<p>作者：Jurica Dujmovic</p>
<p>基础设施巨头如 GE Vernova 和 Bloom Energy 成为 AI 电网的新守门人</p>
<p>人工智能正在重新定价美国经济中的能源，但并不是均匀的。</p>
<p>数据中心的电力需求增长速度是全球电力消费的几倍，而专注于 AI 的设施增长速度更快。</p>
<p>大型科技公司的 AI 扩展面临电力问题，这个问题不再局限于数据中心。短缺正在通过电网蔓延，进入公用事业费率申请，影响能源密集型行业的资产负债表，并直接影响那些以计算成本低廉为假设建立商业模式的软件公司的成本结构。</p>
<p>对于投资者来说，问题不在于 AI 是否在推高能源成本。这个争论已经有了定论。问题在于谁承担这些成本，谁能够将其转嫁，以及哪些公司处于两者之间。</p>
<p>让我们从电网本身开始。彭博社的分析发现，自 2020 年以来，主要数据中心集群附近的批发电价急剧上升，一些地区的涨幅超过 250%。为美国中大西洋和中西部地区提供服务的 PJM Interconnection 显示了这种压力如何迅速从地方需求转移到市场整体定价。其容量价格从 2024-2025 年的每兆瓦日不足 30 美元上涨到 2026-2027 年的 300 美元以上。</p>
<p>一旦新的负荷对可用的发电和输电施加压力，成本就不会整齐地附着在创造需求的公司上。它会通过容量市场、费率申请和工业电费扩散开来。</p>
<p>国际能源署明确指出：数据中心的电力需求增长速度是全球电力消费的几倍，而专注于 AI 的设施增长速度更快。支撑这种需求的资本支出周期是巨大的。Alphabet (GOOG) (GOOGL)、Amazon.com (AMZN)、Microsoft (MSFT) 和 Meta Platforms (META) 预计在 2026 年将花费约 7000 亿美元的资本支出，比前一年增长约 77%。这些支出正在落在电网上，而电网正在将其转嫁出去。</p>
<p>消费者已经感受到影响。公用事业在 2025 年提交了创纪录的 310 亿美元的涨价申请，而学术估计表明，数据中心的负荷可能会在 2030 年前显著推高美国的平均电费，在最受限制的市场中影响更大。美国各州开始作出反应。德克萨斯州的第 6 号参议院法案就是一个例子：大型负荷客户现在被迫承担更多他们所创造的基础设施风险。</p>
<p>对投资者来说，最有用的框架不是简单地看谁在使用能源，而是看谁有能力将上涨的成本转嫁出去，谁被迫承担这些成本。</p>
<p>超大规模云服务商在很大程度上是隔离的。亚马逊、微软、谷歌和 Meta 拥有与发电商直接谈判长期购电协议的规模，能够在市场重新定价之前在电表后面建立发电能力并锁定电价。他们的资本预算使他们能够以小公司无法做到的方式吸收、对冲或预防能源成本的通胀。更有趣的投资问题是其他公司会发生什么。</p>
<p>在实体方面，能源密集型制造商是市场持续低估的最暴露群体。在一个数据中心需求与紧张供应相碰撞并推动容量价格急剧上涨的区域市场中，钢铁厂或铝冶炼厂在争夺电力时没有太多选择。它是一个价格接受者——而价格正在上涨。利润压缩不会在新的发电能力上线之前得到解决——考虑到燃气轮机的交货时间现在长达七年，这并不快。</p>
<p>在软件方面，动态有所不同，但结论相似。AI 热潮以传统 SaaS 经济学未曾预见的方式重构了计算成本。传统软件公司因边际成本接近零而在 80% 到 90% 的毛利率下运营，而 AI 原生公司则结构性地较低——约 50% 到 60%——因为每个查询都会产生实际的推理成本。构建基于第三方应用程序编程接口的 AI 功能的中型软件公司处于中间地带：按令牌付费；通过仍在调整的定价模型传递部分成本，并与拥有计算能力的超大规模云服务商竞争，后者总是能够在价格上压低他们。</p>
<p>最明确的投资案例在于那些别无选择只能建设的基础设施。直接处于超大规模云服务商资本路径上的公用事业是最明显的投资机会。道明尼资源（Dominion Energy）和安特吉（Entergy）都预计到 2029 年将实现强劲的盈利增长，得益于合同数据中心负荷的支持。</p>
<p>5 只值得关注的股票</p>
<p>电网设备和电力管理公司具有更大的不对称上行潜力。GE Vernova（GEV）有效地将其燃气轮机生产销售一空，2026 年第一季度的订单积压达到 100 吉瓦；管理层预计剩余的生产能力将在年底前售罄。伊顿公司（Eaton Corp.，ETN）提供每个数据中心都需要的电气管理系统，无论任何单个 AI 公司发生什么。Quanta Services（PWR）构建这些设施所需的电网连接，这在某些地区现在与芯片供应一样成为限制因素。特灵科技（Trane Technologies，TT）是制冷方面的相应投资——这是一个与计算密度直接相关的非自愿成本。</p>
<p>在发电方面，Bloom Energy（BE）从清洁能源替代品转变为关键基础设施提供商，因为运营商发现燃料电池安装可以绕过在受限市场中需要数年才能完成的并网排队 - 以极短的时间上线。与 Brookfield Asset Management 的 50 亿美元合作在 2025 年底验证了这一地位。最后，NextEra Energy（NEE）拥有 33 GW 的可再生能源和储能开发待办事项，是同一理论的大型公司版本。</p>
<p>不稳定的环境</p>
<p>一个值得关注的警告：监管环境尚未稳定。例如，Constellation Energy（CEG）在 1 月份的一次交易中大幅下跌，当时监管机构表示有意限制电价。当消费者已经看到更高的公用事业账单时，电力的政治经济环境对不受限制的定价并不友好。那些在批发市场定价而非长期合同中有重大暴露的公司面临监管风险，当前的估值可能未能完全反映这一点。</p>
<p>人工智能正在重新定价美国经济中的能源，但并不均匀。重新定价集中在计算密集的地方，通过电网定价机制向外流动，并根据公司是否能够绕过它、将其转嫁，或仅仅是作为无法控制的成本而暴露于此而不均匀地落地。</p>
<p>值得投资的公司是那些拥有合同需求、受控供应链或在物理基础设施中不可替代的地位，这些地位无法通过任何软件优化来消除。需要密切关注的是在人工智能密集电网区域的能源密集型制造商和在第三方计算上大规模运行推理的软件公司，其定价模型尚未跟上其实际成本结构。</p>
<p>电网是人工智能经济运行的基础。其约束和定价现在是投资变量，投资者如果将其视为投资变量，将比那些仍然将其视为纯粹技术故事的投资者更具优势。</p>
<p>更多信息：聪明的钱正在投资这些电力行业股票 - 并为人工智能驱动的增长开辟新的前沿</p>
<p>另请阅读：这是下一个人工智能 “战场” - 投资者如何参与其中</p>
<p>-Jurica Dujmovic</p>
<p>此内容由 MarketWatch 创建，MarketWatch 由道琼斯公司运营。MarketWatch 独立于道琼斯新闻通讯和《华尔街日报》发布。</p>
<p>（结束）道琼斯新闻通讯</p>
<p>05-16-26 1245ET</p>

安特吉

道明尼资源

能源基础设施 Alerian ETF - ALPS

<p>基础设施巨头如 GE Vernova 和 Bloom Energy 正在成为人工智能能源市场的关键参与者，因为数据中心对电力的需求激增。预计到 2026 年，大型科技公司的资本支出将达到 7000 亿美元，这推动了批发电价的上涨，影响了消费者和各行业。虽然超大规模云服务商能够谈判有利的能源交易，但能源密集型制造商面临成本上升。位于数据中心增长附近的公用事业公司，如道明尼资源和安特吉，预计将看到强劲的盈利增长，使其成为有吸引力的投资机会</p>

<p>By Jurica Dujmovic</p>
<div class="lb-trans"><p>作者：Jurica Dujmovic</p>
</div><p>Infrastructure giants such as GE Vernova and Bloom Energy are the new gatekeepers of the AI grid</p>
<div class="lb-trans"><p>基础设施巨头如 GE Vernova 和 Bloom Energy 成为 AI 电网的新守门人</p>
</div><p>AI is repricing energy across the U.S. economy, but not uniformly.</p>
<div class="lb-trans"><p>人工智能正在重新定价美国经济中的能源，但并不是均匀的。</p>
</div><p>Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still.</p>
<div class="lb-trans"><p>数据中心的电力需求增长速度是全球电力消费的几倍，而专注于 AI 的设施增长速度更快。</p>
</div><p>Big Tech's AI buildout has a power problem, and it is no longer confined to data centers. The shortage is moving through the grid, into utility-rate filings, onto the balance sheets of energy-intensive industries, and straight into the cost structures of software companies that built their business models on the assumption that computing was cheap.</p>
<div class="lb-trans"><p>大型科技公司的 AI 扩展面临电力问题，这个问题不再局限于数据中心。短缺正在通过电网蔓延，进入公用事业费率申请，影响能源密集型行业的资产负债表，并直接影响那些以计算成本低廉为假设建立商业模式的软件公司的成本结构。</p>
</div><p>For investors, the question is not whether AI is inflating energy costs. That debate is settled. The question is who absorbs those costs, who can pass them on, and which companies sit on either side.</p>
<div class="lb-trans"><p>对于投资者来说，问题不在于 AI 是否在推高能源成本。这个争论已经有了定论。问题在于谁承担这些成本，谁能够将其转嫁，以及哪些公司处于两者之间。</p>
</div><p>Let's start with the grid itself. A Bloomberg analysis found that wholesale electricity costs near major data-center clusters have risen sharply since 2020, with some areas seeing increases of more than 250%. PJM Interconnection, the grid operator serving much of the U.S. Mid-Atlantic and Midwest, shows how quickly that pressure can move from local demand into marketwide pricing. Its capacity prices rose from under $30 per megawatt-day for 2024-'25 to more than $300 for 2026-'27.</p>
<div class="lb-trans"><p>让我们从电网本身开始。彭博社的分析发现，自 2020 年以来，主要数据中心集群附近的批发电价急剧上升，一些地区的涨幅超过 250%。为美国中大西洋和中西部地区提供服务的 PJM Interconnection 显示了这种压力如何迅速从地方需求转移到市场整体定价。其容量价格从 2024-2025 年的每兆瓦日不足 30 美元上涨到 2026-2027 年的 300 美元以上。</p>
</div><p>Once new load pushes against available generation and transmission, the cost does not stay neatly attached to the companies creating the demand. It spreads through capacity markets, rate filings and industrial power bills.</p>
<div class="lb-trans"><p>一旦新的负荷对可用的发电和输电施加压力，成本就不会整齐地附着在创造需求的公司上。它会通过容量市场、费率申请和工业电费扩散开来。</p>
</div><p>The International Energy Agency puts the trajectory plainly: Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still. The capex cycle behind that demand is enormous. Alphabet (GOOG) (GOOGL), Amazon.com (AMZN), Microsoft (MSFT) and Meta Platforms (META) are on track to spend around $700 billion in capital expenditure in 2026 alone, up roughly 77% from the prior year. That spending is landing on the grid, and the grid is passing it along.</p>
<div class="lb-trans"><p>国际能源署明确指出：数据中心的电力需求增长速度是全球电力消费的几倍，而专注于 AI 的设施增长速度更快。支撑这种需求的资本支出周期是巨大的。Alphabet (GOOG) (GOOGL)、Amazon.com (AMZN)、Microsoft (MSFT) 和 Meta Platforms (META) 预计在 2026 年将花费约 7000 亿美元的资本支出，比前一年增长约 77%。这些支出正在落在电网上，而电网正在将其转嫁出去。</p>
</div><p>Consumers already are feeling the hit. Utilities filed a record $31 billion in rate-increase requests in 2025, while academic estimates suggest data-center load could push average U.S. electricity bills significantly higher by 2030, with far larger effects in the most constrained markets. U.S. states are beginning to respond. Texas Senate Bill 6 is one example: Large-load customers are now being forced to shoulder more of the infrastructure risk they create.</p>
<div class="lb-trans"><p>消费者已经感受到影响。公用事业在 2025 年提交了创纪录的 310 亿美元的涨价申请，而学术估计表明，数据中心的负荷可能会在 2030 年前显著推高美国的平均电费，在最受限制的市场中影响更大。美国各州开始作出反应。德克萨斯州的第 6 号参议院法案就是一个例子：大型负荷客户现在被迫承担更多他们所创造的基础设施风险。</p>
</div><p>The most useful framework for investors is not simply who uses energy, but who has the leverage to pass on rising costs and who is forced to absorb them.</p>
<div class="lb-trans"><p>对投资者来说，最有用的框架不是简单地看谁在使用能源，而是看谁有能力将上涨的成本转嫁出去，谁被迫承担这些成本。</p>
</div><p>The hyperscalers are largely insulated. Amazon, Microsoft, Google and Meta have the scale to negotiate long-term power-purchase agreements directly with generators, build generation capacity behind the meter and lock in rates before the open market reprices. Their capital budgets allow them to absorb, hedge or pre-empt energy-cost inflation in ways smaller companies cannot. The more interesting investment question is what happens to everyone else.</p>
<div class="lb-trans"><p>超大规模云服务商在很大程度上是隔离的。亚马逊、微软、谷歌和 Meta 拥有与发电商直接谈判长期购电协议的规模，能够在市场重新定价之前在电表后面建立发电能力并锁定电价。他们的资本预算使他们能够以小公司无法做到的方式吸收、对冲或预防能源成本的通胀。更有趣的投资问题是其他公司会发生什么。</p>
</div><p>On the physical side, energy-intensive manufacturers are the most exposed group that the market consistently underweights. A steel plant or aluminum smelter competing for electricity in a regional market where data-center demand has collided with tight supply and pushed capacity prices sharply higher does not have many options. It is a price-taker - and the price is going up. The margin squeeze does not resolve until new generation capacity comes online - which, given gas turbine lead times now running up to seven years, is not soon.</p>
<div class="lb-trans"><p>在实体方面，能源密集型制造商是市场持续低估的最暴露群体。在一个数据中心需求与紧张供应相碰撞并推动容量价格急剧上涨的区域市场中，钢铁厂或铝冶炼厂在争夺电力时没有太多选择。它是一个价格接受者——而价格正在上涨。利润压缩不会在新的发电能力上线之前得到解决——考虑到燃气轮机的交货时间现在长达七年，这并不快。</p>
</div><p>On the software side, the dynamic is different but the conclusion is similar. The AI boom has restructured the cost of computing in ways that traditional SaaS economics did not anticipate. Where conventional software companies operate at 80% to 90% gross margins because marginal cost per additional user approaches zero, AI-native companies run structurally lower - around 50% to 60% - because every query incurs real inference costs. Mid-tier software companies building AI features on top of third-party application programming interfaces are caught in the middle: paying per token; passing some of that cost through pricing models that are still being calibrated, and competing against hyperscalers who own the computing power and can always undercut them on price.</p>
<div class="lb-trans"><p>在软件方面，动态有所不同，但结论相似。AI 热潮以传统 SaaS 经济学未曾预见的方式重构了计算成本。传统软件公司因边际成本接近零而在 80% 到 90% 的毛利率下运营，而 AI 原生公司则结构性地较低——约 50% 到 60%——因为每个查询都会产生实际的推理成本。构建基于第三方应用程序编程接口的 AI 功能的中型软件公司处于中间地带：按令牌付费；通过仍在调整的定价模型传递部分成本，并与拥有计算能力的超大规模云服务商竞争，后者总是能够在价格上压低他们。</p>
</div><p>The clearest investment case sits in the infrastructure that has no option except to be built. Utilities directly in the path of hyperscaler capital are the most evident plays. Dominion Energy (D) and Entergy (ETR) are both guiding for strong earnings growth through 2029, backed by contracted data-center load.</p>
<div class="lb-trans"><p>最明确的投资案例在于那些别无选择只能建设的基础设施。直接处于超大规模云服务商资本路径上的公用事业是最明显的投资机会。道明尼资源（Dominion Energy）和安特吉（Entergy）都预计到 2029 年将实现强劲的盈利增长，得益于合同数据中心负荷的支持。</p>
</div><p>5 stocks to watch</p>
<div class="lb-trans"><p>5 只值得关注的股票</p>
</div><p>Grid equipment and power management companies carry more asymmetric upside. GE Vernova (GEV) has effectively sold out its gas turbine production through 2030, with a backlog that hit 100 gigawatts in the first quarter of 2026; management expects the remaining slots will be gone by year's end. Eaton Corp. (ETN) supplies electrical management systems that every data center requires regardless of what happens to any individual AI company. Quanta Services (PWR) builds the grid connections these facilities need, a bottleneck that in some regions now rivals chip supply as a limiting factor. Trane Technologies (TT) is the equivalent play on cooling - a non-discretionary cost that scales directly with computing density.</p>
<div class="lb-trans"><p>电网设备和电力管理公司具有更大的不对称上行潜力。GE Vernova（GEV）有效地将其燃气轮机生产销售一空，2026 年第一季度的订单积压达到 100 吉瓦；管理层预计剩余的生产能力将在年底前售罄。伊顿公司（Eaton Corp.，ETN）提供每个数据中心都需要的电气管理系统，无论任何单个 AI 公司发生什么。Quanta Services（PWR）构建这些设施所需的电网连接，这在某些地区现在与芯片供应一样成为限制因素。特灵科技（Trane Technologies，TT）是制冷方面的相应投资——这是一个与计算密度直接相关的非自愿成本。</p>
</div><p>On the generation side, Bloom Energy (BE) went from clean-energy alternative to critical infrastructure provider after operators discovered that fuel-cell installations could bypass interconnection queues that stretch years in constrained markets - coming online in a fraction of the time. A $5 billion partnership with Brookfield Asset Management in late 2025 validated the position. Finally, NextEra Energy (NEE), with a 33 GW renewable and storage development backlog, is the large-cap version of the same thesis.</p>
<div class="lb-trans"><p>在发电方面，Bloom Energy（BE）从清洁能源替代品转变为关键基础设施提供商，因为运营商发现燃料电池安装可以绕过在受限市场中需要数年才能完成的并网排队 - 以极短的时间上线。与 Brookfield Asset Management 的 50 亿美元合作在 2025 年底验证了这一地位。最后，NextEra Energy（NEE）拥有 33 GW 的可再生能源和储能开发待办事项，是同一理论的大型公司版本。</p>
</div><p>Unsettled environment</p>
<div class="lb-trans"><p>不稳定的环境</p>
</div><p>One caveat worth tracking: The regulatory environment is not settled. Constellation Energy (CEG), for example, sold off sharply in a single session in January when regulators signaled interest in capping electricity prices. The political economy of electricity is not friendly to uncapped pricing when consumers are already seeing higher utility bills. Companies with significant exposure to wholesale market pricing rather than long-term contracts carry regulatory risk that current valuations may not fully price in.</p>
<div class="lb-trans"><p>一个值得关注的警告：监管环境尚未稳定。例如，Constellation Energy（CEG）在 1 月份的一次交易中大幅下跌，当时监管机构表示有意限制电价。当消费者已经看到更高的公用事业账单时，电力的政治经济环境对不受限制的定价并不友好。那些在批发市场定价而非长期合同中有重大暴露的公司面临监管风险，当前的估值可能未能完全反映这一点。</p>
</div><p>AI is repricing energy across the U.S. economy, but not uniformly. The repricing concentrates where computing is dense, flows outward through grid pricing mechanisms, and lands unevenly depending on whether a company can negotiate around it, pass it through, or is simply exposed to it as a cost it cannot control.</p>
<div class="lb-trans"><p>人工智能正在重新定价美国经济中的能源，但并不均匀。重新定价集中在计算密集的地方，通过电网定价机制向外流动，并根据公司是否能够绕过它、将其转嫁，或仅仅是作为无法控制的成本而暴露于此而不均匀地落地。</p>
</div><p>The companies worth owning are those with contracted demand, captive supply chain, or irreplaceable positions in the physical infrastructure that no amount of software optimization can eliminate. The ones to watch carefully are energy-intensive manufacturers in AI-dense grid regions and software companies running inference at scale on third-party computing, with pricing models that have not yet caught up to their actual cost structure.</p>
<div class="lb-trans"><p>值得投资的公司是那些拥有合同需求、受控供应链或在物理基础设施中不可替代的地位，这些地位无法通过任何软件优化来消除。需要密切关注的是在人工智能密集电网区域的能源密集型制造商和在第三方计算上大规模运行推理的软件公司，其定价模型尚未跟上其实际成本结构。</p>
</div><p>The grid is the substrate on which the AI economy runs. Its constraints and its pricing are now investment variables, and investors who treat them as such will have an edge over those still reading this purely as a tech story.</p>
<div class="lb-trans"><p>电网是人工智能经济运行的基础。其约束和定价现在是投资变量，投资者如果将其视为投资变量，将比那些仍然将其视为纯粹技术故事的投资者更具优势。</p>
</div><p>More: The smart money is plugging into these power-sector stocks - and opening new frontiers for AI-driven growth</p>
<div class="lb-trans"><p>更多信息：聪明的钱正在投资这些电力行业股票 - 并为人工智能驱动的增长开辟新的前沿</p>
</div><p>Also read: Here's the next AI 'battleground' - and how investors can get in on the action</p>
<div class="lb-trans"><p>另请阅读：这是下一个人工智能 “战场” - 投资者如何参与其中</p>
</div><p>-Jurica Dujmovic</p>
<div class="lb-trans"><p>-Jurica Dujmovic</p>
</div><p>This content was created by MarketWatch, which is operated by Dow Jones &amp; Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.</p>
<div class="lb-trans"><p>此内容由 MarketWatch 创建，MarketWatch 由道琼斯公司运营。MarketWatch 独立于道琼斯新闻通讯和《华尔街日报》发布。</p>
</div><p>(END) Dow Jones Newswires</p>
<div class="lb-trans"><p>（结束）道琼斯新闻通讯</p>
</div><p>05-16-26 1245ET</p>
<div class="lb-trans"><p>05-16-26 1245ET</p>
</div>

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