The newly enacted Senate Bill 53 in California has raised important questions about whether the state’s approach to AI risk genuinely promotes safety or simply bureaucratic compliance. Authored by Gavin Newsom’s administration, this legislation defines “catastrophic risk” for AI systems as those that could cause either the death or serious injury of more than 50 people, or inflict property damage exceeding US $1 billion. It also targets “frontier models” trained with over 10²⁶ FLOPS and developers with annual revenue above US $500 million.
However, analysts such as Elen Irazabal Arana and Nikolai G. Wenzel argue that the law emphasises paperwork rather than the prevention of actual harm. They note:
“Rather than regulating the actual harm that might come from AI, California has chosen to focus on the models and developers… paperwork does not necessarily mean protection.”
The legislation mandates large frontier developers to publish safety frameworks, report critical incidents and undergo oversight—with fines up to US $1 million for non-compliance. Yet the framework may inadvertently divert expert judgement, as firms focus on ticking procedural boxes rather than confronting genuine system risk:
“When regulation equates safety with documentation… it reduces complex realities to checking boxes and filing forms.”
For the insurance and risk-management sector, this shift matters. Insurers that rely on smart algorithms for underwriting, claims assessment and fraud detection may find that compliance labels carry little assurance of true risk mitigation. Questions remain about how firms interpret thresholds such as “more than 50 deaths” or whether smaller, targeted models are entirely left outside regulation. The law’s long-term success depends on bridging the gap between procedural compliance and adaptive, expert-driven risk controls.
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