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

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

Climate Week, New York- Day 2: The growth of AI requires energy consumption, and that’s a challenge. But AI can also help us solve for climate and community needs at incredible speed. Is this a dilemma? It depends on how you look at it - and I’m grateful to Ben Gemen from Axios for our thoughtful conversation about the big picture. At Amazon we’re taking a holistic approach to both make sure we support the growth of AI sustainably, and simultaneously harness it to help tackle critical climate challenges. Here’s just a few of the ways we’re using the technology (that may surprise you): 📌 After Hurricane Helene, Amazon’s Disaster Relief team used drones to capture 32,000 images across 28 miles of dangerous rivers and rough terrain. AI analyzed these images in seconds (vs. days for humans) and created detailed maps to prioritize search and rescue areas. The information enhanced safety for first responders and cut down on agonizing wait times for worried families. 🌊 Amazon Web Services (AWS) has partnered with The Ocean Cleanup to create an AI-powered "navigation system" that will help identify, track, and predict where plastic is floating in the Great Pacific Garbage Patch. With an estimated 1.8 trillion pieces spanning millions of square miles, this technology will be critical in optimizing cleanup operations. 💧 In Mississippi, we’re collaborating with Arable and MSU on AI-powered sensors to help farmers make smarter irrigation decisions. The sensors analyze real-time soil moisture, weather conditions, and crop water requirements, then AI processes historical patterns to deliver clear, actionable recommendations through a mobile app. This is expected to save 150 million gallons of water annually—enough for 1,600 households! 🍎 Our Amazon Fresh stores in India use machine vision to identify produce with minor imperfections in crates and on shelves, so the items can be redirected for sale at reduced prices rather than going to waste. These are just a few examples of how cutting-edge technology can be used for good! We’re just beginning to scratch the surface, but there’s no doubt AI can be a tool to catalyze meaningful change.

🔴 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

The advent of robotics in gardening and agriculture is poised to revolutionize the industry, driving significant changes in various aspects. What do you think about this solution? Increased Efficiency and Productivity: Precision Farming: Robots equipped with sensors and AI can analyze soil conditions, plant health, and weather patterns to optimize resource allocation, leading to higher yields and reduced waste. 24/7 Operation: Unlike human workers, robots can operate around the clock, maximizing productivity and accelerating crop cycles. Minimized Labor Costs: Automation of repetitive tasks like weeding, harvesting, and planting can reduce reliance on manual labor, lowering operational costs. Enhanced Sustainability: Resource Optimization: Robots can precisely apply water, fertilizers, and pesticides, minimizing environmental impact and reducing costs. Reduced Chemical Use: AI-powered robots can identify and target specific pests and weeds, limiting the need for broad-spectrum chemical treatments. Sustainable Practices: Robots can facilitate sustainable farming practices like precision agriculture and organic farming, promoting long-term ecosystem health. Improved Food Quality and Safety: Consistent Quality: Robots can maintain consistent standards for harvesting and processing, ensuring uniform product quality. Reduced Contamination: Automated systems can minimize the risk of contamination from human error or biological factors. Traceability: Robotics can enable precise tracking of food products from farm to table, enhancing food safety and traceability. Challenges and Considerations: Initial Investment: The high cost of robotic systems may be a barrier for small-scale farmers. Technical Expertise: Operating and maintaining complex robotic systems requires specialized skills and training. Job Displacement: Automation may lead to job losses in certain sectors, necessitating workforce retraining and upskilling. Ethical Concerns: The use of AI and robotics in agriculture raises ethical questions about the role of technology in food production and potential environmental impacts. The Future of Agriculture: The integration of robotics in gardening and agriculture is likely to reshape the industry, leading to increased efficiency, sustainability, and food security. While challenges remain, the potential benefits of this technological revolution are immense. As technology continues to advance, we can expect to see even more innovative applications of robotics in the years to come. #Ai #innovation #technology

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

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 

Digital Circular Economy 🌎 In the shift towards sustainable business practices, digital technologies offer transformative potentials for the circular economy. These technologies facilitate significant improvements across various circular business models, from design and manufacturing to life extension and resource recovery. As depicted in the recent visual framework, each stage of the circular process can be optimized through the strategic deployment of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence (AI), and big data analytics. For instance, IoT can enhance product lifecycle tracking, enabling more efficient reverse logistics and better product lifecycle management. Blockchain technology introduces unparalleled transparency and security in supply chains, making it easier to track the origin and handling of materials, which is crucial for recycling and remanufacturing processes. Meanwhile, AI and big data analytics can predict maintenance needs and optimize resource use, significantly extending the life of products and components. However, while technology provides opportunities for advancing circular business models, it's crucial to recognize and address potential adverse effects. The increased use of digital tools can lead to higher energy demands and contribute to electronic waste. These negative impacts necessitate a balanced approach where the benefits of digital applications are leveraged to enhance sustainability while mitigating undesirable outcomes. This balance is achieved by designing systems and frameworks that not only incorporate digital tools into circular business practices but also ensure that these tools are used in ways that prioritize environmental integrity and resource efficiency. For example, employing cloud computing solutions can decrease the need for physical infrastructure, reducing material use and energy consumption. As industries continue to integrate these technologies, it is imperative to continually assess their impacts, both positive and negative. By understanding and addressing these dynamics, businesses can more effectively harness the potential of digital technologies to drive the development of a more sustainable and economically viable circular economy. This approach ensures that technological advancements contribute effectively to environmental goals and the resilience of business operations. Source: OECD #circulareconomy #sustainability #climateaction #esg #circular #circularity

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

Today, Greenpeace Australia Pacific has released a major new report revealing that the unchecked roll-out of AI data centres in Australia is on track to derail our renewable energy transition, lock us into more polluting and expensive gas, and produce huge amounts of climate-wrecking carbon emissions. The report, written by climate and energy expert Ketan Joshi, is the first of its kind in Australia to map out the climate risks of the current data centre build-out. It’s essential reading to get a clear picture of the climate toll that our frenzied AI data centre build-out will have, and the extent to which Big Tech and Big Gas are joining forces to shill for fossil fuel-powered data centres. Greenpeace Australia Pacific is calling for the development of binding, legislated standards for AI development, including substantiated claims of additional renewable energy—and an urgent moratorium on data centre development until such safeguards are in place. Check out the report and share widely here: https://lnkd.in/gmHYyEti