AI in Sustainable Technology

The next era of datacenters is here. The demand for AI is growing rapidly, and with it comes the need to grow the cloud’s physical footprint. Historically, datacenters have been water-intensive and require using large amounts of higher carbon materials like steel. At Microsoft, we're building datacenters with sustainability in mind, and we're constantly innovating to find new ways to reduce our environmental impact. This includes: 🤝 A first-of-its-kind agreement with Stegra, backed by an investment from Microsoft’s Climate Innovation Fund (CIF) in 2024, to procure near zero-emissions steel from Stegra’s new plant in Boden, Sweden, for use in our datacenters. Powered by renewable energy and green hydrogen, Stegra's facility reduces CO2 emissions by up to 95% versus conventional steel production. By committing to purchase this green steel before it rolls off the line, Microsoft is sending a clear market signal, driving demand for cleaner materials and supporting Stegra’s growth. 💧 We also announced a major breakthrough to make our datacenters more sustainable: microfluidic in-chip cooling technology. Unlike traditional cold plates that sit atop chips, microfluidics brings cooling right inside the silicon itself. Engineers carve microscopic channels directly into the chip, letting liquid coolant flow through and absorb heat exactly where it’s generated. This approach is up to three times more effective than current methods. More efficient cooling allows datacenters to support powerful next-gen AI chips without ramping up energy use or investing in costly new gear. 💵 Through our CIF investments, we’ve catalyzed billions in follow-on capital for breakthrough solutions in low-carbon materials, sustainable fuels, carbon removal, and more. We just released a new whitepaper – Building Markets for Sustainable Growth – that distills five key lessons on how catalytic investment and partnership can move markets and accelerate a global transition in energy, waste, water, and ecosystems. Our journey toward sustainable datacenters is only beginning, and we recognize true progress requires collective action and investment. Read more from Building Markets for Sustainable Growth: https://msft.it/6041sq9xD

Plastic is highly durable and resistant to decomposition. Most plastics take hundreds to thousands of years to break down, meaning that once produced, they persist in the environment for an extremely long time. What do you think about this initiative in Bali? Marine Pollution: A large proportion of plastic waste ends up in the oceans, where it poses a serious threat to marine life. Animals often mistake plastic for food, leading to ingestion and, in many cases, death. Microplastics, which are tiny plastic particles resulting from the breakdown of larger pieces, can enter the food chain, affecting not just marine species but also humans who consume seafood. Harm to Wildlife: Animals can become entangled in plastic waste, leading to injury or death. For example, plastic rings, nets, and bags are common culprits in the harm and killing of birds, fish, and other wildlife. Toxicity: Some plastics contain harmful chemicals, such as BPA (Bisphenol A) and phthalates, which can leach into the environment and potentially enter the human body, causing health issues. The incineration of plastic waste can also release toxic gases, contributing to air pollution. Carbon Footprint: The production of plastic is energy-intensive, relying heavily on fossil fuels. This contributes to greenhouse gas emissions, exacerbating climate change. How AI Can Help Address the Plastic Issue: Waste Sorting and Recycling: AI can enhance recycling processes by improving the accuracy and efficiency of waste sorting. Machine learning algorithms, combined with robotic systems, can identify and separate different types of plastic from other waste materials, increasing the volume of plastic that gets recycled. Plastic Detection in Oceans: AI-powered drones and satellite imaging can be used to detect plastic waste in oceans. By analyzing images with AI, we can better understand the scale of ocean plastic pollution and target cleanup efforts more effectively. Material Innovation: AI can accelerate the development of alternative, more sustainable materials by analyzing vast datasets of chemical compounds and predicting their properties. This can lead to the creation of biodegradable plastics or entirely new materials that have less environmental impact. Supply Chain Optimization: AI can help companies optimize their supply chains to reduce plastic use. By analyzing data on production, packaging, and transportation, AI can suggest ways to minimize plastic waste and encourage the use of sustainable alternatives. Education and Awareness: AI-driven platforms can be used to educate the public about the impacts of plastic pollution and encourage more sustainable behaviors. Personalized recommendations based on AI analysis can guide consumers to make more environmentally friendly choices, such as choosing products with less plastic packaging. #plastic #ai #technology #innovation via @sungai_design

Today at #AdoptAI, I thought back to the moment when artificial intelligence truly clicked for me. It didn’t come from a strategic report or a board discussion. It came from my teenage daughter. One evening, I caught her using Le Chat (France’s homegrown ChatGPT) while doing her homework. My first reaction was the one you would expect: a bit of parental panic of course. But then, I looked closer. She was not using it to cheat. She had uploaded her notes and was asking the AI to quiz her, acting as a study partner with infinite patience. That moment changed my perspective completely. I realized #AI really is about putting us back in control of our own progress, rather than merely replacing human intelligence. It is not a magic wand, but rather a very powerful catalyst to accelerate innovation and human expertise. At the same time, this experience reminded me of the importance of developing AI responsibly and ethically, and of carefully choosing when and how to use it. So, since we are on a quest to massively accelerate #EcologicalTransformation that delivers for our clients, I see AI as the means to multiply Veolia’s impact tenfold. How? We are already partnering with the world’s largest data center operators over 100 sites worldwide to transform these energy-intensive giants into agents of territorial circularity. ➡️ Instead of wasting the massive heat generated by computing power, we can capture it to warm nearby schools, hospitals, and homes, leading to +20% of energy reuse. ➡️ Instead of draining local water supplies, AI-enabled treatment systems can recycle cooling water, reducing the water footprint by up to 75%. ➡️ And instead of letting strategic metals go to waste, we can massively recycle them, getting up to 95% circularity. So yes, the AI boom will undeniably put tremendous stress on our natural resources. But yes, we have the tools to use AI itself to massively optimize the resource intensiveness, not only of data centers, but of all industrial activities. This is how we reconcile the digital and environmental transitions. By 2030, our obsession is zero waste, tracking every drop of water and every kilowatt in real time. At the end of the day, we will know that AI can succeed if we achieve a transition where its environmental benefits exceed the costs. I am fully confident that we can make it happen at Veolia, because we already are for many projects. Thank you to Adopt AI and Samantha Simmonds of the BBC for the opportunity to discuss this all-important topic. The future starts now!  

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

♻️ Recycling, reimagined. I came across Ameru’s AI Smart Bin — and it made me realize something we rarely talk about in sustainability: We don’t fail to recycle because we don’t care. We fail because the friction is too high. This bin doesn’t just collect waste. It sees what you throw, sorts it automatically, and even gives you real-time feedback. The results? ✅ 95%+ sorting accuracy ✅ Analytics that show you how to reduce waste ✅ ROI in under 2 years 👉 Here’s the hidden insight: Let’s be honest: recycling is broken. Most of us want to recycle, but the system is designed for failure — too much friction, too many rules. The real innovation isn’t in AI or edge computing. It’s in making sustainability invisible. No guilt, no extra steps — just default behavior upgraded. 💡 Actionable thought: Whether you’re building tech, a product, or even a habit, ask yourself — how can I make the right choice feel effortless? Because effort scales linearly. But effortlessness? That scales exponentially. PS: Imagine when every trash bin becomes a data point in the circular economy. 👉 Do you think this kind of “invisible innovation” could transform how we recycle at home and at work? #GreenTech #AI #Innovation #Sustainability #CircularEconomy

Sustainability = Innovation 🌎 Integrating sustainability into business strategy requires continuous advancements in technology, processes, and resource management. At the same time, sustainability challenges drive research, development, and operational efficiencies that lead to new market opportunities and competitive advantages. Resource constraints drive material and process innovation. The need for alternatives to finite or harmful materials has accelerated the development of advanced composites, circular economy models, and energy-efficient production systems, improving cost efficiency and resilience. Addressing sustainability challenges requires systems-level innovation. Reducing emissions, optimizing resource use, and minimizing waste require advancements in supply chain management, product lifecycle design, and industrial processes, reshaping entire sectors. Cross-functional collaboration is critical. Sustainability initiatives require input from engineering, data science, regulatory compliance, and finance to develop integrated solutions that meet environmental targets while maintaining operational and commercial viability. Data-driven approaches enhance sustainability performance. Measuring environmental impact enables companies to identify inefficiencies, optimize resource allocation, and refine business strategies based on quantifiable sustainability metrics. Long-term sustainability targets drive investment in research and technology. Businesses are accelerating development in areas such as AI-driven resource optimization, carbon capture, and next-generation materials to align with regulatory requirements and market expectations. Nature-based solutions provide scalable innovation opportunities. Biomimicry has led to advancements in self-healing materials, passive cooling systems, and regenerative agricultural techniques, improving efficiency and resilience across industries. Sustainability is reshaping business models. The transition to circular economy principles, service-based models, and regenerative supply chains is driving competitive differentiation and long-term value creation. Innovation is fundamental to achieving sustainability objectives. The convergence of regulatory frameworks, technological advancements, and market shifts is reinforcing the role of sustainability as a driver of industrial transformation and business resilience. #sustainability #sustainable #business #esg #climatechange

Satellites generate more data in an hour than we can download in a day. Here's why that's about to change. Modern satellites collect an overwhelming amount of information - far more than we can transmit back to Earth quickly. But this isn't just a technical problem. It's potentially costing lives. Here's what's happening right now: When wildfires threaten homes: ↳ Satellite images showing their spread sit trapped for hours During hurricane season: ↳ Vital storm trajectory data reaches emergency teams late - when every minute counts Military operations rely on several-hour-old satellite intelligence ↳ In situations where seconds matter Think about that: We have the data to: • Protect lives • Mitigate disasters • Optimize operations But much of it's stuck in space, waiting to be downloaded. This is why AI-powered satellites are transforming space operations. Take the European Space Agency's new Φsat-2 satellite. Instead of blindly collecting and slowly transmitting back to Earth, it: • Processes images in orbit • Identifies what's actually important • Only sends down actionable intelligence The early indications are game-changing: • 80% reduction in transmission needs • Real-time disaster monitoring • Faster threat detection • Rapid weather pattern analysis Of course, AI in space faces challenges: → Cybersecurity risks → Regulatory constraints → Complex international coordination But the potential rewards are immense for those focusing on: • Reducing data transmission bottlenecks • Providing real-time, actionable insights • Solving critical infrastructure and monitoring challenges This goes beyond a “tech upgrade”. It's a powerful transformation in how we protect communities, save lives, and understand our planet. The old approach: Collect everything, transmit slowly, analyze later. The emerging reality: Think in orbit, send what matters, act immediately. Earth’s early warning systems are getting smarter. P.S: Join high-growth founders and seasoned investors getting deeper analysis on emerging tech trends and opportunities on my newsletter (https://lnkd.in/e6tjqP7y) ____________________________ Hi, I’m Richard Stroupe, a 3x Entrepreneur, and Venture Capital Investor I help early-stage tech founders turn their startups into VC magnets Building in space tech? Let's talk

If you’re overseeing an Agentic AI roadmap, these ten principles can save cost, carbon, and complexity. In the race to deploy autonomous agents, many organizations are quietly accumulating Agentic Debt — systems that are over-orchestrated, expensive to run, and increasingly hard to govern. Engineering excellence in the AI era isn’t about how much autonomy an agent has. It’s about how much efficiency, restraint, and intent are baked into the architecture. Here are the 10 Lean Agentic AI Principles for building production-ready, sustainable systems: 1. Managed Context – Large context is a liability when unmanaged. More memory ≠ more intelligence. 2. Right-Sized Models – Not every prompt deserves a 70B response. Use the smallest brain that gets the job done. 3. Streamlined Orchestration – Agent orchestration is not a playground. Every extra agent is a cost, a delay, and an emission. 4. Think Before Compute – Reflections aren’t free. Validate the need before asking an agent to “think.” 5. Targeted Retrieval – RAG isn’t always right. Retrieve only when it’s truly needed. 6. Account for Hidden Emissions – Emissions don’t show up in logs, but the planet still pays for them. 7. Reuse as Reasoning – Don’t re-run. Re-think. Reuse is the new reasoning. 8. Judicious Tool Use – More tools, more problems. Every tool adds latency and risk. 9. Judgmental Memory – Memory isn’t a journal. Storing everything is hoarding, not intelligence. 10. Governance Over Autonomy – Agentic systems need governance. Left unchecked, autonomy becomes chaos. A lean mindset doesn’t just reduce overhead. It increases predictability, performance, and trust across the entire agentic stack. These ideas are now open-sourced as the Lean Agentic AI Playbook: https://lnkd.in/dp8KZVku. For deep dive , refer to my book - https://leanagenticai.com/ #AgenticAI #LeanAgenticAI #SustainableAI #SoftwareArchitecture #AIStrategy #ResponsibleAI

📊 Check out the Sustainability Risk Tool Dashboard! Over 100 tools to compare across climate, transition, and nature risks! As a climate leader who sees firsthand how quickly the risk landscape is shifting, I know how valuable it is for financial institutions to use the right tools.  That’s why I find the dashboard from United Nations Environment Programme Finance Initiative (UNEP FI) Risk Centre so useful. In my time leading the Risk Programme, I was proud to begin work on the climate risk dashboard, which has grown into the sustainability tool dashboard. This open-access resource offers an overview of more than 100 tools, detailing their features, methodologies and use cases across climate risks, nature and biodiversity, pollution and social risks.  Updated quarterly, it now incorporates insights from UNEP FI’s Climate Risk Landscape Report, giving financial institutions a clearer and more integrated view of the evolving risk tools market. Key functionality includes:  🧩 Classification by risk type to support comparability  🏭 Sectoral coverage from energy to real estate, agriculture and more  📈 Side-by-side comparison to help identify gaps and choose the right tools  🔎 Searchable database of tool descriptions and solutions for targeted use  🌐 Coverage of cross-cutting themes such as biodiversity, water and carbon for holistic assessments Explore the Dashboard here: https://lnkd.in/ebivVmEH  What challenges are you facing in finding the right risk tools? And which ones have been most useful? Share your thoughts in the comments! 

🔴 AI Is Draining Water From Areas That Need It Most 🔴 We analyzed data on thousands of #AI #datacenters, and found that roughly two thirds of them since 2022 are in places with high to extremely high levels of water stress. With terrific reporters Michelle Ma and Dina Bass ⭐ 🎁 : https://lnkd.in/exrEaSWU Each time you ask an AI #chatbot to write an email, it sends a request to a data center and strains an increasingly scarce resource: water. We found that about two-thirds of new data centers built or in development since 2022 are in places already gripped by high water stress. In the US, data centers are increasingly built and planned in these dry areas, more than ever before. But this trend is unfolding globally. Arid regions like Saudi Arabia and the United Arab Emirates are welcoming more data centers than ever before. Meanwhile, in China and India, an even greater proportion of data centers are located in drier areas compared to the US. Some of these sites are literal deserts. Globally, data centers consume about 560 billion liters of water annually and that could rise to about 1,200 billion liters by 2030, as tech firms push for bigger facilities stocked with more advanced AI computing chips that run hot. Now tech companies are trying new solutions, including data center and chip designs that let them use less water. Some are placing hot chips directly on cold plates that use water or else submerging chips and servers in liquid, a process known as immersion cooling. Businesses are also experimenting with synthetic liquids to cool data centers. But some coolants are being phased out from the market because they use so-called forever chemicals, which don’t naturally break down and can persist in animals, people and the environment. As #SiliconValley mulls solutions, water advocates say tech companies need to be more transparent about the problem. Almost no information about data center water usage on an individual system level is publicly available. Jennifer Walker, director of the Texas Coast and Water Program at the National Wildlife Federation, also said state officials need more information for water planning. But when the Texas Water Development Board sent a water use survey to data centers, it received a lackluster response, she said. “We just had one of the hottest summers on record in Texas, and we've had several of those,” she said. “I’m concerned about any super water-intensive industry that is going to come into our state.” 🎁 Read for free here: https://lnkd.in/exrEaSWU