AI in Sustainable Technology

Today, I’m sharing Google's 10th annual Environmental Report, which shows how we’re making AI helpful for everyone—including the planet—while at the same time taking meaningful steps toward our environmental ambitions. A key highlight for me is that we reduced our data center energy emissions by 12% in 2024 despite significant growth in electricity demand, showing it's possible to advance the two great transformations of our time—the AI revolution and clean energy growth—hand in hand. This is about building for the future through new advanced energy innovations and deeper supplier engagement, both of which are core parts of our strategy as we work toward our climate moonshots, 24/7 carbon free energy and Net Zero by 2030. But our impact extends far beyond our own operations. Just five of our AI-powered products—like fuel-efficient routing in Google Maps and our Solar API—enabled others to reduce an estimated 26 million metric tons of CO2e in 2024. That’s more than the emissions from powering 3.5 million U.S. homes for a whole year. This is real-world impact, at scale. We know there is much more work to be done, but I remain hopeful given the positive impact enabled by AI—from transforming how people engage with information, to enhancing business and economic growth, to enabling scientific breakthroughs and driving sustainable innovation for society. Read the full report and engage with it in new AI-enabled ways here: https://goo.gle/4eyEugQ

The Water Footprint of AI: Why We Need to Pay Attention to Its Environmental Cost As artificial intelligence continues to advance, its environmental impact, particularly concerning water consumption in data centres, warrants attention. Understanding AI's Water Usage AI models, especially large language models, require substantial computational resources. This computing power, concentrated in data centres, generates significant heat, necessitating extensive cooling, often through water-based systems. - Per Query Water Usage: Each interaction with AI models like ChatGPT consumes water. For instance, a 20-50 question session can use approximately 500 millilitres of water, primarily for cooling purposes. - Industry Impact: Data centres globally consumed over 660 billion liters of water in 2022 to cool servers running various services, including AI workloads. Key Areas of Concern 1. Water Scarcity: Many data centres are located in regions with limited water resources. In areas like California, where numerous tech companies operate, water-intensive cooling for AI adds strain to local supplies. 2. Seasonal Impact: During summer, data centres often double their water usage to maintain optimal temperatures. With climate change leading to more frequent heatwaves, this demand could increase, exacerbating the impact. 3. Comparative Impact: Training large AI models can consume up to five times more water than traditional data center operations, highlighting the need for efficient resource management. Steps Toward Sustainability To foster a more sustainable AI ecosystem, the tech industry can consider the following measures: 1. Adopt Alternative Cooling Solutions: Implementing methods like liquid immersion cooling, direct air cooling, and utilising recycled water systems can reduce water demands by up to 90% in certain environments. 2. Enhance Transparency and Accountability: Publicly reporting water usage and environmental impact data allows companies to foster accountability and enable informed consumer choices. Currently, only a few tech giants release detailed sustainability reports on water use. 3. Optimise Model Efficiency: Redesigning models to perform with lower computational intensity can significantly reduce both water and energy requirements. Model efficiency improvements, even by 10-15%, can save millions of litres of water annually. While AI offers transformative benefits across various sectors, it's crucial to balance its growth with responsible resource use. Focusing on sustainable AI practices is essential not only for environmental preservation but also for the technology's long-term viability.By embracing these strategies, we can ensure AI's advancement doesn't come at the expense of our planet's resources. Visual: The Times #ai #waterconsumption #sustainability #datacenters #environmentalimpact #greenai

AI has no place in sustainability. There’s a familiar stance I hear a lot in sustainability circles. AI uses a lot of energy. So using it for sustainability sounds… contradictory. But that argument misses the bigger picture. AI isn’t just consuming energy. It’s helping us use less of it too. Used well, AI is already solving real sustainability problems. Not hypotheticals. Not R&D lab demos. Live, operational tools that help businesses reduce emissions, speed up reporting, and make better decisions. Here’s what that looks like in practice: 1. Energy grid optimisation In the UK, the National Grid is using AI to forecast solar energy production by analysing satellite images and weather data. If clouds are expected to lower solar output in, say, Cornwall 30 minutes from now, the grid can prep alternative sources in advance. That means fewer blackouts and lower emissions from fossil backup plants. DeepMind did something similar for wind power. Their AI predicted wind farm output 36 hours in advance, which increased the commercial value of wind energy by around 20 percent. Why? Because energy providers could schedule when to send power to the grid with more certainty. 2. Streamlined carbon accounting AI tools now scan invoices, utility bills and PDF reports to pull out emissions data automatically. They match spend categories to emissions factors and calculate Scope 1, 2 and 3 outputs in seconds. That turns carbon accounting from a once-a-year headache into a real-time management tool. 3. Transparent supply chains Unilever has tested AI platforms that combine satellite imagery with supply data to flag illegal deforestation in palm oil regions. If a patch of rainforest is cleared where it shouldn’t be, AI catches it fast and alerts their team. No need to wait for an audit or third-party tipoff. 4. Faster climate simulations Traditional climate models take weeks or months to run. New AI-driven models can simulate complex climate scenarios up to 25 times faster. That unlocks planning tools for city councils, small businesses and insurers who can’t wait months to model flood risks or heat exposure. Yes, AI needs energy to run. But if it helps avoid 10 times more emissions than it creates, the trade-off makes sense. So the question isn’t whether AI belongs in sustainability. It’s whether we’re serious about using every tool we have to solve the problems in front of us. At Leafr, we’ve seen consultants use AI to cut time and cost on energy audits, validate supplier claims, and surface risks early. When paired with the right human expertise, AI becomes a multiplier. Because the planet doesn’t care if a human or a machine found the emissions. It just cares that they’re found and cut. Follow Gus Bartholomew (Leafr 🌿)for more and repost if you found useful. Use Leafr to find the sustainability specialists you need to support your AI efforts

Today, Nature Communications published our latest research, led by Amit Misra from Microsoft’s AI for Good Lab: a global flood detection model built using 10 years of Synthetic Aperture Radar (SAR) satellite data. It can detect floods through clouds, at night, and in remote areas—filling a critical gap in global disaster data. Already in use in Kenya and Ethiopia, this open-source tool is helping governments respond faster and plan smarter. It’s a powerful example of how AI can drive climate resilience.

How AI is changing storm response in the U.S. — technically. Have you experienced it? Extreme weather response is no longer driven by single forecasts. It’s driven by ensembles + AI acceleration + real-time data fusion. Here’s what’s happening under the hood: AI-accelerated Numerical Weather Prediction (NWP) Deep learning models (graph neural nets, transformers) are trained on decades of reanalysis data to approximate full physics-based solvers. Result: • Inference in seconds instead of hours • Enables rapid ensemble generation (hundreds of scenarios, not dozens) This allows forecasters to update storm tracks and intensity continuously, not on fixed cycles. Multi-modal data fusion AI ingests: • Satellite imagery (GOES) • Doppler radar volumes • Ocean buoys & atmospheric soundings • Ground IoT sensors • Historical climatology Models correlate spatial-temporal patterns across modalities — something classical models struggle with at scale. Severe weather nowcasting Computer vision models detect: • Convective initiation • Tornadic signatures • Rapid intensification signals Lead times improve by 30–60 minutes for fast-forming events — which is operationally massive for emergency management. Probabilistic forecasting, not single answers ML-driven ensembles output probability distributions, not deterministic paths: • Flood depth likelihoods • Wind gust exceedance • Ice accumulation risk This feeds directly into risk-based decision systems. Infrastructure impact modeling Utilities combine AI weather outputs with: • Grid topology • Asset age & failure history • Load forecasts This enables pre-storm optimization: • Crew pre-positioning • Targeted grid isolation • Faster restoration paths Operational decision intelligence AI systems now bridge forecast → action: • When to evacuate • Where to stage responders • Which assets fail first This is no longer meteorology alone — it’s real-time systems engineering. Storms are getting more chaotic. Our response is getting more computational. AI doesn’t replace physics. It compresses it into time we can actually use. #AI #WeatherModeling #Nowcasting #ClimateTech #InfrastructureAI #DigitalTwins #ResilienceEngineering #HPC

An Overheated Amazon Data Center Just Exposed a Growing AI Infrastructure Problem An outage tied to overheating inside an Amazon Web Services data center triggered major disruptions across global trading systems, highlighting a rapidly growing engineering challenge facing the AI and cloud computing industries: heat management at extreme computational scale. According to the report, the AWS facility shut down after temperatures exceeded safe operational thresholds for servers and networking hardware. Modern data centers rely on highly precise cooling systems — including chilled water loops, computer room air handlers, and increasingly direct liquid cooling — to maintain tightly controlled operating temperatures. When cooling systems fail to keep pace with thermal loads, servers automatically throttle performance or shut down to prevent catastrophic hardware damage. The incident reportedly disrupted portions of Amazon’s cloud infrastructure supporting financial and AI-related workloads, contributing to broader trading outages across markets dependent on low-latency cloud services. Amazon had not publicly detailed the exact root cause or duration of the outage at the time of reporting. The event underscores a deeper structural issue now emerging across the technology sector. Artificial intelligence workloads, particularly large-scale model training and inference, generate extraordinary heat densities far beyond those associated with traditional enterprise computing. Advanced AI accelerators and GPU clusters consume immense power while producing concentrated thermal output that existing data center architectures were not originally designed to handle. This creates a growing engineering tension inside the AI economy. Demand for increasingly powerful AI systems is rising faster than the industry’s ability to build cooling, power delivery, and thermal management infrastructure capable of supporting them reliably at scale. The problem is becoming especially critical because modern economies increasingly depend on cloud infrastructure not only for enterprise software, but also for finance, logistics, communications, healthcare, and national security systems. A single cooling failure can now ripple across multiple industries simultaneously. Key Takeaways for the material include the reality that AI infrastructure challenges are no longer limited to software and computing power alone. Thermal engineering, energy distribution, cooling technologies, and physical infrastructure resilience are rapidly becoming strategic bottlenecks in the global AI race. The broader implication is that the future competitiveness of AI ecosystems may depend as much on electrical grids, cooling innovation, and infrastructure engineering as on algorithms themselves. As AI computing density continues rising, thermal resilience could become one of the defining operational challenges of the next generation of digital infrastructure. Keith King https://lnkd.in/gHPvUttw

This isn’t just farming. This is Dyson engineering reimagining how we feed the world. For decades, vertical farming was a futuristic dream—out of reach for most, limited by cost, scale, and complexity. Until now. At Dyson, a team of engineers dared to ask: What if sustainable, high-yield farming wasn’t a privilege, but a global standard? Their answer is a bold innovation—no Big Tech giants required: A vertical strawberry farm powered by ingenuity. Ferris wheel-style rigs rotate 1.2 million strawberry plants toward sunlight and LEDs, maximizing every square meter. Robots pick only the ripest fruit, while UV light keeps mold at bay—no chemicals needed. Anaerobic digesters recycle heat and CO₂, fueling growth and slashing waste. 2.5x more strawberries per square meter than traditional farms. But the real breakthrough isn’t just in the engineering. It’s in the future made possible. → Food security, redefined. Fresh strawberries, grown locally—no matter the season. Fewer food miles. Less waste. → Sustainability, realized. Closed-loop systems. Recycled energy. No chemical pesticides. Farming that heals the planet instead of harming it. → Innovation, democratised. Smart sensors and automation make precision farming accessible, scalable, and resilient for a changing world. Ask yourself: When was the last time you saw a vacuum company change the way we think about food? For millions, this is the taste of what’s possible. This isn’t only about strawberries. It’s about resilience. It’s about abundance. It’s about a future where design meets necessity—and everyone benefits. And for the first time, it’s within reach. When technology meets agriculture, lives change. This is engineering for humanity. Follow me, Dr. Martha Boeckenfeld, for more stories of tech that matters. ♻️ Share with your network to see how bold ideas can reshape the world. #TechForGood #Innovation #Sustainability

Within DP World's sustainability endeavours, I've been deeply immersed in the intersection of technology and environmental consciousness, particularly in the realm of artificial intelligence (AI). The discourse around responsible and sustainable AI is not just timely but imperative in today's rapidly evolving digital landscape, especially as AI continues to grow and is poised for even greater expansion in 2024. This article aptly highlights four crucial paths that companies can take to ensure their AI initiatives align with environmental goals while driving innovation. Efficiency emerges as a central theme, urging companies to adopt specialised AI models tailored to specific use cases rather than opting for resource-intensive, general-purpose models. This approach not only minimises energy consumption but also fosters a culture of innovation by leveraging the vast potential of open-source resources. By using less data, we can better optimise AI algorithms for reduced computational overhead while still maintaining performance and achieving results. The integration of renewable energy sources into AI infrastructure represents a significant step forward in mitigating the environmental impact of AI operations. By hosting AI functions in data centers powered by renewable energy, companies can significantly reduce their carbon footprint while driving sustainable growth. However, as highlighted in the article, challenges such as tracking energy consumption and fostering transparency remain paramount. As we navigate these challenges, it's crucial to prioritise ethical considerations and long-term sustainability in AI development. For us at DP World, as we look to tap into the potential of AI, we take into consideration these sustainable approaches to ensure that our technological advancements align with our environmental objectives and foster a greener future. A concrete example is our multi-programme software suite, CARGOES, which is an AI-driven solution automating every terminal process, from staff rostering to streamlining customs inspections—an infamously arduous process. With AI managing the basics, our Jafza teams can focus on upskilling and handling specialist shipments, thereby expanding our capabilities beyond mere throughput increase. Through the integration of AI technologies like CARGOES into our operations, we not only enhance efficiency and productivity but also reduce our environmental footprint by optimising processes and resource usage. By embracing responsible AI practices and leveraging technology as a catalyst for positive change, we can create a more sustainable future where innovation and societal well-being go hand in hand. https://lnkd.in/dugjCDMq 

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

𝗪𝗲 𝗷𝘂𝘀𝘁 𝗰𝗿𝗼𝘀𝘀𝗲𝗱 𝘁𝗵𝗲 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝘀𝘁𝗼𝗽𝘀 𝗯𝗲𝗶𝗻𝗴 “𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹” 𝗳𝗼𝗿 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 I’m buzzing after seeing our latest research with #Reuters. After years implementing #IndustrialAI for sustainability, watching this shift happen in real-time feels significant. 𝟲𝟯% 𝗼𝗳 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗵𝗮𝘃𝗲 𝗺𝗼𝘃𝗲𝗱 𝗯𝗲𝘆𝗼𝗻𝗱 𝗽𝗶𝗹𝗼𝘁𝘀. Implementation rates jumped from 13% to over 50% in a single year. Organizations deploying industrial AI are seeing: ⚪️ 𝟲𝟱% 𝗮𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗲𝗻𝗲𝗿𝗴𝘆 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 of 23% on average ⚪️ 𝟱𝟵% 𝗰𝘂𝘁𝘁𝗶𝗻𝗴 𝗖𝗢𝟮 𝗲𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗯𝘆 𝟮𝟰% But there’s something even more fascinating underneath. 𝗪𝗲’𝗿𝗲 𝗘𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗨𝗻𝗰𝗵𝗮𝗿𝘁𝗲𝗱 𝗧𝗲𝗿𝗿𝗶𝘁𝗼𝗿𝘆a I’m watching AI evolve from imitation learning—copying how humans solve problems—to exploration learning. 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘄 𝘁𝗮𝗸𝗶𝗻𝗴 𝘂𝘀 𝗯𝗲𝘆𝗼𝗻𝗱 𝘄𝗵𝗮𝘁 𝗵𝘂𝗺𝗮𝗻 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗹𝗼𝗻𝗲 𝗰𝗼𝘂𝗹𝗱 𝗮𝗰𝗵𝗶𝗲𝘃𝗲. This isn’t incremental improvement. We’re talking radical innovation. AI can simulate entirely new designs that were previously impossible to conceive. When you’re juggling decarbonization, circularity, and societal changes simultaneously while navigating a “tsunami of regulations” - this capability becomes transformative. 𝗪𝗵𝗮𝘁 𝗞𝗲𝗲𝗽𝘀 𝗠𝗲 𝗨𝗽 𝗮𝘁 𝗡𝗶𝗴𝗵𝘁 (𝗜𝗻 𝗮 𝗚𝗼𝗼𝗱 𝗪𝗮𝘆) 𝟴𝟭% 𝗼𝗳 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻. Not “AI will help.” But that AI will 𝗱𝗿𝗶𝘃𝗲 innovation. From what I’m seeing? They’re right. We’re using AI to capture regulations requiring hundreds of experts. We’re building multi-agent teams developing entirely new features. We’re optimizing complete process flows. The technical barriers are dissolving faster than expected. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗻𝗼𝘄 𝗶𝘀 𝗵𝗼𝘄 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝘄𝗲 𝘀𝗰𝗮𝗹𝗲 𝘄𝗵𝗮𝘁’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘄𝗼𝗿𝗸𝗶𝗻𝗴. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗼𝗺𝗲𝗻𝘁 𝗙𝗲𝗲𝗹𝘀 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 For years: “Can AI really deliver on sustainability?” Now: “How fast can we deploy this across our operation?” That shift - from skepticism to urgency - tells me we’ve hit critical mass. The business cases are proven. 71% of leaders expect significant impact on the energy transition. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝗜’𝘃𝗲 𝗯𝗲𝗲𝗻 𝘄𝗮𝗶𝘁𝗶𝗻𝗴 𝗳𝗼𝗿. What’s your take? Are you seeing this shift in your work?