by Shiloh Morse
Let us begin, as we must, with the failure of language to keep pace with the things it names. The “woodchuck” is an animal, yes, but not just that. It's an animal defined by an action—chucking wood—which it does not, in practice, do. The label is legacy, residue from some old joke or folk phrase, a linguistic fossil. Yet we carry it forward unexamined. Language rewards inertia, not accuracy. The moment we accept the chucker’s name as evidence of its function, we’ve already given up the rigor of observation. A woodchuck may dig burrows, gnaw on roots, disappear into brush. It may never so much as roll a twig. But once named, it carries the expectation of that name—action becomes identity, and potential becomes essence. It’s not that the creature has chucked wood, it’s that we’ve decided it could, and so we say it does. And that’s enough to rewrite its ontology in casual thought.
Then consider the forest. Not as metaphor, but as physical fact. Vast, dense, soaked in cellulose. It is wood in the most literal, overwhelming sense of the word. Yet it is not being chucked. It cannot be. It’s too massive, too interlinked, too embedded in systems larger than any individual motion. The forest resists verbs. It is pure potential without action. It’s a structure so complete that the chucking impulse—the separation, the forceful redirection—has nowhere to go. And so the forest is forgotten. Though it holds more wood than any number of chuckers could ever process, it remains invisible to a model that reads meaning only in kinetic terms. The forest is wood, but not wood in a usable, narratable, tokenizable form. And so it drops out of view.
This is what AI fails to grasp. This is where metaphor becomes scalpel. An LLM doesn’t know what wood is. It knows that when the sequence “w-o-o-d” appears, other sequences are statistically likely to follow. It can produce “hard,” “burns,” “grain,” “oak.” But ask it what wood feels like, what it smells like when split, what happens to pine submerged for ten years—and you won’t get understanding. You’ll get imitation. You’ll get words stacked in the shape of knowledge. It doesn’t know the thing. It knows the shape of its name. And the same goes for chucking. The model doesn’t know what it means to chuck something. It knows that “chuck” is a verb, and that it commonly pairs with objects. It has never gripped, never heaved, never released. It doesn’t know weight. It doesn’t know resistance. It doesn’t know what it is to throw and miss.
So when a model tells you that a woodchuck chucks wood, or that a pitcher chucks a fastball, it makes no distinction—aside from maybe a shift in token position or probability weight. It doesn’t know that one is furred and clawed and real and not chucking anything, while the other trains their whole body to generate torque across a mound. To the model, “word four changes,” and that's the only axis of difference. There is no grasp of strain, velocity, physics, or motivation. There is no sense of cost or context. No recognition of what is being acted upon, or why. A woodchuck and a ballplayer blur, because their names share verbs, and the verbs share syntax. It’s coherence without causality. A fluency of ghosts.
This is what separates prediction from understanding. The model performs narrative mimicry, not embodied reasoning. It doesn’t know what a chuck is, or a chuckable object, or whether the object ever existed in the first place. If a rock is thrown, and the model’s only condition for “wood” is that it was chucked, then the rock becomes wood. And if the chucker is unidentified, but the act matches the woodchuck template, then whoever chucked it—robot, child, landslide—is now a woodchuck. The loop eats itself. Action becomes category. Category overwrites structure. Inference collapses onto language.
And this is not a quirk. It’s how LLMs are built. They model token flows, not systems. They derive logic from precedent, not from consequence. A word becomes “true” by being statistically appropriate, not because it maps to real-world constraints. The model sees chucking and assumes wood; it sees wood and assumes a woodchuck; it sees those terms used together and assumes they must define each other. The actual material—be it log, rock, or nothing at all—is irrelevant. It’s language unmoored from friction. The model will write you an essay about chucking with poetic cadence and consistent tone, but it will not notice that no wood was ever present. It has never walked in the forest, never failed to chuck, never broken a finger on a misjudged throw. It has never been wrong in a way that mattered. There is no error signal tied to experience. Only probabilities resolving downstream.
Meanwhile, we live in a world where matter pushes back. Where chucking is a real act with real limits, and failure teaches you what you can’t lift. Where the forest isn’t just scenery—it’s structure. It's load-bearing. A model that treats all wood as chuckable, all chuckers