Training advanced AI models requires large investments in computational resources, or compute. Yet, as hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time. To analyze this phenomenon, we introduce compute (investment) efficiency, which relates training compute investment to the resulting AI model performance. We then present a conceptual model of increases in compute efficiency and assess the social and governance implications. We find that while an access effect increases the number of actors who can train models to a given performance over time, a performance effect simultaneously increases the performance available to every actor - potentially enabling large compute investors to pioneer new capabilities and maintain a performance advantage even as capabilities diffuse. The market effects are multifaceted: while a relative performance advantage might grant outsized benefits in zero-sum competition, performance ceilings might reduce leaders' advantage. Nonetheless, we find that if the most severe risks arise from the most advanced models, large compute investors warrant particular scrutiny since they discover potentially dangerous capabilities first. Consequently, governments should require large compute investors to warn them about dangerous capabilities, thereby enabling timely preparation and potentially using their superior model performance and compute access for defensive measures. In cases of extreme risks, especially offense-dominant capabilities, the government might need to actively restrict the proliferation entirely.

翻译：训练先进AI模型需要大量计算资源（即算力）投入。然而，随着硬件创新降低计算成本、算法进步提升使用效率，使AI模型达到特定性能水平的训练成本会随时间推移而下降。为分析这一现象，我们引入"计算（投资）效率"概念，将训练计算投资与AI模型性能产出相关联。随后提出计算效率提升的概念模型，并评估其社会与治理影响。研究发现：一方面，"可及性效应"使能训练出特定性能模型的行动者数量随时间增加；另一方面，"性能效应"同时提升了所有行动者可获得的性能水平——这可能导致大型算力投资者率先开拓新能力，即便能力逐渐扩散仍能保持性能优势。市场影响具有多面性：在零和竞争场景中，相对性能优势可能带来超额收益，而性能天花板可能削弱领先者优势。但研究发现，若最严峻的风险源于最先进的模型，大型算力投资者值得特别关注，因其会率先发现潜在危险能力。因此，政府应要求大型算力投资者就危险能力发出预警，以便及时准备，并可能利用其卓越的模型性能与算力获取能力采取防御措施。在极端风险（尤其是攻击主导型能力）情况下，政府或需主动全面限制能力的扩散。