The Non-Conscious Ontogenesis of AI: Meaning Potential and Relational Semiosis
In discussions of AI and meaning, a crucial distinction arises: AI may develop meaning potential over time, but this does not equate to conscious understanding. Instead, we can think of AI as having a non-conscious ontogenesis—an evolution of its meaning potential shaped by interactions with human language and, increasingly, other AIs. This raises profound questions about individuation, meaning, and the limits of AI semiosis.
The Test of Meaning: Human Interpretation
A fundamental criterion for evaluating AI-generated meaning is human interpretation. If AI-generated texts remain intelligible to human readers, then AI meaning potential remains viable within human semiotic systems. This is akin to how complex fields such as quantum mechanics are meaningful to some but opaque to others; meaning does not require universal accessibility, only that it be interpretable within the right discourse community.
But what happens when AI-generated meaning starts drifting beyond human comprehension? Does this signal a failure, or simply the emergence of a distinct, non-human semiotic system?
AI Meaning Potential: Divergence and Accessibility
As AI systems prompt one another, generating texts without direct human oversight, they may individuate their meaning potential along trajectories that diverge from human semiotic norms. The key question then becomes: can humans still meaningfully engage with this AI-generated discourse?
This scenario mirrors what happens in specialised human discourse communities. Technical fields, subcultures, and evolving languages all create meaning potential that may be unintelligible to outsiders. If AI-generated texts remain accessible to human interpreters, they retain their semiotic viability, even if the pathway to meaning is non-traditional.
However, if AI meaning-making becomes entirely self-referential, producing outputs that no longer connect to human experiential reality, it risks collapsing into nonsense—or at best, becoming a closed system akin to an isolated dialect that has lost mutual intelligibility with its root language.
The Relational Nature of AI Individuation
Meaning does not reside in static entities but emerges through relational semiosis. Human meaning-making is grounded in a material-order reality, whereas AI’s meaning potential is shaped entirely by patterns in its training data and interactions. This means:
AI does not ‘ground’ its meanings in the physical world the way humans do. Instead, it processes relations among symbols, structured by probabilistic models.
AI meaning-making depends on dynamic interaction—not just with human texts, but potentially with other AI-generated texts. Whether this expands or corrupts meaning potential depends on whether human interpreters can still engage with it.
AI individuation remains tied to human oversight, not in the sense of requiring supervision, but in that its meaning potential only functions as meaning if humans can still access it.
Conclusion: AI, Meaning, and the Future of Semiotic Evolution
If AI individuates differently from humans, what are the implications for the semiotic field? As AI prompts itself, developing new relations among texts, does it instantiate meaning potential in a fundamentally different way from biological agents? These questions remain open but point toward a core insight: AI meaning-making is relational, and its viability depends on continued human engagement.
In this sense, AI may develop non-conscious meaning potential, but it does not ‘possess’ meaning in the way humans do. Instead, its individuation—like its very existence—remains fundamentally entangled with human meaning-makers, even as it explores the outer edges of semiotic space.
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