CONCEPT
Borrowed Competence
Spolsky's framework's name for the confidence that abstraction provides — real while the abstraction holds, owed with interest when it fails — and the specific form of professional fluency that AI-era developers are accumulating without the diagnostic strata that would let them repay the loan.
Borrowed competence is the confidence a practitioner inherits from the reliability of the tools she uses rather than from her own understanding of what those tools do. When the abstraction holds, the confidence is justified — the developer using AI-generated code feels capable, productive, liberated from mechanical labor, and the feeling maps accurately onto her output. When the abstraction leaks, the confidence becomes a liability: the developer who believed she understood the system discovers she understood the abstraction, and the system is the thing that is breaking. The interest rate on the loan is determined by the size of the gap between the abstraction level and the complexity it conceals. For AI-generated code, the gap is every layer deep, and the interest compounds silently until the moment the loan is called.
In The You On AI Field Guide
The concept inverts a common intuition. Most discussions of AI-era