![\"\"](\"http:\/\/www.fresnoforeclosure.com\/wp-content\/uploads\/2025\/04\/e27-34-1.png\")
<\/p>\n<\/p>\n
<\/p>\n
Artificial intelligence (AI) is expanding at breakneck speed, presenting a paradox for global energy systems. On one hand, AI-driven innovations promise efficiency gains in renewable energy management and smarter grids. On the other, the<\/span> surging power demands of AI<\/span><\/a> threaten to strain electricity infrastructure and increase reliance on fossil fuels. <\/span><\/p>\n Current projections indicate data centres — the digital fortresses powering AI — could consume over<\/span> 1,000 TWh of electricity by 2026<\/span><\/a>, roughly double their 2022 usage. (For perspective, that\u2019s comparable to Japan\u2019s annual power consumption, or about 90 million US homes.) <\/span><\/p>\n In the European Union alone, data centre energy use is forecast to reach<\/span> 150 TWh by 2026<\/span><\/a>, ~ four per cent of EU demand. Gartner even predicts that <\/span>40 per cent of existing AI data centres will hit power capacity limits by 2027, underscoring the urgent infrastructure challenge.<\/p>\n This surge places immense pressure on power grids. Cutting-edge AI models require enormous energy: Training a single large language model (LLM) like OpenAI\u2019s GPT series can<\/span> devour tens of gigawatt-hours of electricity<\/span><\/a> . Some hyper-scale AI data centres already draw 30-100 megawatts each, and future facilities may<\/span> exceed 1,000 MW (1 gigawatt) — about the output of a large power plant<\/span><\/a> .<\/span><\/p>\n One industry analysis notes tech giants are<\/span> pursuing \u201cgigawatt-scale\u201d data centre campuses to support AI workloads<\/span><\/a> . By 2030, Microsoft and OpenAI\u2019s planned \u201cStargate\u201d supercomputer could<\/span> require an astonishing five GW of power<\/span><\/a>.<\/p>\n In response, tech companies are exploring diverse energy strategies. Google, for instance, is investing in advanced nuclear power: it signed a deal to purchase energy from small modular reactors (SMRs), aiming to<\/span> add 500 MW of carbon-free power by 2030<\/span><\/a>.<\/span><\/p>\n Microsoft is turning to nuclear with the<\/span> Three Mile Island nuclear power plant deal<\/span><\/a>, Amazon, and Meta are turning to conventional power plants — in some regions, new natural gas-fired generators — to guarantee reliable juice for AI data centres, a strategy<\/span> supported by utilities<\/span><\/a>. In Wisconsin, regulators approved a US$2 billion gas plant<\/span> deemed \u201ccritical\u201d for Microsoft\u2019s new AI hub<\/span><\/a>.<\/span><\/p>\n These moves underline a hard truth: <\/span>renewables alone can\u2019t yet meet AI\u2019s ravenous base-load demand, prompting a dual-track energy race between carbon-free solutions and fossil fuels.<\/span><\/p>\n This brings up pressing questions for business leaders:<\/span><\/p>\n This three-part guide examines the forces at play — from data centre trends and energy innovations to policy and geopolitical factors — to help corporate decision-makers navigate AI\u2019s energy revolution.<\/p>\n The goal: understand the macro and geopolitical impacts of AI\u2019s energy consumption, and chart a course that leverages AI\u2019s power responsibly and sustainably.<\/p>\n Global data centre electricity consumption reached an estimated 460 TWh in 2022, with AI and cryptocurrency operations accounting for roughly 14 per cent of that load, according to the<\/span> International Energy Agency (IEA)<\/span><\/a>. <\/span><\/p>\n Now AI is pushing those numbers dramatically higher. Projections show data centres worldwide could<\/span> consume over 1,000 TWh by 2026<\/span><\/a> — roughly doubling in just four years. By 2030, some<\/span> forecasts see a further 160 per cent increase<\/span><\/a> in data centre power demand driven by AI.<\/span><\/p>\n Also Read:\u00a0Eco-investing: Driving change through climate technology and strategic finance<\/a><\/strong><\/p>\n This growth is concentrated in key AI hubs and \u201ccloud clusters\u201d with serious consequences for local grids:<\/span><\/p>\n The energy intensity of AI is a key reason demand is outpacing capacity. A few eye-opening facts illustrate the scale:<\/p>\n Despite these efforts, power constraints are emerging as a growth limiter for AI. Industry analysts warn that in the next few years, many data centre operators (especially those not backed by big tech) may find it difficult or prohibitively expensive to get the electricity they need.<\/p>\n Gartner projects that by 2027, 4 in 10 AI data centres worldwide<\/a> could hit their power capacity ceiling, meaning their expansion will be stalled by energy shortages. For enterprises, this could translate to slower cloud rollouts or higher costs as energy prices rise.<\/p>\n However, within this hard truth lies a hidden opportunity — AI itself can help solve the energy challenge. As we\u2019ll explore, the same technology driving up consumption can also drive greater efficiency and new solutions, if wielded wisely.<\/p>\n Also Read:\u00a0The key to tackling climate change: Electrify shipping<\/a><\/strong><\/p>\n Not all AI is equally power-hungry. There is a vast gap in energy consumption between large, state-of-the-art AI models and more traditional algorithms. Understanding this spread can help leaders choose the right AI tools for the job — balancing capability and cost. The table below compares examples of AI models:<\/p>\n Table: Energy requirements for training various AI models range over orders of magnitude<\/a>. Cutting-edge deep learning models (top rows) consume enormously more energy than smaller neural nets or classical machine learning methods (bottom rows). Choosing a right-sized model can avoid wasting power.<\/p>\n<\/div>\n As the table shows, today\u2019s largest AI models (like GPT-3\/4) dwarf earlier AI in power needs. Training GPT-4 can use about 50,000\u00d7 more energy than training a typical convolutional neural network (CNN) like ResNet-50 used for image recognition.<\/p>\n And an old-school algorithm like k-nearest neighbors (KNN) or an ARIMA forecast model might use a million-times less energy — essentially negligible in comparison.<\/p>\n This doesn\u2019t mean companies should avoid large AI models altogether; rather, it underscores the importance of right-sizing AI to the task. You don\u2019t always need a billion-parameter model if a simpler one works — and the energy (and cost) savings from a leaner approach can be huge.<\/p>\n Key takeaway: AI\u2019s energy footprint isn\u2019t uniform. Generative AI and other complex models can be incredible but come with extreme energy costs.<\/p>\n Business leaders should evaluate whether a smaller, more efficient model could meet their needs. In many cases, optimized or \u201cdistilled\u201d models, or running AI at the network edge, can deliver acceptable performance while using a fraction of the power. This efficiency-centric approach to AI adoption will become increasingly vital as energy pressures mount.<\/p>\n The tug-of-war between AI\u2019s energy demand and clean energy supply is pushing companies down two very different paths. On one side, some firms and regions are doubling down on fossil fuels to keep the lights on for AI. On the other, there\u2019s a growing movement toward a nuclear revival (along with renewables) to power AI sustainably.<\/p>\n Also Read:\u00a0What does Trump mean for SEA climate scene?<\/a><\/strong><\/p>\n On the fossil fuel front, oil and gas producers see AI\u2019s rise as a new source of demand for hydrocarbons. BP\u2019s CEO Murray Auchincloss, for example, predicts<\/a> AI\u2019s infrastructure build-out could drive an extra 3-5 million barrels per day of oil demand growth through the 2030s, as data centres and associated supply chains consume more energy (fuel for generators, diesel for construction, etc.). Likewise, Shell\u2019s latest Energy Security Scenarios<\/a> project natural gas demand reaching 4,640 billion cubic meters annually by 2040, partly to fuel backup generators for data centres and provide grid stability in an AI-enabled economy.<\/p>\n These trends raise concerns that AI could inadvertently lock in a new wave of fossil fuel dependence right when the world is trying to decarbonise. For instance, in the US, some utilities are proposing<\/a> 20+ GW of new gas-fired power plants by 2040largely to meet data centre growth.<\/p>\n This runs directly against climate goals — building gas infrastructure that could last 40-50 years to serve what might be a short-term spike in AI-related demand.<\/p>\n Conversely, a potential \u201cnuclear renaissance\u201d is being driven by AI\u2019s 24\/7 power needs and corporate clean energy pledges. Nuclear power offers steady, carbon-free electricity that is highly appealing for always-on AI workloads. We\u2019re seeing concrete steps in this direction:<\/p>\n The contrast is striking: Will the AI era deepen our fossil fuel dependence or accelerate the shift to alternative energy?<\/p>\n In practice, both are happening — but the balance could tip one way or the other based on economics and policy. Natural gas plants currently often win on cost and speed (a gas turbine can be built faster than a nuclear plant and is a proven solution to instantly boost capacity).<\/p>\n Indeed, \u201cthe only concrete plans I\u2019m seeing are natural gas plants,\u201d notes one energy consultant about data centre expansions<\/a>. Yet, as carbon costs rise and modular nuclear tech matures, nuclear and renewables could prove the more attractive long-term play.<\/p>\n For corporate leaders, this means energy strategy is becoming inseparable from AI strategy. Companies may need to directly invest in energy projects (like Microsoft\u2019s and Google\u2019s deals) to ensure their AI ambitions have a viable power supply. Those that succeed in securing reliable, clean energy will not only meet sustainability goals but also gain an operational advantage (avoiding the risk of power constraints slowing their AI deployments).<\/p>\n This is part one of a three-part series exploring AI’s energy impact. <\/em><\/p>\n Part two of this series examines how AI can enhance energy efficiency and optimise grid management to address this challenge.<\/em><\/p>\n This article was originally published here<\/a> and co-authored by Xavier Greco<\/a>, Founder and CEO of ENSSO.<\/em><\/p>\n —<\/p>\n Editor\u2019s note:\u00a0e27<\/b>\u00a0aims to foster thought leadership by publishing views from the community. Share your opinion by\u00a0submitting<\/a>\u00a0an article, video, podcast, or infographic.<\/p>\n Join us on\u00a0Instagram<\/a>,\u00a0Facebook<\/a>,\u00a0X<\/a>, and\u00a0LinkedIn<\/a>\u00a0to stay connected.<\/p>\n Image courtesy: DALL-E<\/p>\n The post The AI-energy paradox: Will AI spark a green energy revolution or deepen the global energy crisis? \u2014 Part 1<\/a> appeared first on e27<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence (AI) is expanding at breakneck speed, presenting a paradox for global energy systems. On one hand, AI-driven innovations promise efficiency gains in renewable energy management and smarter grids. On the other, the surging power demands of AI threaten to strain electricity infrastructure and increase reliance on fossil fuels. Current projections indicate data […]<\/p>\n","protected":false},"author":1,"featured_media":927,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[14],"tags":[],"_links":{"self":[{"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/posts\/925"}],"collection":[{"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/comments?post=925"}],"version-history":[{"count":3,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/posts\/925\/revisions"}],"predecessor-version":[{"id":930,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/posts\/925\/revisions\/930"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/media\/927"}],"wp:attachment":[{"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/media?parent=925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/categories?post=925"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.fresnoforeclosure.com\/index.php\/wp-json\/wp\/v2\/tags?post=925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
The energy cost of AI: Hard truths and hidden opportunities<\/b><\/h1>\n
\n
\n
Comparing AI models: Power hunger from GPT to KNN<\/b><\/h2>\n
![\"\"](\"http:\/\/www.fresnoforeclosure.com\/wp-content\/uploads\/2025\/04\/e27-29-1.png\")
<\/p>\nFossil fuel lock-in vs a nuclear renaissance<\/b><\/h2>\n
\n