「英中对照」贝恩：2031年AI近6万亿美元营收能否支撑数据中心建设存疑

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The artificial intelligence construction boom has more money to spend and more problems to solve.人工智能建设热潮有更多资金可花，也有更多问题需要解决。

Annual AI infrastructure spending could reach about $1.5 trillion by 2031, according to Bain, a Boston-based global management consulting firm. Through 2030, the firm projects a cumulative $5 trillion to $6.5 trillion in data center spending. To justify those outlays, the AI market would need to approach $6 trillion in revenue annually by 2031, according to the company.年度AI基础设施支出可能达到约到2031年1.5万亿美元。据总部位于波士顿的全球管理咨询公司Bain称，到2030年，该公司预计累计5万亿至6.5万亿美元的数据中心支出。该公司称，要证明这些支出合理，AI市场到2031年的年收入需接近6万亿美元。

The eye-popping price tag is “conceivable,” considering how quickly the world’s largest technology companies have ramped up data center spending, said Peter Hanbury, a partner at the consulting firm. However, he questions whether these AI companies will eventually produce enough revenue to keep construction going at that pace.考虑到全球最大科技公司加大数据中心支出的速度之快，这一令人瞠目的支出规模是“可以想象的”，该咨询公司合伙人Peter Hanbury表示。不过，他质疑这些AI公司最终能否创造足够收入，使建设保持这一速度。

For contractors, immediate headwinds still revolve around power constraints and electrical labor, said Hanbury. Lead times on key materials remain long enough that decisions made today determine capacity several years from now, according to Bain.Hanbury称，对承包商而言，眼下的不利因素仍集中在电力限制和电气劳动力方面。据Bain称，关键材料的交付周期仍然很长，以至于如今作出的决策将决定数年后的容量。

Increased community opposition is also impacting timelines. Local opposition blocked or delayed at least 75 projects worth $130 billion in the first quarter of 2026, nearly matching the impacts in all of 2025, according to the firm.日益增加的社区反对也在影响工期。当地反对势力阻止或推迟了至少75个价值1300亿美元的项目；据该公司称，这发生在2026年第一季度，影响几乎与2025年全年持平。

Here, Hanbury talks with Construction Dive about the duration of the AI investment wave, construction constraints and viability of the buildout.在本文中，Hanbury接受Construction Dive采访，探讨AI投资浪潮的持续时间、建设限制以及扩建的可行性。

Editor’s note: This interview has been edited for brevity and clarity.编者注：为篇幅和清晰度考虑，本访谈已经过编辑。

CONSTRUCTION DIVE: How realistic is the projection of $5 trillion to $6.5 trillion in data center spending needed by 2030?CONSTRUCTION DIVE：到2030年所需5万亿至6.5万亿美元数据中心支出的预测有多大现实性？

PETER HANBURY: I think it’s realistic based on the spending trajectory we are already seeing. The largest hyperscalers, Microsoft, Google, Amazon, Meta and Oracle, could spend roughly $780 billion in capital expenditures in 2026 alone, nearly five times what they were spending three years earlier.PETER HANBURY: 我认为，基于我们已经看到的支出轨迹，这是现实的。最大的超大规模云服务商——微软、谷歌、亚马逊、Meta和甲骨文——仅在2026年的资本支出就可能达到约7,800亿美元，几乎是它们三年前支出的五倍。

Not all of that is data center capital expenditure, but it shows the scale of capital already being deployed. Against that backdrop, a cumulative $5 trillion to $6.5 trillion of data center investment through 2030 is certainly conceivable.并非所有这些都是数据中心资本支出，但它显示了已在部署的资本规模。在此背景下，到2030年累计5万亿至6.5万亿美元的数据中心投资无疑是可以想象的。

The bigger question is whether the economics can sustain that level of investment over time. Our AI work estimates that annual AI infrastructure spending could reach about $1.5 trillion by 2031. If capital expenditure represents roughly 25% of industry revenue, supporting that spending would require an AI market approaching $6 trillion in annual revenue.更大的问题在于，经济效益能否长期支撑这种投资水平。我们的AI研究估计，到2031年，年度AI基础设施支出可能达到约1.5万亿美元。如果资本支出约占行业收入的25%，支撑这一支出将需要AI市场的年收入接近6万亿美元。

So the near-term issue is not necessarily whether enough capital exists.因此，近期问题不一定在于是否存在足够的资本。

What would have to go right over the next four years to justify that level of investment?要证明这种投资水平合理，未来四年需要顺利实现哪些条件？

A few things have to happen at the same time.必须同时具备若干条件。

First, AI has to move beyond use cases focused on efficiency and productivity and start creating entirely new sources of revenue and economic value. We would need substantial growth from new sources of value such as autonomous systems, physical AI, new consumer experiences and entirely new AI-enabled products and industries.首先，AI必须超越以效率和生产力为重点的用例，开始创造全新的收入来源和经济价值。我们需要自主系统、物理AI、新型消费者体验以及全新的AI驱动产品和行业等新价值来源实现大幅增长。

Second, the physical bottlenecks have to ease. Capital doesn’t help if a project can’t get power, chips, skilled labor or permission to build. Power, in particular, needs system-level solutions, more generation, faster interconnection, behind-the-meter capacity, storage and greater coordination among utilities and technology companies.其次，物理瓶颈必须得到缓解。如果项目无法获得电力、芯片、熟练劳动力或建设许可，资本便无济于事。尤其是电力，需要系统级解决方案、更多发电容量、更快互联、表后容量、储能以及公用事业公司与科技公司之间更有力的协调。

Third, the capital model will have to become more creative. Hyperscaler balance sheets can carry an enormous amount, but at this scale we are likely to see more risk-sharing across developments, infrastructure investors, utilities, sovereigns and governments, as well as approaches that include jointly funded power infrastructure.第三，资本模式必须更具创造性。超大规模云服务商的资产负债表能够承载巨额规模，但在如此规模下，我们可能会看到各开发项目、基础设施投资者、公用事业公司、主权实体和政府之间更多的风险分担，以及包括共同出资建设电力基础设施在内的方式。

And finally, the industry will have to become much more disciplined about which projects get built. Not every announced gigawatt will make sense.最后，行业必须对建设哪些项目更加审慎。并非每一个已公布的吉瓦级装机都合理可行。

If those things happen, the investment level is achievable.如果这些条件得以实现，这一投资水平是可以达到的。

From a construction perspective, what does the industry have to do to deliver that amount of capacity on time?从建设角度来看，行业需要做些什么才能按时交付如此大规模的容量？

It will require the industry to move from managing individual projects to running industrial-scale programs. That means integrated power and site planning, earlier procurement and more prefabrication, modular designs and coordinated portfolios of contractors and suppliers.这要求行业从管理单个项目转向运营工业级规模的项目集。这意味着一体化的电力与场地规划、更早采购与更多预制、模块化设计，以及协调一致的承包商和供应商组合。

At this scale, the slowest constrained input sets the schedule for the entire program.在这种规模下，最缓慢的制约性投入要素决定整个项目集的进度。

Which construction trade do you see becoming the biggest bottleneck?您认为哪个建筑工种将成为最大瓶颈？

Electrical labor is likely to be the sharpest constraint, particularly high-voltage, substation and mission-critical electrical talent. Mechanical and pipefitting trades will also come under pressure as liquid cooling scales, along with smaller but highly specialized pools of controls and commissioning talent.电气劳动力可能是最严峻的制约，尤其是高压、变电站和任务关键型电气人才。随着液冷规模化，机械和管道安装工种也将面临压力，规模较小但高度专业化的控制与调试人才库同样如此。

The issue becomes more acute as campuses scale because these projects need large numbers of specialized workers at the same time. That will push more work toward offsite fabrication, prefab assemblies, regional labor strategies and longer-term partnerships with key trades.随着园区规模扩大，这一问题变得更加严峻，因为这些项目同时需要大量专业工人。这将推动更多工作转向场外预制、预制装配、区域劳动力战略，以及与关键工种建立更长期的合作关系。

What should contractors watch for when evaluating their data center pipelines?承包商在评估其数据中心项目管线时应注意哪些事项？

Contractors should increasingly underwrite the pipeline.承包商应越来越多地对项目管线进行风险审核。

I would ask four questions: Is the power real? Is the customer and financing commitment real? Is the permission to build real? And is the design stable enough to build?我会问四个问题：电力是否落实？客户与融资承诺是否落实？建设许可是否落实？设计是否足够稳定、能够施工？

Permission increasingly means more than a permit. It includes community support and social license around power use, water, noise, emissions and other local impacts.许可的含义日益超出一张许可证。它包括围绕用电、用水、噪声、排放及其他本地影响的社区支持和社会许可。

Power is becoming the gating item. A data center can be built in a few years, but adding major new grid capacity can take four years or more. At the largest campuses, that means power infrastructure is now firmly on the data center projects’ critical path.电力正成为门槛性事项。数据中心可在几年内建成，但新增大型电网容量可能需要四年或更久。在最大型园区，这意味着电力基础设施如今已稳固处于数据中心项目的关键路径上。

Contractors are increasingly working around substations, transmission, interconnection and, in some cases, onsite generation and storage as part of the same delivery program. A gigawatt-scale data center looks more like a power project with a very large computing load.承包商越来越多地在同一交付项目集中围绕变电站、输电、并网，以及在某些情况下的现场发电和储能开展工作。一个吉瓦级数据中心看起来更像是一个带有超大规模计算负荷的电力项目。

Are there any other trends contractors should be paying attention to?还有哪些其他趋势值得承包商关注？

One of the biggest changes is chip-to-grid codesign. Historically, the facility and IT stack could be designed with a fair amount of separation. The building did not need to change dramatically with every new chip generation. AI is collapsing those boundaries.最大的变化之一是从芯片到电网的协同设计。从历史上看，设施与IT堆栈在设计时可以保持相当程度的分离。建筑并不需要随着每一代新芯片发生巨大变化。AI正在打破这些边界。

Changes in graphic processing units and custom silicon drive rack density. Rack density drives networking and cooling. Cooling and compute density change electrical architecture. And all of those choices affect the building and ultimately the power source.图形处理器和定制芯片的变化推动机架密度提升。机架密度推动网络与冷却。冷却和计算密度改变电气架构。而所有这些选择都会影响建筑，并最终影响电力来源。

For contractors, the implication is the server roadmap is increasingly helping design the building. And that silicon roadmap is becoming more varied and faster-moving as Nvidia continues to accelerate its platform cadence while hyperscalers simultaneously push custom silicon.对承包商来说，这意味着服务器路线图正日益助力建筑设计。随着 Nvidia 持续加快平台迭代节奏，同时超大规模云服务商推进定制芯片，该芯片路线图正变得更加多元、变化更快。

The players that can coordinate compute, power, cooling and construction will have an advantage over those optimizing each layer independently.能够统筹计算、电力、冷却与施工的参与者，将比那些独立优化每一层的参与者更具优势。