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The rise of artificial intelligence is transforming every industry, but it also creates enormous demand for digital infrastructure and natural resources. Data centers, the engines of this transformation, consume vast amounts of water and energy. A single hyperscale data center consumes up to 5 million gallons of potable water every day. In Phoenix, 58 centers together demand more than 170 million gallons daily, enough to serve up to several hundred thousand households. This is the internets hidden water footprint, amplified by AI, cloud computing, and data-heavy services. Training a single large AI model in a Microsoft data center can require about 185,000 gallons of clean water. By 2027, AI-related data centers could consume 1.7 trillion gallons annually, nearly matching the domestic water use of some developed nations. Most data centers still rely on evaporative cooling, which consumes massive volumes and discharges chemical-laden wastewater. The challenge is not only scale but also geography. More than 40% of U.S. data centers are located in water-stressed basins. AIs rapid growth demands a new approach. Water cannot become the bottleneck to the next chapter of human progress. FROM COMMUNITY PUSHBACK TO BUSINESS RISK Public concerns are already reshaping the industry: Oregon: Google faced lawsuits over water secrecy. Indiana: Amazons Project Rainier is under state scrutiny for allegedly draining wells while pumping millions of gallons per hour. Georgia: Families near Metas complex report unusable wells. Virginia: Utilities now require new data centers to secure their own water sources or adopt closed-loop systems. Investors are paying attention. Water use per AI training cycle is emerging as a core accountability metric, alongside carbon intensity. Communities are responding with moratorium requests. THE CALL FOR INDUSTRY LEADERSHIP The industry can no longer rely on incomplete data, inconsistent reporting, or distant offsetting schemes. Declaring a water positive target by some far-off date is no longer enough. Communities demand tangible action where the water is drawn. The technology exists today. Around the world, data center and cloud providers are proving that sustainability and scalability can coexist, with each breakthrough setting a new benchmark for what is possible. Microsoft has deployed closed-loop systems in Arizona and Wisconsin, saving up to 125 million liters per site. Google used reclaimed wastewater at 22% of its campuses as of 2023. Amazon is building new centers with closed-loop treatment, recycling every drop used for cooling. NVIDIA is partnering with Singtel to deploy next-generation liquid and immersion cooling systems designed to achieve industry-leading water efficiency in Singapores new AI data centers. The opportunity is clear: Water must be engineered into AIs growth, not treated as an afterthought. THE TECHNOLOGIES DRIVING SUSTAINABLE AI Building a sustainable digital future requires bold adoption of both proven and emerging solutions that reduce environmental impact while enabling continued growth. The tools already exist. What we need now is the conviction to scale them. Smarter cooling technologies Closed-loop and liquid cooling: Advanced systems can reduce water consumption by as much as 30 to 50% while maintaining the high-performance environment that AI workloads demand. Water recycling at scale: Leaders like AWS plan to deploy treated wastewater at more than 120 data centers by 2030, setting a new baseline for responsible water use. AI-Driven optimization Smart workload scheduling: By applying AI to manage computing loads, operators have shown they can cut water consumption by a third without increasing carbon emissions. This type of efficiency breakthrough makes sustainability scalable. Alternative water sources Seawater desalination: In coastal or arid regions, seawater offers an abundant alternative. Advanced desalination technologies convert it into a reliable cooling supply without burdening municipal drinking water systems. High-value water reuse: Modern treatment technologies can transform sewage, brackish groundwater, and industrial effluent into high-quality process water, eliminating dependence on limited freshwater supplies. This is our approach at Gradiant, where our feedwater-agnostic treatment systems enable data centers to operate using seawater, wastewater, or other unconventional sources, reducing dependence on fresh supplies. By recycling blowdown and cooling tower reject, we achieve zero-liquid discharge and drastically reduce freshwater withdrawals, even in the largest hyperscale AI facilities. With the right technologies, sustainable AI data center growth can align with both environmental and business imperatives. AIS GROWTH HINGES ON WATER The next era of AI will be defined by those who treat water as critical infrastructure. Companies that lead will gain faster permitting, avoid regulatory shocks and operational disruptions, and build lasting trust with the communities that host them. Water is not compliance. It is resilience. It is innovation. It is license to operate. AIs future depends on leadership that recognizes water as the defining resource of our digital age, one that must be safeguarded through innovation rather than depletion. Advanced recycling, seawater desalination, and next-generation water treatment will be the pillars of responsible growth. The companies that act now will determine not only how AI grows but whether it grows responsibly, securing both digital progress and planetary resilience. Prakash Govindan and Anurag Bajpayee are the cofounders of Gradiant.
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Ralph Lauren revealed Team USA’s Milan Cortina Winter Olympics looks Thursday, complete with Americana knit sweaters and plenty of vintage call-backs.The formal opening ceremony look pairs a patterned red, white and blue knit sweater with tailored cream trousers and a matching wool coat. Moving sportier, the closing ceremony outfit features a graphic puffer coat inspired by vintage ski kits over a color-blocked sweater.“We are creating something that we know has to become timeless and has to be something that people will wear forever and appreciate forever,” said David Lauren, the Chief Branding and Innovation Officer at Ralph Lauren. “So in creating jackets like this and creating things, we’re looking at the things that we most cherish. Things that are already enduring parts of the Ralph Lauren lexicon, and then we’ll build on that.”Beyond the ceremony looks, a Team USA collection, which will also be given to athletes as Olympic village wear, became available to public Thursday.The collection follows similar design themes as the opening and closing ceremony looks, with classic red, white and blue patterning on lots of knits, and includes Ralph Lauren’s versions of winter staples like bomber jackets and hockey jerseys.The process of creating these looks is a long one. The Ralph Lauren team, which has been designing Team USA’s Olympic apparel since 2008, starts on each Olympics’ looks about 2 1/2 years out from the Games, meeting with athletes and brainstorming ideas for the kits. As Milan-Cortina’s looks are unveiled, Lauren said the looks for the 2028 Los Angeles games are already months in the making.He knows the cultural importance each Olympics’ outfits holds, and the attention they garner in the fashion world and among American consumers.“The fact that we know people will want them and collect them and chase them down across eBay, is just an exciting part of the game,” he said.Sometimes, even international Olympic athletes are on the lookout for them. Beyond being an addition to an American athlete’s Olympic wardrobe, the pieces are also sometimes used as bargaining tokens in the Olympic village.Para snowboarder Brenna Huckaby and snowboarder Red Gerard explained to The Associated Press that there’s a tradition of swapping team sweaters and jackets with other nations at the Olympics, if there’s a certain country’s design that catches an athlete’s eye. That’s only if there’s a piece of their collection that they’re willing to let go of, that is.“I rarely trade, because I almost always love every single piece of Team USA stuff,” said Huckaby, modeling the color-blocked closing ceremony sweater that she said “is going to be on rotation after.”“But every now and then there will be some random thing that another country has. And it’s so hard to sit with all my bags, all my stuff open, like, ‘OK, what am I willing to part with?’ That is probably, aside from competing, the hardest part of the Games,” she said. AP Olympics: https://apnews.com/hub/milan-cortina-2026-winter-olympics Alyce Brown, AP Sports Writer
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Welcome to AI Decoded, Fast Companys weekly newsletter that breaks down the most important news in the world of AI. Im Mark Sullivan, a senior writer at Fast Company, covering emerging tech, AI, and tech policy. This week, Im focusing on the increasing pressure on the AI industrys wunderkind, OpenAI. I also look at the change in AI leadership at Apple, and at the music industrys new cooperation with AI music generation apps. Sign up to receive this newsletter every week via email here. And if you have comments on this issue and/or ideas for future ones, drop me a line at sullivan@fastcompany.com, and follow me on X (formerly Twitter) @thesullivan. Is OpenAI still the king? The AI industry has always been very competitive, and its getting even more so. A relatively small group of AI labs are slugging it out to release the smartest models, and, by extension, the smartest chatbots. Ever since OpenAI released its ChatGPT chatbot three years ago, the upstart company has been seen as the leader, but that status has been called into serious question by Googles new Gemini 3 Pro model (and the Gemini app).ChatGPT has grown quickly. The official number is 800 million weekly active users. Googles number is 650 million monthly active users for the Gemini chatbot. So, apples and oranges. SimilarWeb provides a somewhat better comparison, saying that Geminis share of web traffic grew from 5.7% a year ago to more than 15% today. Meanwhile, ChatGPTs 87% share a year ago shrunk to 71.3% today. OpenAI is feeling the pressure from Gemini (and probably from Anthropics new Claude Opus 4.5 model). CEO Sam Altman sent a memo to staff Monday declaring a “code red” effort to improve ChatGPT, according to The Information and other outlets. The effort includes reducing investments in enhancing the health information available on ChatGPT, as well as reducing work on the shopping experience, and the advertising that could go around that. “Our focus now is to keep making ChatGPT more capable, continue growing, and expand access around the worldwhile making it feel even more intuitive and personal,” ChatGPT product lead Nick Turley tweeted Monday. In a wider sense, OpenAI is losing billions, and spending billions, a fact that must make its investors both nervous and curious. Leaked documents and analyst estimates show OpenAI will lose between $9 billion and $11 billion in 2025 (spending roughly $22 billion while bringing in about $13 billion in revenue). The company recently told investors that its spending through 2029 could rise to $115 billion. Altman has said his company, partners, and investors will commit as much as $1.4 trillion to infrastructure (chips, data centers, etc.) in the next eight years. OpenAI is an aggregator, as the analyst Ben Thompson points out. The fact that its willing to de-emphasize its shopping and advertising experiences, which are potential revenue generators, shows that its still in the mode of growing users, and not yet in the mode of growing revenue. And the way that aggregators (like Facebook) grow is by becoming more things to more people in order to maximize attention and engagement on its platform, regardless of whether the users are paid subscribers. In the aggregator model, actually monetizing all those eyeballs comes later. The confidence in that model, which requires constant growth toward a critical mass of users, has afforded OpenAI a certain swagger, and even a cavalier attitude about making returns for its investors. One of those investors, Altimeter Capitals Brad Gerstner, asked Sam Altman during an October podcast (12:30 mark) how he explains to the markets spending more than a trillion on infrastructure when his company is operating deep in the red. Altman was exasperated. Brad, if you want to sell your shares, Ill find you a buyer, he said. I just . . . enough. But it’s no longer clear that OpenAI has the best models and the go-to chatbot. Setting aside the shopping and advertising work, OpenAI is right to reassign its talent to work on new models and new skills for ChatGPT. This also might mean taking talent off fun projects like the Sora app, which seems far afield from the mission of making ChatGPT the highest performing chatbot available. On the other hand, things can change very quickly in the AI world. Reports say OpenAI is already set to release a new reasoning model codenamed Garlic that will overtake Gemini 3 on a number of key benchmarks. Well see if Garlic gets a better reception than GPT-5. Apple must keep publishing AI research under Subramanya This week Apple announced that its AI boss, John Giannandrea, will be leaving the company. Giannandrea had been a successful AI leader at Google, but his name is linked to Apples failure to seize on generative AI to improve its Siri voice assistant and make the iPhone and other iDevices smarter and more personalized. Hell be handing the reins to another Google vet, Amar Subramanya, who once led engineering on Googles Gemini chatbot, and is stepping down after seven years on the job. Apples stock price got a slight boost on the news, as some investors saw Apple signaling a new urgency to bring AI to its devices. Subramanyas remit will be restoring Apple to some kind of parity with its peers in developing AI models and applying them in meaningful ways. As Mark Zuckerberg can attest, achieving that goal will depend on recruiting and retaining top-shelf AI researchers. Giannandreas AI/ML group saw a lot of churn and lost a number of top shelf researchers to Meta and others, including Ruoming Pang and Robby Walker. One reason for this was the groups habit of investing time and labor in technical approaches to problems only to see them scrapped. Another was the slow pace of developing and releasing new AI features for products like Siri. Another problem is publishing. Apple is famously secretive about its R&D in all areas of the company. The company likes to talk about customer-facing products, and dislikes talking publicly about the technology that makes them work. AI researchers arent OK with that. They want to publish their research. They want the exposure and influence that can bring within an ultra-competitive industry. When Giannandrea came to Apple, the company began allowing its AI talent to publish more of their researchto the extent they could do so without revealing trade secrets. Apple now has a Apple Machine Learning Research web page that lists published papers, technical reports, and conference submissions. It will be crucial that Subramanya keeps this practice going, or expands it. Otherwis Apple risks losing key researchers to competitors. Record Labels are having their iTunes moment with AI The Music Industry has stopped suingAI music generation appsinstead, its making deals with them: The three major record labels have now signed licensing agreements with AI music startups. Warner Music Group, Universal Music Group, and Sony Music Entertainment have made licensing deals with an AI music startup called Klay Vision. The agreements grant Klay Vision permission to train its music generation models on music catalogs owned by the labels, replacing previous models that relied on scraped or unauthorized data. AI-generated music is getting more popular. An AI-generated song using a simulation of a real human country singers voice recently hit number one on the Billboard Country Digital Song Sales ranking. Suno, another AI music company that previously faced lawsuits from major labels, has signed what it calls a “first-of-its-kind partnership” with Warner Music. The deal moves the company toward licensed, artist-opt-in AI models. The moment feels similar to the record labels decision in the early 2000s to sell digital music on Apples iTunes platform. The labels saw CD sales tank as consumers downloaded free MP3s from sites like Napster and Limewire. More AI coverage from Fast Company: The Trump administration keeps taking stakes in chipmakers it may come back to haunt them Will chatbots ever be funny? Why these comedians arent worried about an AI takeover, yet Can your AI adapt to multiple cultures? 10 ways I use AI to be a better journalist Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? Sign up for Fast Company Premium.
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