
在超大规模企业加码布局算力基础设施的时代,民众对数据中心的抵触情绪,与其对公用事业费用飙升的担忧紧密交织。舆观(YouGov)去年开展的一项民调显示,在1000名美国受访者中,超三分之二的人认为,如果本地新建数据中心,电价将会上涨。今年早些时候,高盛(Goldman Sachs)预测,人工智能基础设施建设将推动2026至2027年电价上涨6%,到2028年电价将再上涨3%。
美国电力研究院(EPRI)最新发布的一份工作论文,让人工智能热潮与美国居民电费账单之间的关系变得更加复杂。研究表明,至少在2024年之前,数据中心运营并未像消费者担忧的那样推高电价,反而压低了居民零售电价。研究人员依托美国联邦能源监管委员会(FERC)2015至2024年的数据,以及美国能源信息署(EIA)的电力零售营收数据,发现数据中心用电需求与电价存在因果关系:数据中心装机容量每翻一番,零售电价平均下降3.5%;在州级层面,降幅约为6%。
这种关联很大程度上可以用规模经济来解释。
该研究的合著者、美国电力研究院研究员阿萨·瓦滕(Asa Watten)在接受《财富》杂志采访时表示:“电力市场的运行逻辑和其他商品市场并不相同。”
大豆、汽油的价格由生产成本决定,电价则遵循成本回收机制,取决于用电量。固定成本可分摊到更多用户、更多千瓦时(电能标准计量单位)上;用电量越大,固定成本摊薄效应越强,电价也就越低。除此之外,数据中心用电负荷增长促使更多发电机组并网运行,其中不少机组的能效正持续提升,进一步压低了用电成本。
但这一规律潜藏风险:这一利好态势未必能延续,一旦趋势逆转,或将预示人工智能未来发展面临更为严峻的系统性问题。苗头或许已经显现。美国最大电网运营商PJM在本周发布的报告中预测,未来三年美国居民用电成本将增加63亿美元,主要原因在于数据中心用电需求攀升。数据中心建设热潮(预计到2030年总投资规模将达到7万亿美元),已与用电成本上涨呈正相关。美国能源信息署的数据显示,弗吉尼亚州拥有全美数量最多的数据中心,当地居民电价过去一年涨幅超13%。
数据中心与电价未来的关系由什么因素决定?
瓦滕解释称,未来电价最关键的影响因素,在于这场规模空前、快速推进的人工智能建设浪潮能否兑现其炒作预期。
他表示:“如果电网为满足数据中心预期用电需求而扩建产能,但实际需求不及预期,那么数据中心就会推高电价,和过去压低电价的情况截然相反。”
数据中心将产生巨额固定成本,若缺乏足够客户消化算力资源,瓦滕补充道:“成本分摊基数就会低于预期。固定成本只能由更少的用户承担,这与规模效应的逻辑背道而驰,进而推高电价。”
当下市场围绕人工智能泡沫及其破裂时点争论不休,已有迹象表明投资者对人工智能前景的疑虑不断加深。本周四,特斯拉与Alphabet宣布增加人工智能资本支出后,两家公司股价应声大跌。
亿万富翁投资者马克·库班(Mark Cuban)在本周的《All-In》播客节目中警告称,“大量数据中心……最终会被改造成匹克球场”。他认为,尽管超大规模企业对人工智能应用规模将持续增长的判断并无偏差,但能效提升会降低人工智能使用成本,这意味着数据中心产能将会出现过剩。
但瓦滕表示,事情也有乐观的一面。他不愿过多揣测人工智能行业前景及其对数据中心建设的影响,但总体而言,能效会持续提升。除数据中心扩张外,电动汽车、电热泵普及推动的电气化进程,有望持续压低居民用电成本,这一利好并不依赖人工智能热潮。
瓦滕称:“能效提升和整体用电成本下降,会给周边用户带来正向溢出效应。举例来说,如果推进得当,电动汽车普及同样可以压低电价,至少能避免电价持续上涨。”(财富中文网)
译者:中慧言-王芳
在超大规模企业加码布局算力基础设施的时代,民众对数据中心的抵触情绪,与其对公用事业费用飙升的担忧紧密交织。舆观(YouGov)去年开展的一项民调显示,在1000名美国受访者中,超三分之二的人认为,如果本地新建数据中心,电价将会上涨。今年早些时候,高盛(Goldman Sachs)预测,人工智能基础设施建设将推动2026至2027年电价上涨6%,到2028年电价将再上涨3%。
美国电力研究院(EPRI)最新发布的一份工作论文,让人工智能热潮与美国居民电费账单之间的关系变得更加复杂。研究表明,至少在2024年之前,数据中心运营并未像消费者担忧的那样推高电价,反而压低了居民零售电价。研究人员依托美国联邦能源监管委员会(FERC)2015至2024年的数据,以及美国能源信息署(EIA)的电力零售营收数据,发现数据中心用电需求与电价存在因果关系:数据中心装机容量每翻一番,零售电价平均下降3.5%;在州级层面,降幅约为6%。
这种关联很大程度上可以用规模经济来解释。
该研究的合著者、美国电力研究院研究员阿萨·瓦滕(Asa Watten)在接受《财富》杂志采访时表示:“电力市场的运行逻辑和其他商品市场并不相同。”
大豆、汽油的价格由生产成本决定,电价则遵循成本回收机制,取决于用电量。固定成本可分摊到更多用户、更多千瓦时(电能标准计量单位)上;用电量越大,固定成本摊薄效应越强,电价也就越低。除此之外,数据中心用电负荷增长促使更多发电机组并网运行,其中不少机组的能效正持续提升,进一步压低了用电成本。
但这一规律潜藏风险:这一利好态势未必能延续,一旦趋势逆转,或将预示人工智能未来发展面临更为严峻的系统性问题。苗头或许已经显现。美国最大电网运营商PJM在本周发布的报告中预测,未来三年美国居民用电成本将增加63亿美元,主要原因在于数据中心用电需求攀升。数据中心建设热潮(预计到2030年总投资规模将达到7万亿美元),已与用电成本上涨呈正相关。美国能源信息署的数据显示,弗吉尼亚州拥有全美数量最多的数据中心,当地居民电价过去一年涨幅超13%。
数据中心与电价未来的关系由什么因素决定?
瓦滕解释称,未来电价最关键的影响因素,在于这场规模空前、快速推进的人工智能建设浪潮能否兑现其炒作预期。
他表示:“如果电网为满足数据中心预期用电需求而扩建产能,但实际需求不及预期,那么数据中心就会推高电价,和过去压低电价的情况截然相反。”
数据中心将产生巨额固定成本,若缺乏足够客户消化算力资源,瓦滕补充道:“成本分摊基数就会低于预期。固定成本只能由更少的用户承担,这与规模效应的逻辑背道而驰,进而推高电价。”
当下市场围绕人工智能泡沫及其破裂时点争论不休,已有迹象表明投资者对人工智能前景的疑虑不断加深。本周四,特斯拉与Alphabet宣布增加人工智能资本支出后,两家公司股价应声大跌。
亿万富翁投资者马克·库班(Mark Cuban)在本周的《All-In》播客节目中警告称,“大量数据中心……最终会被改造成匹克球场”。他认为,尽管超大规模企业对人工智能应用规模将持续增长的判断并无偏差,但能效提升会降低人工智能使用成本,这意味着数据中心产能将会出现过剩。
但瓦滕表示,事情也有乐观的一面。他不愿过多揣测人工智能行业前景及其对数据中心建设的影响,但总体而言,能效会持续提升。除数据中心扩张外,电动汽车、电热泵普及推动的电气化进程,有望持续压低居民用电成本,这一利好并不依赖人工智能热潮。
瓦滕称:“能效提升和整体用电成本下降,会给周边用户带来正向溢出效应。举例来说,如果推进得当,电动汽车普及同样可以压低电价,至少能避免电价持续上涨。”(财富中文网)
译者:中慧言-王芳
In the era of hyperscalers, the rising unpopularity of data centers has become inextricably linked with the fear of skyrocketing utility prices. A YouGov poll administered last year found that among 1,000 Americans, more than two-thirds expected electricity prices to rise if a data center was built in their area. Earlier this year, Goldman Sachs projected the AI infrastructure buildout to increase electricity costs by 6% between 2026 and 2027, and an additional 3% by 2028.
But a recent working paper from the Electric Power Research Institute is complicating the relationship between the AI boom and what it means for Americans’ electric bill. The research suggested that up until at least 2024, data center operations defied consumer anxieties and actually caused retail electricity costs to decrease. Using data from the Federal Energy Regulatory Commission (FERC) and retail revenue from the U.S. Energy Information Administration between 2015 and 2024, researchers found a causal relationship between data center demand and electricity prices: For every doubling of data center capacity, average retail electricity prices decreased by 3.5%. On a statewide level, this decrease was about 6%.
Much of this relationship can be understood through economies of scale.
“Electricity markets are different than a lot of markets that they interact with,” Asa Watten, the study’s coauthor and EPRI researcher, told Fortune.
Unlike soybeans or gasoline, where prices are determined by the cost of production, electricity prices are based on cost recovery, or how much of it is consumed. As fixed costs are divided among more consumers and kilowatt hours (the standard unit of energy), more kilowatt hours mean a greater division of fixed costs and lower prices. That’s in addition to load increases from increased data center usage, which causes more generators to come online, many of which are becoming more energy efficient.
There’s a catch to this pattern: It’s not guaranteed to continue, and if the trend reverses, it could signal a broader and bigger problem with the future of AI. The writing may already be on the wall. PJM, the largest power grid operator in the country, projected in a report this week that a $6.3 billion increase in consumer electricity costs over the next three years can be mostly attributed to increased data center power demands. The rise in data center construction—expected to reach $7 trillion in spending by 2030—is already correlated with increased power costs. In Virginia, the state with the most data centers, residential electricity prices have increased by more than 13% in the last year, according to data from the U.S. Energy Information Administration (EIA).
What will determine the future relationship between data centers and electricity costs?
Watten explains that the largest determinant of future electricity prices will be if the rapid and rampant AI buildout lives up to the hype.
“If the grid builds capacity, expecting a lot of demand from data centers, and that doesn’t show up, that could be a clear story of how data centers could increase prices in the future in a way that they did not do in the past,” he said.
Data centers are expected to incur a great deal of fixed costs, and if customers for those data centers aren’t there, “then your denominator is less than you thought it would be,” Watten continued. “You’re spreading those fixed costs amongst fewer people. It’s the opposite of what we want to be doing, so that could increase prices.”
Amid a debate of an AI bubble and when it will pop, there are some signs that investors are growing more skeptical of the technology’s promise. On Thursday, share prices for Tesla and Alphabet took a tumble following both companies announcing an increase in AI capital expenditures.
In an episode of the All-In podcast this week, billionaire investor Mark Cuban warned “a lot of data centers…are going to be turned into pickleball courts” because while hyperscalers are correctly assuming AI adoption will continue to increase, AI will become cheaper to use because of increased power efficiency, meaning all of the capacity being created through data centers will not be necessary.
But there is an optimistic read here, Watten said. He doesn’t like to speculate on what the future of AI holds and what that means for the data center buildout, but generally speaking, energy will continue to become more efficient. Electrification, though more electric vehicles and electric heat pumps in addition to data center growth, could continue to reduce household energy costs in a way that could happen independent from an AI boom.
“This clearly efficiency-increasing thing or total budget-reducing thing could have positive spillovers to your neighbors,” Watten said, “such that more electric cars means that if done well, prices are also going down—or at least not going up.”