Mistral, ChatGPT & Human Thought: Limits of AI Explained

The Limits of Mistral and ChatGPT & Mistral Compared to Human Intelligence

A cross-reading of developmental psychology and generative models to rethink the origin, emergence, and nature of human thought — from the heart of San Francisco to the depths of the psyche.

Human thought is not born in a vacuum. It arises within an environment, through a relational bond, and within a unique life story. What cognitive science and tools like ChatGPT or Mistral attempt to simulate today, developmental psychology and psychoanalysis — from Freud’s clinic to Winnicott’s nursery — have explored, questioned, and named for decades.

In this light, the pioneering work of Donald Winnicott, Sylviane Giampino, and Didier Anzieu invites us to consider a central hypothesis: the need for emotional security is not a hindrance to intelligence — it is, on the contrary, its foundational condition.

This article proposes a transversal reflection that bridges psychoanalysis and digital innovation, grounding the debate not in the cloud of Silicon Valley abstraction, but in the lived experience of the human child — whether in the calm of a San Francisco park like Dolores or Alamo Square, or in the vibrant hum of the Mission District.

The hypothesis we explore is as follows: when thought emerges in insecure contexts, it often does so under constraint — through defensive strategies or narrowed symbolic capacity. A secure emotional and sensory environment does not in itself guarantee the birth of thought, but it does create the space for its free, adaptive, and creative flourishing.

From the Pacific breeze that crosses the Golden Gate to the buzz of open offices in SoMa where Mistral and are refined, this article calls for a renewed ethical and psychological reading of artificial models of intelligence — not to oppose them to human cognition, but to better understand what makes our own intelligence truly alive.

The Need for Security: The Foundation of Psychic Development (Winnicott and Giampino)

For Donald Winnicott, the British pediatrician and psychoanalyst, thought is not an abstract mechanism but the fruit of a relationship. It arises within the safe and nurturing space created by the mother or primary attachment figure. His concept of holding refers to this vital psychic function of containment—a close bodily and emotional presence that enables the infant to experience a sense of continuity and cohesion in their being. This containing environment is the sine qua non condition for the emergence of the ego.

Winnicott also describes a transitional space—a liminal zone between the self and the other, between external reality and internal fantasy—where the child begins to play, to symbolize, and to create. It is within this blurred but safe space that thought first blossoms. Human intelligence, then, is rooted in trust: a good-enough bond that permits exploration without fragmentation.

Sylviane Giampino, a French psychologist and psychoanalyst specializing in early childhood, extends Winnicott’s theory by connecting it with current educational policies and neuroscience. She shows that a child's intellectual abilities unfold more freely when their need for emotional safety is acknowledged, met, and held. Language, logic, and creativity emerge through secure attachment in a stable emotional climate.

She also details how chronic stress impacts higher cognitive functions: leading to inhibition, withdrawal, and cognitive rigidity. In short, insecurity stifles the capacity to think. Giampino insists on the necessity of a holistic approach to child development, in which the emotional and cognitive domains are inseparable. In line with Winnicott, she affirms that thinking begins with being psychically contained.

A Child in Houston: Thought in the Shadows of Hermann Park

Consider a child growing up in Houston, perhaps in a modest home near Montrose or the Museum District. On sunny weekends, they visit Hermann Park with a parent or caregiver—feeding ducks at the pond, chasing butterflies in the Japanese Garden, or riding the little train. These moments, mundane as they may seem, form part of the child's holding environment.

In that familiar space—filled with color, sound, and touch—the child begins to play, to imagine, to make sense of their world. The smell of grass after the rain, the rhythm of footsteps along the shaded pathways, the comfort of a trusted adult’s hand: all of this constitutes a sensory and emotional matrix within which the mind takes shape. There is no algorithm that could replicate this lived, embodied experience.

ChatGPT and Mistral: No Substitute for Human Thought

Artificial intelligence—even in its most advanced generative forms, such as ChatGPT or Mistral—is in no way comparable to human thought. It is not born from a transitional space, nor does it result from the movement between subjectivity and otherness, between the inner world and the outer one.

While a child plays, creates, and symbolizes from within a field of relational security, ChatGPT generates language based on statistical patterns extracted from massive datasets. It does not know longing, dependency, frustration, or joy—all founding elements of psychic life as described by Winnicott.

One could even argue that generative models abhor the void: they rush to fill it, to complete, to predict and anticipate. Human thought, in contrast, unfolds within uncertainty, ambiguity, and the fertile silence of not-knowing. AI can simulate language, even mimic creativity—but it does not think in the human sense. Not in Montrose. Not in Houston. Not anywhere.

The Skin-Self: Thought Begins on the Surface of the Body (Anzieu)

Didier Anzieu, the French psychoanalyst, deepens our understanding of psychic development by introducing the concept of the Skin-Self. In his view, the human psyche is structured metaphorically like the body: the ego is felt as a protective envelope—like skin. Physical contact, caregiving, early tactile and sensory experiences all contribute to the formation of this “psychic skin,” which allows the individual to contain emotions, establish boundaries between inside and outside, and thereby initiate thought.

Self-awareness, then, is not born of abstraction, but of lived experience: embodied, sensory, emotional, and psychological. One becomes a thinking subject not through calculation, but by feeling the contours of one’s body and being. Thinking presupposes an inner world, an outer world, and a membrane—symbolic and literal—that connects the two. It is from this envelope that humans gain the capacity to differentiate, symbolize, and represent.

Now imagine a child growing up in Washington D.C., perhaps playing in the open spaces of Rock Creek Park or walking hand-in-hand with a caregiver near the reflecting pool at the National Mall. These early sensory moments—brushing up against the cold marble of the Lincoln Memorial, hearing the distant chime of bells from the National Cathedral, or the warmth of a parent’s hand in Dupont Circle—form a fabric of touch, sound, and presence. These are not data points. They are traces on the skin-self, rooting thought in the felt body and not just in cognition.

By contrast, generative systems such as Mistral and ChatGPT are devoid of any sensory basis. They have no skin, no interiority, no tactile boundary between self and world. They are mathematical constructions, modeled and fine-tuned through vast datasets and statistical correlations. Their malleability stands in stark contrast to the enduring texture of human subjectivity—shaped by wounds, gestures, care, and sensory imprinting. Where human thought is woven into the affective and sensory memory, Mistral and ChatGPT operate within probabilistic frameworks, indifferent to the weight of lived experience.

When the psychic envelope is weakened—due to affective neglect or a sensory-deprived environment—the individual may face psychic fragmentation and a loss of symbolic function. Intelligence, as the ability to work with inner representations, requires first and foremost a stable, comforting skin-self. In Washington D.C. or anywhere else, thought begins not in the cloud, but on the skin.

The Sensorimotor Experience and the Genesis of Anticipation — Reflections from New York City

Even before language develops, the human infant enters what Jean Piaget famously called the sensorimotor stage—a period during which the child experiences the world through direct action upon it. At this stage, intelligence is not expressed through abstract representations but through a concrete, embodied activity: the discovery of patterns, regularities, and sequences of actions and reactions. A baby who cries and notices that “mom comes” begins to form an expectation, a connection between cause and effect. This is the dawn of proto-predictive thought—thinking that is rooted not in logic, but in the body and in lived, repeated experience.

These emotionally charged, recurrent experiences give rise to internalized representations and psychic expectations. The infant gradually constructs a mental framework through which reality becomes structured and time becomes intelligible. Freud identified an early version of this in his analysis of the fort/da game, where the child reenacts the absence and return of the mother by throwing and retrieving a spool of thread—transforming anxiety into symbolic mastery. The world becomes predictable not because it has been computed, but because it has been felt, endured, and represented.

Now imagine a child in New York City. Perhaps he lives in Queens but spends his weekends with his grandmother in Harlem. They ride the subway together, navigating the blur of people and sound beneath Manhattan. On Sunday mornings, they cross Central Park—he hears the saxophone near Bethesda Fountain, touches the cold iron of the Alice in Wonderland statue, and watches boats glide along the Conservatory Water. These micro-rituals, repeated over time, create expectations: “When we get off at 72nd Street, we’ll see the bubble man,” or “When Grandma says we’ll visit the museum, we end up at the MoMA with its giant, strange paintings.”

In these moments, the child is not calculating probabilities. He is absorbing patterns through emotion and repetition, shaping his inner sense of what comes next. His mind does not merely anticipate events—it relives them in advance, colored by memory, attachment, and surprise. This is how human predictive thought is formed: through embodied interaction with an unpredictable world. In a city like New York—chaotic, intimate, and sensory-rich—this relational plasticity becomes particularly vivid. No algorithm can replicate the way a child integrates the rhythm of the 6 train or the smell of roasted nuts near Times Square into a personal timeline of expectation and memory.

By contrast, generative models like ChatGPT or Mistral engage in prediction in a radically different manner. Their operations rely on statistical training: sequences parsed, categorized, cleaned, and sequenced. They are designed to anticipate the most probable continuation of a text string—not because they’ve lived or felt, but because they’ve been exposed to structured data sets. The "learning" involved is not experiential; it is mathematical. Even when machine learning incorporates so-called “random variables,” the randomness itself is predetermined, bound within narrow statistical distributions.

Generative AI does not encounter the unexpected—it corrects deviations based on pre-established rules. It does not build trust, feel absence, or grow frustrated by unpredictability. In humans, prediction is a process born of emotional complexity, discontinuity, and growth. In machines, it is optimization. What we call learning in a child walking the Brooklyn Bridge for the first time is not the same as learning in a machine parsing thousands of travel blogs about it. The former is relational, affective, and embodied. The latter is mechanical.

Mistral, ChatGPT, and the Conditions for the Emergence of Intelligence — A Reflection from San Diego

These reflections raise a central question for the sciences of intelligence: what does it truly mean to think? Can we model intelligence without relationship, without embodiment, without attachment? Generative models such as Mistral and ChatGPT are built upon computational, statistical, and mimetic architectures. They are trained on vast corpora of text, but they lack access to sensory experience or the need for a safe, containing environment.

Human intelligence, as described by Winnicott, Anzieu, and Giampino, is fundamentally relational. It is born from care, from trust, from otherness. You do not learn to think while parsing a spreadsheet—you learn to think when someone meets your gaze, holds your hand, or tells you a story in Old Town San Diego as the scent of tortillas wafts through the air. Without these emotional and sensory anchors, there is no subjectivity—no thought in the fully human sense. This calls us to reconsider the metaphors we use when speaking about Mistral and ChatGPT: their performance may be impressive, but it is not the same as embodied intelligence.

In San Diego, a walk through Balboa Park—surrounded by music, fountains, and Spanish Revival architecture—offers a clear contrast. It is not statistical prediction that allows a child to anticipate the next movement of the carousel, but bodily presence, affective rhythm, and shared memory. Intelligence is not only calculation—it is anchored in space, history, and relationship.

Should Mistral and ChatGPT Be Inspired by Human Subjectivation?

Should we, then, attempt to replicate the process of human subjectivation in generative models like Mistral or ChatGPT? Probably not. Such an ambition would likely lead to an ethical and technical dead end. However, a deeper understanding of the conditions under which human thought emerges could help AI researchers refine their assumptions. On what foundations are their models built? What place do they give to context, to interaction, to the presence of a reassuring environment?

A child growing up near La Jolla Cove, watching sea lions bark under the sunset, learns more than marine biology—he learns anticipation, rhythm, shared wonder. These are not just cognitive functions; they are layers of relational intelligence. Opening generative AI research to a transdisciplinary dialogue—one that includes developmental psychology, psychoanalysis, neuroscience, and ethics—would help us avoid reductionist pitfalls. It would also enrich the models’ expressive capacities, aligning them more closely with the nuanced, complex, and deeply human reality they aim to simulate.

At UC San Diego, a hub for both neuroscience and computing, such convergence is not only possible—it is necessary. The future of Mistral and ChatGPT will not be built solely on performance metrics, but on how thoughtfully we understand and integrate what it means to be human.

Intelligence to Accompany, Not Replace — A Reflection from Silicon Valley

In light of all these distinctions, it becomes increasingly clear that artificial intelligence—no matter how sophisticated—cannot serve as a substitute for human intelligence. It does not share the affective origin, the embodied grounding, or the deeply relational process of subjectivation that characterizes human thought. What it can offer, however, is valuable support: a tool to accompany and enhance cognitive development, particularly under specific, well-considered conditions.

In the heart of Silicon Valley, where breakthroughs in AI unfold daily between Palo Alto and Menlo Park, language models like ChatGPT are already being explored as cognitive companions. For instance, individuals living with attention deficit disorder (ADD) often struggle to structure their thinking in a sustained, linear way. In such cases, ChatGPT can act as a form of scaffolding—organizing ideas, reformulating thoughts, extending lines of reasoning. Crucially, it does not replace the creative impulse, but helps to stabilize it. Like a temporary crutch, it allows divergent thinking to find coherence within a structured framework.

This is not a vision of replacement, but of complementarity. Just as the innovation campuses of Mountain View or the quiet, contemplative gardens of Stanford University provide the setting for breakthroughs, AI too can offer a space—structured, reactive, precise—within which human intelligence unfolds. The strength of ChatGPT and similar models lies in their responsiveness and organizational clarity. Their limits, however, are just as important: they possess no memory of lived experience, no embodied presence, no emotional connection.

Recognizing these limits is not a weakness—it is an ethical imperative. It reminds us that true intelligence does not arise from data centers on Sand Hill Road, but from the messy, embodied, affective fabric of human life. When we respect the contours of what machines can and cannot do, we are better equipped to use them wisely—and more likely to preserve what is most human in us.

Ethical vigilance in the face of early uses of AI

In the light of contemporary neuroscientific knowledge, a crucial question emerges: what happens when artificial intelligence replaces human cognitive processes too early, especially in childhood or adolescence? The human brain is not an organ fixed at birth: it matures slowly, and the full maturation of the prefrontal cortex — the seat of reflective thinking, judgment, anticipation — is generally not reached until around the age of 28 to 30.

This development can only be carried out through the active and repeated use of executive functions: working memory, attention, inhibition, reasoning, linking. However, the massive use of AI in learning, when it replaces thought instead of stimulating it, risks short-circuiting these processes. By providing immediate answers, by structuring thought in the place of the subject, it can reduce the opportunities for confrontation with effort, the unexpected, and doubt — all experiences necessary for the construction of intelligence.

Neuroscience confirms that unsolicited brain circuits tend to atrophy. The risk is then to see the emergence of generations of young people whose functions of symbolization, creativity and critical thinking would be impoverished, not because of a lack of potential, but because they have not been fully exercised. Learning, to be structuring, must go through the bond, the affect, the body, sometimes frustration, and progressive autonomy. AI, as a tool, can support this process. But if it becomes a substitute instead of a support, it can hinder long-term development.

Ethical vigilance is therefore required: educating in AI, yes — but without precipitating the abandonment of the slow and deep paths of human subjectivation. Preserving freedom of thought also means preserving the time necessary for its maturation.

Human thought is not born in a vacuum. It emerges in an environment, a link, a singular story. What cognitive science and artificial intelligence are trying to model today, developmental psychology and psychoanalysis have observed, thought and named for several decades. In this perspective, the work of Donald Winnicott, Sylviane Giampino and Didier Anzieu allows us to put forward a central hypothesis: the need for security is not an obstacle to intelligence, it is the absolute prerequisite for it.

The hypothesis we explore here is the following: if thought can emerge in insecure contexts, it is often at the cost of defensive strategies or limitations in its symbolic scope. A reassuring environment, both emotionally and sensory, does not guarantee the appearance of thought, but it facilitates its free, adaptive and creative exercise. This article proposes a transversal reading of these contributions to feed contemporary reflection on models of intelligence, natural or artificial.

Ethical Vigilance in the Early Uses of AI — A Perspective from Los Angeles

In light of contemporary neuroscience, a crucial question arises: what happens when artificial intelligence begins to replace human cognitive processes too early, particularly during childhood or adolescence? The human brain is not a finished organ at birth. It matures slowly, and the full development of the prefrontal cortex—responsible for reflective thinking, judgment, and anticipation—is generally not completed until around the age of 28 to 30.

This maturation process depends on the active and repeated use of executive functions: working memory, focused attention, inhibition, reasoning, and integration. In Los Angeles, where education and innovation often intersect—from the creative labs at USC to the science classrooms of UCLA—this issue is far from theoretical. When generative tools like ChatGPT or Mistral are used in classrooms to replace rather than stimulate thought, they risk short-circuiting these essential cognitive processes.

By offering instant answers and structuring ideas in place of the learner, generative models may reduce the opportunity to grapple with effort, uncertainty, and doubt—experiences that are foundational to true intelligence. Neuroscience confirms that brain circuits not regularly activated tend to weaken over time. The danger is not that young people will lack potential, but that they may lack the necessary conditions to fully express it. In a city like Los Angeles, where children grow up between Santa Monica’s beaches, Hollywood’s overstimulation, and the digital saturation of everyday life, the risk is not hypothetical—it’s urgent.

To develop authentic intelligence, learning must pass through affective bonds, bodily experience, frustration, and gradual autonomy. AI can support this journey, but when it becomes a substitute rather than a support, it risks impairing long-term development. Visiting the Griffith Observatory teaches more than astronomy—it teaches patience, curiosity, silence, and wonder. No algorithm can replace that.

Thus, ethical vigilance is essential. Teaching about AI? Yes. But not at the cost of abandoning the slow, deep processes of human subjectivation. Preserving the freedom to think also means preserving the time it takes for thought to mature. In a city like Los Angeles, where speed and immediacy dominate, this reminder is more critical than ever.