What the Stochastic Parrot Leaves Out: AI, Meaning, and the Limits of Critique
By J. Owen Matson, Ph.D.
One of the odd habits of our age of algorithms is how quickly we reach for animal metaphors to calm ourselves—or to panic more eloquently. When faced with technologies that feel alien, we try to shrink them down to something we already know, like comparing a complex technical system to a barnyard creature. Take the now-ubiquitous phrase “stochastic parrot.” It’s catchy, slightly funny, and just smug enough to feel clever. The image works because it combines randomness with mimicry: a bird that repeats words without the faintest idea what they mean.
The phrase has been passed around endlessly, like a meme with tenure. It’s perfect for those who want to dismiss large language models with a single, quotable jab. After all, if GPT is just a parrot, then its sentences are no more meaningful than a ventriloquist’s dummy rattling off Shakespeare—mouth sewn shut, applause optional.The Stochastic Parrot as Remedy to a History of Computational Abstraction
There is something almost charmingly old-fashioned about the sudden popularity of the parrot as a way of describing today’s artificial intelligence. It flutters into the conversation like a bird from an earlier age of criticism, tail feathers arranged just so, bringing with it a certain air of sensible authority. The phrase “stochastic parrot,” now endlessly repeated in articles and online debates, suggests a creature that can mimic words with dazzling accuracy while remaining oblivious to their meaning. Those who favor the term often present it as a healthy corrective, a verbal peck on the knuckles for technologists who speak of machines as if they were simply waiting to be crowned as new forms of mind. The parrot does its work through comedy as much as argument: it offers a flash of deflation, a joke that reassures its audience that, despite the hype, no computer has yet crossed the threshold into true understanding.
The phrase resonates because it emerges from a very long and complicated history of trying to explain intelligence by turning it into something that can be measured, modeled, and built. For decades, scientists and engineers have worked with ideas that treat thought as a kind of traffic system, full of predictable flows and intersections. In these models, language is broken down into symbols that can be shuffled about like paper slips in a filing cabinet, meaning is treated as a signal waiting to be delivered, and intelligence is reduced to a statistical pattern in which the best guess for your next word is whichever one most frequently follows the word before it. This tradition has produced many brilliant insights and breakthroughs, but it has also relied on an enormous simplification of what thinking involves. Each new metaphor has been offered with confidence: the brain as a computer, neurons as circuits, ideas as programs, words as mathematics dressed up for a press conference.
Because of this background, the parrot metaphor feels familiar even to those encountering it for the first time. It carries the residue of earlier debates about what counts as mind and what can be simulated by machine. In its humor there is also a trace of weariness, as though the parrot were speaking on behalf of a public grown tired of grand promises about artificial intelligence. At the same time, the metaphor performs a kind of sleight of hand. By offering a quick, vivid image, it allows us to dismiss these systems without looking too closely at how our very language for describing them shapes what we can imagine about their place in the world. The parrot, like a figure in an old fable, delivers its message while concealing the deeper histories of thought and ambition that gave it voice in the first place.
The parrot arrives as both mascot and warning sign. It signals a shift in how people talk about thinking machines, a shift that has been slowly gathering through many different strands of theory and critique. Some of these come from feminist approaches to science that pay close attention to bodies and power, others from the study of old and forgotten technologies, and still others from the kind of research that maps out human and non-human connections with the same stubborn detail one might use to chart a water system. Though these traditions have their disagreements, they share a conviction that thinking is never weightless or abstract. It happens through real things—through wires, through human labor, through the heat produced by server farms running day and night in places like Nevada. Thought, in this view, travels along the same channels as electricity and sweat.
This way of seeing challenges the idea that language or intelligence can float free of the material world. It suggests that every act of speaking or writing has a hidden cost, like the fuel that keeps a power plant running. Language can be elegant and inspiring, but it also depends on the physical grind of keyboards, circuit boards, cooling systems, and paychecks. Even the most ethereal sentence owes its existence to an entire infrastructure humming beneath it. Cognition itself begins to look like an economy, one that produces meaning through layers of work, energy, and hidden debts.
Seen from this angle, the parrot metaphor begins to make more sense. Those who invoke it are trying to pin down these strange systems, to bring the lofty promises of artificial intelligence back to the ground. They imagine peeling back the feathers to reveal the tangles of wiring beneath, exposing the labor and machinery that make the mimicry possible. In this way, the parrot becomes more than a joke: it becomes a reminder that every dazzling display of machine intelligence is built on a very physical, very human history of effort and exchange.
How the Stochastic Parrot Reduces the Complexity of AI-Generated Meaning
When people use the parrot metaphor to describe artificial intelligence, they mean to keep things grounded, to remind us that imitation is not the same as understanding. Yet in trying to simplify matters, the metaphor ends up doing its own kind of shrinking, as though mimicry and intelligence belonged on opposite sides of some neat dividing line. There is a habit among certain critics of treating simulation as a kind of bad behavior, like a student caught copying homework or an actor who never breaks character. Machines, however, feel no shame over their lack of childhood memories or secret inner lives. The question is not whether they feel, but how their patterns of operation might change the ways we ourselves think, speak, and interpret.
Language has always been a restless thing. It spreads across people and places, shifting as it goes, with meaning emerging from the sheer jumble of exchanges rather than from any single source of intent. A word spoken centuries ago can ripple forward into new contexts, acquiring layers it never originally possessed. Meaning, in this sense, has never been a treasure stored deep inside an individual mind, waiting to be uncovered. It is something produced through the constant meeting and clashing of systems, ideas, and voices.
If large language models like GPT have anything truly remarkable about them, it lies in how they amplify this process. They generate responses by taking in vast amounts of language and running it through cycles of recombination so intense they resemble a kind of pressure cooking. Out comes speech that feels immediate and surprising, even if every fragment is drawn from what came before. These systems do not simply mimic—they interfere, reshape, and insert themselves into the flow of human communication. In that sense, they resemble many of the conversations we already have, where people repeat, rephrase, and remix what they have heard until it becomes hard to tell who first spoke an idea. Anyone who has spent two weeks in graduate school will recognize the pattern.
AI's Umwelt
We might finally bid farewell to the drawing-room fantasy of a chatty bird on a brass perch, endlessly repeating the same overheard fragments, and instead take a closer look at what is really happening inside today’s language-generating machines. These systems do not possess voices or inner lives, nor do they simply reflect our own patterns of speech like a parrot reciting a dinner conversation. What they have instead is something more peculiar and more revealing, a kind of enclosed world of perception and response. The theorist N. Katherine Hayles borrows the word umwelt from the biologist Jakob von Uexküll to describe this phenomenon. Every living creature, from a tick to a whale, inhabits such a world, one shaped entirely by the sensations and signals it can detect. A tick, for example, perceives the world almost exclusively through heat and smell. Its entire reality consists of these few inputs, even though a human observer might experience the same environment through dozens of other senses. Machines, though they are not alive, construct their own version of this limited world, made entirely of data rather than smell or warmth.
Inside the machine, language is broken down into tiny units called tokens, a process not unlike chopping a sentence into its smallest possible pieces. These tokens are then mapped into complex patterns, almost like a three-dimensional puzzle of extraordinary scale. Through countless exposures to text, the machine learns how these pieces tend to fit together. This learning is statistical rather than symbolic: it does not understand meaning in the way humans do but instead builds an intricate network of probabilities. Within this network, the machine begins to create a sense of what might matter and what can be ignored, highlighting certain relationships and suppressing others. Over time, this becomes its landscape of relevance, a dynamic environment that allows it to generate responses. What emerges from this process is less like a parrot imitating speech and more like a composer working with a vast library of musical phrases, combining them in ways that feel continuous and alive.
To call this mimicry is to miss the point entirely. The machine’s responses are part of a recursive process, which is to say that every new output reshapes the environment from which the next will arise. A sentence generated today becomes part of the background that influences tomorrow’s sentence. This constant feedback loop gives the machine its uncanny ability to sound coherent. Central to this process is a mechanism known as attention, a kind of built-in mathematics that determines which pieces of language should be weighted more heavily at any given moment. Instead of drawing on external truths or human intention, the machine constructs a temporary, local sense of meaning on the fly, like a cook improvising a dish from whatever ingredients happen to be at hand.
These structures may be mathematical at their core, but they should not be mistaken for hollow imitations of thought. They create possibilities, opening certain pathways of expression while closing off others. In doing so, they shape what kinds of sentences, ideas, and even interpretations can appear. The parrot metaphor keeps us tethered to an outdated idea of the mind, one that assumes every outward expression must match an inward, pre-existing intention. Hayles invites us to a more challenging perspective: to treat these systems as forms of interpretation in their own right. Their cognitive landscapes remain alien, not because they lack meaning, but because they generate meanings under conditions that do not map neatly onto our own categories. These machines do not simply reflect our speech back to us. They speak from within their own strange architectures, intersecting with our world only at fragile points of translation, like two maps of the same terrain drawn with incompatible scales. The challenge lies in learning to read those points of intersection without reducing them to either parrots or people.
The parrot metaphor, for all its comic charm, has a way of distracting us from the real questions. By treating artificial intelligence as nothing more than mindless repetition, it shuts down the more interesting inquiry: how meaning comes into being, what conditions allow it to take shape, and how to understand a kind of thinking that works less through deliberate reasoning than through endless reconfiguration. The image of the parrot suggests failure, as though these systems can only copy and never create. Yet language models do create, though in a peculiar way. They build upon each of their own utterances, like a river depositing silt that slowly reshapes its own banks. With every response, they adjust the field of possibilities for the next one, exerting a quiet pressure on what kinds of sentences and ideas can emerge. Meaning, in this light, resembles a compost heap—layered, fermenting, unpredictable—continually breaking down and reforming through interaction. The machine does not sit back and contemplate the meanings of sentences as a philosopher might; instead, it alters the very environment in which meanings can arise, including our own. To dismiss this as parroting is to cling to the comfortable illusion that only beings like ourselves are capable of producing meaning, when meaning has always been messy, born from the accidents of infrastructure, the slips of language, the strange event of a sentence answering a question no one quite knew how to ask.
Recognizing this does not require us to abandon the idea of intelligence or to imagine machines as soulful companions. It only asks us to consider what we mean by “meaning” when the systems generating it do so through patterns rather than through individual personalities. A language model need not be dressed up in a lab coat or invited to dinner for us to admit that it is doing something more than reciting pre-recorded phrases. The real issue is what its language does to us: how it changes what we expect from conversation, how it invites certain interpretations while closing off others, how it slowly reshapes the ways we speak and think. This is hardly a new problem. It is the same problem posed by books, by newsfeeds, by classrooms and governments. We have always been in dialogue with parrots of one kind or another, though the feathers may be literal in some cases and purely metaphorical in others. What matters is who taught them to speak, what histories their voices carry, and whom they address when they speak.
The parrot, then, remains a useful reminder, provided we resist the temptation to take it too literally. It shows us how easily language slips into metaphor, and how those metaphors can constrain our thinking. Meaning has never arrived as a single, tidy package. It has always been entangled, recursive, built up over generations, shaped by context and by the media through which it travels. And every so often, it is delivered by a parrot whose language sounds strange and familiar at once—a voice speaking in patterns we are only beginning to understand.
For insight into debates around the Stochastic Parrot metaphor, see the comments in this post on LinkedIn: Against the Stochastic Parrot: AI's Umwelt as Alien Epistemology
Also see Ilkka Tuomi's Please Kill the Parrots