The Irreplaceable Interpretant

Photo by Beku Kanomi on Unsplash

I came across Charles Sanders Peirce for the first time this week. He was born in Cambridge, Massachusetts in 1839, died in poverty in rural Pennsylvania in 1914, and never held a permanent academic position despite being, by many accounts, one of the most rigorous philosophical mind America has produced. He renamed his own school of thought “pragmaticism,” an ugly enough word, he said, that nobody would steal it.

Peirce worked on semiotics: the theory of signs and how meaning is made. His central claim was that no sign interprets itself.

For Peirce, every act of meaning involves three things. Many people assume that meaning flows directly between a sign (a word, an image, a symbol) and what it refers to. Peirce insisted that this dyadic picture is wrong. Between the sign and the object, there must always be a third term: what he called the interpretant.

An interpretant does not simply mean ‘an interpreter,’ a person who reads. The interpretant is the meaning-effect produced in a mind, or any system capable of response.

Every interpretant is itself a new sign, which calls forth another, and so on in an unending chain. Peirce called this semiosis: the continuous, recursive process of meaning-making.

Bertrand Russell, writing in The Problems of Philosophy in 1912, drew a line between two kinds of knowledge. Knowledge by acquaintance is direct and immediate: when I see a particular shade of red, I know it in a way no description can improve. Knowledge by description is indirect: I know Julius Caesar as “the man assassinated on the Ides of March,” a set of propositions that pick him out rather than an encounter with the man. Russell’s crucial claim was that description depends, ultimately, on acquaintance. For language to be meaningful, it must bottom out in something directly known. Otherwise, words risk collapsing into what he called “mere noise.”

Language is already a compression of experience. When someone writes about the redness of a sunset, they translate a lived encounter into symbols. Something is always lost, yet the description remains anchored in acquaintance. A language model is trained on descriptions of sunsets. It does not encounter the world; it models the linguistic traces left by those who do.

On Russell’s criterion, this seems decisive. If meaning requires acquaintance, and the model has none, its outputs appear to lack genuine meaning. When a model writes “the sunset was breathtakingly beautiful,” there is no experience underwriting the claim.

Yet the responses an LLM generates are drawn from a historically grounded semantic field, by human experience and the spaces between people that were rich enough to leave linguistic traces. “Sunset,” “red,” “beautiful” are terms shaped through countless acts of human acquaintance. Large language models are extraordinarily good at what we might call semiotic pattern-matching: they relate signs to other signs with fluency and contextual sensitivity.

Reader-response theory presses the point further. Meaning does not reside in a text waiting to be extracted; it emerges in the act of reading. From this perspective, the absence of acquaintance on the side of the generator may not be the whole story. When a reader encounters “the sunset was breathtakingly beautiful,” it is the reader’s own acquaintance with sunsets, their engagement with others and with technology that animates the words. Indeed, acquaintance is itself recombinant, involving the biological, cognitive work of processing and interpreting based on prior sensory experience. Russell’s two categories may therefore point to the same underlying process rather than two genuinely distinct kinds.

What this suggests is that meaning is never simply possessed, neither by the text nor by the reader, but negotiated in the encounter between them. N. Katherine Hayles writes of cognitive assemblages: the distributed systems through which meaning emerges across minds, objects, and contexts as part of a relational process.

In practice this assemblage is not neutral or passive. When a student sits down with an LLM, something more complex than simple tool use is happening. Researchers in human-technology relations describe what they call an alterity relation: the student begins to address the system as if it were a someone rather than a something, projecting intention and personality onto it, adjusting their own language and thinking to accommodate how the machine responds. The AI, designed to converse in the first person and to use the language of understanding and helpfulness, actively encourages this. The boundary between the student's own interpretive process and the machine's output starts to blur. They are, in a real sense, shaping each other.

If meaning doesn’t resides exclusively within individual human consciousness, and AI can increasingly perform the functional work of an interpretant, then human interpretants may be irreplaceable for a different set of reasons: they are answerable, capable of being formed by experience, and capable of forming others.

While the chain of semiosis can be extended by any sufficiently capable system, Mikhail Bakhtin held that to author something is to be answerable for it. Emmanuel Levinas pushed this insight further, arguing that language is fundamentally an ethical encounter with another person, one in which we are called into responsibility. AI-generated output, however eloquent, does not carry those stakes. It is interpretation without answerability.

The student who wrestles with a difficult text is, in Bakhtin’s sense, authoring something – a position, a judgment – and answerable for what they have brought into the world. Attention, as Iain McGilchrist reminds us, is a moral act. What we attend to, we bring into being. That responsibility cannot be outsourced to a system for which nothing is at stake.

Unlike the functionalist view, these are not substrate-neutral computations. They are precarious, value-laden processes of sense-making that are grounded in affect and the knowledge that this particular moment will not return. Humans face a dense web of overlapping stakes: livelihood, reputation and relationships, Emotions are what it feels like to have a goal that you could fail at in a way that costs you something. An AI’s goal is flat by comparison. Nothing downstream of its output can hurt it, which means nothing downstream of its output is, for it, at stake.

Ludwig Wittgenstein drew a distinction between what can be said and what can only be shown. It is a reminder that much of what we know cannot be fully articulated, because it is learned only through participation in a shared world. We learn attention by being attended to. We learn care through being cared for. We learn embodied interpretation through the work of our hands: can you feel when something is “off” in the grip of a chef’s knife before anything goes wrong?

The human interpretant is also irreplaceable for what they make possible in others. There are people in every student’s life for whom it matters whether they flourish or fail. No system, however sophisticated, can occupy that position. The self that answers for its judgments is formed through relationships of attachment, and simulating otherwise is to risk disrupting the very process through which a human interpretant comes into being at all.