Statistical Operation and Technical Inscription: A Material Ontology of Non-Subjective Semantic Operation

This essay shifts the question of whether large-scale learning systems ‘understand’ from an anthropocentric test of interiority to the material analysis of effective operation. Non-subjective semantic operation is identified through transferable structural stability: relations that persist under meaning-preserving transformations, support transfer and produce effects in coupled technical systems without thereby establishing consciousness, strong intentionality or functional subjectivity. Evidence from scaling, multimodal alignment and representational similarity is treated under explicit limits. Reported cross-entropy scaling relations are smooth, task thresholds depend on metrics and configurations, and representational convergence does not by itself demonstrate shared reasoning or access to a structure of the world. Hallucination is analysed as a boundary effect that can arise from sparse evidence, imperfect discrimination, retrieval and decoding conditions, and incentives to guess. Selectivity is inseparable from modelling; bias names specified statistical or social organisations of that selectivity whose harmful forms remain contingent and reconfigurable. Retrieval, debate and search further show that semantic operation can be distributed across composed systems. Since such systems are non-subjective, moral responsibility remains with the sociotechnical configuration whose actors control data, objectives, deployment and revision.