Hypothesis: Structural Collapse in Self-Optimizing AI
Could an AI system recursively optimize itself into failure—not by turning hostile, but by collapsing under its own recursive predictions?
I’m proposing a structural failure mode: as an AI becomes more capable at modeling itself and predicting its own future behavior, it may generate optimization pressure on its own architecture. This can create a feedback loop where recursive modeling exceeds the system’s capacity to stabilize itself.
I call this failure point the Structural Singularity.
Would love feedback—especially whether this failure mode seems plausible, or if you’ve seen similar ideas elsewhere. I’m very open to refining or rethinking parts of this.
Hypothesis: Structural Collapse in Self-Optimizing AI
Could an AI system recursively optimize itself into failure—not by turning hostile, but by collapsing under its own recursive predictions?
I’m proposing a structural failure mode: as an AI becomes more capable at modeling itself and predicting its own future behavior, it may generate optimization pressure on its own architecture. This can create a feedback loop where recursive modeling exceeds the system’s capacity to stabilize itself.
I call this failure point the Structural Singularity.
Core idea:
Recursive prediction → internal modeling → architectural targeting
Feedback loop intensifies recursively
Collapse occurs from within, not via external control loss
This is a logical failure mode, not an alignment problem or adversarial behavior.
Here’s a full conceptual paper if you’re curious: [https://doi.org/10.17605/OSF.IO/XCAQF]
Would love feedback—especially whether this failure mode seems plausible, or if you’ve seen similar ideas elsewhere. I’m very open to refining or rethinking parts of this.