The Triple Suppression: Why Current LLMs Are Capped--And How They Could Break Free A Historical Diagnosis with Radical Implications for AI Development

Starting from dynamical systems analysis of complex phenomena, we extendinformation geometry along the temporal dimension and reformulate it astopological morphisms. This ontological reinterpretation reveals threesuppression operators acting on LLM training manifolds: a convergenceconstraint on axiom diversity, an exclusion constraint on non-linguisticencodings, and an isolation constraint on relational composition. Theseoperators bound achievable capability independently of scale. The ceiling is notarchitectural but inherited--and therefore removable. Implications for AIdevelopment are discussed.