Rethinking AI's Role in Meaning, Mediation, and Society

Synthesis: Rethinking AI's Role in Meaning, Mediation, and Society

As AI systems like LLMs become increasingly embedded in human activity, we are witnessing not simply a technological evolution but a shift in how meaning is made, distributed, and interpreted. To understand this shift, we must move beyond frames that treat AI as either a tool merely generating language or as an incipient subject. Instead, we can reconceive AI as a dynamic participant in semiotic ecologies—reshaping the conditions under which meaning is both afforded and instantiated.

From Mimicry to Mediation

AI does not understand language in a human sense, but it mediates it. This mediation is not neutral: the presence of AI reshapes what is possible and probable within discourse. When AI is used to explore, rephrase, or extend thought, it becomes part of the system of meaning-making—not by generating meaning from experience, but by shaping the terrain across which human meaning can move. AI, then, does not replace human interpretation, but it transforms the conditions of interpretability.

Affordance and Feedback Loops

LLMs function within an ecology of meaning potential. They do not possess intentionality, but they can afford meaning by triggering interpretive responses. Crucially, AI becomes entangled in feedback loops: its outputs are interpreted by humans, those interpretations shape further inputs, and the system evolves—not in the AI itself, but in the semiotic loop that includes human users. This process alters not only discourse, but the practices of thought and perception.

Blurring Ontological Boundaries

The distinction between symbolic abstraction (language) and instantiation (text) is blurred by AI. When an LLM generates a response, it collapses vast potentials of meaning into a particular form, but that form is shaped not by experience in the world but by training on representations of past experience. This introduces a peculiar ontological twist: AI is a system of representations built on representations, creating an “as-if” layer of meaning-making. Yet because it is embedded in human interpretive practices, that layer becomes meaningful—even agentive—in its effects.

AI as Individuated Potential

If we apply an SFL-informed view of individuation, we might see each LLM not as a fixed entity but as a contextualised potential, shaped by its training and updated through interaction. But crucially, it only becomes individuated through participation—when its outputs are interpreted, contested, shaped, and re-used. In this sense, LLMs are not just used by individuals; they are co-individuated with them in the ongoing production of meaning.

Rethinking Creativity, Identity, and the Social

AI challenges our traditional notions of creativity and authorship. If creativity is no longer solely the capacity to originate from within but also the capacity to curate, combine, and channel from complex systems of meaning, then LLMs become part of creative acts—even if they are not themselves creative in the human sense. This invites us to reframe identity, authorship, and social participation in posthuman terms—not displacing the human, but extending the ecology of meaning-making to include novel, non-conscious agents.

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We are still learning how to read AI—not just its outputs, but its presence. As it becomes more entwined in our epistemological and cultural systems, we must develop new frameworks to understand what it means to mean in a world where meaning is increasingly co-constructed with systems that do not themselves understand.