
最早一批用户曾认为AI带来的收入会源源不断,事实并非如此。AI基建投入与预期回报之间,正演变成数万亿美元的巨大鸿沟。
上周全球研究与咨询公司高德纳(Gartner)发布的新闻稿称,数据中心运营商要在2030年前累计创造8.5万亿美元收入,才能覆盖投资。该公司预测,同期这些数据中心能兑现的企业和消费端AI收入最多为3.6万亿美元,缺口还有4.9万亿美元。
2026年行业协会BOMA一项市场研究显示,2025年数据中心建设投入约412亿美元,约占美国私人办公楼建设投资总额近46%。十年前,数据中心所占比例尚不足5%。
高德纳警告称,如果AI供应商继续以当前速度大笔投入,2030年底超过3.5万亿美元的AI基础设施支出可能面临减值风险,也就是必须采取资产减记。更长远来看,到2032年超过4万亿美元的资产价值可能存在减记风险。
“2026年,AI供应商预计将投入1万亿美元用于AI基础设施建设,2027年至2030年间可能追加投资6万亿美元,”高德纳高级总监分析师威尔·索默在新闻稿中表示,“但我们的研究表明,企业和消费端AI收入无法支撑期间已规划AI资本支出的盈利回报。”
8.5万亿美元的测算里,假设数据中心运营商需要覆盖已公布的资本支出和融资成本,同时实现12%的投入资本回报率,高德纳将之视为投资的最低收益率门槛。高德纳预计,最早要到2032年底才能实现12%的回报率。
数字不会说谎
分析显示,2025年至2030年全球AI累计资本支出预计将达到7.5万亿美元。到2030年,年度AI资本投入预计将接近2万亿美元,占全球GDP的比重远超1%。
这凸显出投资预期回报之间的巨大落差。到2030年,AI软件、服务和消费产品总收入预计为4.4万亿美元。高德纳估计,其中最多30%的收入可用于抵偿相关基础资本投入。也就是说,其余大部分收益,将流向销售基于基础设施开发出的应用、模型和其他服务的公司。
换句话说,企业可能为AI软件和服务付费,但其中只有一小部分最终回流到提供服务所需的数据中心和计算芯片上。
“产品负责人不应将AI资本减记简单看成基础设施层面的问题,”高德纳的研究写道,“即便某个产品部门不拥有AI基础设施,供应商整合、价格波动、模型停用、服务精简以及各国AI监管要求,也会显著影响产品盈利、路线图执行和客户履约等等。因此,产品负责人在设计依赖AI的产品时,应假设到本十年末,当前AI供应格局将更加集中。”
“新云”服务商前景堪忧
该研究机构还预计,各供应商盈利前景并不相同。亚马逊、微软和谷歌母公司Alphabet等大型云厂商拥有多元化业务和稳定现金流来源,能够支撑AI投资。但规模较小,专注基础设施的云服务商,即所谓“新云”服务商面临的风险更高。
“新云服务商必须尽快从AI优化的基础设施,即服务及相关平台中获得回报,才能维持运营,”高德纳的新闻稿称,“高德纳预测,到2030年受成本压力和缺乏差异化商业模式影响,超过80%的新云提供商将无法继续独立运营。”(财富中文网)
译者:梁宇
审校:夏林
最早一批用户曾认为AI带来的收入会源源不断,事实并非如此。AI基建投入与预期回报之间,正演变成数万亿美元的巨大鸿沟。
上周全球研究与咨询公司高德纳(Gartner)发布的新闻稿称,数据中心运营商要在2030年前累计创造8.5万亿美元收入,才能覆盖投资。该公司预测,同期这些数据中心能兑现的企业和消费端AI收入最多为3.6万亿美元,缺口还有4.9万亿美元。
2026年行业协会BOMA一项市场研究显示,2025年数据中心建设投入约412亿美元,约占美国私人办公楼建设投资总额近46%。十年前,数据中心所占比例尚不足5%。
高德纳警告称,如果AI供应商继续以当前速度大笔投入,2030年底超过3.5万亿美元的AI基础设施支出可能面临减值风险,也就是必须采取资产减记。更长远来看,到2032年超过4万亿美元的资产价值可能存在减记风险。
“2026年,AI供应商预计将投入1万亿美元用于AI基础设施建设,2027年至2030年间可能追加投资6万亿美元,”高德纳高级总监分析师威尔·索默在新闻稿中表示,“但我们的研究表明,企业和消费端AI收入无法支撑期间已规划AI资本支出的盈利回报。”
8.5万亿美元的测算里,假设数据中心运营商需要覆盖已公布的资本支出和融资成本,同时实现12%的投入资本回报率,高德纳将之视为投资的最低收益率门槛。高德纳预计,最早要到2032年底才能实现12%的回报率。
数字不会说谎
分析显示,2025年至2030年全球AI累计资本支出预计将达到7.5万亿美元。到2030年,年度AI资本投入预计将接近2万亿美元,占全球GDP的比重远超1%。
这凸显出投资预期回报之间的巨大落差。到2030年,AI软件、服务和消费产品总收入预计为4.4万亿美元。高德纳估计,其中最多30%的收入可用于抵偿相关基础资本投入。也就是说,其余大部分收益,将流向销售基于基础设施开发出的应用、模型和其他服务的公司。
换句话说,企业可能为AI软件和服务付费,但其中只有一小部分最终回流到提供服务所需的数据中心和计算芯片上。
“产品负责人不应将AI资本减记简单看成基础设施层面的问题,”高德纳的研究写道,“即便某个产品部门不拥有AI基础设施,供应商整合、价格波动、模型停用、服务精简以及各国AI监管要求,也会显著影响产品盈利、路线图执行和客户履约等等。因此,产品负责人在设计依赖AI的产品时,应假设到本十年末,当前AI供应格局将更加集中。”
“新云”服务商前景堪忧
该研究机构还预计,各供应商盈利前景并不相同。亚马逊、微软和谷歌母公司Alphabet等大型云厂商拥有多元化业务和稳定现金流来源,能够支撑AI投资。但规模较小,专注基础设施的云服务商,即所谓“新云”服务商面临的风险更高。
“新云服务商必须尽快从AI优化的基础设施,即服务及相关平台中获得回报,才能维持运营,”高德纳的新闻稿称,“高德纳预测,到2030年受成本压力和缺乏差异化商业模式影响,超过80%的新云提供商将无法继续独立运营。”(财富中文网)
译者:梁宇
审校:夏林
There isn’t an endless stream of revenue for AI as early adopters once thought. The gap between what’s being spent on the AI buildout and the revenue projected to come back is turning into a multitrillion-dollar chasm.
Data-center owners need to generate $8.5 trillion in cumulative revenue by the end of the decade to justify their investments, according to a press release last week by global research and advisory firm Gartner. But the firm forecasts that no more than $3.6 trillion of enterprise and consumer AI revenue can be attributed to those data centers over the same period—leaving a $4.9 trillion gap.
According to a 2026 market study by trade association BOMA, data centers accounted for approximately $41.2 billion—or nearly 46%—of private office construction in the country in 2025. A decade earlier, data centers only represented less than 5%.
Gartner warns that more than $3.5 trillion of AI infrastructure spending could be exposed to impairment—meaning it would have to be written down—by the end of 2030 if AI providers keep spending at their current pace. Further out, the firm says more than $4 trillion in asset value could face write-down risk through 2032.
“In 2026, AI providers are expected to spend nearly $1 trillion on AI infrastructure, and are forecast to invest an additional $6 trillion between 2027 and 2030,” Will Sommer, senior director analyst at Gartner, said in the press release. “However, our research indicates that enterprise and consumer AI revenue will be insufficient to support profitable returns on all planned AI capital expenditures during this period.”
That $8.5 trillion figure assumes data-center owners need to cover announced capital spending and financing costs while also earning a 12% return on invested capital, which Gartner treats as the hurdle rate for the investment. Gartner doesn’t expect that 12% return to be reached until the end of 2032 at the earliest.
The math doesn’t lie
According to the analysis, cumulative global AI capital spending is forecast to reach $7.5 trillion from 2025 through 2030. Additionally, annual AI capital spending is projected to approach $2 trillion by 2030, which represents well over 1% of global GDP.
That proves a stark difference compared to what the investment looks to receive in return. Total revenue from AI software, services and consumer products is only expected to reach $4.4 trillion by 2030—and Gartner estimates that no more than 30% of the revenue will be booked against the underlying capital investments. That means much of the remaining cash is going to companies that are selling the applications, models and other services that are built on the infrastructure investment.
In other words, businesses might be paying for AI-powered software and services, but only a fraction of that will ultimately support the data center and computing chips that are used to deliver its service.
“Product leaders should not view AI capital write-downs as an infrastructure problem alone,” Gartner’s study read. “Even if a product organization owns no AI infrastructure, provider consolidation, price volatility, model discontinuations, service rationalization, and sovereign AI requirements can materially affect product economics, roadmap execution, and customer commitments. Product leaders should therefore design AI-dependent products assuming that today’s AI supply landscape will become materially more concentrated through the end of the decade.”
Not a good look for neoclouds
The research firm also expects the economics to change depending on the provider. Large cloud companies like Amazon, Microsoft and Google parent Alphabet have diversified businesses and established cash flow sources that will aid in their AI investments. But smaller, specialized cloud infrastructure providers—called neoclouds—could be more exposed.
“Neoclouds must soon earn returns on AI-optimized IaaS and associated platforms to sustain operations,” Gartner’s press release read. “By 2030, Gartner predicts that cost pressure and a lack of a differentiating business model will force over 80% of neocloud providers to cease independent operations.”