Across leading AI companies where breakdowns are available, the chips and computing time to run them account for 54% to 62% of total spending
AI硬件成本占AI公司总支出的一半以上(54%-62%)，这凸显了计算资源在AI开发中的核心地位。如此高的比例表明，AI公司的竞争很大程度上转化为对计算资源的获取和利用能力的竞争。这也解释了为什么各大公司愿意为芯片支付高价并积极投资自研芯片。
When our engineers no longer spend time supervising Codex sessions, the economics of code changes completely. The perceived cost of each change drops because we're no longer investing human effort in driving the implementation itself.
大多数人认为AI编程会增加监督成本，但作者认为通过Symphony系统，人类监督成本实际上大幅下降，因为AI能够自主完成大部分实现工作。这个观点挑战了人们对AI编程成本结构的普遍认知，暗示正确的AI编排可能根本性地改变软件开发的经济模型。
He, Z., Nagel, S., & Song, Z. (2020). Treasury Inconvenience Yields during the COVID-19 Crisis (Working Paper No. 27416; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27416