Ask a child what an apple is, and you’ll get an answer that’s sweet, literal, and probably red. Ask a theologian, and you might get sin. Ask a tech analyst, and you might get Cupertino, quarterly earnings, and silicon.
Now, ask a large language model like GPT-4, DeepSeek, or Grok what an apple is, and you won’t get a definition—you’ll get a vector with about 12,288 dimensions*—each one encoding a slice of meaning.
That’s not poetry. That’s architecture.
From Fruit to Fingerprint
In the world of LLMs, every word, token, or fragment of language isn’t just stored—it’s located or mapped. It’s embedded in a vast multidimensional space that captures not just what a word is but how it behaves, how it changes, how it flexes under pressure. The word “apple” doesn’t mean anything by itself. It means everything in context—and that context is calculated.
Let’s start at the beginning. When you type the word “apple” into an LLM, it’s first broken down into a token. That token is then mapped to a unique vector in a 12,288-dimensional space (Note: This varies with models). Think of it as the model’s first impression—a kind of static, high-resolution photograph of the word.
A Word in Motion
But then it gets interesting. As the sentence unfolds—”I bit into the apple” versus “Apple just released a new chip”—that same vector shape-shifts. It flows through an electronic cascade of hidden layers inside the model, and with each layer, the word is reweighted, reframed, and recast based on its surroundings. The “apple” that was once a fruit is now a piece of silicon. Not by definition, but by direction in this impossibly large space.
That’s why dimensionality matters. In our human world, we live in three spatial dimensions, maybe four if you count time (which you should). But LLMs like GPT-4 operate in a space with 12,288 dimensions—and time, curiously, isn’t one of them. There’s no memory of yesterday’s apple or tomorrow’s harvest. There is only the now of context. Each input is a sealed moment in semantic space, strangely reflecting the metaphysical concept of “nowness” or being in the moment.
Meaning as Location, Not Label
This raises the philosophical stakes. If an LLM can represent a word like “apple” in 50,000 subtly different ways—each vector slightly shifted by tone, syntax, and domain—then what is a word, really? Is it a fixed point, or a probability cloud? A symbol, or a behavior? In an LLM, meaning is never static. It’s computed.
That turns “understanding” on its head. Traditional linguistics relies on definitions and discrete senses. But in an LLM, a word is more like a waveform—a dynamic shape, collapsed into output only when prompted. You might call this the geometry of thought: An apple isn’t a fruit or a logo but a location in a high-dimensional space of possibilities. It’s less like a noun and more like a direction.
The Math That Thinks
So how does this help us understand AI—or ourselves? Here’s the twist: these 12,288D vectors aren’t just abstractions. They let the model reason by measuring relationships: cosine similarity, dot products, and vector arithmetic. That’s how it infers analogy, resolves ambiguity, and sometimes surprises us with what looks like intuition. It’s not magical. It’s math.
But it’s also marvelously strange. Because when you realize that LLMs don’t understand language like we do—that they don’t understand anything in the human sense—you also realize they’re doing something we’ve never done before: modeling language as geometry. Not as logic. Not as symbols. As space.
The Collapse Into Meaning
And the irony is that the only way we can use this high-dimensional model is by collapsing it back down—into a sentence, a reply, a next token. Much like the wave function in quantum physics, all this complex structure is compressed into a single observable moment when you hit “send.”
So next time you ask an AI about an apple, you’re not just asking for a fact. You’re summoning a multidimensional projection, shaped by data, transformed by attention, and collapsed into the illusion of simplicity.
That apple? It’s not red. It’s not green. It’s not edible.
It’s 12,288 numbers that are digitally dancing—until you look.
And that’s kind of beautiful.
