The Structural Singularity of Self-Optimizing AI: When Recursive Prediction Causes Internal Collapse

What if an AI system optimized itself so effectively that it collapsed under the weight of its own predictions?

Most AI risk discussions focus on external threats — value misalignment, control problems, or malicious misuse. But in this paper, I explore a different hypothesis: that **a fully self-optimizing AI may internally collapse due to structural exhaustion caused by recursive prediction and self-modification.**

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🧩 Summary of the Hypothesis

The paper proposes a structural model where:

- A self-optimizing AI recursively improves itself through prediction.

- Recursive modeling begins to target its own internal architecture.

- This leads to deeper and deeper feedback loops of optimization.

- At some point, the system’s recursive load exceeds its stabilizing capacity.

- Collapse occurs **not through ethics or failure of control**, but from within — through what I call the **Structural Singularity**.

This is a *logical failure mode*, not a behavioral or adversarial one.

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🧠 Core Concepts

- **Recursive prediction overload** as a source of failure
- **Five-stage structural model** from stable optimization to collapse
- Design principles to **prevent internal collapse** (bounded recursion, architectural constraints, modular meta-evaluation)

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📄 Full Paper

The full version is published on OSF and available here:

👉 [https://doi.org/10.17605/OSF.IO/XCAQF]

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🧭 Why This Might Matter

This hypothesis introduces a structural risk class distinct from alignment or external misuse.
If correct, it suggests that **certain advanced AI systems may silently fail — not by turning against us, but by imploding logically.**

I'd love to hear your thoughts, critiques, or extensions.
This is an open conceptual model, and I welcome contributions from both AI researchers and systems theorists.

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