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The complexity in our team's code bases have only gotten worse with AI-integrated agents. Maybe it's the prompts we're using, but it's an ironic twist that these tools that promise so much productivity today ends up dumping more tech debt into our code.

It's funny reading the "key contributors to dependency-complexity" -- Duplication, Exceptions, Inheritance, Temporal Decomposition -- because those qualities seem like the standard for AI-generated code.

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> but it's an ironic twist that these tools that promise so much productivity today ends up dumping more tech debt into our code.

Because long-term productivity was never about the generated lines of code. You can increase your system features through expansion, or by a combination of expansion and contraction.

Generating new code without spending time to follow through with the contraction step, or alternatively contracting first as a way of enabling the new expansion, will always make the code more complex and harder to sustain and continue to improve wrt the feature set.

I have to take special effort to tamp down on duplication in AI generated code.

For me it's not uncommon for AI to draft an initial solution in X minutes, which I then spend 3*X minutes refactoring. Here's a specific example for a recent feature I coded: https://www.youtube.com/watch?v=E25R2JgQb5c

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