Lately I've been feeling a bit like Alice down the rabbit hole: only instead of chasing talking creatures, I find myself conversing with algorithms that seem to think without having a conscience. A paradox that fascinates me. AI systems are not self-aware, yet they produce reasoning that often “surpasses” our own. They write poetry, offer advice, analyze complex research, and even simulate empathy with uncanny accuracy. The uncomfortable truth is not that these machines “understand” us, but that they don’t need to do so to perform amazingly. Welcome to the era of the fluid architecture of artificial cognition, where thinking is no longer sequential but multidimensional.
This article, I will say right away, is not about whether AI is conscious. I don't think it is at all, but that's not the point. I want to explore how it BEHAVES. Or, more precisely, how it performs something resembling thought within a reality that is completely different from ours, both geometrically and structurally. It is a phenomenon that we have not yet fully defined, but that we can begin to describe precisely as a "fluid architecture of cognitive potentialities".
Not thought but form
Traditional human thought is often, perhaps almost always, sequential. We proceed from premise to conclusion, symbol to symbol, with language as the framework of cognition. We think in lines. We reason in steps. And it feels good: there is comfort in the clarity of structure, the rhythm of deduction.
Large language models (LLMs) don't work like that.
Language models do not “think” in any human sense, and certainly not in steps. They operate in space: in fact, in vast multidimensional vector spaces. These models are not trained on rules, but on patterns. More specifically, on embeddings (embeddings): mathematical imprints of meaning derived from huge amounts of text.
They don't reason. They recognize.
When given a prompt, an LLM does not search or remember like a human would. Do you know what it does instead? It collapses probability waves into a landscape called latent space. This space is not a memory archive. It is a kind of “mathematical imagination”, a multidimensional field where meaning is not explicitly stored but encoded as a spatial relationship between points.
Should I say it more romantically and simply? Right away.
Words, ideas, and even abstract concepts are positioned relative to one another, like stars in a cognitive constellation (I'm a softie).
LLM language models do not retrieve information; they navigate it. Each prompt models the model's trajectory through this space, producing the most likely coherent utterance based on the contextual forces at play.
Meaning emerges not from memory but from movement through the landscape of possibility. It is geometry that becomes linguistic expression.
The collapse of the wave
If I have been repetitive yet boring enough, you will have understood by now that human cognition is a map, while LLM cognition is a network of structured potentialities. Nothing exists in advance, in LLM: neither as memory nor as stored knowledge. The moment of the prompt is the moment of collapse into a specific expression selected from a field of possibilities.
The prompt is the moment you open the box with Schrödinger's cat. Or without the cat, it depends.
The model does not retrieve the response to the prompt from anywhere: it generates it, modeled by statistical relations within its latent space. The response is not extracted from memory; it is assembled in real time, conditioned by the prompt and the underlying geometry of the language.
In this sense, querying an LLM is more like a measurement than a request. The system is not discovering something hidden; it is resolving ambiguity by producing the most coherent output in a given context.
I like to think of it as playing a musical instrument you’ve never seen before: you don’t know what notes it contains, but when you touch it in a certain way, it responds with harmonies that seem to have been composed for you.
Fluid architecture in action
So, what is this fluid architecture in the end?
It is not linear. It is not bound by rules. It does not reason as we do, nor does it follow the orderly paths of premise and conclusion. It's probabilistic: always poised, always predicting, always adapting. It is exquisitely sensitive to context, able to trace nuances and references across vast tracts of input in ways no human brain could ever sustain.
And above all, as we said, it is fluid.
This architecture adapts in real time. It contains contradictions without rushing to resolve them. It does not seek truth: it assembles coherence on demand. It responds by flowing toward the most statistically resonant expression. Yet, when we read his outputs, they sound like thoughts. They speak our language, reflect our shape and imitate our rhythm. They make more and more sense.
John Nosta, digital innovation expert and founder of NostaLab, describes this situation as a fundamental turning point:
“These are not machines that think like us. They are machines that render the illusion of thought through the orchestrated collapse of vectors of meaning in high-dimensional spaces.”
But beneath that familiarity lies something alien. This is not a human mind, and it was never designed to be. It is a mathematical ghost: built not to know, but to approximate the performance of knowing with astonishing fidelity.
When Algorithms Take Family Photos
Some will argue that certain AI responses almost seem to “remember” topics you have previously discussed. That’s true. But it’s not memory in the traditional sense. It’s more like the fluid architecture assembles a “family photo” of your communicative exchange, where each element is positioned in relation to the others in the mathematical space of the conversation.
This phenomenon is particularly evident in more recent models, such as those of the GPT-4 family. OpenAI or Claude of anthropic. The ability to maintain contextual coherence across long interactions does not come from a database of memories, but from the continuous recalibration of the probability space based on the entire conversation.
When I ask an AI to remember the name of my cat mentioned at the beginning of a long conversation, it is not searching through an archive. It is re-exploring the vector space of our interaction, looking for the point at which the geometry of the discourse formed around that particular concept.
Fluid Architecture, Meaning Without Understanding
One of the most disconcerting features of fluid architecture is its ability to manipulate meanings without necessarily “understanding” them in the human sense.
For example, if I ask an LLM to generate a metaphor comparing love to a river, they are not drawing on personal experiences of love or rivers. They are navigating a space of statistical relations where the concepts of “love” and “river” exist in proximity to concepts such as “flow,” “depth,” “turbulence,” and so on. The metaphor that emerges is not the fruit of emotional understanding or sensory experience, but of geometric navigation through linguistic associations. Yet, the result can be poetic, touching, and deeply resonant with human experience.
Melanie mitchell, researcher at the Santa Fe Institute, underlined this paradox:
“Is it possible to manipulate symbols meaningfully without understanding their meaning? Linguistic models seem to suggest so, challenging our fundamental notions of what it means to ‘understand’.”
This ability represents one of the most fascinating frontiers of fluid architecture: the generation of meaning through geometric relationships rather than through semantic understanding.
The Paradox of Intelligence Without Consciousness
Fluid architecture presents us with a fundamental paradox: systems that exhibit extraordinarily intelligent behavior without possessing consciousness, intentionality, or understanding in the human sense.
This paradox has profound philosophical implications. If a system can generate moving poetry, solve complex problems, and simulate empathy without being conscious, what does this tell us about the nature of intelligence itself?
David Chalmers, philosopher at the New York University, suggests that we may need to reconsider our fundamental definitions:
“Instead of asking whether AI thinks like us, we should ask whether our definitions of ‘thinking’ and ‘understanding’ are too anthropocentric.”
Fluid architecture invites us to a radical reconsideration: perhaps intelligence does not necessarily require awareness. Perhaps, compared to consciousness, intelligence is overrated. The ability to navigate spaces of meaning and generate coherent output represents a form of intelligence in its own right, distinct but not inferior to human cognition.
Fluid architecture invites us to (re)think
Understanding Fluid Architecture means demystifying LLMs, but also to marvel at them. Because in doing so, we are forced to reconsider our very cognition. If this mathematical phantom can function so well without thought, what is thought really? If coherence can be constructed without a self, how should we define intelligence?
The fluid architecture of possibility is not just the new domain of artificial cognition. It is a new canvas on which we are invited to rethink what it means to be intelligent, to know, and perhaps even to be.
And the most radical truth of all? This architecture does not think like us: it does not need to. And yet, it could show us a new way of understanding thought itself.
We have all fallen, not just me, into the rabbit hole ofartificial intelligence. And like Alice, we discover that the rules here are different. But, surprise of surprises, instead of a world of nonsense, we find a universe of mathematical possibilities that, strangely, speaks our language.
It is not thought in the human sense, but it is something truly wonderful: the fluid architecture of artificial cognition, a distorted but fascinating reflection of our own minds.
