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

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!  

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

Robots are starting to reshape the installation of solar panels. Chinese company Leapting recently rolled out its AI-controlled robot in Australia for its first commercial deployment — installing 10,000 panels at Neoen’s Culcairn solar farm in NSW. It makes a lot of sense. The largest solar farms have over a million panels, each weighing around 30 kg and requiring 3-4 people to handle. This translates to an installation rate of about 100 panels per 8-hour day. By comparison, Leapting says its robot can install 3-5x as many, at an average rate of 60 modules an hour. As well as the sheer scale of the work involved, large scale solar farms are often located in remote areas with harsh construction environments and strong sunlight, not to mention the heat in countries like Australia. Leapting hopes the use of robots will help address worker shortages and reduce the amount of downtime due to injury. The robot itself consists of a 2.5m high robotic arm mounted on a self-guided and self-propelled crawler. It has its own navigation system, uses visual recognition to adapt to different terrain, and multimodal sensors ensure each panel goes in the right place. Next up, Leapting will deploy this robot and several others to another solar farm in Australia, where together they will install half a million panels. And Leapting isn’t alone — many other companies are exploring the use of robots to speed up solar module installations. Expect to see a lot more of this in the coming years. Video credit: Leapting #energy #renewables #energytransition

Microsoft and Meta Embrace New Power Design for AI Infrastructure: As data center rack densities rise to support more powerful GPUs for AI workloads, power distribution must also evolve. That's why Microsoft and Meta are collaborating on a design that will shift power conversion into a separate rack, laying the groundwork for denser and more configurable server racks. This disaggregated rack design, known as Mt Diablo, will initially use 48Vdc but will enable a shift to a 400Vdc power distribution system for AI data centers. The Mt Diablo project was disclosed at the recent Open Compute Project Foundation summit, and the architectural spec will be contributed to OCP to encourage further collaboration and development. "The need for scalability and future-proofing is driven by high-power server racks, which will exceed a few hundred kilowatts and are moving towards a megawatt," said Microsoft. "Our solution is to separate the single rack into an server rack and a power rack, each optimized for its primary function. With this approach, we can right-size the power shelf count to meet each configuration’s unique needs." The Meta team describes it as "a cutting-edge solution featuring a scalable 400 VDC unit that enhances efficiency and scalability. This innovative design allows more AI accelerators per IT rack, significantly advancing AI infrastructure." The companies say this approach will allow them to deploy 35% more accelerators in each rack, and the shift to 400Vdc will bring greater efficiency as data centers shift to extremely dense AI clusters. Mt Diablo has a modular design to support scalability and future-proofing as server racks grow denser, as well as different power configurations. Here's where you can learn more: Microsoft blog post: https://lnkd.in/e_tcGkEy Meta's blog post: https://lnkd.in/e6UeS86Q Open Compute presentation: https://lnkd.in/emjHAGji

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

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.

Last month's World Economic Forum's Annual Meeting in Davos served as a crucial platform for leaders to engage in meaningful discussions surrounding the complexities of #GenAI.  I’ll devote the next few weeks to addressing these critical questions and hearing your thoughts on AI topics that need attention.         First, I’m exploring whether AI will catalyze inclusive growth or contribute to greater societal inequality. As we stand at the beginning of the #generativeAI era, our choices about ethical AI development, government oversight, and equal access and distribution of its benefits will set the tone for how businesses operate and how we live and interact with each other.          Drawing historical parallels makes it easy to see how innovation impacts societal gaps. For example, the Industrial Revolution deepened societal inequalities as the elite benefited while the lower class endured harsh working conditions. However, trade unions intervened to support workers’ rights, leading to more inclusive growth.      Similarly, a "digital divide" emerged in the Digital Revolution, with a small segment of the population reaping disproportionate benefits. But the benefit extended to all as governments facilitated more equitable access.       As we navigate the GenAI era, however, there is reason for optimism.  1. Ubiquity of smartphone access (w/ connectivity) brings AI to every user across the developed and developing world, instantly.  2. The emerging world stands to gain more quickly as a chronic shortage of teachers and medical professionals can be compensated for, leading to healthier and better educated populations, thus shrinking the human capital divide.  3. Governments, civic and non-profit agencies can have access to almost real time data about people in need, thus helping provide better citizen services.  4. Across most job types, GenAI is more likely to “augment” than “replace” leading to more productivity-driven GDP growth than we’ve seen in the last few decades. This should lead to inclusivity if governments distribute the benefits more equally.    The key factors in ensuring inclusive growth are equal access, equitable distribution of benefits (including through taxation), ethical development (free of bias) and responsible usage. Governments will have a huge part to play by ensuring these through optimal policies and regulation.     At NTT, we believe in the democratization of GenAI. We envision a future where a variety of special purpose LLMs operate in concert with each other on cheap and power efficient infrastructure (vs. a few general purpose LLMs with a winner-takes-all dynamic). To that end, we are innovating to enable low cost, low power consumption infrastructure IOWN, and have developed our very light weight and power efficient LLM (Tsuzumi) that can inter-operate with other LLMs.  Do you think GenAI will be the great equalizer? I’d love to hear what you’re doing to ensure the technology drives inclusive growth.