This analysis reveals LLM capabilities as an aggregation of human intelligence, suggesting new insights into their development.
We argue that LLM capabilities are best understood as structured aggregation of collective human intelligence, not autonomous machine reasoning. The framework is grounded in proven mathematical identities (cross-entropy pretraining implements the linear opinion pool; RLHF implements Borda count and logarithmic opinion pooling), the Diversity Prediction Theorem, and the Conditional Jury Theorem. v3 changes: 14 citation corrections verified across all 119 references via systematic fact-checking, Varela enactive cognition framework integrated, one fabricated reference replaced.
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Nyx Redondo (2026) studied this question.
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