Linguistic drift at the frontier

The author argues that frontier language models may be driving linguistic drift by repeatedly introducing or amplifying uncommon terminology, using the increasing use of “seam” to describe software component boundaries as a central example. They report GitHub searches suggesting that usage of the term surged in 2026 and was disproportionately associated with AI coding tools, particularly Claude Code, while an analysis of README corpora from before 2025 and from 2026 visually and statistically identified broader differences in writing style. The piece warns that such shifts could create confusion when AI-generated language enters operational workflows, illustrating the concern through a hypothetical airline-ticket triage system in which an unfamiliar model-generated synonym disrupts human interpretation. As a practical response, it demonstrates a Pydantic AI Capability called VocabularyGuard, which can detect vocabulary associated with a selected linguistic profile and either rewrite outputs with another model or request a retry, applying this control to structured-output fields, tool arguments, and prose responses. The author presents the project as an early experiment rather than a proven solution, calls for more rigorous research and real-time tracking of AI-related language change, and includes an appendix describing a corpus-comparison classifier based on weighted n-gram log-odds scoring.

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