The Decoherence Trap: When AI drowns out the signal in an endless sea of noise

Decoherence is the buildup of locally sensible but mutually incompatible or redundant models and fixes inside a system, such that the dominant cost of any change becomes translating between those models and preventing unintended side-effects. It accelerates with the system’s rate of change and accelerates faster the more decoherent the system already is, producing runaway, exponential complexity. Widespread use of AI is driving decoherence far faster than historical practices did: that both blocks rapid, isolated self-improvement (ruling out a neat intelligence explosion) and creates pressure toward a different disaster called DECOHERENCE COLLAPSE. The Ship of Theseus and a plumbing-in-an-office-building thought experiment illustrate the difference between replacing components cleanly and incrementally creating a patchwork that is functional but combinatorially complex.

That patchwork forces each subsequent change to require deeper, costlier analysis because any local repair can alter global behavior; management pressure to move faster leads engineers to make brittle, expedient joins that worsen the system, creating a vicious, exponential feedback loop. In software this problem is invisible to outsiders, so proposals to unify models or pay down debt are hard to sell. Practices like standardization, isolation, tooling and careful unification can delay collapse, but once multiple transitions interleave the maze becomes combinatorial and even top engineers begin to fail, making large-scale failure increasingly likely.