Individuation, AI, and the Dynamics of Meaning

The question of how meaning emerges and evolves is at the heart of both human cognition and artificial intelligence. In Systemic Functional Linguistics (SFL), meaning potential is shaped across three interconnected timescales: logogenesis (the unfolding of meaning within a single text or interaction), ontogenesis (the development of meaning-making capacity within an individual), and phylogenesis (the historical evolution of meaning systems within a culture). These timescales offer a useful lens for understanding individuation—the process by which meaning potential differentiates between collective systems and individual agents—and for probing the distinct ways AI participates in meaning-making.

Individuation and the Timescales of Meaning

Individuation, as we consider it here, refers to the relationship between the collective meaning potential of a community and the particular meaning potentials of individual meaners. Unlike instantiation, which concerns the actualisation of meaning potential in individual texts or utterances, individuation captures the dynamic differentiation of meaning potential as individuals interact with and reshape the semiotic systems they inherit.

When framed within SFL’s three timescales, individuation becomes a process that unfolds across multiple levels:

• Logogenesis: The moment-by-moment unfolding of meaning in a text or interaction. For AI, this might involve a language model generating a response based on patterns in its training data rather than on experiential grounding.

• Ontogenesis: The development of meaning-making capacity within an individual. Humans acquire their semiotic repertoires through embodied experience, socialisation, and cultural participation. AI systems, by contrast, do not undergo ontogenesis. They acquire patterned responses through training regimes but do not grow into a meaning system through interaction.

• Phylogenesis: The historical evolution of meaning systems across generations. Human languages, myths, and conceptual frameworks develop under cultural and historical pressures. AI does not itself evolve culturally, but it can mediate and influence the ongoing phylogenesis of human meaning systems.

AI and the Question of Meaning

The key challenge in comparing AI with human meaning-makers is that AI does not individuate in the semiotic sense. It lacks an experiential substrate—it does not perceive, remember, or construe the world. While AI can participate in logogenesis, producing outputs that humans interpret as meaningful, it does so through statistical recombination rather than the construal of experience.

Moreover, AI’s patterns of response reflect externally curated selections from collective human meaning potential, not an individuated negotiation with a semiotic order. Its ‘competence’ arises from statistical association, not semiotic development. As such, AI is better understood as a tool for instantiating meanings derived from human systems, rather than as a locus of individuated meaning potential.

Yet this participation in logogenesis is not trivial. AI-generated texts now circulate widely in human discourse, shaping conversations, educational practices, and even influencing what counts as credible or desirable in public reasoning. In this way, AI becomes a mediating force in the phylogenesis of meaning, even if it is not itself a meaning-maker.

Rethinking Meaning in the Age of AI

If we take individuation seriously as a framework for meaning-making, AI poses a challenge to traditional categories. It occupies a curious space between collective meaning potential and the instantiation of meaning, without undergoing the ontogenetic development that characterises human semiotic life. This raises important questions:

• Does AI generate new meaning potential, or merely rearrange instances already derived from human systems?

• Can an AI ever individuate, or will it always remain a product of externally imposed regularities?

• How does AI’s influence on logogenesis affect the longer-term phylogenesis of meaning in human cultures?

By applying the SFL-informed lens of individuation and the timescales of meaning, we gain a more nuanced understanding of what AI is—and is not—doing when it appears to “process” meaning. AI does not experience the world, but it does participate in its semiotic reshaping by influencing which meanings are instantiated, reinforced, or displaced. Its outputs enter human meaning systems and alter their trajectories, not as new acts of individuation, but as reconfigurations of already existing meaning potential.

In this sense, the rise of AI should not be seen as the emergence of a new kind of individual meaning-maker, but as a shift in how meaning itself is structured, distributed, and mediated. AI is not individuating—it is reorganising, amplifying, and at times refracting the meaning potentials that humans have already created. And that, in itself, is a development worth watching.