AI and Energy Transformation

Improving AI's energy efficiency is key to solving the world's biggest challenges. The first step is understanding its environmental impact. For the first time, Google is releasing a comprehensive methodology for measuring the energy and water impact of Google's AI models. Here are some of our key findings: Today, a median Gemini text prompt uses: 📺 0.24 watt-hours of energy, the equivalent of watching TV for a little less than nine seconds 💧0.26 milliliters of water, about five drops of water We’re approaching efficiency from many angles — investing in new infrastructure, engineering smarter and more resilient grids, and scaling both mature and next-generation sources of clean energy. The results are telling. Over a 12-month period, while delivering higher-quality responses: ⚡ the median energy consumption per Gemini Apps text prompt decreased by a factor of 33x 👣 the median carbon footprint per Gemini Apps text prompt decreased by a factor of 44x By sharing our methodology, we hope to contribute to collective understanding and drive industry-wide progress towards more efficient and beneficial AI for everyone — including the planet. Learn more in the video below and dive into all the details in our Keyword blog here: g.co/AI/energyefficiency #GoogleSustainability #Gemini

NEW RESEARCH - WHY THE ENERGY TRANSITION IS DISRUPTIVE & COULD BE MUCH FASTER THAN WE THINK: The clean energy transition isn’t just about swapping out old tech for new—it’s a complex, non-linear process full of feedback loops, tipping points, and unexpected consequences. Our new “Systems Archetypes of the Energy Transition” brief is a must-read for anyone shaping policy, investing, or innovating in this space. Key takeaways: 1) Feedback loops drive change: Reinforcing loops (like learning-by-doing and economies of scale) have made solar, wind, and batteries cheaper and more widespread, often outpacing even the boldest forecasts. 2) Path dependence is real: Early advantages for a technology (think BEVs vs. hydrogen cars) can snowball into market dominance, making policy choices and timing critical. 3) Limits and synergies: As renewables grow, market dynamics like “cannibalisation” can dampen investment—unless we design markets and storage solutions to keep the momentum going. 4) Policy design is everything: Well-intentioned fixes (like price caps or broad subsidies) can backfire, while smart, targeted interventions can unlock positive feedbacks across sectors. 5) Tipping points and decline: The decline of fossil fuels isn’t just a mirror image of clean tech growth—it comes with its own feedbacks, risks, and opportunities for a just transition. The brief also offers practical guidance on using causal loop diagrams and participatory systems mapping—powerful tools for understanding and managing the complexity of the transition. If you’re working on energy, climate, or innovation policy, I highly recommend giving this a read. Let’s move beyond linear thinking and embrace the systems view—because the future will be shaped by those who understand the dynamics beneath the surface. This briefing was led by Simon Sharpe at S-Curve Economics CIC, Max Collett 柯墨, Pete Barbrook-Johnson, me at Environmental Change Institute (ECI), University of Oxford & Oriel College, Oxford & the Regulatory Assistance Project (RAP) and Michael Grubb at UCL Institute for Sustainable Resources.

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

For decades, energy progress meant one thing: build more. Today, we’re reaching the limits of that logic. More alone is no longer enough. We're facing a massive decoupling. Energy demand - driven by the force of AI, electrification, and industrial reshoring - is moving at a speed that physical infrastructure and permitting cycles simply cannot match.   This creates a new mandate for leadership. The primary constraint is no longer just generation capacity; it is the intellectual efficiency of the systems we already have.   We see this efficiency coming to life as electrification expands where we use energy, automation drives precision into our operations, and digitalization captures data at every layer. Together, these forces are reshaping energy systems into something far more dynamic than traditional approaches can keep up with.   The result is a shift from static infrastructure to living networks. Buildings, data centers, and industrial sites are no longer passive consumers at the end of a line; they are active participants that use energy technology to generate, store, and intelligently manage the power they need.   To navigate this, we need Energy and Industrial Intelligence.   Energy and Industrial Intelligence works when it’s part of the system. It is about linking trusted data from the physical edge - the actual motors, breakers, and servers - to the strategic layer. When you connect the physical to the digital, you stop guessing where energy is wasted and start orchestrating how it is used.   I shared this perspective recently at Innovation Summit India. My message was clear: We have entered the Era of Intelligence. The next phase of advancing energy technology won’t be defined by how fast we build, but by how intelligently we design, operate, and scale what already exists.   I’ve expanded on how we bridge this gap in my latest article. Link in the comments.   #EnergyTechnology #AdvancingEnergyTech #EnergyIntelligence

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

The shift from "smart" to "autonomous" infrastructure isn't optional – it's essential for the electrification of everything. When electricity grids started accepting renewable power from volatile sources in the 1990s, smart systems with dashboards and sensors were the answer. They’ve been a great success, enabling energy savings and managing decentralized power. But today’s challenges demand more than human decision-making supported by data – they require systems that act autonomously in milliseconds. The distinction is like GPS versus an autopilot. GPS tells you where to go; the autopilot flies the plane. As fluctuations in supply and demand bring existing grids to their limits, depending on dashboards is like flying through turbulence by hand. Autonomous buildings juggle multiple power sources minute-by-minute. Autonomous grids detect faults and reroute power in milliseconds using digital twins. The business case is compelling: smart buildings command higher valuations and higher rent, while saving on energy costs. Autonomous buildings can bring even more benefits. For grid operators, digitalized networks can double existing asset capacity and cut transformer upgrade costs significantly. The technology exists – AI, digital twins, and advanced semiconductors. What we need now is scale. Without autonomy, electrification risks stalling. With it, we get resilience, profitability and accelerated clean energy transition. #AutonomousInfrastructure #SmartGrids #DigitalTransformation #AI #Electrification

Energy still shapes global power—but technology now decides who leads. What do you think? Countries with the largest oil reserves (Saudi Arabia, Venezuela, Iran, Iraq, Russia, Canada) have long influenced geopolitics. Oil still supplies ~30% of global primary energy, and ~95% of global transportation depends on it. But the real power shift is happening elsewhere. 📊 Why tech, AI, and semiconductors now matter more than reserves AI data centers already consume ~2–3% of global electricity—expected to double by 2030 Advanced semiconductor fabs require: Massive, uninterrupted power Ultra-stable grids Water + energy co-optimization A single leading-edge fab can consume >100 MW—equivalent to a mid-size city 🔌 Energy → Compute → Power Oil-rich countries may control supply, but: Compute-rich countries control productivity Chip-rich countries control AI, defense, and economic scaling Grid-resilient countries control uptime and competitiveness That’s why we see: Oil exporters investing in AI infrastructure, hyperscale DCs, and chip manufacturing Import-dependent nations doubling down on energy efficiency, nuclear, and advanced semiconductors Governments linking energy policy directly to AI and semiconductor sovereignty 🧠 The new power equation ➡️ Energy abundance ➡️ Grid intelligence ➡️ Semiconductor capacity ➡️ AI at scale Oil remains a strategic asset—but AI, semiconductors, and energy-efficient compute are becoming the real levers of global influence. The future belongs to countries that convert energy into intelligence. #Energy #AI #Semiconductors #DataCenters #Geopolitics #EnergyTransition #Compute #TechnologyLeadership #FutureOfEnergy

This isn’t just clean energy. This is how we power a digital future—without burning the planet to do it. The rise of AI, streaming, and cloud computing is fueling an energy crisis. By 2025, data centers alone will consume 20% of global electricity. That’s more power than many countries use—combined. But two countries are showing us a smarter way forward. France didn’t build new land. It built solar stations on parking lots. Overhead canopies that generate energy, provide shade, and repurpose space we already have. Switzerland didn’t build new grids. It built solar into its railways. A startup named Sun-Ways is turning train tracks into power plants: -48 panels per 100 meters -No disruption to train operations -No additional land needed And this is just the beginning. Sun-Ways aims to scale across 5,000 km of track. That’s 2.5 million panels. Enough to supply 2% of Switzerland’s energy. But the real breakthrough isn’t just solar tech. It’s a shift in mindset: → From endless expansion to smart reinvention → From grid strain to grid intelligence → From energy extraction to energy integration The spaces we pass every day—commutes, car parks, rail lines—are becoming part of the solution. Not tomorrow. Today. Because sustainability isn’t just about reducing emissions. It’s about rethinking how we build, move, and power our lives. This is clean energy. This is infrastructure with intention. This is how we keep the lights on—in every sense. When innovation meets possibilities, life changes. This is technology for humanity and our planet. Follow me, Dr. Martha Boeckenfeld , for more of tech that matters. ♻️ Share this post to trigger smarter conversations about our energy future. #CleanEnergy #TechForGood #Innovation

AI is starting to change the grid in a way most people aren’t seeing yet. Utilities are quietly building “digital twins” of their systems—AI models that simulate the grid in real time. Not just power plants and wires, but rooftop solar, batteries, EVs, and demand response all interacting dynamically. And here’s what those models are showing: We don’t always need to build our way out of the problem. In many cases, flexible resources—virtual power plants, smart charging, distributed storage—can meet peak demand faster and cheaper than new generation or transmission. They can relieve congestion. They can defer upgrades. They can keep the system stable. In other words, as many of us have been saying, the grid we already have is more capable than we’ve been giving it credit for. But here’s the catch: Our regulatory system hasn’t caught up. California, for example, allows DERs and VPPs to participate in markets—but the rules that determine what counts as reliable capacity haven’t fully caught up to what these resources can actually do. But this isn’t just a California issue. From New York to PJM to the Midwest, markets allow flexibility—but still struggle to value it, to count it, as reliable capacity. So we have a mismatch: • Engineering reality is moving fast • Regulatory frameworks are not And that mismatch is expensive. It means we default to building more infrastructure than we may actually need. It means higher costs for ratepayers. And it means we’re slower to integrate the clean energy already coming online. The opportunity here is enormous. If we update the rules—so utilities can be rewarded for using flexibility, not just for building assets—we can: • Lower costs • Move faster • Make the grid more resilient Same electrons. Smarter system. That’s the next chapter of the energy transition. #EnergyTransition #DataCenters #AI #ElectricGrid

For decades, energy dictated how technology evolved. Today, that dynamic is reversing. Technology, particularly AI, is now influencing where energy flows, how it’s used, and how demand is created in the first place. And we’re already seeing the impact. The same technology driving a surge in demand, particularly from AI infrastructure, is also becoming the only viable way to manage it. This creates a new kind of tension.   Energy systems can’t scale to meet this shift without becoming more intelligent, more adaptive, and more integrated with digital technologies. At the same time, companies are being asked to do more with less: less energy, less carbon, and fewer skilled resources. For me, this is where the real opportunity lies. AI is not replacing systems. It’s augmenting them. It enables faster demand-response, more targeted interventions, and greater capacity from existing infrastructure. But what’s changed is how it’s being used. We’re moving from siloed systems to integrated, intelligent environments — where AI, automation, and software work together to predict, adapt, and optimise continuously. And that shift goes beyond efficiency. It starts to redefine who is operating the system and where value is created. As this “energy inversion” plays out, the question for many organisations is: Are you operating within the intelligence layer — or outside of it?