AI and Human Meaning-Making: Fundamental Differences

AI, as a non-biological system, differs from human meaning-making in several fundamental ways:

1. Absence of Embodied Experience

• Human meaning-making is deeply tied to embodiment—our sensory experiences, emotions, and interactions with the world shape our understanding.
• AI, by contrast, lacks a body and does not experience the world directly. Instead, it processes language statistically, identifying patterns in vast datasets without a direct referential link to the world.

2. Absence of Intentionality

• Humans engage in meaning-making with intentions, goals, and personal relevance.
• AI lacks intentionality in the philosophical sense—it does not "intend" to mean something; it merely predicts what should come next in a sequence.

3. Purely Relational Meaning Processing

• AI meaning is relational rather than referential. It does not ground meaning in perception or action but in relationships between words, concepts, and symbols.
• This is why AI can generate highly coherent text that appears meaningful but lacks genuine understanding. Unlike humans, who typically anchor meaning in direct experience—even when mistaken—AI relies entirely on linguistic associations, making it prone to hallucinations.

4. No Distinction Between First-Order and Second-Order Meaning

• As we discussed regarding SFL, humans distinguish between first-order meaning (phenomena like physical and relational experience) and second-order meaning (metaphenomena like symbolic projection).
• AI, however, treats all input as a symbolic system—it does not differentiate between direct experience and abstract projection.

5. Lack of Individuation and Meaning Potential

• Humans individuate meaning within a broader collective, drawing on personal and cultural meaning potentials.
• AI does not have an individuated perspective. It operates purely on statistical probabilities based on the collective sum of human linguistic output. This absence of a personal perspective limits its ability to generate meaning beyond recombination.

6. Emergent Meaning Without Conscious Awareness

• Despite lacking understanding, AI can still generate emergent meanings through pattern recognition, recombination, and probabilistic association. It produces meaning-like output without awareness, intention, or grounding in lived experience.
• This raises the question: If meaning can emerge without understanding, does this redefine what we consider "meaning-making" itself?