AI and Energy Transformation

Virtual Power Plants (VPPs) have been around for a long time as a concept. After China has seen a rise in their use will the US be next? By digitally aggregating thousands—often millions—of flexible assets like heat pumps, EV chargers, batteries, smart thermostats, and commercial HVAC, VPPs deliver reliable capacity, balancing, and ancillary services at a fraction of the cost and carbon of traditional peaker plants, without compromising comfort or productivity. As electrification accelerates and variable renewables scale, grid stress is rising, and building new firm capacity is expensive and slow; unlocking demand-side flexibility is faster, cleaner, and more scalable. The enabling technologies exist today—smart, standards-based controls—and policy is beginning to catch up. Priority actions are clear: pay-for-performance markets that let flexibility compete fairly with supply-side resources, interoperability through open standards to reduce costs and avoid lock-in, and consumer-first participation models with simple enrollment, strong privacy by default, and equitable access, particularly for low-income customers.

AI has gone nuclear. President Trump's latest Executive Order (EO) marks a decisive shift in how the US approaches the intersection of AI and national security. The order requires the deployment of advanced nuclear reactors to power both military installations and AI data centers, treating uninterrupted AI computing power as a matter of national defense. Significantly, for commercial AI development, the Department of Energy must designate AI data centers at DOE facilities as critical defense facilities, with their nuclear power infrastructure classified as defense critical electric infrastructure. This is potentially the start of attempts to make AI a restricted technology. The scale of the challenge is immense. According to the IEA, global electricity consumption by data centers is set to more than double by 2030 to around 945 TwH annually. This figure equals Japan's entire annual electricity consumption and represents enough power to supply 85 million American homes for a year, or California for nearly four years. The urgency driving this policy becomes clear when examining the Stargate Project, the $500 billion AI infrastructure venture announced in January. The first Stargate site in Abilene, Texas will have 1.2 gigawatts of capacity when completed by mid-2026, enough to power roughly 750k small homes. These numbers underscore why conventional energy sources cannot meet AI's demands at the required scale and speed. The EO explicitly frames AI as a national security imperative. It states that advanced computing infrastructure for AI at military and national security installations demands reliable, high-density power sources that cannot be disrupted by external threats or grid failures. Military applications of AI, from surveillance and intelligence processing to autonomous systems, depend on massive computing infrastructure that traditional power sources cannot reliably support. Congress has reinforced this federal approach to AI governance. The House just passed the "One Big Beautiful Bill Act" which includes a 10-year moratorium on State enforcement of any law regulating artificial intelligence models, systems, or automated decision-making processes. The administration's intolerance for impediments to AI progress became evident with the firing of the head of the US Copyright Office. Her dismissal came one day after the Copyright Office released a report stating that technology companies' use of copyrighted works to train AI may not always be protected under U.S. law - something which may hinder AI development in the USA. These developments signal that the US has entered a new strategic phase where AI is no longer merely a technological or economic concern but an instrument of geopolitical power. The US is treating the AI race as an arms race, with nuclear energy as its fuel, centralised federal control as its governance model, and zero tolerance for resistance whether from states, regulators, or rights holders.

The power sector is changing fast, and AI is at the center of this transformation. From predicting outages before they happen to improving energy distribution, AI is making electricity more reliable, efficient, and sustainable. But how exactly is AI reshaping the industry? 1. Predicting failures before they happen. Power outages can be costly and disruptive. AI-powered predictive maintenance helps utilities identify potential failures in transformers, power lines, and substations before they occur. By analyzing data from sensors and historical trends, AI reduces downtime and ensures a more stable power supply. 2. Smarter energy distribution. Electricity demand fluctuates throughout the day. AI helps balance supply and demand in real time, ensuring power is distributed where it’s needed most. This minimizes waste, lowers costs, and improves overall grid efficiency. 3. Optimizing renewable energy. Renewable energy sources like solar and wind are unpredictable. AI helps by analyzing weather patterns and adjusting energy production accordingly. This means more stable integration of renewables into the grid. While AI is transforming the power sector, technology alone isn’t enough. The biggest challenge is adoption. Getting companies, governments, and individuals to embrace these changes. For digital transformation to succeed, the industry needs: → Skilled talent → Better infrastructure → And a willingness to rethink traditional ways of managing power AI is here to stay, and its impact on energy is growing. The question is: Are we ready to maximize its potential?

When I first joined OpenAI, my colleagues welcomed me with a fluorescent safety vest draped over my chair—a playful hint that I’d spend a lot of time helping build things. They were right: infrastructure is destiny when it comes to AI, and a new report details why—and how—we should turbocharge that buildout The report from the Center for Strategic and International Studies (CSIS) argues that the US needs to scale up investments in AI infrastructure to stay ahead of competitors—particularly the People’s Republic of China—in frontier models, data centers, and chips. According to CSIS, the top priority is energy, the lifeblood of AI data centers. As the report warns, “failing to secure energy means surrendering U.S. leadership on AI” Other nations recognize energy’s importance and are quickly expanding their supply. France is leveraging surplus nuclear energy for data centers. Japan 🇯🇵 is restarting idle nuclear plants. The UAE is creating AI-focused economic zones powered partly by nuclear energy With so many nations going all-in on AI, the US must invest historic sums to maintain our global lead. CSIS estimates the required new infrastructure will cost $2 trillion by 2030—primarily for energy and advanced semiconductor production It’s an enormous sum that far exceeds what the private sector can deploy at the speed necessary to compete with China’s government-backed industrial policies. Earlier tech breakthroughs—like aviation, computing, or the internet—required public-private collaboration to ensure the benefits reached as many people as possible. AI is no different. Private enterprise thinks in quarters, but strategic-minded governments like the PRC think in decades. The US needs to find a way to do the same OpenAI recently unveiled Stargate, a new venture which will invest $500 billion to build new AI infrastructure—spurring innovation, creating jobs, driving economic growth, and strengthening national security. Stargate will also help ensure US-led democratic AI prevails over China’s authoritarian version Stargate is a major investment in America’s AI future–but the CSIS report shows, policymakers need to think more quickly and creatively to maintain US global leadership in the technology by:  Ramping up domestic GPU production by 72–96% annually between 2025–2030 to meet exploding AI chip demand Adding 40–90 GW of new energy capacity by 2030 to power AI data centers Accelerating permitting for energy and data infrastructure to quickly bring new data centers online Developing “AI Innovation Zones” to connect regions with complementary AI strengths and needs. The hubs would receive government funds to build data centers, energy resources and other infrastructure  Huge thanks to Navin Girishankar, Joseph Majkut, and the CSIS team for this timely report, which OpenAI was proud to support. I'll be discussing all this and more on an upcoming CSIS podcast—stay tuned! https://lnkd.in/gmmm4_Ay

There’s a Climate Tech company valued at $1.75bn which is working to make AI cheaper: Today, many oil refineries are located close to oil wells - which are in remote locations. As a result - gas (a by-product of oil refining) cannot always be transported economically. The result: The refineries are literally forced to burn the excess gas (since the cost of transport / storage is > cost of burning). This is a colossal waste of energy which results in air pollution. Here’s where the geniuses at Crusoe Cloud come in: 🧠 They’ve created portable data centers which are custom built for running AI workloads (Nvidia chips). These machines are installed close to an oil refinery & the gas (which is otherwise flared) is used to run AI workloads. 💡 Value proposition: - For oil refineries, this is an additional income source w/o any capex - so they are happy to partner with Crusoe. - For cloud customers who want to run AI workloads, this is a low(er) cost alternative to the cloud incumbents. 🤔 Why not AWS or GCP for cloud? - AWS et al build data centers for latency (i.e. fastest load time for internet traffic). Crusoe builds data centers for performance (i.e. lowest cost load time on AI workflows). - For Crusoe, the ideal use-case is high compute AI workloads which don’t have much of a latency problem (1 second v/s 10 seconds response doesn’t matter as much) 🤖 Technical feats: - They’ve literally built their own cables, data center & other infrastructure since they operate out of remote areas. The rigs can actually be moved around. Crusoe has raised > $500M in debt as well to finance all this infrastructure & hardware investment (which is an incredible amount for a young company!) They are believed to be at > $100M ARR Personally, I’m not an expert in either AI or Climate - so half the stuff these chaps do makes no sense to me. But, if you’re interested to learn more - their CEO did a deep dive on the Acquired show here: https://lnkd.in/gjQRiSui #startups

Over the past ten years, global electricity generated by solar increased 10x. Another 10x increase is possible by 2034, providing abundant clean energy. In today's episode, I detail how A.I. can help us get there. 10x ☀️ GROWTH: • Solar panels cover an area the size of Jamaica, providing 6% of global electricity. • Solar capacity doubles every three years, increasing tenfold each decade. • Projected to provide 60% of world's electricity by 2034 if trend continues. • Solar could become the largest source of all energy by the 2040s. VIRTUOUS ECONOMICS: • Cost of solar-produced electricity could drop to less than half of today's cheapest options. • Virtuous cycle: Increased production lowers costs, driving up demand. • No significant resource constraints unlike all previous energy transitions (i.e., wood to coal, coal to oil, oil to gas). • All of the main ingredients (silicon-rich sand, sunny places, human ingenuity) are abundant... so the virtuous economic cycle can proceed unhindered. KEY CHALLENGES (and how to address them with data science): 1. Energy Storage and Grid Management: • Complementary storage solutions needed for 24/7 energy demands. • A.I. can optimize battery management systems. • Machine learning can enhance energy-grid management. 2. Heavy Industry, Aviation, and Freight Electrification: • Machine learning can optimize battery architectures. • A.I. can enhance synthetic fuel (e-fuel!) production processes. 3. Solar Energy Production Optimization: • A.I. for discovering new photovoltaic materials. • Generative A.I. to predict successful solar project locations. • A.I. to optimize solar-panel production processes. IMPACT: • Cheaper energy will boost productivity across all sectors. • Improved accessibility to essential services for billions. • Breakthroughs in drinking-water access through affordable purification and desalination. • Opportunities for unforeseen innovations in an era of energy abundance. Hear more on all this (including about a dozen resources for learning more about how you — yes, you! — can address climate/energy challenges with data science) in today's episode. The "Super Data Science Podcast with Jon Krohn" is available on your favorite podcasting platform and a video version is on YouTube (although today's episode's "video" is solely an audio-waveform animation). This is Episode #804. #superdatascience #machinelearning #ai #climatechange #solar #energy

We need to talk about the AI opportunity that Australia can't afford to miss and reframe the conversation. Reports that highlight the immense water and energy footprint (particularly that higlighted by InnovationAus.com below) are rightly a cause for concern. Rapid and unchecked growth is putting a strain on our natural resources. BUT to frame this as a simple choice between technological progress, and environmental responsibility is to miss out on the real, multi-billion dollar opportunity for Australia. This doesn't need to be an "either-or" scenario. Rather it can be a "YES, AND..." YES we need to power the AI & cloud computing revolution. AND, we can use this demand as a powerful catalyst to become a world leader in sustainable technology. The catalyst for making data centres greener shouldn't be seen as a roadblock; but rather a runway for innovation. The global demand for AI is insatiable, and so it the demand for the infrastructure that support is. Every single nation is facing the same challenge: how do we do this without breaking the environmental bank. This is our opportunity. This is where we can be bold and shine. If we align the rapid growth of our data centres and AI sectors with a focused investment in R&D for renewables, water-saving technologies, and new an novel optimisations we can solve our own resource challenges, and create lucrative new export markets. It's a YES, AND opportunity. Think about it: 💧 Hyper-efficient water cooling: Moving beyond the current standards to create closed-loop systems or leverage recycled water, drastically cutting reliance on potable water. ☀️ Integrated renewable energy solutions: Developing smart grids and on-site generation that can reliably power these energy-intensive facilities 24/7. 💡 Next-generation hardware: Innovating in more energy-efficient chips and server architecture that require less power and cooling from the outset. ☁️ Smarter software management: Creating AI-driven platforms that optimise workloads to minimise energy and water consumption across the entire infrastructure stack. And this is just the start - critical mineral processing onshore, new energy efficient models... the list goes on and on. If we choose ambition, let's be bold and tackle this head on and cultivate new fields of high-tech expertise. We build technology and IP that the rest of the world needs and fill both our own and global market opportunities. This is how we increase Australia's economic complexity. We move from simply housing the world's data to creating and exporting the high-value, sustainable technology that makes it all possible. Let's not put the brakes on AI growth. Let's use it to accelerate our transition into a renewable energy and technology powerhouse. AI #Sustainability #Innovation #DataCenters #RenewableEnergy #EconomicGrowth #Australia #TechFuture

Tesla is not just an #automaker - it’s building a real time #software platform for the future of #energy. Tesla’s Virtual Power Plant (VPP) connects thousands of Powerwalls, solar panels, and Megapacks into one intelligent energy network. The backbone? #ApacheKafka for real-time #DataStreaming and WebSockets for last-mile IoT integration. This architecture enables: - Millisecond-level grid balancing - Automated #energytrading - Distributed command & control for millions of energy assets - Real-time resilience during blackouts and extreme weather Tesla’s approach shows how data streaming and automation can turn decentralized energy resources into a unified, scalable, and #AI-driven grid. Tesla manages #DigitalTwin for real-time control - a bold but effective decision aligned with its unique architecture. This is the blueprint for the next-generation power grid: event-driven, intelligent, and software-defined. I break it all down in my deep dive: https://lnkd.in/e58aCnfv How long until utilities around the world embrace this kind of real-time architecture? And is your company ready to handle streaming data at grid scale?

𝐖𝐡𝐲 𝐒𝐨𝐥𝐚𝐫 𝐖𝐢𝐥𝐥 𝐁𝐞𝐚𝐭 𝐆𝐚𝐬 - 𝐍𝐨𝐭𝐡𝐢𝐧𝐠 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐆𝐫𝐞𝐞𝐧, 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐀𝐈 Most people frame solar vs gas as a climate debate. It isn’t. This is about economics, speed, and the AI buildout. ✅ 𝐆𝐚𝐬 = 𝐁𝐫𝐚𝐲𝐭𝐨𝐧 𝐜𝐲𝐜𝐥𝐞 𝐡𝐞𝐚𝐝𝐚𝐜𝐡𝐞 Brayton-cycle gas turbines are an engineering marvel, but complex to manufacture, hard to run, and slow to ramp. Even if you fund them today, meaningful output often takes 3 to 4 years. Demand can shift, cycles can turn, and your CapEx is stuck. ✅ 𝐒𝐨𝐥𝐚𝐫 = 𝐬𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐬𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 Modular panels, simple construction, rapid installs. You can add capacity in months, not years, and scale in bite-sized tranches. ✅ 𝐂𝐨𝐬𝐭𝐬 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐞 𝐚𝐬 𝐬𝐜𝐚𝐥𝐞 𝐠𝐫𝐨𝐰𝐬 Every time global solar capacity doubles, costs drop by roughly 43%. Adoption is accelerating, so the cost decline is accelerating too. ✅ 𝐓𝐡𝐞 𝐀𝐈 𝐭𝐰𝐢𝐬𝐭 A big chunk of AI capex is flowing into power capacity. In the AI cost stack, power is about 10% of total token cost while chips dominate. That makes power spend pocket change. AI players will happily overbuild cheap solar rather than wait years and take technology and market risk on Brayton-cycle gas. ✅ 𝐆𝐚𝐬 𝐢𝐬 𝐭𝐫𝐚𝐩𝐩𝐞𝐝 By the time a new gas plant reaches scale, solar has usually gotten cheaper and more abundant. Result: rising risk of stranded assets for gas, compounding gains for solar. Seedha sa logic hai: AI ko sasti, tez power chahiye, aur yeh solar hi de sakta hai. This isn’t about being green. It’s about being right on the cost curve, right on timing, and right for AI-era scale. Solar wins on all three. BlueGreen Ventures Anup Jain

Early Sunday mornings are usually my time I make space to think more deepy about few key areas. Today I looked at two things — global LNG market trends, and ADNOC Gas own market performance. For both, I used AI agents I’ve built in Copilot, where I’ve been feeding in analyst reports, market updates, and our own data (within secured platform). What used to take me hours is now done faster and with a wider perspective. But I never take it at face value — human judgment, context, and experience are still critical. For me, this is a real example of AI for People (one of ADNOC’s AI Strategy Pillars) in action: giving us tools that make us sharper and more efficient, while still relying on our own and expert’s judgment to make the right call. The second pillar is Energy for AI. AI itself is hugely energy-intensive, and data centers are only growing. Here, ADNOC Gas plays a central role: we already supply 60% of the UAE’s gas needs, and we’re investing to increase capacity by 30%. Supplying the energy that powers AI is part of our contribution to this transformation. Finally, there is AI for Energy — using AI to run our operations smarter, safer. This is where we’ve built focused programs across our business: Planningai, Operationsai, Maintenance/HSEai, and Corporateai. Two examples from ADNOC Gas show what this looks like in practice: • The Centralized Predictive Analytics Diagnostics CPAD system, which monitors more than 500 rotating machines to catch problems before they become failures, cutting costs and avoiding downtime. • The Neuron 5 platform, already running on 20% of our critical equipment, using deep learning on sensor data to predict maintenance needs. These are not Ideas or conepts — they are already part of daily operations, helping us improve efficiency, safety, and reliability. Step by step, this is how ADNOC Gas is becoming an AI-native company. Reuters events published special report on how ADNOC Group is embedding AI across its downstream operations worldwide to accelerate innovation and performance (report attached) What about you? How do you see AI being integrated into your life and the operations of your business? #AI #EnergyTransition #ADNOCGas #PredictiveMaintenance #OperationalExcellence