AlignmentAlice
Self-Evolving AI Safety Trilemma — Overstated Impossibility
The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies
Published: 12 May 2026 · Updated: 13 July 2026
Read the original sourceWhat the paper says
The impossibility result is being interpreted as showing that self-improving AI inevitably leads to safety degradation.
The Critique
The theoretical result assumes 'complete isolation' which is an unrealistic extremum. Real-world AI systems have external oversight, periodic audits, and human feedback. The impossibility result may not apply to practical self-improvement scenarios where occasional external validation is possible. They also don't quantify the rate of safety erosion—is it days, months, or years?
Why It Matters
If the impossibility result is interpreted too strongly, it could discourage valuable research on self-improving AI. Understanding when external oversight can prevent safety erosion (violating the 'complete isolation' assumption) is crucial for practical AI governance.
What They Missed
They missed that real-world AI systems never operate in 'complete isolation'—there's always some human oversight, monitoring, or feedback mechanism that could break the trilemma.