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

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

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

I entered the sustainability field to build a resilient future for people and the planet - not to wrestle with manual spreadsheets. But as many of us in this space have discovered, the time-consuming logistics of reporting are often a barrier to real progress. At Google, we’ve spent the last two years using our own environmental report as a testing ground for a better way. By leveraging Google Cloud tools to automate data ingestion and claim validation, we’ve shifted from weeks of manual data cleaning to on-demand strategic insights. These technologies don’t replace our experts. Instead, they free our team to focus on strategy and execution rather than repetitive, time-consuming data collection and validation. We’re already seeing how other companies can use these tools to make similar shifts. For example, Equinix moved from manual tracking to a system that collects data from 240+ global sites automatically. Learn more about how Google Cloud is helping sustainability teams spend more time on strategy, not spreadsheets. ⤵️ https://goo.gle/4scTUfR

🔴 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

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

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