) in the Market Research Industry.

https://www.esomar-northamerica.com/

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