"If you’re a developer in 2026, you’re most likely using large language models (LLMs) in your flow. While agentic coding tools can work wonders, their output often flies in the face of do not repeat yourself (DRY), a core software development principle stating that every piece of knowledge must have a single, unambiguous, authoritative representation within a system.
GitClear and GitKraken research analyzed 623 million real-world code changes from 2023 to 2026, and discovered some sobering data on the shape of code commits “before” and “after” the AI takeover.
The study found AI-assisted commits make up one quarter of all commits, alongside rising technical debt across eight maintainability-related code quality metrics.
The clearest signal? Code reuse is down significantly from pre-AI levels, marking a shift away from shared libraries.
“Every time you want something, AI creates a new package for it,” says Bill Harding, CEO of GitClear and author of the report. “That general approach to building has all sorts of consequences.”"
https://leaddev.com/ai/code-maintainability-plummets-in-the-ai-coding-era
#AI #GenerativeAI #AICoding #VibeCoding #SoftwareDevelopment #Programming #CodeMaintenance
@remixtures This should be obvious to anyone who has used an LLM for coding.
@h4890 Yes, that's one of the reasons you unfortunately always need to use another LLM to review the work of the original LLM. The LLM who first generated the code - or document, in the case of technical writing - is just incapable of cleaning/decluttering the mess it originally made. It just ends up creating more mess. Without relying on another LLM, the final output wil be just plain "ugly".