How AI Changed the Economics of Writing Clean Code

AI coding tools have collapsed the cost of writing code, but the cost of reading and understanding code remains unchanged. This creates a new economic argument for using abstractions like interfaces: the boilerplate cost that historically justified skipping them is now near zero, while the cognitive load reduction they provide is as valuable as ever. Backed by neuroscience (Cognitive Load Theory, fMRI studies), historical CS wisdom (Dijkstra, Parnas, Fowler), and recent data (GitClear's code churn analysis, METR productivity study, Anthropic's comprehension study), the argument is that AI-generated code without good abstractions accumulates 'comprehension debt' — invisible erosion of team understanding that doesn't show up in velocity metrics. The contrarian cases against abstraction (performance, premature abstraction) are addressed and shown to be arguments against bad abstraction, not abstraction itself. The conclusion: with AI handling boilerplate, there's no longer an economic excuse to skip interfaces and proper abstractions.