The structural vulnerability of practitioners who possess borrowed chunks rather than earned ones — highly capable within the operational parameters of their tools, profoundly exposed when conditions depart from routine.
Fragile expertise is a specific cognitive condition that Miller's framework identifies as a predictable consequence of borrowed rather than earned compression. The fragile expert produces work indistinguishable from that of a deep expert under routine conditions. Her outputs meet the specifications. Her ratings match. Her metrics perform. The difference emerges only when novelty arrives — a bug that resists standard diagnosis, a requirement that falls outside training distribution, a system behavior whose root cause lies in mechanisms the tool did not expose. At that point, the deep expert draws on thousands of recoding episodes to navigate the unfamiliar. The fragile expert reaches into her compression and finds a label where a chunk should be. She knows what the system does. She does not know why. She cannot repair what she does not understand. She is not incompetent — she is competent within a specific range, and catastrophically exposed outside it.
Fragile Expertise
In The You On AI Field Guide
The fragility is invisible under normal operations, which