Programming Mastery as Epistemic Training: How Debugging Expertise Transfers to Sophistry Detection in the AI Era

Sophisticated AI-generated argumentation satisfies surface syntactic and semantic criteria whileembedding structural invalidity at the level of type, composition, and implicit constraint —invalidity that standard critical thinking education, which addresses the deliberative layer ofreasoning, is not designed to detect. We propose the Threshold Transfer Hypothesis:mastery-level experience with statically-typed programming languages, specifically experiencethat crosses a phenomenological threshold of fluency, generates zero tolerance for approximatevalidity that transfers directly to pre-reflective detection of sophistry in natural language.We argue that the decisive cognitive transition — from mental simulation of code execution todirect reading of code meaning — produces a practitioner who processes programming languageand natural language in the same cognitive mode: one in which structural validity is felt ratherthan computed and anomalies register before deliberative analysis begins. This transition, whichmost working programmers do not reach regardless of experience, is not a transfer of skills but arestructuring of cognitive processing that eliminates transfer cost entirely.We develop a three-level model of programming expertise (symbol manipulation, mentalsimulation, language reading), analyze the successive internalization of type-system constraints,function composition, cast-structure validity, and the Input-Process-Output schema ascorresponding dimensions of argument validity sensitivity, and account for the structural gapbetween declarative knowledge and proceduralized conviction — a gap that explains why thiscapability cannot be taught through instruction and why its transmission requires witnessedembodied demonstration.The thesis is supported by phenomenological evidence, reinterpretation of Pea and Kurland's(1984) null transfer finding as predicted rather than disconfirming, and analysis of a naturalhuman-AI collaborative workflow — sense anomaly, name via AI — that demonstrates theirreducibly human character of the pre-reflective sensing function. We conclude with an empiricalresearch program and educational implications for the AI era.