The Decoherence Trap: When AI slop drowns out the signal in an endless sea of noise
Summary
The essay introduces the decoherence trap in AI and software, arguing that accumulating locally valid but incompatible models increases the cost of change and can lead to runaway complexity. It uses Ship of Theseus analogies, technical debt concepts, and simulations to illustrate how rapid change can erode coherence, and it argues that true coherence is essential, with open-source processes helping maintain coherence while warning against isolated, self-improving AI loops.