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

Large language models have become highly proficient at generating syntactically correct code, but this doesn't address the problem of code "sloppiness"—unnecessary abstractions, duplication, and poor architectural decisions that lead to bloated projects. The industry currently lacks rigorous, quantitative methods to measure code quality, relying instead on vague assessments and AI judges that prove unreliable due to inconsistency and bias. Developing proper metrics for code sloppiness is essential as AI-generated code volumes grow exponentially and exceed human capacity for meaningful review.