Technohybridism: Recursive Semantic Normalization in AI-Mediated Societies

arXiv:2611.07984v1 Announce Type: new Abstract: Language-generating AI systems are no longer only objects of linguistic interaction: their outputs increasingly re-enter the communicative and institutional practices from which their training material was drawn. Drawing on social theory, social epistemology, and the philosophy of language, this article develops a sociotechnical account of that re-entry. We introduce recursive semantic normalization: the process through which machine-generated formulations, once recurrently taken up, evaluated, reused, and institutionally retained, participate in the reproduction and transformation of semantic regularities. Under the heading of technohybridism, we argue that the relevant unit of analysis is the hybrid semantic system, a recursively coupled configuration of human practices, computational generation, and institutional uptake. First, we distinguish the concept from neighbouring accounts, including model collapse, looping effects, machine habitus, machine culture, and the epistemology of AI testimony. Second, we develop an analytical model that separates generation, selection, uptake, and persistence and distinguishes machine-attributable change from change that would have occurred anyway. Third, we analyse uptake as routinized, institutionally diffused, and socially stratified practice and state conditions under which machine-derived regularities become norms. Fourth, we identify social-epistemic consequences: the laundering of provenance that weakens epistemic vigilance, the erosion of the cognitive diversity on which collective

The article discusses how AI-generated content can alter semantic regularities and potentially weaken epistemic vigilance and cognitive diversity.

The claim outlines how AI-generated language recursively normalizes and alters human communicative practices, leading to epistemic risks such as weakened vigilance, reduced cognitive diversity, and narrowed interpretive resources. These effects imply societal degradation in knowledge quality and pluralism, posing material concerns without asserting outright collapse or crisis.

This is a conceptual, theory-building paper proposing a framework, not an empirical finding. It articulates plausible but unverified mechanisms of epistemic erosion from AI language reuse, with no measured outcomes presented, warranting a moderate negative score capped by low evidential strength.

The article theorizes a process where AI-generated language recursively influences human semantic norms, potentially leading to a loss of cognitive diversity and weakened epistemic vigilance. This suggests a significant negative impact on human understanding and institutional practices. The claims are theoretical, not empirical results.

The text describes a process where AI-generated language shapes human communication and institutions. It warns of reduced epistemic vigilance and cognitive diversity, which are concerning for societal flourishing.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   df2bbd24-28e2-4337-a054-e810760b3a99
           https://arxiv.org/abs/2611.07984
   ↓
EVIDENCE   3 extracted excerpts
   ↓
MODEL RUN  5 runs, methodology 1.0.1
   ↓
SCORE      -50  (Adverse)
   ↓
CONFIDENCE 79%