I’ve spent over 25 years watching software systems accumulate entropy and drift into disrepair and failure. I’ve led several projects and know the …

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Drawing on 25 years of experience and systems thinking literature (Brooks, Meadows, Dekker), the author argues that AI-driven productivity gains will likely accelerate software entropy rather than reduce it. Four structural forces are examined: path dependence locking in early design choices, competing stability-seeking vs. growth-seeking feedback loops, delayed feedback that masks accumulating problems, and divergent mental models held by different engineers. AI amplifies all four: it can't circumvent path dependence safely, will accelerate conflicting feedback loops, may introduce more delayed maintenance debt, and will generate slightly different system models for each user — analogous to 100 developers each with their own understanding of the codebase. The conclusion is that good software engineering principles become more important, not less, in an AI-augmented world.

Productivity and Entropy