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

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

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

AI is running out of power in the Western world. What's the solution? Demand is exploding so super fast that the electrical grid can’t keep up. AI Agents use 100x more energy than a Google search. Millions of AI agents will simultaneously research companies, write reports, book meetings, analyse legal documents, and execute workflows 24/7!! Compute for AI is going parabolic. We’re still underestimating the scale of the energy bottleneck behind AI. There isn’t enough power available for all the data centres. The US, for example, is heading towards a 70-80 GW power deficit. That’s equivalent to the electricity demand of roughly 60-67 million average US homes!! Gas is the current makeshift solution, but new gas turbines take around 7 years to deploy, and they're terrible for CO2 emissions and the health of those living nearby. There’s also real fear that electricity prices will keep rising. The world needs immediate solutions. Here are some of the startups that I find really promising that are building the solutions: Starcloud: Their solution is to move data centres into space. The idea is to use abundant solar power and radiative cooling to run AI compute without relying on Earth’s overstretched grids. Fuse Energy: Their aim is to make household energy cheaper, cleaner and more digitally managed. They’re scaling extremely fast, generating hundreds of millions in recurring revenue, and have already reached a $5 billion valuation! Exowatt: In a nutshell, they capture solar energy during the day, store it as heat and then convert it back into electricity when needed. So effectively, solar becomes 24/7 power. Which is a game-changer considering AI needs constant, reliable energy! The AI boom could turn electricity into one of the world’s most strategic resources. Have you heard of any other interesting solutions tackling this problem?? And longer term… Do you think AI companies should build their own power infrastructure?

🔴 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

How AI is helping protect giraffes 🦒 For decades, tracking individual giraffes meant sketching spot patterns in notebooks or spending weeks poring over photographs. Now artificial intelligence has given conservationists in Tanzania a faster way, reports Abhishyant Kidangoor. The Wild Nature Institute, co-founded by biologists Monica Bond and Derek Lee, has teamed up with Microsoft’s AI for Good Lab to launch Project GIRAFFE, an open-source tool that uses algorithms to identify and re-identify individual animals by their markings. “It can now be done in minutes, and we can have the output the same day we collect the data,” says Lee. The technology matters because survival and reproduction rates, movements, and social behavior can only be measured if scientists know which giraffes they are seeing. Once-healthy populations have dwindled across Africa due to poaching and habitat loss. The Masai giraffe, Tanzania’s national animal, has declined by half in recent decades. Project GIRAFFE allows researchers to process millions of photographs gathered during annual surveys. The data help pinpoint strongholds for the species and reveal landscapes where giraffes are struggling. Protecting their habitat brings wider gains, since many other species share the same ecosystems. “A big part of our mission is to see that people and the giraffes are both thriving together,” says Lee. 📰 story: https://lnkd.in/gnArQm2U

Artificial Intelligence's rapid growth is not just a trend, it's a force that is driving up electricity demand, which is already challenging the power grid and tech companies. The strain is real and immediate. The boom in Artificial Intelligence is leading to a significant increase in electricity usage, putting a strain on the already stressed power grid. From simple ChatGPT queries to complex AI-generated images and videos, the demand for power is escalating rapidly. Data centers, which consumed more power than entire countries in 2023, are at the forefront of this surge. Experts predict that if AI's power needs continue to grow at this rate, it could potentially outpace the grid's capacity, leading to a significant increase in reliance on non-renewable energy sources, a scenario that should raise concerns. ⚡ Soaring Electricity Consumption: Even simple AI tasks, like ChatGPT queries, consume significant power, equivalent to a 60-watt bulb running for 10 minutes, highlighting the intensive energy needs of AI technology. 🌍 Massive Data Center Demand: In 2023, data centers used more electricity than nations such as Italy and Taiwan. Their energy demand has surged over seven times since 2008 despite advancements in energy-efficient chips. 📈 Projected Growth: According to the Boston Consulting Group, data centers' power consumption could rise to 7.5% of the global total by 2030, tripling from current levels. This could overwhelm existing power generation capacities and strain renewable energy sources. 🌪️ Regional Vulnerabilities: In regions like Texas, which experienced deadly blackouts in 2021, the rising energy demands from AI data centers and crypto miners could lead to grid instability and increased risk of outages. ♻️ Energy Source Challenges: While tech companies aim to use green energy, the high consumption by data centers often exhausts available renewable resources. This forces power providers to rely more on non-renewable energy sources to meet overall demand. #AIBoom #ElectricityDemand #PowerGrid #DataCenters #RenewableEnergy #TechIndustry #EnergyConsumption #AIGrowth #SustainableTech #EnergyChallenges 

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

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