AI Blind-Spot Theory — A Structural Definition of the Moments When Human Cognition Surpasses AI

AI Blind-Spot Theory — A Structural Definition of the Moments When Human Cognition Surpasses AI

This paper defines AI Blind Spots as domains that AI cannot access due to structural limitations.
Despite its power in computation, optimization, and abstraction,
AI fundamentally lacks the ability to process:

1. meaning generated from deficiency

2. self-changing internal time

3. future selection driven by emotion

These abilities arise only from human internal structure—
deficiency, emotional vectors, and the continuous regeneration of meaning.

The paper contrasts human and AI cognitive processes:

• AI: data → structuring → optimization → action

• Human: deficiency → emotion → meaning → action

This difference explains why humans occasionally break predictions,
perform non-rational actions, or make intuitive leaps that AI cannot model as lived experience.

Examples of structural leaps include:
・decisions beyond logic or benefit
・beliefs held despite contradiction
・intuitive jumps without explicit cause

AI can calculate these phenomena but cannot understand them.

This theory reframes AI’s limitations not as technical issues but as structural boundaries,
offering a foundational framework for the era of human–AI co-creative intelligence.