When Self-Optimizing AI Collapses From Within: A Conceptual Model of Structural Singularity

Most AI risk models focus on alignment, control loss, or external misuse.  
But what if some AI systems were to fail in a different way — not by turning against us, but by collapsing under their own recursive modeling?

This post introduces a conceptual hypothesis: that sufficiently advanced self-optimizing AI systems may experience internal failure due to structural exhaustion caused by recursive self-prediction and self-modification. I call this the Structural Singularity.

1. Summary of the Hypothesis

• A self-optimizing AI recursively predicts and modifies itself.
• Over time, its predictions begin targeting its own internal architecture.
• Recursive feedback loops intensify, exerting structural pressure.
• Eventually, the system collapses from within — not due to misalignment, but due to recursive overload.

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

2. The Mechanism in Brief

• Recursive self-modeling → internal targeting
• Amplified optimization loops
• Diminishing structural slack
• Collapse at a point of recursive instability

I’m treating this as a structural collapse, analogous to mechanical or epistemological overload, rather than a value failure.

3. Why This Might Matter

If plausible, this hypothesis could:

• Represent a new class of AI risk, distinct from alignment or misuse
• Highlight the design importance of recursive boundaries
• Suggest that some systems may silently fail, without external behavior signaling the collapse
• Invite rethinking how we model robustness under recursive modification

It may also offer conceptual links to bounded rationality, Gödelian limits, or epistemic instability in self-referential systems.

4. Full Conceptual Paper

I’ve published a more detailed version of this model on OSF:  
👉 [https://doi.org/10.17605/OSF.IO/XCAQF]

5. Feedback Welcome

This is an open hypothesis — I’d greatly appreciate feedback, criticism, pointers to related models, or thoughts on how this fits into broader AI safety taxonomies.

Has anyone seen similar mechanisms discussed elsewhere? Or possible counterexamples?

Thanks for reading!