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Mutually Assured Deregulation
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文章指出,全球政策制定者拥抱“监管牺牲”理念,即通过放弃安全监管以换取AI的领先地位,但这一做法存在致命缺陷,强调强化监管框架的重要性。

arXiv:2508.12300v1 Announce Type: cross Abstract: We have convinced ourselves that the way to make AI safe is to make it unsafe. Since 2022, policymakers worldwide have embraced the Regulation Sacrifice - the belief that dismantling safety oversight will deliver security through AI dominance. Fearing China or USA will gain advantage, nations rush to eliminate safeguards that might slow progress. This Essay reveals the fatal flaw: though AI poses national security challenges, the solution demands stronger regulatory frameworks, not weaker ones. A race without guardrails breeds shared danger, not competitive strength. The Regulation Sacrifice makes three false promises. First, it promises durable technological leads. But AI capabilities spread rapidly - performance gaps between U.S. and Chinese systems collapsed from 9 percent to 2 percent in thirteen months. When advantages evaporate in months, sacrificing permanent safety for temporary speed makes no sense. Second, it promises deregulation accelerates innovation. The opposite often proves true. Companies report well-designed governance streamlines development. Investment flows toward regulated markets. Clear rules reduce uncertainty; uncertain liability creates paralysis. Environmental standards did not kill the auto industry; they created Tesla and BYD. Third, enhanced national security through deregulation actually undermines security across all timeframes. Near term: it hands adversaries information warfare tools. Medium term: it democratizes bioweapon capabilities. Long term: it guarantees deployment of uncontrollable AGI systems. The Regulation Sacrifice persists because it serves powerful interests, not security. Tech companies prefer freedom to accountability. Politicians prefer simple stories to complex truths. This creates mutually assured deregulation, where each nation's sprint for advantage guarantees collective vulnerability. The only way to win is not to play.

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AI安全 监管框架 政策误区
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