Computing power, or "compute," is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.

翻译：算力，或称"计算能力"，对于人工智能（AI）能力的开发与部署至关重要。因此，各国政府和企业已开始将算力作为治理AI的手段。例如，政府正投资国内算力基础设施、控制流向竞争国家的算力资源，并向特定领域提供算力补贴。然而，这些努力仅触及了利用算力治理AI开发与部署的冰山一角。相较于AI的其他关键要素（数据与算法），AI相关算力具有独特的干预优势：它可检测、可排他、可量化，且其供应链高度集中。这些特性，加之算力对前沿AI模型的根本重要性，表明算力治理有助于实现共同政策目标，如确保AI的安全与有益应用。更具体而言，政策制定者可通过算力提升AI监管透明度、引导资源分配以促进有益成果，以及强化对不负责任或恶意AI开发与使用的限制。然而，尽管基于算力的政策与技术在上述领域具有潜力，其实现准备程度存在显著差异：部分设想已进入试点阶段，另一些则因基础研究不足而受阻。此外，对算力治理采取简单化或范围失当的路径，可能在隐私保护、经济影响以及权力集中化等领域引发重大风险。我们最终提出若干防护措施，以最大程度降低算力治理带来的这些风险。