CONCEPT
Smuggled Expertise and the Training-Data Problem
The structural illusion by which AI systems appear to possess expertise they have extracted from human experts — the representations manifest in training data without any of the developmental process that built them.
When an AI system produces expert-level output, the natural inference is that the system possesses expertise. The Ericsson framework exposes this as a category error. The system produces output that matches expert output because it was trained on the products of human expert cognition — code written by developers who underwent deliberate practice, briefs drafted by lawyers who read cases attentively, diagnoses made by physicians who built pattern-recognition through years of clinical experience. The system extracted statistical patterns from these products. It did not develop the representations that produced them. The human judgment embedded in the training data is smuggled into the system's output, where it appears to be the system's own competence. The distinction matters because smuggled expertise has no developmental trajectory — it cannot grow, cannot adapt to genuinely novel situations, and cannot recognize when the patterns it has absorbed are inadequate to the problem at hand.
