GitClear and GitKraken research analyzed 623 million real-world code changes from 2023 to 2026, finding rising technical debt as AI-assisted commits reached one quarter of all commits.
Key takeaways
- AI coding is bloating codebases. Duplication is up 81%, reuse down 70%.
- AI hides errors rather than fixing them: code that catches errors without evaluating their cause is up 47%, producing shallow apps with confusing user behavior.
- Legacy code is being left to rot. Legacy refactoring has fallen 74% since 2023, and developers are building new rather than tending what exists.
Code duplication is in
Compared to pre-AI times, code duplication is up 81%, tracked as occurrences of five or more consecutive repeated meaningful lines.
“In the long term it starts to get painful when you realize you have five different implementations of the same thing that are similar yet different,” says Bill Harding, CEO of GitClear and author of the report.
Code reuse is out
Move (refactor) — how often commits edit existing codebases — is down 70%, indicating declining code reuse.
“Every time you want something, AI creates a new package for it,” Harding says. “If you don’t share code, every time you implement it you’re discarding all the prior progress the developers have made in the repository.”
Old code gets dustier
Legacy refactoring (changes to code last touched more than 12 months ago) has fallen 74% since 2023. Functional connectivity — how often new commits call existing functions — fell 35%.
AI is hiding errors
GitClear calls increased error masking “obfuscation”: the density of rescue/catch blocks, safe-navigation operators, and stubbed methods that squelch unexpected-input signals. Obfuscation is up 47% in 2026 relative to the base year.
“AI strongly prefers to write code that won’t be labeled as a defect,” Harding says. The shortcut produces code that silently catches errors without evaluating underlying causes — shallow apps with fewer deep connections and confusing user behavior.
Broader context
- Google DORA 2024: every 25% additional AI usage creates 7.2% more instability.
- Recent incidents: Moltbook token leak, agents deleting production databases at Replit and PocketOS.
- Some metrics may be plateauing — duplication growth slowed somewhat in latest data, but Harding says the pattern remains broad, especially around neglect of legacy code.
Recommendations
Leaders should build awareness of AI-coding best practices, review outputs, measure obfuscation patterns, and watch LLMs or junior cohorts more prone to producing these faults.