GoKawiilEssay Warns AI-Driven 'Decoherence' Could Trigger System-Wide Collapse
A new essay introduces 'decoherence' as the buildup of locally useful but incompatible or redundant models within a system, where the cost of change becomes dominated by translating between these models and avoiding unintended side effects. The author argues decoherence has always existed but is now being produced at an unsustainable rate due to widespread AI adoption, using an aging office building's plumbing as an analogy for different types of technical debt.
GoKawiil's interpretation of the reporting above, not reported fact.
The essay suggests this dynamic rules out a runaway AI intelligence explosion, since decoherence limits how fast AI can improve both in isolation and in the real world. Instead, the author frames the more pressing risk as 'Decoherence Collapse'—a scenario where systems become so fragmented by incompatible models that maintaining or upgrading them grows prohibitively costly, though the full mechanics of this collapse are only partly outlined here.
- Decoherence is defined as the accumulation of locally valid but mutually incompatible or redundant models in a system.
- The essay argues decoherence grows exponentially relative to a system's rate of change and current AI usage is accelerating it unsustainably.
- The author claims this process makes a sudden AI intelligence explosion unlikely, but warns of a slower, systemic 'Decoherence Collapse' instead.
Source: maevyn.codes, 2026-10-19
Published there as: “The Decoherence Trap”
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