If coding is solved, what now?: Measuring the sloppiness of code

The article argues that while LLMs can now generate syntactically correct code, the bigger enterprise risk is accumulating “slop” — unnecessary abstractions, duplication, and overly complex functions that quickly inflate codebases and reduce human control. For CIOs and technology leaders, the strategic implication is that AI-assisted development shifts the bottleneck from code production to code quality governance, making software maintainability, readability, and architectural discipline critical operating concerns for IT organizations adopting agents at scale. It highlights emerging metrics such as verbosity and erosion as a way to quantify code quality and manage AI-generated technical debt before it becomes a drag on delivery velocity and long-term platform health.