Brockman 用通俗比喻（密封泳池 vs 水龙头）拆解了 AI 数据中心的真实用水逻辑，关心 AI 环境影响或基础设施成本的读者值得一看，能帮你避开常见的舆论误区。
OpenAI 联合创始人 Greg Brockman 在播客中解释，公众对 AI 数据中心用水量的批评部分基于误解。他指出，现代数据中心大多采用闭环冷却系统，水在密封管道中循环使用，而非像传统冷却塔那样持续消耗新水。关键区别在于“取水量”与“消耗量”：一个数据中心可能储存大量水，但日常新水补充量远低于公众想象。OpenAI 在 Stargate 项目的官方博客中也证实，其阿比林站点每栋建筑的初始注水量约等于两个奥运泳池，但满负荷运行后全年用水量仅相当于一栋中型办公楼或四个普通家庭。Brockman 强调，AI 基础设施并非没有资源成本，但公众讨论常混淆不同冷却设计，导致对用水量的误判。
Greg Brockman explains how the public story about …
Greg Brockman explains how the public story about AI data center water use is partly wrong.
Because the cooling-systems use a closed-loop design that circulates the same stored water instead of constantly pulling fresh water.
i.e. it works less like a running tap and more like a sealed pool, where water absorbs heat from servers, moves through cooling equipment, then returns to the same circuit.
The argument here is not that AI infrastructure has no resource cost, but that public debate often mixes up different cooling designs and treats every data center as if it burns through water the same way.
The important distinction is water withdrawal versus water consumption, because a site can hold a large amount of water inside its pipes while using far less new water day to day.
OpenAI's official blog on Stargate project also says the same thing:
"Water is one area where details matter. Like many data centers, the Abilene site uses closed-loop cooling rather than traditional evaporative cooling towers. Once the system is filled, water continuously moves through sealed pipes and is recirculated rather than consumed.
For Abilene, the one-time initial fill for each building is equal to roughly two Olympic-sized swimming pools. After that, annual water use for the entire cooling system at full buildout is expected to be comparable to a medium-sized office building, or about four average households."
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From 'The Knowledge Project Podcast' YT channel (link in comment)