The Larynx problem

Large language models have achieved remarkable fluency in producing human-like text, leading many to conflate linguistic competence with intelligence. This paper ar- gues that this conflation is a category error with deep historical roots — analogous to mistaking the larynx for the brain. We term this the Larynx Problem: LLMs are trained on the output channel of human intelligence — language — not on intelligence itself. The brain does not think in words; it processes information as distributed activation patterns across functionally specialized regions, producing language only at the ter- minal encoding stage. We trace how this error arose from a 1969 substitution in AI research goals — from how does intelligence emerge? to how do we program intel- ligent behavior? — a substitution that the Transformer era made invisible by achiev- ing unprecedented performance on its own terms. We then describe an alternative grounded in the FNC Framework and the Free Energy Principle, instantiated as the Bisociation Engine: a cognitive architecture in which language generation is one mod- ule among many, specialist knowledge domains compete in a global workspace, and creativity is operationalized as bisociation — the productive collision of structurally separated frames of reference. Empirical comparison shows that the Engine’s domain- pair rankings are negatively correlated with those of a leading LLM (Spearman ρ = −0.25), confirming that the two systems optimize for structurally different proper- ties. The destination the Dartmouth pioneers set in 1956 — machines that think, not machines that describe thought — remains the right one. The path needs correcting.