The Economic Effects of AI Energy Consumption

International Data Center Authority (IDCA) report reveals a critical gap in our AI infrastructure planning. The numbers are staggering: AI data centers will consume 12% of US electricity by 2028 - equivalent to Argentina's entire power usage. This unprecedented surge threatens both grid stability and consumer energy costs. Biden's executive order aims to fast-track energy resources for data centers, reflecting the urgency of our infrastructure needs. But without massive increases in power generation, we're looking at potential brownouts and significant price hikes for everyday Americans. The real story isn't just about domestic energy - there's a major competition brewing between nations over AI infrastructure dominance. While the media focuses on OpenAI vs xAI, sovereign nations are quietly racing to secure their energy futures. Energy capacity will determine who leads the AI revolution. Google's taking initiative with plans for two nuclear-powered data centers - a bold move that could reshape how we power our AI future. This signals a crucial shift toward sustainable, high-capacity power solutions for tech infrastructure. The irony? We already have viable solutions at scale: - Distributed power generation technology is ready now - Advanced solar technology costs dropped 80% in a decade - Biogas from organic waste could power millions of homes Yet major energy companies like Southern Company continue restricting consumer energy independence, maintaining their grip on the market. Meanwhile, we're shuttering nuclear plants instead of modernizing them. Each closed facility represents thousands of megawatts of clean energy capacity we can't afford to lose. There's potential for AI itself to revolutionize energy production - the very technology driving this surge might help solve it through advanced grid management and fusion breakthrough research. Critical priorities needed now: - Nuclear power expansion and modernization - Energy independence initiatives at state and federal levels - Infrastructure modernization for grid resilience - Innovation-friendly policies to unlock new energy solutions Without strategic planning, this unprecedented energy demand won't just impact tech companies - it will reshape our entire energy landscape and economy. The future of AI depends on solving this energy equation. The nations that figure this out first will lead the next technological revolution. #AIInfrastructure #EnergyPolicy #Innovation #TechFuture

DeepSeek announced a much more energy efficient AI tool. Does that resolve our AI-energy conundrum? No. If you've been anywhere near the business press this week, you've heard about the announcement from DeepSeek that is rocking global tech markets. DeepSeek is Chinese startup that has released an open source AI tool that is much more computationally efficient than the existing AI tools from the big US tech companies. And with this increased computational efficiency comes less energy demand. As Reuters asks, isn't this bad news for the thesis that AI uptake is going to require massive power buildout? No. As exhibit A in my case, I point to the Jevons paradox. The Jevons paradox says as you make a resource more efficient to use, the world will demand more of that resource. The paradox got its name when English economist William Stanley Jevons noticed in 1865 that as coal use became more efficient, industry's demand for coal increased. And I think we'll see precisely this effect with AI. AI, in fact, may be the perfect embodiment of the Jevons paradox. We can use AI for countless applications. Some are very serious, like exploring for new, more effective pharmaceutical compounds. Others are more frivolous, like generating silly videos or songs. (Though keep in mind some of the most frivolous uses of any technology can encourage the kind of creativity that unlocks highly consequential subsequent innovation.) We have an endless number of "problems" to throw at AI. The constraint will very much be its cost. As energy efficiency per unit of AI output improves, costs go down, and as a result, demand will go up. AI isn't like coal, a finite resource embedded in the earth's crust that generally becomes more difficult to extract as we go. AI is as plentiful as our ingenuity will allow. With more ingenuity comes less cost, and then more demand for this lower cost AI. And if you believe, as I do, that AI can be an incredibly consequential force for net public good, then driving energy efficiency up, so that we can drive aggregate usage up, is a good thing. #energy #ai #ksg https://lnkd.in/g-dPNhZu

Demand for U.S. power is projected to grow rapidly for the first time in decades, driven by the power needs of Artificial Intelligence. AI is having a huge impact on many sectors of the economy, and already we are seeing significant growth in data center development based on expected power needs associated with AI. This power crisis likely will cause major challenges for utilities and those focused on eliminating the use of fossil fuels. Tech firms at the forefront of the AI revolution are starting to think about their power needs and making investments into non-fossil fuel sources, including nuclear. Alphabet Inc., Amazon, Meta, and Microsoft recently made announcements about bringing back decommissioned and/or building new nuclear facilities. Despite concerns about safety and waste disposal, supporters of nuclear power will be happy by the technology sector’s backing of this power source, including small modular reactors. However, a bigger concern was the recent announcement by FirstEnergy, an Akron, Ohio-based utility, that it plans to continue operating its Fort Martin and Harrison coal-fired plants in West Virginia, having previously announced it would close these facilities by 2030. It is likely that FirstEnergy will not be the only utility to break its pledge to decommission coal power plant in order to ensure it has enough power for future AI needs. This decision to extend the use of coal power plants is deeply troubling, particularly after witnessing the environmental impact from Germany’s decision to close its nuclear reactors following the Fukushima nuclear disaster. Short on power, Germany turned to lignite, the dirtiest type of coal, and in less than 5 years, Germany reversed its decades long trend of lower emissions, as CO2 emissions started rising again. The German experience is something that we don’t want to emulate in the U.S. We should not stand in the way of progress, and AI has the ability to have a positive impact on many industries and our daily lives. Nevertheless, we must be careful to ensure we are not moving backward on our environmental commitments. Increasing power needs from data centers and electric vehicles can, and should, be met by rapid growth in renewable energy. With renewable energy and energy storage prices continuing to decline, the U.S. should be able to meet its power needs without hurting the environment. https://lnkd.in/gHNaKYsr EcoTech Capital Cy Obert #cleantech; #climatetech; #energytransition; #sustainability

Artificial Intelligence is revolutionizing industries, but it's also driving a surge in energy consumption. ⚡With computational demands doubling approximately every 100 days, AI is projected to account for 3.5% of global energy use by 2030. In the U.S., data centers could consume nearly 9% of the nation's energy, significantly impacting power grids and carbon emissions. However, AI also offers transformative solutions: 🔋 Smart Grid Optimization: AI enhances the efficiency and resilience of energy distribution networks. 🔒 Advanced Energy Storage: Machine learning accelerates the development of long-term, cost-effective energy storage systems. 💡 Clean Energy Research: AI expedites the discovery of materials for sustainable energy technologies. 🔑 The Path Forward: Balancing AI's energy demands with its potential to drive innovation is crucial for a sustainable energy future. This balance requires strategic investment and a commitment to integrating AI responsibly within the energy sector. Read more here ➡ https://lnkd.in/gwtJNw-B #AI #EnergyConsumption #SustainableTech #SmartGrids #EnergyTransition

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