Andrew Ng argues model liability cannot guarantee downstream AI control
Andrew Ng argued that California SB 1047's model-level liability could not ensure harmless downstream use because open-model alignment can be removed and closed models can be jailbroken, proposing regulation of dangerous applications rather than general-purpose AI technology.
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Why it moved the index
Magnitude 27 reflects a concrete governance-control gap: model providers cannot reliably prevent downstream harmful adaptation or jailbreaks, complicating enforceable frontier-risk policy. Confidence 58 reflects a specific first-person argument grounded in known alignment-removal and jailbreak mechanisms, capped because it infers policy effectiveness without empirically measuring SB 1047's safety or innovation effects.
Assessment history
- R1Toward 27 · confidence 58
Adds a distinct application-versus-model governance mechanism beyond the existing general argument against an AI-development pause.
14 Aug 2026