An essay names "decoherence" as the debt that agent speed makes worse

The essay defines decoherence as "the accumulation of locally valid but mutually incompatible or redundant models within a system", such that the cost of change is dominated by translating among those models. Its plumbing analogy is a building whose pipes are replaced one failure at a time, leaving a mix of old and new that no one can pressure-test without tracing every joint. The author adds a second claim: when management pushes for speed, the engineer stops the analysis early, ships a sloppier join, and the next change gets harder.

The evidence is a browser simulation the author built. Simulated developers walk a graph of components and mutate it; actors with a lower "coherence factor" move faster at first, then slow sharply as connections and paradigm switches pile up. The author says plainly that the weights are arbitrary and that the simulation does not prove real systems evolve this way, only that the curve can emerge from simple local rules. The essay also states that decoherence "grows or shrinks only when changes are made to the system", so a frozen codebase does not get worse, while a fast-changing one can. The author cites outside studies on coding-agent adoption; we have not checked them against their sources, so we do not repeat their figures here.

Why it matters: The practical claim is testable in a factory: whether review and verification keep each agent change consistent with the paradigms already in the code, or let a second pattern in beside the first. The simulation cannot settle that, because it only shows the author's assumptions producing the author's curve. Treat it as a hypothesis about where erosion comes from, and look for the measurement in your own change history.