Economics of Writing Clean Code

[mklabgr]

The article argues that AI has fundamentally changed the economics of writing clean, well-structured code: because tools like Copilot can generate boilerplate, interfaces, type definitions, and refactorings almost instantly, the traditional objection that good abstractions require “too much extra code” is far weaker. The real bottleneck is now human comprehension, since developers still spend large amounts of time reading, debugging, reviewing, and understanding code. Interfaces, modular design, and well-chosen abstractions reduce cognitive load by letting programmers reason about what a component does without constantly inspecting how it works. 
The author also warns that AI can increase “comprehension debt”—teams may produce more code while understanding less of it—and cites studies suggesting more code churn, duplication, weaker debugging knowledge, and sometimes even lower real productivity despite developers feeling faster. The conclusion is not that more abstraction is always better: premature or poorly designed abstractions can hurt performance and flexibility. Instead, developers should use AI to generate straightforward implementations quickly, allow patterns to emerge, and then refactor toward clear, human-readable abstractions, because in the AI era the cost of writing code has fallen dramatically while the cost of understanding bad code has not. 

ARTICLE