Trends in Energy and AI Integration

AI’s exponential energy appetite is quietly rebooting America’s nuclear industry. In 2024, Big Tech had a critical realization: artificial intelligence isn’t just a software revolution - it’s a thermodynamic one. Training a single GPT‑4‑class model consumes ~500 MWh, that’s enough to power ~15 U.S. homes for a year. But inference is the real sinkhole. It’s always-on, everywhere, all at once. AI server racks consume >100 kW per rack, 10x more than traditional racks. Renewables can’t keep up. The sun sets. The wind stalls. Batteries are expensive, and at this scale, insufficient. Clean power isn’t the same as reliable power. And for 24/7 inference, only one option checks every box: nuclear - clean, constant, controllable baseload power. So what do trillion-dollar firms do when they realize their business model runs on electrons? They start buying the grid. ▪️ Microsoft partnered with Constellation Energy to restart Three Mile Island Unit 1 by 2028, supplying 835 MW of baseload power to its AI data centers - the first large-scale restart of a decommissioned U.S. reactor. Oh, and it’s betting on fusion too: Microsoft’s backing Helion, targeting the first commercial fusion prototype by 2028. When you have Microsoft money, you can place moonshots on the sun. ▪️Google is doing what Google does: building a portfolio. It inked a deal in October 2024 with Kairos Power for molten-salt SMRs (6–7 reactors by 2035, first demo 2030). Two weeks ago, it added Elementl Power - 1.8 GW of advanced nuclear capacity. ▪️Amazon Web Services (AWS) locked down up to 1.9 GW from Talen Energy's Susquehanna plant and, last year, dropped $650 million to buy a nuclear-powered data center campus outright. ▪️Meta finally joined the party last week, signing a 20‑year Purchase Agreement with Constellation to draw 30 MW from the Clinton nuclear plant in Illinois. The capacity is modest, but it signals a strategic shift - away from carbon offsets and toward operational baseload coverage. Even Meta sees the writing on the grid. This isn’t a hypothetical future - it’s happening now.  3 major nuclear PPAs signed within 2 weeks. Soaring federal support. Billions in private bets. What began as a GPU arms race is now an energy land grab. The next big AI breakthrough might not be a model, it might be a reactor.

AI has an insatiable appetite for energy. But, can AI help energy companies cook up a buffet? GE Vernova just acquired Alteia, the energy sectors first major acquisition to aimed at simultaneously powering the AI revolution and using AI to manage the resulting grid complexity. The acquisition will enable GE Vernova to, rather than building generic AI capabilities, develop visual intelligence specifically for energy infrastructure – enabling utilities to "see" their grids through AI-powered damage assessment, vegetation management, and asset inspection. Their GridOS® platform represents an AI-native approach to grid management, designed from the ground up for renewable energy integration rather than simply adding AI features to existing systems. GE Vernova's $9B commitment through 2028 represents one of the most aggressive AI investment strategies in the energy sector, far exceeding most competitors' disclosed AI-specific spending. This signals that leading energy companies view AI as fundamental infrastructure for future competitiveness, not just a technology add-on. Meanwhile, competitors across energy’s competitive landscape are taking their own approaches to AI. Siemens Energy leads with the most comprehensive strategy among traditional competitors, launching an industrial foundation model with Microsoft and pursuing workforce transformation (AI-powered learning for 250k+ employees), autonomous manufacturing (targeting 30% productivity gains), and AI-driven sales optimization. Schneider Electric, ABB, and Honeywell focus on partnerships and smaller acquisitions for IoT integration, predictive maintenance, and building automation. Notably, while some competitors have broader industrial AI portfolios, none match GE Vernova's strengthend, specific focus on AI for grid asset management; a critical differentiator as AI and visual data analysis become increasingly important for grid reliability. Every major energy company has embraced cloud partnerships (Microsoft Azure, AWS, NVIDIA) to support AI ambitions, but GE Vernova's sector-specific partnerships like its Chevron joint venture for AI data center power infrastructure demonstrate how companies are creating entirely new revenue streams. Traditional energy companies appear to be lagging in AI adoption, creating market share opportunities for AI-forward competitors. GE Vernova's is looking to win with a strategy of building proprietary AI capabilities through strategic acquisitions, rather than relying solely on partnerships. The companies that successfully integrate AI into their core operations – rather than treating it as an add-on – will likely capture disproportionate value as the energy sector digitizes.

𝐀𝐈’𝐬 𝐄𝐧𝐞𝐫𝐠𝐲 𝐀𝐩𝐩𝐞𝐭𝐢𝐭𝐞 𝐈𝐬 𝐑𝐞𝐝𝐫𝐚𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐔.𝐒. 𝐏𝐨𝐰𝐞𝐫 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 U.S. data center power demand is projected to grow from 25 GW in 2024 to over 80 GW by 2030. By then, data centers could account for 12% of the country’s total electricity demand, up from just 4% in 2023. The accelerated adoption of AI, cloud computing, and high-density digital infrastructure is driving this transformation. McKinsey & Company estimates over $500 billion in investment may be required to support 50+ GW of new capacity. Key implications for utilities and infrastructure investors: 𝙏𝙧𝙖𝙣𝙨𝙢𝙞𝙨𝙨𝙞𝙤𝙣 𝙖𝙣𝙙 𝙙𝙞𝙨𝙩𝙧𝙞𝙗𝙪𝙩𝙞𝙤𝙣 𝙖𝙧𝙚 𝙚𝙢𝙚𝙧𝙜𝙞𝙣𝙜 𝙖𝙨 𝙥𝙧𝙞𝙢𝙖𝙧𝙮 𝙘𝙤𝙣𝙨𝙩𝙧𝙖𝙞𝙣𝙩𝙨, not generation Project timelines are being limited by grid 𝙞𝙣𝙩𝙚𝙧𝙘𝙤𝙣𝙣𝙚𝙘𝙩𝙞𝙤𝙣 𝙙𝙚𝙡𝙖𝙮𝙨, 𝙡𝙖𝙗𝙤𝙧 𝙨𝙝𝙤𝙧𝙩𝙖𝙜𝙚𝙨, 𝙖𝙣𝙙 𝙚𝙦𝙪𝙞𝙥𝙢𝙚𝙣𝙩 𝙡𝙚𝙖𝙙 𝙩𝙞𝙢𝙚𝙨 Flexible, 𝙗𝙚𝙝𝙞𝙣𝙙-𝙩𝙝𝙚-𝙢𝙚𝙩𝙚𝙧 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙤𝙣 𝙖𝙣𝙙 𝙨𝙩𝙤𝙧𝙖𝙜𝙚 𝙖𝙨𝙨𝙚𝙩𝙨 𝙬𝙞𝙡𝙡 𝙥𝙡𝙖𝙮 𝙖 𝙜𝙧𝙤𝙬𝙞𝙣𝙜 𝙧𝙤𝙡𝙚 Secondary markets such as 𝙄𝙤𝙬𝙖, 𝙄𝙣𝙙𝙞𝙖𝙣𝙖, 𝙖𝙣𝙙 𝙉𝙤𝙧𝙩𝙝 𝘾𝙖𝙧𝙤𝙡𝙞𝙣𝙖 𝙖𝙧𝙚 𝙗𝙚𝙘𝙤𝙢𝙞𝙣𝙜 𝙝𝙞𝙜𝙝-𝙜𝙧𝙤𝙬𝙩𝙝 𝙯𝙤𝙣𝙚𝙨 for hyperscale and modular data centers Near-term grid resilience will depend on a diversified mix of generation, including natural gas, while long-term planning must align with decarbonization mandates and load flexibility. This is not a future challenge; it is already underway. #UtilityLeadership #GridModernization #EnergyInfrastructure #AIandEnergy #DataCenterDemand #PrivateCapital #InfrastructureInvesting #ElectricityMarkets #TransmissionExpansion #GridStrategy https://lnkd.in/g6pvCGnS

AI Will Use More Power, But Boost Renewables, IEA Projects The growth of AI data centers will test the global energy sector’s ability to keep pace, but could serve as a catalyst for growth in renewable energy and grid management, the International Energy Agency (IEA) said in a major new report. Unsurprisingly, the IEA projects that electricity demand from data centers worldwide is set to more than double by 2030 to around 945 terawatt-hours (TWh). But the IEA also offered context on AI's potential to support an energy transition. “Concerns that AI could accelerate climate change appear overstated, as do expectations that AI alone will address the issue,” the IEA said in its report. “While the increase in electricity demand for data centers is set to drive up emissions, this increase will be small in the context of the overall energy sector and could potentially be offset by emissions reductions enabled by AI if adoption of the technology is widespread.” In the short term, AI demand will boost the use of natural gas, particularly in the US. But the IEA sees a growing role for renewables globally, especially in the 2030-35 time frame. The report also finds that AI can be a crucial tool for the energy sector, accelerating innovation in energy storage. “With the rise of AI, the energy sector is at the forefront of one of the most important technological revolutions of our time,” said IEA Executive Director Fatih Birol. “AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments and companies – how we use it.” Here's the full IEA report: https://lnkd.in/eCEA-Rz6

A topic as urgent as the relationship between AI and energy deserves a bigger platform. That’s why I’m so excited about the International Energy Agency (IEA)’s trailblazing Energy and AI Observatory—and even more excited Tapestry is a part of it! The IEA’s Energy and AI Observatory is the biggest deep-dive I’ve found so far on this critical subject matter, bringing together comprehensive data, case studies, and visualizations that show AI’s transformative potential to address humanity’s growing appetite for energy. And rather than speculation and hypotheticals, this important resource features real, tangible use cases of how AI technology is being applied to energy challenges all over the world. As I shared during several workshops organized by the IEA, the century-old electric grid faces unprecedented strain in order to accommodate both our surging demand and the intricacies of how we make, move, and use energy in the modern era. Estimates show that we’ll need to build out an additional 80 million kilometers of new grid in the next decade (the equivalent of what we’ve constructed in the past 100 years!), and connect thousands of new energy generation sources each year compared to the dozens we’re accustomed to. While AI is not a panacea, it’s a powerful tool for managing the scale and complexity of this challenge. AI has the potential to make the grid visible in a way that’s never before been possible: allowing for real-time analysis that human operators can’t achieve alone, optimizing planning and operations, enhancing its ability to withstand disruptions, and more. For instance, the IEA found in a recent study that using AI, we have the potential to free up 175 gigawatts of transmission capacity—enough to power roughly 150 million homes. Tapestry is developing a suite of AI-powered tools to help grid operators and planners meet this moment, and I’m pumped to see our work featured in the Energy and AI Observatory alongside Google, Siemens, Hitachi, and others. If you enjoy nerding out on the future of energy as much as I do, this incredible new resource is definitely worth your time: https://lnkd.in/gc3xwkpE