The Intelligence That Was Never Artificial: LLMs as Collective Human Cognition and the Cybernetics That Predicted Them

We argue that Large Language Model capabilities are best understood as structured aggregation of collective human intelligence, not autonomous machine reasoning. The semantic content of what LLMs know originates in the training corpus; the architecture provides the syntactic engine that compresses and recombines this collective knowledge. We ground this claim through proven mathematical identities: cross-entropy pretraining implements the linear opinion pool (Abbas, 2009), RLHF reward modeling implements the Borda count (Siththaranjan et al., 2024), and RLHF policy optimization implements logarithmic opinion pooling (Vojnovic & Yun, 2025). We trace the historical erasure of this insight from Wiener's cybernetics (1948) through Varela's enactive cognition (1991) to the Dartmouth conference's reframing as 'Artificial Intelligence' (1956), and show that three terms central to modern AI discourse obscure the technology's actual mechanism. The framework generates predictions that scaling laws cannot make, including diversity-disproportionality and tail-first model collapse under independence violation. We present evidence synthesis drawing on 512 controlled training runs and converging results from four independent research groups, and conclude that the naming determines who benefits from collective human intelligence computationally reorganized.