AI Can Fix Technical Debt Fast — But It Creates New Debt Even Faster

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AI coding tools and agents can accelerate refactoring, dependency migration, and legacy code cleanup, tackling backlogs that teams rarely had time to address. However, the same speed that makes AI useful also makes it easier than ever to accumulate technical debt at scale, sometimes generating entire systems before engineers fully understand what they have built. Technical debt has traditionally arisen not just from poor engineering but from reasonable decisions that became outdated as systems grew, requirements shifted, and real-world usage revealed unforeseen needs. The concern is that AI compresses implementation time without compressing the learning time required to understand what a system truly needs to become. As a result, engineering teams may find themselves inheriting large, complex codebases shaped by AI-generated shortcuts, with the same structural problems that have always defined legacy software.

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