Understanding the Environmental Impact of AI Infrastructure

Can you believe it has already been a year since #ChatGPT launched? Since its emergence, #artificialintelligence has captured global dialogue, from its potential #workforce impact to implications for education and art. But we’re missing a critical angle: AI’s #carbonfootprint. Examining ChatGPT’s usage can help us gain insight into its environmental impact. As of February 2024, the platform’s 100 million+ weekly active users are each posing an average of 10 queries… That’s ONE BILLION queries per week, each generating 4.32g of CO2. By plugging these estimations into an emissions calculator, I found that EVERY WEEK the platform is producing emissions roughly equivalent to 10,800 roundtrip flights between San Franciso and New York City (enough to melt 523,000 square feet of Arctic sea ice). Scientists have already warned the Arctic could be free of sea ice in summer as soon as the 2030s. And something tells me they weren’t factoring ChatGPT and other energy-demanding AI models into those projections. Further, this is based on estimated *current* ChatGPT use, which will only grow as society gets accustomed to the tool and as AI becomes more a part of everyday life. Some analyses indicate that by 2027, ChatGPT’s electricity consumption could rival that of entire nations like Sweden, Argentina, or the Netherlands. The platform is taking precautions, however, such as using Microsoft’s carbon-neutral #Azure cloud system and working to develop more #energyefficient chips—so it could certainly be worse. But, it could also be better. So let’s hold OpenAI accountable to mitigate their damage before it gets out of control. Join me in letting them know the public is watching their environmental impact and that they must responsibly manage the platform’s rapidly growing carbon footprint. (Pictured: Microsoft GPU server network to power OpenAI's supercomputer language model. Image courtesy of Microsoft/Bloomberg).

Headline: China Sinks Data Centers into the Ocean to Tackle AI Cooling Crisis ⸻ Introduction: To support its aggressive push into artificial intelligence and cloud computing, China is rapidly expanding its data center infrastructure. But this expansion poses a growing challenge: how to cool vast server farms without depleting precious water supplies. In a bold and innovative move, China is deploying data centers underwater, turning to the ocean as a sustainable cooling solution—and in doing so, it may be outpacing the rest of the world. ⸻ Key Details: 1. AI Demands Fuel Data Center Growth • China’s economic strategy prioritizes AI, digital infrastructure, and cloud computing as critical engines of future growth. • These technologies depend on high-performance data centers, which consume massive energy and water resources for cooling. 2. Water Scarcity vs. Data Center Demand • Traditional land-based data centers use hundreds of thousands of gallons of water per day to dissipate heat. • Many are located in arid regions like Arizona, Spain, and parts of the Middle East due to their low humidity, despite water scarcity in these areas. • As these centers proliferate, they compete directly with agriculture and human consumption, prompting sustainability concerns. 3. China’s Ocean-Based Solution • In response to the growing water challenge, China is leading the deployment of underwater data centers, placing them offshore to utilize natural ocean cooling. • This method drastically reduces water usage and energy costs while avoiding the land-use conflicts associated with traditional facilities. • China’s efforts appear to be ahead of other nations, which have only experimented with submerged servers on a limited scale. 4. Environmental and Strategic Implications • Underwater data centers may reduce carbon footprints and eliminate the need for massive evaporative cooling systems. • However, there are questions about long-term maintenance, ecological impact, and geopolitical access to maritime infrastructure. • The shift could reinforce China’s position in the global AI arms race by improving data center efficiency and reducing operational constraints. ⸻ Why It Matters: As AI continues to drive demand for computing power, the environmental costs of data centers—especially water usage—are becoming unsustainable. China’s underwater strategy not only offers a bold path to sustainability but also serves as a geopolitical differentiator in the digital era. If successful at scale, ocean-based data centers could reshape the future of computing infrastructure worldwide, offering a cleaner, cooler alternative to traditional server farms on land. https://lnkd.in/gEmHdXZy

At a recent conference, someone asked me about AI’s environmental impact, a question we all need to sit with more seriously. Training large AI models consumes enormous resources. GPT-3 alone used over 1,200 MWh of electricity and approximately 700,000 liters of water. As demand for compute grows, so does AI’s energy footprint, expected to reach 20 to 50 percent of global data center usage by 2025. This is not sustainable unless we act deliberately. Fortunately, researchers and industry leaders are innovating: optimizing architectures, capping compute, and powering data centers with renewables. Small changes in model design and infrastructure can reduce energy usage by up to 90 percent without sacrificing performance. “AI’s true power lies in advancing both innovation and sustainability. Progress that drains the planet isn’t progress at all” Asha Saxena #SustainableAI #ResponsibleTech #LeadershipInAI #AIandClimate #TheAIFactor

Energy is the key. Over the weekend I got a lot of messages about articles and stories talking about the links between the energy hungry AI models and the path to net zero. On one hand, the computational power required to train and run AI models is soaring, placing increasing demands on our energy grids. On the other, AI itself holds remarkable potential to drive innovations in climate technology, potentially aiding our quest for net zero emissions. This dichotomy presents both a formidable challenge and a beacon of hope in our journey toward a sustainable future. Advanced AI models, particularly those involved in machine learning and deep learning, require substantial computational resources. Training a single AI model can consume as much electricity as several hundred homes use in a month. As AI becomes more integrated into our daily lives, from autonomous vehicles to personalized medicine, the demand on energy grids will inevitably rise. This surge complicates our path to achieving net zero emissions, as increased energy demand generally translates to higher carbon footprints unless met entirely by renewable sources. However, the same technology that poses such a challenge also harbors solutions to some of the most pressing environmental issues. AI can optimize energy consumption in industries and homes, create more efficient renewable energy systems, and improve waste management practices. For example, AI algorithms can predict energy demand more accurately, enabling smarter grid management and reducing reliance on fossil fuel-powered peaker plants. In renewable energy, AI can enhance the efficiency of solar panels and wind turbines by optimizing their placement and operation based on weather predictions. Moreover, AI-driven innovations in materials science are paving the way for more efficient batteries and renewable energy storage solutions, addressing one of the significant hurdles in the transition to green energy. AI also plays a crucial role in monitoring and combating climate change. Through the analysis of satellite imagery and environmental data, AI can track deforestation, ocean health, and the melting of polar ice caps with unprecedented precision and speed. This capability not only informs better policy and conservation efforts but also helps in quantifying the impact of climate action, making it a potent tool in the global effort to mitigate climate change. The dual role of AI as both a contributor to and a solver of the energy and climate crises underscores the need for a balanced approach in its development and deployment. By prioritizing energy-efficient AI models and leveraging AI to accelerate the transition to renewable energy, we can harness the power of AI to move closer to our net zero goals, turning a formidable challenge into a formidable ally in the fight against climate change. At Nadia Partners we are building companies on both sides to help achieve both goals. Anybody who can help us please tag or share!

#SustainableAI :: Understanding #AI's full #climate impact means looking past model training to real-world usage, but developers can take tangible steps to improve efficiency and monitor #emissions -- writes Lev Craig on TechTarget. Building more sustainable AI will require a multifaceted approach that encompasses everything from model architecture to underlying infrastructure to how AI is ultimately applied. A 2019 paper estimated that training a big transformer model on GPUs using neural architecture search produced around 313 tons of carbon dioxide emissions, equivalent to the amount of emissions from the electricity that 55 American homes use over the course of a year. It remains difficult to accurately estimate the environmental impact of AI. But although obtaining accurate sustainability metrics is important, developers of machine learning models and AI-driven software can make more energy-efficient design choices even without knowing specific emissions figures. For example, eliminating unnecessary calculations in model training can reduce overall emissions. Incorporating sustainability monitoring tools can help technical teams better understand the environmental impact of their models and the applications where those models are used. For example, the Python package CodeCarbon and open source tool Cloud Carbon Footprint offer developers snapshots of a program or workload's estimated carbon emissions. Similarly, IBM - Red Hat's #Kepler tool for #Kubernetes [https://lnkd.in/gr_yzdAW] aims to help DevOps and IT teams manage the energy consumption of Kubernetes clusters. As regulations evolve, taking a sustainability-first approach will set companies up for success compared with simply playing whack-a-mole with each new policy that comes out. Thus, putting in the upfront effort to build more sustainable AI systems could help avoid the need to make costly changes to infrastructure and processes down the line. #Sustainability #carbonfootprint #netzero #ESG Sandro Mazziotta :: Vincent Caldeira https://lnkd.in/gjNF3EUk

Data Centers & The Thirst for Water: A Sustainability Challenge in the AI Era. In the digital age, data is often called the "new oil." However, while the environmental impact of the oil industry is well-documented, the growing water consumption by data centers across the globe remains relatively uncharted territory. According to recent data from Bloomberg, data centers are anticipated to consume a staggering 450 million gallons of water daily by 2030, a significant leap from around 205 million in 2016. For example, a $1.1B data facility in Spain’s Talavera de la Reina, planned by Meta, can potentially consume an alarming 176 million gallons annually. Meta has given assurances about restoring water, but the specifics remain uncertain. This isn’t a localized issue. From the copper-rich terrains of Chile to the Silicon Valleys in the US and even the tulip-laden fields of the Netherlands, the war between data center operations and water conservation is becoming palpable. A critical area that needs immediate attention is transparency. Only 39% of data centers recorded water usage last year, which is troubling. It underscores the need for stringent guidelines and better reporting standards. Now, you know me, I've been pushing for AI and its benefits very heavily. However, with the AI-driven demand for computing power skyrocketing, sustainability initiatives need to keep up. While AI brings immense potential in revolutionizing sectors and optimizing processes ( I have been preaching about this), we must pause and reflect on its environmental implications. In my 35+ years working at the intersection of IT and innovation, I've witnessed the transformative power of technology. But as stewards of our planet, we must strike a balance. Sustainable AI and green data centers aren’t just buzzwords; they’re imperatives for the next frontier of tech and the future of our planet. What ideas do you have? Environment vs AI? Please let me know what your thoughts are. #DataCenters #Sustainability #WaterCrisis #AIChallenges #generativeai

As we enter into a global AI arms race, the data economy is ‘returning’ from distributed processing to an industrial-like era of large ‘factories’ with massive local resource footprints. We’ve all heard the statistic about ChatGPT using a bottle of #water per conversation, but these filings are bringing new insights to light about the very localized interplay between AI and water/energy. It turns out that the environmental impact of developing AI is highly contingent on the placement of relatively few massive compute facilities, as the training of these products generally needs to be localized due to the massive flux of data. This highlights the need to better understand #datacenter cooling practices and alternatives, as well as seasonality of cooling demand in the face of a changing climate. If the statistics below are correct for Iowa, we can only imagine the summertime cooling demand for ‘AI Factories’ in desert latitudes (see below quote on Las Vegas). Fortunately, there ARE alternatives. We can integrate datacenters with local water and wastewater infrastructure to leverage water for its value as a heat sink— but without evaporative losses. Since me and Ufuk Erdal Ph.D., P.E. starting presenting on this issue roughly two years ago, awareness has grown tremendously about the problem, but relatively few of us are discussing novel cooling solutions. To learn more, check out our upcoming presentation at #Weftec2023… “Google reported a 20% growth in water use in the same period, which Ren also largely attributes to its AI work. Google’s spike wasn’t uniform -- it was steady in Oregon where its water use has attracted public attention, while doubling outside Las Vegas. It was also thirsty in Iowa, drawing more potable water to its Council Bluffs data centers than anywhere else… In July 2022, the month before OpenAI says it completed its training of GPT-4, Microsoft pumped in about 11.5 million gallons of water to its cluster of Iowa data centers, according to the West Des Moines Water Works. That amounted to about 6% of all the water used in the district, which also supplies drinking water to the city’s residents. In 2022, a document from the West Des Moines Water Works said it and the city government “will only consider future data center projects” from Microsoft if those projects can “demonstrate and implement technology to significantly reduce peak water usage from the current levels” to preserve the water supply for residential and other commercial needs.”

Microsoft just bought over $1B worth of human poop. Yes, seriously. And it might be one of the smartest AI investments they’ve made. Here’s what actually happened: Microsoft signed a 12-year deal with Vaulted Deep, paying to remove 4.9 million metric tons of human and agricultural waste. Why? To offset the carbon emissions its data centers,and AI ambitionsare generating at massive scale. Because GenAI isn’t just an innovation race. It’s an infrastructure war. Every prompt. Every training run. Every inference. They all burn compute. And compute burns carbon. So Microsoft’s move to bury waste and earn carbon credits? → A hedge against regulatory heat → A message to Wall Street → A roadmap for anyone building in AI Here’s what most teams get wrong about scaling GenAI: Ambition grows faster than infrastructure Shipping AI ≠ Scaling it Outcomes matter more than acronyms The lesson for founders, CMOs, and GTM leaders? If your AI story doesn’t include sustainability, you’re shipping half a strategy. And it’s not just Microsoft. Top GSIs like Accenture, Deloitte, Capgemini, and Infosys are investing billions to align AI growth with governance, ESG standards, and infrastructure constraints. And the same goes for agencies. From Publicis Sapient to WPP to agency-integrators like Accenture Song. AI is forcing a shift from storytelling at scale to sustainability at scale. Because if creative platforms are powered by LLMs, clients will ask not just how it performs, but what it costs the planet. Their clients are no longer asking: “Can we use GenAI?” They’re asking: “Can we trust it?” “Can we report on it?” “Can we scale it without breaking the planet, or the brand?” - The result? A new partner mandate: → Help me build smarter → Help me scale responsibly → Help me report transparently Because if Microsoft is already solving for carbon scrutiny... How long until your customers, partners, and investors start asking too? #ArtificialIntelligence #Sustainability #TechNews

Research continues to show the high environmental cost of GenAI tool development and deployment. We’ve created this classroom guide to help educators get a better understanding and engage their students in thoughtful discussions on the potential impacts of GenAI on the planet. Researchers estimate that creating ChatGPT used 1,287 megawatt hours of electricity and produced the carbon emissions equivalent of 123 gas-powered vehicles driven for one year. It's development created substantial heat that required a significant amount of water to cool down those data centers – and for every 5-50 prompts it requires about 16oz of water. Generating an image can be especially energy-intensive, similar to fully charging your smartphone. Creating 1,000 images with Stable Diffusion is responsible for as much CO2 as driving 4.1 miles in a gas-powered car. Some researchers estimate the carbon footprint of an AI prompt to be 4-5 times that of a normal search query. And the impact of escalating use predicted by 2027 could mean AI servers will use as much electricity as a small country. Check out the carousel for more including discussion questions and further reading. Or download a PDF version for your classroom here: https://lnkd.in/eaCtnN3n AI for Education #aiforeducation #aieducation #AI #GenAI #ChatGPT #environment #sustainability

Energy Impact of AI I have spoken about the energy impact of software and AI in particular, and as we wind down the weekend, I came across a thought-provoking stat, which will change the way I interact on the web: A single ChatGPT query can emit up to 4.32** grams of CO₂ In comparison, a Google search emits only about 0.2 grams of CO₂ per query. To put this into perspective - Here’s what the equivalent CO2 usage is by query: 15 queries = watching one hour of videos 16 queries = boiling one kettle 20-50 queries is the equivalent of consuming 500ml of water. 139 queries = one load of laundry washed at 86 degrees Fahrenheit, then dried on a clothesline 92,593 queries = a round-trip flight from San Francisco to Seattle The computational intensity, data center operations, and energy demands of large language models like ChatGPT contribute to its significantly higher carbon footprint. As we embrace the power of AI, it's crucial to consider the environmental impact of these technologies. This disparity highlights the need for more energy-efficient AI models and the importance of using renewable energy sources to mitigate the carbon footprint of our digital interactions. So next time let's search Google before we start chatting with these LLM models! ** Source: https://lnkd.in/gCTAHBdN