AI and the Neglect of Meaning: From Unlimited to Indefinite

The story of artificial intelligence is not just one of increasing computational power—it’s also a story about how machines relate to meaning. Or more precisely, how meaning has been progressively sidelined in favor of statistical pattern manipulation.

To understand this, we can trace AI’s evolution through a key distinction: the difference between handling unlimited inputs and handling indefinite inputs—and what happens to meaning along the way.

Traditional Programming: Unlimited Precision, Defined Boundaries

In traditional programming, meaning is explicit and fixed. Every symbol has a defined role:

• balance always represents a monetary amount.
• account_id always represents a unique identifier.
• transaction_type always means “deposit” or “withdrawal.”

Programs can process unlimited inputs within these categories—a banking system might handle millions of transactions daily—but every input must conform to predetermined semantic structures.

def process_transaction(account_id, amount, transaction_type):
    if transaction_type == "deposit":
        return balance + amount
    elif transaction_type == "withdrawal":
        return balance - amount

Ask it to process “I want to feel financially secure,” and it breaks—because that input doesn’t fit any defined semantic category.

GOFAI: Knowledge Representation with Defined Semantics

Good Old-Fashioned AI (GOFAI) tried to expand computational reasoning by encoding expert knowledge in defined rules:

• IF fever > 101°F AND white_blood_count > 12000 THEN bacterial_infection
• IF chest_pain AND ST_elevation THEN myocardial_infarction

These systems could handle unlimited combinations of symptoms, but every term had to be semantically defined in advance. A doctor’s intuition that “this patient looks unwell” couldn’t be processed—because “looks unwell” wasn’t in the ontology.

GOFAI worked in neat, bounded domains, but collapsed when real-world meanings blurred or shifted.

Neural Networks: Flattening Meaning into Math

Neural networks marked a turning point: they didn’t rely on explicit rules but learned patterns from data.

In supervised learning, though, semantics still came from humans via labels. A face recognition model, for example, maps indefinite pixel patterns (shadows, angles, distortions) to definite labels (“John Smith”).

This is semantic flattening: turning meaningful categories into vectors in mathematical space. To the model, “John Smith” is just coordinates—it doesn’t know he’s a person with history and relationships.

The same holds for:

• Speech recognition (sounds → word labels)
• Language translation (source words → target words)
• Medical imaging (scan features → diagnostic categories)

The richness of meaning is reduced to statistical association.

Generative AI: Indefinite In, Indefinite Out

With generative AI, we enter a new era: systems trained not on precise labels but on vast, indefinite text and image data.

Indefinite Training

A sentence like “The melancholy of autumn evenings speaks to forgotten dreams” has no fixed meaning. It could be poetic, psychological, or metaphorical. LLMs don’t resolve this—they simply learn statistical patterns across indefinite possibilities.

Indefinite Prompts

When you prompt: “Write a story about artificial loneliness,” you leave the meaning open. The model responds without requiring a clear definition—it works within indefiniteness itself.

The Tokenization Trick

The key is tokenization: converting words into tokens treated as equally “flat” objects. To the model, democracy, love, quantum, and mathematics are all just tokens in vector space.

This enables flexibility, but at the cost of meaning: the model can eloquently “discuss democracy” without any concept of politics, or “write about love” without ever experiencing it.

Image Generation: Visual Indefiniteness

The same applies to generative art. Prompting “a melancholic robot contemplating infinity” requires the model to interpret indefiniteness. But it doesn’t know what melancholy or contemplation are. It just assembles patterns statistically associated with those tokens.

The result looks meaningful—but it is meaning by correlation, not comprehension.

The Creativity Paradox

This explains why generative AI can feel so creative. Ask it to “explain quantum mechanics as if you’re a jazz musician,” and it blends patterns across science, music, and teaching styles in novel ways.

But the creativity is statistical recombination, based on an indefinite input prompt supplied by the user, not genuine understanding. The model has no grasp of what quantum mechanics is, or what it feels like to play jazz.

The Semantic Trade-off

The evolution of AI shows a clear trade-off:

• Traditional Programming: Semantic precision, limited flexibility.
• GOFAI: Richer semantic categories, but rigid boundaries.
• Neural Networks with labels: Indefinite input, boundaries imposed by labels.
• Generative AI: Maximum flexibility, but complete semantic flattening.

In short: the more AI handles indefiniteness, the more it neglects meaning.

What This Reveals About Intelligence

Generative AI feels intelligent because it navigates indefiniteness so well. But it achieves this through a fundamental limitation: static flattening. Every input gets processed through the same pre-determined tokenization scheme, creating one fixed mathematical representation regardless of context.

When humans face ambiguity, we don't just tokenize it—we dynamically contextualize it. We can restructure how we parse the same input based on our current state, purpose, and situational framework. The phrase "The melancholy of autumn evenings speaks to forgotten dreams" becomes a different semantic object when we encounter it as:

• Literary analysis (focus on metaphorical structure)
• Personal reflection (activation of autobiographical associations)
• Therapeutic dialogue (emphasis on emotional resonance)
• Philosophical inquiry (phenomenological dimensions)

Each contextualization creates a different meaning-space, a different way of organizing semantic relationships. Current AI systems, locked into their training-determined representations, cannot perform this dynamic recontextualization.

This is why human thinking remains fundamentally different: we don't just engage with meaning—we can reconstruct the very framework through which meaning emerges in real-time, based on context and purpose.

The next step in AI evolution will not be better statistics operating on static representations, but a radical shift toward dynamic semantic architectures—systems that can contextually reorganize how they parse and structure meaning, rather than being forever bound to their training-time flattening schemes. This is why Geneosophy is needed.

Until then, AI's journey remains clear: from unlimited precision to indefinite flexibility—but always through the bottleneck of static semantic flattening, missing the dynamic contextual multiplicity that makes human meaning-making so powerful.