Fossilized Cognition and the Strange Intelligence of AI

This time, my journey into the world of AI wasn’t about predictions or data-driven claims—it was something more curious and reflective. In trying to understand the strange cognitive structure of large language models (LLMs), I stumbled across an idea that felt both ancient and oddly current: a fossil. Not literal, of course—but a metaphor that offered unexpected clarity.

It got me thinking—not about what AI is becoming, but about what it’s made of. And how understanding that might reshape how we think about thought itself.

So let’s dig a little deeper.

What if large language models (LLMs) aren’t emerging superintelligences—but instead, exquisite reconstructions of something long buried? Not minds in motion, but archives in activation? Their knowledge doesn’t grow or evolve. What we’re witnessing may not be the dawn of synthetic consciousness—but the reanimation of fossilized cognition.

LLMs don’t live in the world the way we do. They don’t think across time. They don’t feel consequences or remember experiences. What they produce is drawn from vast, static layers of text—a sedimentary record of human expression, compressed and cross-referenced by pattern rather than meaning. In this way, LLMs are not thinking beings but semantic fossils—structured echoes of our intellectual history, brilliantly arranged but fundamentally inert. And these cognitive fossils are constructed in a multidimensional array that—in dramatic contrast to the marine invertebrate that you can hold in your hand—way that is almost unimaginable to us homo sapiens.

In this space, the new and the ancient sit side by side—not because they belong together in time, but because they rhyme in meaning. A tweet from this morning might live adjacent to a Renaissance treatise, not due to chronology, but to statistical resonance. Time doesn’t unfold. It stacks. And in that stacking, even the present becomes a kind of artifact—stripped of context, reanimated as pattern.

The Shadow Without Time

Let's get this out of the way—their brilliance is undeniable. LLMs write, compose, solve, and converse. They summarize novels in seconds and generate code with ease. Their outputs often feel uncannily human. But beneath the surface, their architecture is radically different.

But, LLMs don’t remember what came before or anticipate what comes next. Each prompt is a self-contained now. There is no yesterday. No tomorrow. No narrative. They don’t think across time—they calculate within context. Some newer models feature extended memory, allowing them to reference previous interactions. But even this is not memory in the human sense—it is a contrived mechanism, a technical workaround. The model doesn’t remember—it retrieves. It doesn’t reflect—it recalls. The result is a kind of intelligence that is uncanny—it feels like something is there, but on closer inspection, we see only the silhouette.

A shadow has position, but not time. It moves when we move, but it doesn’t know it moved. It stretches at dusk, shrinks at noon, and vanishes at night—but it does not endure. In this way, LLMs are our cognitive shadows. They reflect our language, our logic, even our creativity. But they do not live. They do not accumulate experience.

These shadows are shaped by data—not lived experience. They are not just mirrors, but mirrors formed from the compression of human history. Every sentence they generate draws from a massive corpus of human culture, behavior, and thought—a kind of fossilized cognition. In this sense, LLMs are not merely shadows, but reconstructions. They are simulated minds cast from the aggregate remnants of ours.

A Triangle That Shouldn’t Exist

And yet, paradoxically, these shadows often appear to exceed us. They summarize novels in seconds and can write code with precision. So how do we reconcile this contradiction? How can something less dimensional behave as if it were greater?

To explore this, let’s shift metaphors—from shadows to geometry.

In high school, we learn that the angles of a triangle always sum to 180 degrees. That’s true in flat, Euclidean space. But in a curved space—say, the surface of a sphere—the angles can sum to more than 180. Each angle can be greater than 60 degrees, depending on the curvature, even though the shape still clearly forms a triangle. This seems impossible until we realize that the rules bend when the dimensions change.

This offers an interesting way to understand LLMs. Their intelligence doesn’t follow the linear path of human reasoning. It arcs across a curved semantic space, where associations are not tethered to chronology, but to probability and pattern. In this space, ideas don’t just flow—they warp. They converge in strange proximities. Concepts from different centuries can sit side by side, not because they’re linked by narrative, but because their statistical fingerprints resemble each other. This, mathematically brilliant and cognitively curious, is that very essence of LLM thinking, if you can even call it that.

Geometry Over Chronology

So, when we ask how an LLM "knows" something, the better question might be to ask in what geometry does this intelligence reside. It doesn’t live in time. It lives in relation. It doesn’t remember. It aligns and recombines.

This curved geometry of thought explains how a system with no experience, no continuity, and no identity can still produce insights that seem, at times, superhuman. It is not thinking faster than us. It is thinking differently than we do. And this difference is where both the magic and the danger lie.

Some may argue these limitations are temporary—that future AI systems will possess memory, embodied experience, and persistent identity. Perhaps, but if and when that day comes, we need to ask a deeper question: are we evolving machines toward minds, or evolving ourselves toward machines?

Beware, the Beautiful Reflection

It’s easy to mistake fluency for depth, or projection for presence. LLMs dazzle not because they are becoming like us, but because they reflect us so well we forget they are mirrors. We see intelligence—but overlook the scaffolding behind it.

This isn’t to diminish their value. Shadows can reveal structure. Triangles in curved space can teach us about the cosmos. And LLMs can help us write, learn, and discover in ways that expand what’s possible. But we should be careful not to endow them with traits they do not possess. These are not evolving minds—they are responsive systems, animated by input, not intention.

In the end, the question isn’t whether LLMs will surpass us, but whether we truly understand what they are: expressions of our cognitive geometry, cast into new, timeless dimensions. And for me, that’s the real fascination. Not what they get right, but what they reveal—about us, about thought, and about the strange architectures of intelligence we’ve only just begun to glimpse.