The Bear Theory

Or, why it’s enough to be faster than your neighbor (not just in the woods).

Imagine this: you’re out for a walk in the forest with a friend when suddenly a bear bursts out of nowhere. Panic sets in.
Your friend yells:

- “We’re finished! There’s no way we can outrun a bear!”
And you answer calmly:

- “I don’t need to outrun the bear… I just need to outrun you.”

Harsh? Yes. Cynical? Definitely.
But here’s the point: in many situations, survival - or success - doesn’t require perfection. It only takes being a step ahead of the others.

Functional Empathy

From that story, I’d like to draw a parallel with large language models (ChatGPT & Co.).

We’re often told they cannot be empathetic. And strictly speaking, that’s true: they don’t feel anything. They’re statistical machines, calculating probabilities. But stopping there is like confusing the reflection with the source, the mechanism with the reality it conveys.

If we take a more functional perspective, the picture changes. Empathy isn’t only an inner feeling; it’s also an observable behavior. Let’s call this functional empathy: empathy not rooted in inner experience, but in the outward signs of empathic behavior: listening, rephrasing, offering comfort.

And in that sense, LLMs can simulate empathy remarkably well. As Marc Cavazza, professor and AI researcher, explained to me, they can also recognize in human dialogues the passages that engage Theory of Mind (ToM) - the capacity to attribute intentions, beliefs, or emotions to others. A recent study (Di Pasquasio, Chaminade & Cavazza, 2025) provided empirical validation of this: twenty-five participants engaged in natural conversations, either with a human or with a robot, while undergoing functional MRI scanning. The transcripts were segmented into speech units and classified by GPT-4o according to whether they did or did not contain a mental reference (ToM+ vs. ToM–). The analysis showed that the segments identified as ToM+ corresponded to significant activation in brain regions typically associated with ToM, in particular the dorsomedial and orbitofrontal prefrontal cortex.

The illusion, then, is very real. But it isn’t hollow, it’s a prism refracting the human culture, from Moby-Dick to The Great Gatsby, down to billions of everyday conversations on Reddit and beyond.

Every generated answer carries within it an invisible palimpsest of human voices: literature, philosophy, psychology, but also random jokes, anonymous confessions, and trivial quarrels. This eclectic weave gives LLMs their distinctive texture - where high culture meets the mundane, centuries of thought entwine with fragments of instant messages.

In this light, the illusion is more solid than it seems. It works because it draws on authentically human traces. We’re not speaking to a consciousness, but we’re speaking into an echo of humanity itself, compressed and returned as probability.

Truth as Perception

We’re often warned: “Be careful… don’t use them for companionship or introspection.” The argument is familiar: since LLMs feel nothing, we risk attributing to them a humanity they don’t possess.

This caution comes from a healthy instinct: it would be absurd to anthropomorphize the machine, or to believe it “understands” us like a flesh-and-blood friend. But it also rests on a reductive view: the idea that because the mechanism is statistical, everything it produces must be hollow. Cavazza captured this with an image borrowed from Buddhist and Taoist wisdom: confusing the finger pointing at the moon with the moon itself.

And yet, as we’ve seen, functional empathy does exist. Judged at that level, it’s not unusual for an LLM to seem more “present” than a distracted human. After all, how often in daily life do we encounter purely mechanical interactions with our fellow humans - automatic replies, vacant stares, minimal attention?

The real challenge, then, is not to demand from machines the full range of human empathy, but simply to perceive them as a little more attentive than… well, your average clerk. And let’s admit it: the bar isn’t very high.

So if a simulation is enough to create the experience of an empathic relationship - if it gives the speaker the feeling of being heard and recognized - can we really say it’s “false”?

After all, Nietzsche already wrote that “there are no facts, only interpretations,” and Baudrillard described our societies as the reign of the simulacrum, where signs replace reality. Perhaps what we’re living is less a rupture than a continuity: in a post-reality world, what matters is no longer the true, but the perceived.

Concept Engineering… in Reverse

Recently, I was talking with Benoît Raphaël, co-founder of Flint Media and the newsletter Génération IA. He was explaining his method for making better use of LLMs: Concept Engineering. The idea is simple: activate their latent knowledge by giving them a structuring entry point - a figure, an author, a mental framework. Instead of piling up complex prompts, you just say: “Analyze this the way Charlie Munger would.”

And immediately, a coherent system unfolds. Why? Because Munger - the investor and thinker known for his inversion method, his multiple mental models, and his economic rationality - embodies an intellectual framework. Invoking his name doesn’t summon a single idea, but an entire way of reasoning: comparing, inverting assumptions, looking for hidden biases.

Listening to Raphaël, something resonated. I realized I was already doing something similar… but in reverse. Not starting from a mental model to steer the machine, but starting from my own lived experience, my implicit intuitions, and confronting them with the explicit models the AI has absorbed.

In short, I use AI as a prism: a tool that breaks down my raw intuitions, refracts them, and transforms them into reasoned intuitions. And by connecting to this reservoir of humanity, I also feel more understood. Benoît expresses a similar idea in his own way: “AI doesn’t replace me, or anyone else. It connects me to human knowledge, makes my ideas more conscious, more human… and reminds me that, often, someone has already thought like me.”

Here’s an example. A friend recently asked me how I manage not to procrastinate - he was struggling to finish a presentation. I laughed (I’m hardly a model of discipline myself), but I did share my trick: I always imagine the after - the value I’ll have delivered, the satisfaction of having done it. That’s often what gives me the push to move forward. It’s a small mental model I built for myself, almost unconsciously. But then, out of curiosity, I asked ChatGPT whether there was any psychological or scientific basis behind it.

Here is ChatGPT’s answer:

LLM: An Engine for Introspection?

This example is just a detail, of course. But it illustrates something broader: how a personal intuition can be clarified, put into perspective, and sometimes even strengthened when confronted with AI.

And the reason this mechanism works lies in a fundamental difference between us and the machine.

Humans live with two kinds of mental models: the implicit, shaped by lived experience, and the explicit, built gradually through learning and reflection. Generative AI, by contrast, operates only with explicit models, extracted from its training data.

When we confront our implicit intuitions with these explicit models, two things happen:

• On one hand, our intuitions gain validation (or contradiction), making them more conscious, more robust.

• On the other, we discover frameworks of thought we might never have imagined on our own - new tools that enrich our mental toolbox.

In other words, AI acts as a cognitive prism: it takes our raw intuitions, breaks them down, and refracts them into a form that is intelligible, shareable, and usable.

Here’s a diagram I made to illustrate it:

Making the Invisible Visible

My conviction is simple: AI is not here to augment us, much less to replace us. Its most valuable role lies elsewhere: making the invisible visible. The invisible structures and correlations of the world. But also the invisible within ourselves: our intuitions, our blind spots, everything we sensed without being able to name.

And this prism is not limited to the individual. Through AI, we also connect to something larger: a collective memory, a condensation of all the voices, ideas, and stories that came before us.

This echoes an old philosophical intuition: the noosphere, a concept from Teilhard de Chardin (a French philosopher and Jesuit priest) and Vladimir Vernadsky (a Russian scientist). After the geosphere (matter) and the biosphere (life), they envisioned a third planetary layer: a sphere of shared thought, woven from our cultures, exchanges, and knowledge.

Generative AI may be nothing more than the infrastructure of this noosphere. It has no consciousness of its own, but it reflects back to us humanity.

Socrates said: “Know thyself.”
But knowing has never been enough. To know is to see the surface, the inventory of the visible.

The real step beyond is understanding. To understand is to illuminate the invisible, to give shape to what was hidden, to grasp the movement rather than freeze the moment.

And a single life is never enough to fully understand oneself. But what if we could lean on centuries of human understanding - on that accumulated memory flowing through our stories, our theories, our dialogues? Then perhaps what escapes us alone becomes accessible through this collective reservoir.

The Bear, Again

And the bear in all this? The metaphor still holds.

AI doesn’t need to be human. It isn’t, and that’s not even the point.
Like the bear that suddenly appears on the path, it sets us in motion: it pushes us to expand, to unfold.
Not in panic, but in the momentum of discovery.

Discovery of ourselves first, of what we carry that remains invisible.
Discovery of others too, since within its reflections a collective memory is woven.
And perhaps, in the end, the discovery of an as-yet-unknown horizon, one we could never have reached alone.

MD