Want to get a data center online quickly? As the data-center boom puts pressure on the grid, some companies say the answer isn't just more power plants but software that dials down centers' energy-guzzling ways when demand spikes. At the end of a tense and scoreless first half of a soccer match between the English men's team and rival Germany, millions of Brits let out a collective sigh and did what they so often do in moments of stress: They made tea. That wave of electric kettles clicking on, however, caused a different kind of stress: a huge and sudden increase in demand for electricity. But National Grid, which operates the local transmission network, was ready. Just as those kettles started heating up, an AI program sent instructions to a data center in London to slow down some of the facility's power-hungry chips. This reduction helped make sure there was enough supply to match demand, staving off potential blackouts or damage to electrical hardware.

The federal government is planning to let a rule regulating federal data center operations sunset in September with no replacement. The US government is quietly planning to allow a rule outlining the standards for federal data center usage and operations, known as the Federal Data Center Enhancement Act (FDCEA), to expire, according to sources who spoke to WIRED. Neither Congress nor the Trump administration appears to be making significant moves to protect or extend the rule, or put alternate plans in place. Data centers have become a hot-button issue in recent months, as the tech industry goes all in on artificial intelligence and the infrastructure needed to power it. According to a Gallup poll from May, more than 70 percent of Americans oppose the construction of data centers, the energy-and water-intensive buildings that power the AI boom, in their communities.

SpaceX's stock market debut has thrust the richest man in the universe into an unexplored frontier of wealth. There are thousands of billionaires across the world. But there is only one trillionaire. Elon Musk became the first person to amass a personal fortune of over $1,000,000,000,000--that's 12 zeros--after shares of his rocket company SpaceX debuted on the Nasdaq stock exchange on Friday. SpaceX's initial public offering on Thursday valued the company at nearly $1.8 trillion, up from its most recent private valuation of around $1.25 trillion.

With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system. China is submerging data centers into the ocean to keep them cool.Photograph: Shanghai Hailanyun Technology China has become the first country in the world to operate an underwater data center, or UDC, powered by wind. Located off the coast of Shanghai, the complex represents a significant advance in the country's strategy to secure energy supplies in the face of the accelerated growth of artificial intelligence, reduce dependence on fossil fuels, and reduce the environmental impact of its technology infrastructure. The initiative is the result of a collaboration between private company HiCloud Technology and state-owned China Communications Construction, which involved an investment of 1.6 billion yuan, equivalent to about $236 million. With an initial capacity of 24 megawatts, the facility is submerged at a depth of 10 meters in the Lin-gang Special Zone, within the China Pilot Free Trade Zone in Shanghai.

To meet this moment, we need YOU. For five decades, has been exposing the corruption that the powerful would rather keep buried. That fight for the truth is at a pivotal point, and it takes readers like you to make it possible. To meet this moment, we need YOU. That fight for the truth is at a pivotal point, and it takes readers like you to make it possible. Amid public outcry over water-guzzling server farms, a Guardian analysis indicates trouble ahead.

Elizabeth Warren is a U.S. Senator from Massachusetts. Senator Elizabeth Warren speaks on the floor of the New York Stock Exchange on Wall Street on April 17, 2025 in New York City. Senator Elizabeth Warren speaks on the floor of the New York Stock Exchange on Wall Street on April 17, 2025 in New York City. Elizabeth Warren is a U.S. Senator from Massachusetts. Americans are hanging on by their fingernails in an economy that funnels wealth to the ultra-rich and leaves crumbs for working people.

Best Power Banks Best Smart Rings Routers vs. Modems Choose the Right Laptop Smart Sprinklers Deals Delivered The company is mapping Alexandria and, soon, Arlington--right across from the power center of Washington, DC. Self-driving cars aren't yet permitted to operate in Virginia. But Alphabet-owned Waymo began transporting its cars to the state last week, a Waymo representative told Virginia officials, to map Arlington and Alexandria, in the northern part of the state. For most autonomous vehicle companies, mapping, or the creation of sensor-aided and ultra-precise digital representations of streets and the features around them, is the first step required to launch a local robotaxi service. Drivers will operate the mapping vehicles for now, Waymo says.

Ford and GM are backing away from electric vehicles and moving into the battery storage business. And it all comes back to AI. Automakers make cars--it's in the name. But lately, politics, current events, and Wall Street's latest preoccupation, artificial intelligence, have them looking a lot more like energy companies. The pivot, analysts say, could give US auto manufacturers struggling through a transition to electric vehicles an easier path over the next few years. Whether it works will come down to the same technology that automakers once promised would power the majority of their lineups: batteries .

Americans really don't want AI data centers close to their homes Americans really don't want AI data centers close to their homes AI companies are spending astronomical sums of money on building data centers as quickly as possible in order to increase their compute power. But the majority of Americans don't want that infrastructure close to their homes, according to a Gallup survey . The polling company asked 1,000 adults across the US about their views on AI data centers, and 71 percent were against having one in their local area. Almost half of the respondents (48 percent) were strongly opposed. On the flip side, just seven percent were strongly in favor of having a data center close to their home.

Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers. It is imperative for AIGC service providers (ASPs) to strategically schedule AIGC workloads to reduce data center energy costs while guaranteeing high-quality content generation. However, the distinctive characteristics of AIGC services pose critical challenges, including model heterogeneity across ASPs, implicit service quality evaluation, and complex inference process control. To tackle these challenges, we propose a joint energy management and coordinated AIGC workload scheduling framework, which introduces an explicit mathematical characterization of service quality to promote both job transfer among ASPs and fine-grained inference process configuration. Moreover, various energy resources within data centers are jointly considered to enhance power usage flexibility. Subsequently, a system utility maximization problem is formulated to balance AIGC service revenue with operational penalties and costs. Nevertheless, the strong coupling among job scheduling decisions induces severe reward sparsity, which limits the effectiveness of existing deep reinforcement learning (DRL) algorithms. To address this issue, we develop a diffusion model-aided reward shaping approach to synthesize complementary reward signals through a multi-step denoising process. This approach is seamlessly integrated with DRL to enable efficient learning of scheduling policies under sparse environmental feedback. Experiments based on real-world models and datasets demonstrate that our scheme effectively accommodates electricity price fluctuations and AIGC model heterogeneity, while achieving superior learning convergence and system utility compared with benchmark methods.