AI and Energy Transformation

Something VERY cool just happened in California and… it could be the future of energy.   On July 29, just as the sun was setting, California’s electric grid was reaching peak demand.   However, instead of ramping up fossil fuel resources, the California Independent System Operator (CAISO) and local utilities decided to lean on a network of thousands of home batteries.   More than 100,000 residential battery systems (made up primarily by Sunrun and Tesla customers) delivered about 535 megawatts of power to California’s grid right as demand peaked, visibly reducing net load (as shown in the graphic).   Now, this may not seem like a lot but 535 megawatts is enough to power more than half of the city of San Francisco and that can make all the difference when a grid is under stress.   This is what’s called a Virtual Power Plant or VPP. It’s a network of distributed energy resources that grid operators can call on in an emergency to provide greater resilience to our energy systems. Homeowners are compensated for the dispatch, grid operators are given another tool for reliability, and ratepayers are saved from instability. It’s a win-win-win.   Now, this was just a test to prepare for other need-based dispatches during heat waves in August and September. But it’ historic.   As homeowners add more solar and storage resources, the impact of these dispatch events will become even more profound and even more necessary. This was the second time this summer that VPPs have been dispatched in California and I expect to see even more as this technology improves.   Shout out to Sunrun, Tesla, and all companies who participated. Keep up the great work.

Last week 100,000 home batteries operated like a mid-sized power plant. On July 29, California aggregated more than 100,000 residential batteries and discharged them for two hours during the evening peak. The result: 535 MW of coordinated output, comparable to a gas peaker plant, but distributed across rooftops instead of built on a single plot of land. These were some of the most promising outcomes: Truly additive output: The batteries weren’t just doing what they normally do. Compared to the prior day’s profile, almost all 535 MW was additional discharge triggered by the event, which is clear evidence this was coordinated grid support, not incidental customer behavior. Stable performance: Telemetry showed steady power delivery for the full two-hour window with no noticeable drop-off. That’s the level of reliability grid planners typically expect from conventional plants. Well-timed to system stress: The event aligned with CAISO’s net peak (that’s California’s grid operator, balancing demand minus wind and solar). Hitting that window matters because this is when power is most scarce and expensive, and when the “duck curve” ramps hardest. Visible grid impact: Net load dropped measurably during the dispatch, demonstrating that thousands of small batteries can move the needle at the system level. Program design matters: Nearly 90% of participants were enrolled in California’s Demand-Side Grid Support program, with others in the Emergency Load Reduction Program. Incentive structures like these are what make broad participation possible across multiple aggregators and OEMs. The takeaway is bigger than one test: virtual power plants are crossing the line from pilot to planning-grade resource. If properly integrated—through refined dispatch algorithms, better coordination with CAISO, and markets that actually value flexibility—they can defer costly peaker plants, absorb excess solar, and flatten the evening ramp without the stranded costs of centralized infrastructure. The technology is ready. The economics pencil out. The question now is whether market design will catch up. ---- Read the full report from The Brattle Group here: https://lnkd.in/gwYbFiPz

The UAE–U.S. Energy–AI Agreement Under the leadership of HH Sheikh Mohamed bin Zayed Al Nahyan, the UAE signed a landmark MoU with the U.S. National Energy Council during ADIPEC Exhibition and Conference 2025, in the presence of Dr. Joe Dugan. The partnership embeds artificial intelligence across energy, manufacturing, and infrastructure — transforming collaboration into economic engineering for the future. Global Context • Energy powers 8% of global GDP (~US$7.6T) yet drives most CO₂ emissions. • Demand keeps rising toward 2040 without stronger efficiency or digitalisation. • The AI-in-energy market could surpass US$50B by 2030, growing 30%+ annually. • AI could cut 2.4 Gt of CO₂ by 2030 — equal to removing 500M cars. Why the UAE Is Moving • Diversification: Non-oil GDP ≈ 75%, targeting 85% by 2031; AI to add ~14% of GDP. • Energy sovereignty: Domestic demand +30% by 2030 → AI for smart generation & storage. • Industrial leap: Operation 300Bn → AED 300B industrial GDP; AI +35-40% productivity. • Climate leadership: ~AED 600B (US$163B) clean-energy investment; AI accelerates Net Zero 2050. How AI Transforms Energy • Upstream O&G −30% OPEX, +15% yield. • Power +20% efficiency. Grids −25% losses, +30% reliability. • Manufacturing +40% output, −20% cost. • Renewables +18% utilisation. Carbon capture −12% cost. Nationally: +22% energy productivity, +AED 90B (US$24.5B) output, −70M tons CO₂ yearly. The UAE’s Edge • #1 in MENA, #19 globally in AI readiness • ADNOC Group > US$1B digital value; Masdar targets 100 GW by 2030 (51 GW achieved). • First UAE–U.S. Energy-AI Corridor bridging East, West & Global South. Vision Alignment • Vision 2031: Double GDP to AED 3T (~US$816B). • Net Zero 2050: AED 600B clean energy. • Operation 300Bn: AED 300B industry by 2031. • Digital Economy 2032: 20% of GDP digital. • AI Strategy 2031: Top-10 AI nation. All pillars converge — energy becomes data, manufacturing becomes intelligent, industry becomes sovereign. Strategic Impact • Sovereign Energy Intelligence Network across refineries, grids & renewables. • +40% industrial output, −20% cost. +4.5% annual non-oil GDP growth. • −25% carbon intensity by 2030. • UAE as the Energy-AI hub linking U.S., Asia & Global South. Economic Dividend • Global AI could add US$15.7T to GDP by 2030: energy & manufacturing ≈ 40%. • Capturing 0.5% = US$75B new GDP for UAE. • U.S. partnership unlocks frontier compute, R&D & sovereign AI infrastructure. Long-Term Vision The UAE is not digitizing energy — it’s redefining power. By fusing energy, intelligence and industry, the nation is building the world’s first Sovereign Energy-AI Economy — one that creates, predicts and protects its own growth. From energy exporter to intelligence superpower, the UAE proves that the future belongs not to those who own the oil, but to those who own the intelligence that powers the world.

The problem isn't the demand from AI, but rather the outdated and inefficient energy infrastructure and the continued reliance on expensive fossil fuels. AI is energy-intensive, and countries that choose to scale renewable power and battery storage the fastest will have a decisive strategic advantage and cheaper electricity bills. Nations that choose to cling onto energy systems of centuries’ past, like the current administrations decision to keep uneconomic fossil-fired power plants open, will increase ratepayer costs by $3–6 billion dollars. AI data centers will undeniably increase electricity demand, but the rise in electricity bills is not an inevitable consequence of this demand. Instead, it is a result of inefficient legacy infrastructure, politically motivated decisions to subsidize uneconomic fossil fuel plants, and active resistance from the fossil fuel industry that seeks to portray clean energy as expensive, despite its proven economic advantages. The solution is rapidly deploying renewables, leveraging AI for grid optimization, and removing policies that artificially prop up the expensive, outdated fossil fuel system.

We’ve been treating AI’s energy problem as a datacenter problem. But the datacenter is the most optimized part of the stack. The real inefficiency is happening higher up. Google estimates that 60% of AI’s energy now comes from inference. Meta says 60 to 70%. AWS, 80-90% of its ML compute demand. A single prompt is insignificant. Billions across apps, agents & API calls aren’t. We’re on track for trillions. The mismatch? We’re optimizing infrastructure while most of the energy and cost are created at the application layer. Even the cleanest datacenter can’t compensate for a stack that sends every request to a frontier model, runs jobs at the costliest hours of the grid, and treats all queries as equally urgent. The biggest gains won’t come from better cooling or more renewable PPAs. They will come from how we design, route & operate the models themselves. There is a way to architect sustainability as a first-order principle in the AI lifecycle itself. So what does a more efficient stack look like? I’m seeing some really cool stuff these days. It begins with grid-aware infra. Platforms like Emerald AI align compute with renewables, shifting batch workloads to cleaner hours and routing traffic to cleaner regions. Crusoe rethinks the foundation entirely by converting stranded natural gas and heat recovery into compute. Training visibility changes how teams build. CodeCarbon exposes the emissions of every experiment forcing real decisions. Once numbers are visible, priorities shift. Does a 2% accuracy gain justify 10× more compute? Then comes inference intelligence. ChatGPT's routing prevents unnecessary over-computing, while GreenPT builds efficiency into the foundation so every inference run uses less power by default. One optimizes after building. The other designs for efficiency from the start. This is where FinOps & sustainability converge. User visibility matters too. Most people have no idea how much energy their prompts consume. When that information becomes visible in real time, behavior shifts. People choose lighter models, batch calls, and refine their prompting. Shared baselines are emerging. The GSF’s SCI turns sustainability into a measurable standard. GPU-level tools like Neuralwatt replace estimates with real power data and expose waste at the hardware level. But none of this works if the layers stay disconnected. This is the logic behind Antarctica: a single observability layer that connects cost, usage, energy, and user behavior across cloud and AI. Grid carbon intensity, training emissions, inference energy, hardware telemetry, and application analytics converge into one source of truth. To make inefficiency measurable at the point of decision. And in AI, every inefficiency appears twice: once as wasted energy and once as wasted dollars. So let’s make this practical now. I’m putting together a shared list of tools that actually improve efficiency across the AI stack. Which ones would you recommend?

✍️ 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗔𝗜: 𝗔 $𝟮 𝗧𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗨𝗦, 𝗠𝗘𝗡𝗔, & 𝗖𝗜𝗦. 🌎 🙋♂️ Through my ventures in data center investments and business development across the MENA, US, and CIS regions, including my work with AI-driven healthcare initiatives, I've seen firsthand the escalating demands for AI infrastructure. 📣𝙒𝙝𝙞𝙡𝙚 𝙗𝙞𝙡𝙡𝙞𝙤𝙣-𝙙𝙤𝙡𝙡𝙖𝙧 𝘼𝙄 𝙙𝙚𝙖𝙡𝙨 𝙢𝙖𝙠𝙚 𝙙𝙖𝙞𝙡𝙮 𝙝𝙚𝙖𝙙𝙡𝙞𝙣𝙚𝙨, 𝙖 𝙘𝙧𝙞𝙩𝙞𝙘𝙖𝙡 𝙘𝙝𝙖𝙡𝙡𝙚𝙣𝙜𝙚 𝙡𝙤𝙤𝙢𝙨: ‼️We're facing an $800 billion revenue shortfall for data centers, necessitating an estimated $2 trillion in investment by 2030 to maintain the current pace. It isn't just growth; it's a gold rush for computing power, the new most valuable commodity. 🪄 Consider these points: ✔️ AI's compute demand is doubling at twice the rate of Moore's Law, a pace of progress we've never seen before. ✔️ AI server growth is projected at a 41% CAGR, driving the overall data center market to a 23% CAGR. ✔️ To meet this demand, we need to invest $500 billion annually in data centers over the next decade. ✔️ The cost to construct a data center building has surged by 322% in just four years, before even adding a single chip or server. 📍 In healthcare AI—a sector I've focused extensively on in the MENA region—the infrastructure demands are particularly acute. Medical imaging, AI, and genomics processing require sustained high-performance computing, making reliable, cost-effective data center access critical for healthcare innovation. ♾️ This explosive growth is creating a significant energy bottleneck. Power demand from AI centers is set to quadruple in the next decade. By 2035, they could consume 1,600 terawatt-hours of power, equivalent to 4.4% of global electricity demand. 🔎 The AI revolution is still in its early stages. Addressing this $2 trillion challenge requires collaboration among investors, technology innovators, energy providers, and policymakers worldwide, from the US to the CIS and from Europe to the MENA region. 🖍️ 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗮 𝗳𝗼𝘂𝗿-𝗽𝗿𝗼𝗻𝗴𝗲𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: ↗️ Alternative energy partnerships (nuclear, renewable microgrids), ↗️ Next-generation cooling technologies (liquid cooling, immersion cooling), ↗️ AI-optimized chip architectures that improve performance per watt, and ↗️ Strategic geographic distribution to leverage regional energy advantages. ⁉️ The future of AI depends on the physical infrastructure we build today. Which emerging markets do you see as most promising for sustainable AI infrastructure development? 🧐 How are you balancing immediate scaling needs with long-term sustainability commitments? 📶 Let's connect and discuss the future of AI infrastructure. #DataCenters #AI #Investment #Energy #PhysicalAI #AICenters #MENA #CIS #Healthcare #Data #Infrastructure

Innovation has always moved the energy transition forward quietly at first, then decisively reshaping entire systems. Over the past decade, we have seen how new ideas can shift entire sectors: solar has become mainstream, electric mobility has accelerated, and grids have become smarter and more flexible. But these transformations didn’t happen overnight. Whether it was engines, batteries, HVAC, solar PV or even the early neural network models, each breakthrough took decades to mature before it could scale. AI may be the first technology with the potential to compress that entire cycle—and that creates both opportunity and responsibility. A few themes stand out to me: 1. AI as a Force Multiplier for Climate Action: AI is already improving renewable energy predictability, cutting building emissions, enhancing farm productivity and strengthening carbon accounting. At scale, such solutions could help eliminate gigatonnes of emissions—by augmenting decision-making. 2. Governing AI’s Own Footprint Is Essential: AI’s rapid growth comes with meaningful energy demand. Its impact on climate will depend on how responsibly we manage data infrastructure—ensuring transparency, efficiency and a shift toward renewable-powered computing. 3. From Reactive to Proactive Resilience: High-resolution AI models are helping governments and cities move from monitoring climate risks to prognosticating them—informing resilient infrastructure, emergency preparedness and long-term adaptation planning. 4. Democratising Access Matters: Advanced economies currently dominate robotics, automation and industrial software. For AI-enabled climate solutions to scale equitably, they must be accessible to the Global South. India, given its digital public infrastructure and renewable energy momentum, is well positioned to lead. If 2025 was the year of expectation, 2026 must be the year of integration—where AI is embedded across science, policy, agriculture, infrastructure and energy systems. As a practical tool for resilience, efficiency and decarbonisation. #EnergyTransition #AIForClimate #ClimateTech

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.

Could powering #AI be a #gamechanger that accelerates the #cleanenergy revolution?   During his visit to Hong Kong, NVIDIA CEO Jensen Huang expressed his hopeful vision for the future of AI. Beyond envisioning a world of global cooperation to advance tech development, Huang argued that “using energy for intelligence is the best use of energy at the moment” to strive for a better world.   Chatting with Prof. Harry Shum, who is a renowned computer scientist and AI expert in his own right, Huang gave his three-pronged reasoning. First, the goal of AI is not to train models but to use it to discover new and more efficient solutions for the world. We can therefore use AI models to come up with everything from new CO2 storage solutions and wind turbine designs to novel materials for solar panels.   Second, AI doesn’t require physical proximity. Whereas all our existing appliances and even our EVs must be close to us for use, AI transcends this boundary. The beauty of this? We can put these supercomputers off-grid, powering it using sustainable energy while it learns and trains itself to create solutions for our homes and cities.   Finally, Huang argues that AI is a panacea to uncover the science we need to solve our #wastecrisis. Not just plastic, food and textile waste, but literal energy waste. By using algorithms to consider vast datasets—specific demands, supply, price, area conditions—we can more efficiently determine the storage and distribution of energy and facilitate the decarbonisation of our grid.   I believe that we’re at a crossroad: An AI revolution in the midst of our #climatecrisis. AI could either help reduce our carbon emissions and enable a clean energy overhaul, or it could increase energy demand. Humanity has to choose how we wish to proceed. If we want the former, which is what Huang argues is the potential of AI, we must work together to balance the resource use that it will inevitably require in order to reap its benefits.   With societal pressure, governmental support and the right green finance ecosystem to fund planet-first projects, tech companies will be more likely to innovate towards the direction for our climate, rather than against it. We need to make sure that AI is geared towards solutions for our global challenges. This task is urgent, given that the computational power needed to sustain AI’s growth is projected to double every single 100 days.   #NVIDIA is already proving that #TechForGood is possible, with its new superchip designed to deliver a 25-fold reduction in energy-use. We now need the whole AI ecosystem on board to accelerate our global clean energy transition.   As a council member, I am thrilled to welcome Huang as a member of the The Hong Kong University of Science and Technology community. I am not only encouraged by his commitment to harness #AIForGood, but also his enthusiasm and faith in Hong Kong as a global innovation hub.   Are you hopeful about AI too?   #AIForSustainability

AI always has a price. We just don’t always see it. 💯 A chatbot might feel free. You text. It replies. But someone is paying—servers, energy, water, hardware. Training large AI models now uses enormous power. One retraining run for a big language model can use between 324 and 1,287 MWh of electricity. That’s as much as 100 homes use in a year. Operating those models costs too. Cooling systems alone eat up 30–50% of energy in AI data centers. Add networking and backup systems, and the footprint grows. Data center power is rising fast. Global demand could reach 945 TWh by 2030—close to 3% of all electricity worldwide. In the US, AI-heavy servers drive electricity use up 30% each year. The thirst is worse for water. Training GPT-3 may have evaporated 700,000 liters of freshwater. Global AI demand could draw 4.2–6.6 billion cubic meters of water by 2027. That’s more than the annual water use of the UK. There are signs of improvement. Google measured that a typical Gemini Apps text prompt now uses just 0.24 Wh of energy—and five drops of water. That’s less energy than nine seconds of TV, thanks to smarter design and renewable energy sourcing. Still, monetizing AI is not simple. Big firms face real losses. Lenovo’s infrastructure group saw $4.3 billion in AI-related sales. But the operating loss was $86 million. They nearly lose a dollar for every $8 in server sales. In short: AI demands resources. It's not magic. It brings real costs—energy consumption, water use, hardware turnover. But firms also care about margins, so they’re investing in efficiency. We all benefit if they push for transparency and responsibility.