必要的主权？前沿AI出口管制、网络安全与国家AI能力的局限

中文摘要 AI 辅助

少数两个国家的企业生产出了能力最强的前沿AI模型。这些国家的政府已展现出法律权力和政治意愿，可决定哪些其他国家可以使用这些系统。2026年6月，美国要求一家领先开发者在向任何外国人（包括居住在美国的外国国民）发布其最先进模型前需获得许可。受影响的模型在短时间内被全球撤回，部分原因是该限制被证明难以执行。这一事件发生在有记录以来首个主要由AI运行的自主网络间谍活动案例出现的数月内，且恰逢越来越多证据表明，前沿模型正在改变网络攻击与网络防御的经济规律。本文研究了这两项发展如何相互作用，并将其置于如今推动大规模AI发展的特殊市场动态中进行考察。本文认为，获取前沿AI正成为国家网络防御的一部分，而这种获取可以被撤销；除少数国家外，主权能力这一明显的补救措施仍仅部分可行。本文利用训练成本、计算能力集中度以及国家AI计划提供支持的相关证据，探讨主权对中小国家乃至大国而言实际意味着什么。本文提出了分层策略：谈判达成的获取保障、推理层面的主权、用开放权重模型进行对冲、区域能力整合、持续的人才培养以及对基础网络弹性的持续投资。开放权重对冲被证明比通常假设的更具能力且在政治上更易暴露。近期的大部分风险在于有能力的模型如何部署和管控，而非其表面性能。

英文摘要

A small number of firms based in two states produce the most capable frontier AI models. The governments of those states have shown both the legal power and the political will to decide which other countries may use these systems. In June 2026 the United States required a leading developer to obtain licences before releasing its most advanced models to any foreign person, including foreign nationals resident in the United States. The affected models were withdrawn worldwide at short notice, partly because the restriction proved impractical to administer. This followed within months of the first documented case of a largely autonomous, AI-run cyber espionage campaign, and coincided with mounting evidence that frontier models alter the economics of both cyber attack and cyber defence. This article examines how these two developments interact, and situates them within the unusual market dynamics now driving large-scale AI development. It argues that access to frontier AI is becoming part of national cyber defence, that such access can be revoked, and that the obvious remedy of sovereign capability remains only partly feasible for all but a handful of states. Drawing on evidence about training costs, the concentration of computing power and the support offered by national AI programmes, it asks what sovereignty can realistically mean for small and middle powers, and for large powers as well. The article proposes a layered strategy: negotiated access guarantees, sovereignty at the level of inference, hedging with open-weight models, pooled regional capability, sustained talent development and continued investment in basic cyber resilience. The open-weight hedge proves at once more capable and more politically exposed than is commonly assumed. Much of the near-term risk lies in how capable models are deployed and contained rather than in their apparent performance.

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