At face value, this essay is about understanding a fairly esoteric governance tool called compute thresholds. However, in order to grapple with whether these thresholds will achieve anything, we must first understand how they came to be. This requires engaging with a decades-old debate at the heart of computer science progress, namely, is bigger always better? Hence, this essay may be of interest not only to policymakers and the wider public but also to computer scientists interested in understanding the role of compute in unlocking breakthroughs. Does a certain inflection point of compute result in changes to the risk profile of a model? This discussion is increasingly urgent given the wide adoption of governance approaches that suggest greater compute equates with higher propensity for harm. Several leading frontier AI companies have released responsible scaling policies. Both the White House Executive Orders on AI Safety (EO) and the EU AI Act encode the use of FLOP or floating-point operations as a way to identify more powerful systems. What is striking about the choice of compute thresholds to-date is that no models currently deployed in the wild fulfill the current criteria set by the EO. This implies that the emphasis is often not on auditing the risks and harms incurred by currently deployed models - but rather is based upon the belief that future levels of compute will introduce unforeseen new risks. A key conclusion of this essay is that compute thresholds as currently implemented are shortsighted and likely to fail to mitigate risk. Governance that is overly reliant on compute fails to understand that the relationship between compute and risk is highly uncertain and rapidly changing. It also overestimates our ability to predict what abilities emerge at different scales. This essay ends with recommendations for a better way forward.

翻译：表面上看，本文旨在探讨一种名为"计算阈值"的深奥治理工具。然而，要评估这些阈值能否产生实际效果，我们首先需要理解其产生背景。这要求我们深入探讨计算机科学进步的核心议题——"规模越大是否必然越好？"这一持续数十年的争论。因此，本文不仅对政策制定者和公众具有参考价值，也能帮助计算机科学家理解计算资源在突破性进展中的作用。特定计算拐点是否会改变模型的风险特征？鉴于当前普遍采用的治理方法认为计算量越大则危害倾向越高，这一讨论显得尤为紧迫。多家前沿AI领军企业已发布负责任扩展政策，无论是白宫关于AI安全的行政命令还是欧盟《人工智能法案》，都将FLOP（浮点运算次数）作为识别强大系统的指标。值得关注的是，当前部署的实际模型无一达到行政命令设定的计算阈值标准。这表明治理重点往往不在于审计已部署模型的实际风险与危害，而是基于"未来计算水平将引发不可预见新风险"的预判。本文的核心结论是：现行计算阈值机制存在短视性，难以有效缓解风险。过度依赖计算量的治理方式未能认识到：计算与风险的关系具有高度不确定性且快速演变，同时高估了我们预测不同规模下能力涌现的预判能力。文末提出了更具建设性的治理路径建议。