Optimizing Code for AI Readers: Why Pattern Uniformity Matters More Than Abstraction

As AI agents become primary code readers, the efficiency calculus of code design shifts fundamentally. The true cost driver isn't abstraction itself, but heterogeneous patterns scattered across a codebase—forcing agents to re-derive understanding for each variation. Standardizing on a single pattern applied consistently proves more efficient than minimizing abstractions, as it amortizes token costs and enables constant-time feature extensions.