Large language models are reshaping software development economics in unexpected ways. Unlike human developers who instinctively seek the shortest path, LLMs implement complete solutions regardless of complexity. For these models, writing two hundred lines of code costs the same cognitive effort as two lines of imports. This fundamental difference creates a new bottleneck in the development cycle.

Code reviews have become the expensive step. Reviewers face technically correct but over-engineered implementations that require time-consuming analysis and pushback discussions. The same complexity debates repeat across multiple pull requests, consuming valuable engineering time that was previously spent on initial implementation.

Rewriting, however, is now remarkably cheap. When developers spot unnecessary complexity, they can prompt the same AI to simplify, swap in libraries, or remove unneeded features. The turnaround is often faster than the original implementation, making post-hoc cleanup more efficient than upfront prevention.

This shift changes how developers approach planning and implementation. More time goes into upfront design discussions to establish scope and library choices before code generation begins. The result is a workflow that front-loads architectural decisions while making iteration cycles dramatically faster.