AI‑Induced Stylometric Convergence

This essay examines stylometric convergence as a structural outcome of predictive mediation. It analyzes how retrieval compression, semantic averaging, and system‑level optimization reduce stylistic contrast across writers, producing a cross‑platform drift toward statistical coherence. The work details how predictive systems penalize deviation, how smoothing pressures reshape cadence and density, and how homogenization threatens lineage, identity, and conceptual precision. It concludes with a set of counter‑convergence techniques—structural asymmetry, density layering, lineage anchoring, rhythmic irregularity, and semantic contrast—designed to preserve stylistic autonomy within AI‑mediated writing environments. This essay operates within the SignalRupture canon, extending the diagnostic lineage established in Quiet Governance, Systemic Erosion Theory, Infrastructural Exposure Theory, Slow Harm Theory, Social Infrastructure Theory, and Morality on Trial, alongside the applied surfaces Metadata Suppression and Stylometric Resistance and the stylometric capsule Triggering the System’s Immune Response.