The Decoherence Trap

Decoherence is the build-up of locally coherent but mutually incompatible or redundant models inside a system so that the dominant cost of change becomes translating between those models and preventing unintended side‑effects. It scales with the system’s rate of change and feeds on itself: more decoherence accelerates future decoherence, producing exponential runaway unless transitions are consciously completed and unified. Widespread AI tooling is accelerating the creation of such local models, which paradoxically makes a rapid intelligence explosion unlikely but increases the risk of a systemic DECOHERENCE COLLAPSE. The essay frames the problem with three kinds of debt - infrastructural wear, technical-advancement migration, and the third, decoherence, which arises from how migrations are executed - and uses Ship of Theseus and plumbing analogies to show how incremental, ad hoc replacements produce complex, fragile patchworks.

Practically, decoherence turns maintenance and extension into a combinatorial maze: every patch, hybrid, or partial migration creates dependencies that require expert tracing and careful analysis; pressured timelines lead to sloppier joins that further fragilize the system, creating positive feedback. Invisible codebases hide that complexity from decision-makers, making cleanup proposals hard to sell. Isolation, best practices, and tooling can delay collapse, but beyond a threshold even top engineers will fail to safely reason about the system. The core remedy is deliberate unification and completing migrations rather than perpetual, piecemeal patching.