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If those small nuclear power plants which can be transported for us military are perfected, maybe 1000 of those around the place could be the answer, these run on nuclear fuel that is way more stable and safer than the glowing rods the Springfield nuclear power plant uses (nuclear fuel used in a steam turbine run from a nuclear chain reaction are not fluro green)

Anyway they have failsafe measures and don't need the containment barriers typical nuclear power plant need

Meet the Nvidias of Power — 5 Stocks Winning Big Tech's $700 Billion AI Energy Grab

Infrastructure giants such as GE Vernova and Bloom Energy are the new gatekeepers of the AI grid. AI is repricing energy across the U.S. economy, but not uniformly.Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still.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.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.The hyperscalers are largely insulated. Amazon, Microsoft, Google and Meta

Meet the Nvidias of Power — 5 Stocks Winning Big Tech's $700 Billion AI Energy Grab

Disclaimer: Investing carries risk. This is not financial advice. The above content should not be regarded as an offer, recommendation, or solicitation on acquiring or disposing of any financial products, any associated discussions, comments, or posts by author or other users should not be considered as such either. It is solely for general information purpose only, which does not consider your own investment objectives, financial situations or needs. TTM assumes no responsibility or warranty for the accuracy and completeness of the information, investors should do their own research and may seek professional advice before investing.

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{"i18n":{"language":"en_US"},"data":{"magic":2,"id":563323341525808,"tweetId":"563323341525808","gmtCreate":1778559899403,"gmtModify":1778561898905,"author":{"id":4235649508181162,"idStr":"4235649508181162","authorId":4235649508181162,"authorIdStr":"4235649508181162","name":"wig trader","avatar":"https://community-static.tradeup.com/news/cce95fd388d3781c6f60635e31c475df","vip":1,"userType":1,"introduction":"","boolIsFan":false,"boolIsHead":false,"hat":"https://static.tigerbbs.com/306aa047bc14b6942cda1aed925cf8c3","crmLevel":1,"crmLevelSwitch":0,"individualDisplayBadges":[],"wearingBadges":[],"fanSize":3,"starInvestorFlag":false},"themes":[],"images":[],"coverImages":[],"title":"","html":"<html><head></head><body><p>If those small nuclear power plants which can be transported for us military are perfected, maybe 1000 of those around the place could be the answer, these run on nuclear fuel that is way more stable and safer than the glowing rods the Springfield nuclear power plant uses (nuclear fuel used in a steam turbine run from a nuclear&nbsp;chain reaction are not fluro green)</p>\n<p>Anyway they have failsafe measures and don't need the containment&nbsp;barriers typical nuclear power plant need</p></body></html>","htmlText":"<html><head></head><body><p>If those small nuclear power plants which can be transported for us military are perfected, maybe 1000 of those around the place could be the answer, these run on nuclear fuel that is way more stable and safer than the glowing rods the Springfield nuclear power plant uses (nuclear fuel used in a steam turbine run from a nuclear&nbsp;chain reaction are not fluro green)</p>\n<p>Anyway they have failsafe measures and don't need the containment&nbsp;barriers typical nuclear power plant need</p></body></html>","text":"If those small nuclear power plants which can be transported for us military are perfected, maybe 1000 of those around the place could be the answer, these run on nuclear fuel that is way more stable and safer than the glowing rods the Springfield nuclear power plant uses (nuclear fuel used in a steam turbine run from a nuclear chain reaction are not fluro green) Anyway they have failsafe measures and don't need the containment barriers typical nuclear power plant need","highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"favoriteSize":0,"link":"https://ttm.financial/post/563323341525808","repostId":2634259534,"repostType":2,"repost":{"id":"2634259534","kind":"highlight","weMediaInfo":{"introduction":"Dow Jones publishes the world’s most trusted business news and financial information in a variety of media.","home_visible":0,"media_name":"Dow Jones","id":"106","head_image":"https://static.tigerbbs.com/150f88aa4d182df19190059f4a365e99"},"pubTimestamp":1778558171,"share":"https://ttm.financial/m/news/2634259534?lang=en_US&edition=fundamental","pubTime":"2026-05-12 11:56","market":"nz","language":"en","title":"Meet the Nvidias of Power — 5 Stocks Winning Big Tech's $700 Billion AI Energy Grab","url":"https://stock-news.laohu8.com/highlight/detail?id=2634259534","media":"Dow Jones","summary":"Infrastructure giants such as GE Vernova and Bloom Energy are the new gatekeepers of the AI grid. AI is repricing energy across the U.S. economy, but not uniformly.Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still.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.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.The hyperscalers are largely insulated. Amazon, Microsoft, Google and Meta","content":"<html><head></head><body><blockquote><p>Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still.</p></blockquote><p>Big Tech&#39;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><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><p>Let&#39;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 <a href=\"https://laohu8.com/S/MDWT\">Midwest</a>, 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-&#39;25 to more than $300 for 2026-&#39;27.</p><p>Once new load pushes against available generation and transmission, the cost does not stay neatly attached to the companies creating the demand. It spreads through capacity markets, rate filings and industrial power bills.</p><p>The International Energy Agency puts the trajectory plainly: Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still. The capex cycle behind that demand is enormous. Alphabet <a href=\"https://laohu8.com/S/GOOG\">$(GOOG)$</a> <a href=\"https://laohu8.com/S/GOOGL\">$(GOOGL)$</a>, <a href=\"https://laohu8.com/S/AMZN\">Amazon</a>.com (AMZN), Microsoft <a href=\"https://laohu8.com/S/MSFT\">$(MSFT)$</a> and <a href=\"https://laohu8.com/S/META\">Meta Platforms</a> (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><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><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><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><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><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><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. <a href=\"https://laohu8.com/S/D\">Dominion</a> Energy (D) and Entergy <a href=\"https://laohu8.com/S/ETR\">$(ETR)$</a> are both guiding for strong earnings growth through 2029, backed by contracted data-center load.</p><h3 id=\"id_2237614856\">5 stocks to watch</h3><p>Grid equipment and power management companies carry more asymmetric upside. GE Vernova (GEV) has effectively sold out its gas turbine production through 2030, with a backlog that hit 100 gigawatts in the first quarter of 2026; management expects the remaining slots will be gone by year&#39;s end. Eaton Corp. <a href=\"https://laohu8.com/S/ETN\">$(ETN)$</a> supplies electrical management systems that every data center requires regardless of what happens to any individual AI company. Quanta Services <a href=\"https://laohu8.com/S/PWR\">$(PWR)$</a> builds the grid connections these facilities need, a bottleneck that in some regions now rivals chip supply as a limiting factor. Trane Technologies <a href=\"https://laohu8.com/S/TT\">$(TT)$</a> is the equivalent play on cooling - a non-discretionary cost that scales directly with computing density.</p><p>On the generation side, Bloom Energy <a href=\"https://laohu8.com/S/BE\">$(BE)$</a> 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 $5 billion partnership with <a href=\"https://laohu8.com/S/BAM\">Brookfield Asset Management</a> in late 2025 validated the position. Finally, NextEra Energy <a href=\"https://laohu8.com/S/NEE\">$(NEE)$</a>, with a 33 GW renewable and storage development backlog, is the large-cap version of the same thesis.</p><h3 id=\"id_1824143791\">Unsettled environment</h3><p>One caveat worth tracking: The regulatory environment is not settled. <a href=\"https://laohu8.com/S/CEG\">Constellation</a> 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><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><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><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></body></html>","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>Meet the Nvidias of Power — 5 Stocks Winning Big Tech's $700 Billion AI Energy Grab</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nMeet the Nvidias of Power — 5 Stocks Winning Big Tech's $700 Billion AI Energy Grab\n</h2>\n\n<h4 class=\"meta\">\n\n\n<div class=\"head\" \">\n\n\n<div class=\"h-thumb\" style=\"background-image:url(https://static.tigerbbs.com/150f88aa4d182df19190059f4a365e99);background-size:cover;\"></div>\n\n<div class=\"h-content\">\n<p class=\"h-name\">Dow Jones </p>\n<p class=\"h-time\">2026-05-12 11:56</p>\n</div>\n\n</div>\n\n\n</h4>\n\n</header>\n<article>\n<html><head></head><body><blockquote><p>Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still.</p></blockquote><p>Big Tech&#39;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><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><p>Let&#39;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 <a href=\"https://laohu8.com/S/MDWT\">Midwest</a>, 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-&#39;25 to more than $300 for 2026-&#39;27.</p><p>Once new load pushes against available generation and transmission, the cost does not stay neatly attached to the companies creating the demand. It spreads through capacity markets, rate filings and industrial power bills.</p><p>The International Energy Agency puts the trajectory plainly: Data-center electricity demand is growing several times faster than global electricity consumption, and AI-focused facilities are growing faster still. The capex cycle behind that demand is enormous. Alphabet <a href=\"https://laohu8.com/S/GOOG\">$(GOOG)$</a> <a href=\"https://laohu8.com/S/GOOGL\">$(GOOGL)$</a>, <a href=\"https://laohu8.com/S/AMZN\">Amazon</a>.com (AMZN), Microsoft <a href=\"https://laohu8.com/S/MSFT\">$(MSFT)$</a> and <a href=\"https://laohu8.com/S/META\">Meta Platforms</a> (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><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><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><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><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><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><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. <a href=\"https://laohu8.com/S/D\">Dominion</a> Energy (D) and Entergy <a href=\"https://laohu8.com/S/ETR\">$(ETR)$</a> are both guiding for strong earnings growth through 2029, backed by contracted data-center load.</p><h3 id=\"id_2237614856\">5 stocks to watch</h3><p>Grid equipment and power management companies carry more asymmetric upside. GE Vernova (GEV) has effectively sold out its gas turbine production through 2030, with a backlog that hit 100 gigawatts in the first quarter of 2026; management expects the remaining slots will be gone by year&#39;s end. Eaton Corp. <a href=\"https://laohu8.com/S/ETN\">$(ETN)$</a> supplies electrical management systems that every data center requires regardless of what happens to any individual AI company. Quanta Services <a href=\"https://laohu8.com/S/PWR\">$(PWR)$</a> builds the grid connections these facilities need, a bottleneck that in some regions now rivals chip supply as a limiting factor. Trane Technologies <a href=\"https://laohu8.com/S/TT\">$(TT)$</a> is the equivalent play on cooling - a non-discretionary cost that scales directly with computing density.</p><p>On the generation side, Bloom Energy <a href=\"https://laohu8.com/S/BE\">$(BE)$</a> 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 $5 billion partnership with <a href=\"https://laohu8.com/S/BAM\">Brookfield Asset Management</a> in late 2025 validated the position. Finally, NextEra Energy <a href=\"https://laohu8.com/S/NEE\">$(NEE)$</a>, with a 33 GW renewable and storage development backlog, is the large-cap version of the same thesis.</p><h3 id=\"id_1824143791\">Unsettled environment</h3><p>One caveat worth tracking: The regulatory environment is not settled. <a href=\"https://laohu8.com/S/CEG\">Constellation</a> 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><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><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><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></body></html>\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"LU0640476718.USD":"THREADNEEDLE (LUX) US CONTRARIAN CORE EQ \"AU\" (USD) ACC","IE00B1XK9C88.USD":"PINEBRIDGE US LARGE CAP RESEARCH ENHANCED \"A\" (USD) ACC","LU0048584097.USD":"FIDELITY FUNDS  GLOBAL THEMATIC OPPORTUNITIES \"A\" (USD) INC","BK4587":"ChatGPT概念","LU0138981039.USD":"FIDELITY FUNDS SUSTAINABLE MULTI ASSET INCOME \"A\" INC","HK0000819398.USD":"BLACKROCK PREMIER SYSTEMATIC GLOBAL EQUITY HIGH INCOME FUND A10 (USD)INC","LU0308772762.SGD":"Blackrock Global Allocation A2 SGD-H","LU1280957306.USD":"THREADNEEDLE (LUX) US CONTRARIAN CORE EQUITIES \"AUP\" (USD) INC","LU2089284900.SGD":"Allianz Global Sustainability Cl AM Dis H2-SGD","SG9999004303.SGD":"Nikko AM Shenton Global Opportunities SGD","LU0203347892.USD":"SCHRODER ISF QEP GLOBAL ACTIVE VALLUE \"A\" (USD)  INC AV","LU1988902786.USD":"FULLERTON LUX FUNDS GLOBAL ABSOLUTE ALPHA \"I\" (USD) ACC","LU2237438978.USD":"Amundi Funds US Pioneer A2 (C) USD","LU0444973449.USD":"CT (LUX) I GLOBAL TECHNOLOGY \"DU\" (USD) ACC","BK4588":"碎股","IE00BDCRKT87.USD":"PINEBRIDGE GLOBAL DYNAMIC ASSET ALLOCATION \"ADC\" (USD) INC","LU1145028129.USD":"ALLIANZ INCOME AND GROWTH \"AQ\" (USD) INC","SGXZ81514606.USD":"大华环球创新基金A Acc USD","IE00BD4GTV84.USD":"LEGG MASON CLEARBRIDGEINFRASTRUCTURE VALUE \"A\" (USDHDG) ACC","IE00BJJMRY28.SGD":"Janus Henderson Balanced A Inc SGD","LU2463028550.USD":"AB SICAV I AMERICAN MULTI-ASSET PORTFOLIO \"A\" (USD) ACC","LU0320765059.SGD":"FTIF - 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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.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.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.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.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.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.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.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.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.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.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.5 stocks to watchGrid 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.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.Unsettled environmentOne 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.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.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.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.","news_type":1,"symbols_score_info":{"AMZZ":0.6,"BEZ":0.6,"AMZU":0.6,"YMAG":0.63,"MSFU":0.6,"MSFL":0.6,"GOOX":0.6,"MSFD":0.6,"NVDA":0.6,"USAW.SI":0.6,"AMZP":0.6,"AIYY":0.6,"GGLL":0.6,"TSLA":0.6,"NEE":1.85,"AZYY":0.6,"CEGX":0.6,"MSFO":0.6,"GGLS":0.6,"GEVG":0.6,"AMZW":0.6,"GEV":1.91,"MSFX":0.6,"MSFW":0.6,"GOOW":0.6,"GOU":0.6,"GOOP":0.6,"AMZY":0.6,"TT":1.87,"AMZD":0.6,"GOOY":0.6,"USJW.SI":0.6,"ETN":1.89,"MSFY":0.6,"PWR":1.88,"BEG":0.6,"BE":1.9,"GEVX":0.6,"BEX":0.6,"AAPL":0.6}},"isVote":1,"tweetType":1,"viewCount":9,"commentLimit":10,"likeStatus":false,"favoriteStatus":false,"reportStatus":false,"symbols":[],"verified":2,"subType":0,"readableState":1,"langContent":"EN","currentLanguage":"EN","warmUpFlag":false,"orderFlag":false,"shareable":true,"causeOfNotShareable":"","featuresForAnalytics":[],"commentAndTweetFlag":false,"andRepostAutoSelectedFlag":false,"upFlag":false,"length":393,"optionInvolvedFlag":false,"xxTargetLangEnum":"ORIG"},"commentList":[],"hasMoreComment":false,"orderType":2}