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. To do so, we need to engage with a decades-old debate at the heart of computer science progress, namely, is bigger always better? Does a certain inflection point of compute result in changes to the risk profile of a model? 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. This discussion is timely given the wide adoption of compute thresholds in both the White House Executive Orders on AI Safety (EO) and the EU AI Act to identify more risky systems. A key conclusion of this essay is that compute thresholds, as currently implemented, are shortsighted and likely to fail to mitigate risk. The relationship between compute and risk is highly uncertain and rapidly changing. Relying upon compute thresholds 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、相当深奥的治理工具。然而，要判断这些阈值能否真正发挥作用，我们必须首先理解其产生的背景。为此，我们需要回溯计算机科学进展中一个持续数十年的核心争论：规模扩大是否必然带来进步？计算能力的特定拐点是否会改变模型的风险特征？因此，本文不仅对政策制定者和广大公众具有参考价值，也能帮助计算机科学研究者深入理解计算资源在实现技术突破中的作用。当前，计算阈值已被广泛纳入《白宫人工智能安全行政命令》和《欧盟人工智能法案》中，用于识别高风险系统，使得这一讨论尤为及时。本文的核心结论是：现行计算阈值方案存在短视性，很可能无法有效缓解风险。计算能力与风险之间的关系具有高度不确定性且快速演变，依赖计算阈值会高估我们预测不同规模下模型能力涌现的能力。文章最后提出了更具建设性的改进建议。