On a cold afternoon standing on a bluff overlooking 907F and her Junction Butte packmates, I could hear the wind cross the ridge before I saw it. It slid through the timber, rattled last year’s leaves, and pulled at my coat. If you listen long enough, you begin to notice the spaces between gusts. And, as a linguist, I thought of the underlying pattern in the wind.

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What you don’t see is the dance of the molecules—millions of them—colliding and ricocheting in all directions. A single molecule might hit another close by, which hits another farther away, and in this way a movement in one place ripples invisibly into motion somewhere else. Language is like that. Each word is a molecule in a larger current—seemingly disconnected from distant words, yet bound to them through invisible chains of relationship.

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Our words are part of an invisible map, laid out in the brain like trails on a mountainside. Every trail connects to others. You can go from the word wolf to howl to moon in a few steps, and the path makes sense. We can link thousands of such trails and cross the map any way we choose. That’s why we can say The wolf howled under the moon because it missed its pack, and someone else can follow us there.

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Other animals also have words—signals encoding information. A prairie dog’s sharp chirp can indicate hawk overhead. A dolphin’s whistle can identify a particular friend. Wolves have over twenty distinct sounds they make to communicate. These are words in the sense that they point to something in the world. But they live on smaller maps. A few trails. A handful of landmarks. That is why context is so important to understanding how wolves interpret their smaller vocabulary.

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Chaser the collie, Kanzi the bonobo, Koko the gorilla, and Alex the parrot each learned over a thousand human words, carrying language across the species divide and showing that, with patience, the distance between minds can be smaller than we imagine.

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In a recent study of more than 260,000 button presses collected from 152 dogs using FluentPet, researchers found that many pets weren’t just hitting single buttons at random, but often paired two in sequence—“outside + potty”—to say something closer to a sentence. Nearly a third of all interactions were multi-button combinations, and analysis showed these pairings happened too often to be explained by chance or imitation alone. It was a small but telling bridge across the gap between human language and animal expression, proof that, given the right tools, a dog’s thought can stretch past a single word.

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But what these remarkable animals have never achieved is combining their words into long, endlessly varied sentences.

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Somewhere in our deep past, our brains (or one of our close relatives) crossed a threshold. Our vocabularies swelled, our connections multiplied, and the trails joined into a network so dense that you could walk it forever without retracing your steps. Scientists can model this “percolation point” in a computer: add enough words and enough links, and the network suddenly becomes a whole world. For English speaking children, that turning point comes when they have maybe two hundred and fifty words. That’s when their sentences leap from two words to three, four, and more—combinations no other animal makes on its own as far as we can tell today.

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Inside the skull, this map is not built in one place. The brain’s “language network” lives mostly in the left frontal and temporal lobes, but there are outposts in the hippocampus, the cerebellum, and other corners. The hippocampus, long thought to be just a keeper of memory, also acts like a weather forecaster—predicting what words should come next and adjusting when they don’t. Work by Evelina Fedorenko and colleagues shows that these language circuits are separate from the systems we use for reasoning and other kinds of thought.

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Artificial intelligence language models work in a similar way—though they are made of code, not neurons. They build what are called high-dimensional “latent spaces” where each word, phrase, and sentence is a point in a vast cloud. The geometry of this cloud—its distances, directions, and clusters—encodes meaning. Think of it like constellations in the night sky: each star is a word, and their positions relative to each other tell you which belong to the same figure. In the Goldstein et al. (2024) study, researchers showed that the brain’s semantic space in the inferior frontal gyrus shares geometric patterns with the latent space of a powerful AI model (GPT-2). Using “zero-shot” tests—where the model had to predict the brain’s activity for words it had never been trained on—they found the AI could accurately interpolate those brain patterns by relying on the geometry of relationships among other words. In other words, both brain and machine can jump to a new, unvisited point in semantic space simply by knowing the shape of the space around it.

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That’s what “geometry” means here: the pattern of relationships. Just as in a map of mountain peaks, the distances and directions between points tell you where the next ridge lies, in a semantic map, the “angle” between wolf and howl and moon can tell you where night probably sits. Both brains and AI use these relationships to predict what should come next, to fill in blanks, and to weave meaning from context.

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Simply because AI has a map for language does not mean it can think without human intervention. This distinction—between the maps for language and the machinery for thought—matters. It means that just because an animal doesn’t have human-style sentence building, it doesn’t mean its mind is simple. Thought is not locked inside language. People in deep coma or under anesthesia may be unable to speak or move, yet evidence suggests that some can still think—holding images, memories, or even abstract problems in their minds. The machinery for thought can run without the machinery for speech.

And if that can be true for us—silent but thinking—then perhaps it is true for the elk in the meadow, the raven in the wind, or the wolf on the ridge. They may not have our kind of words, but they may have their own mountains of thought. And if we accept that, we might choose to treat them as fellow travelers through their own unseen maps.​

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