AI's Growing Demand for Energy Spurs Interest in Next-Generation Nuclear Power
The increasing demand for energy from AI data centers is driving unprecedented investment in next-generation nuclear power plants, which could be cheaper to construct and safer to operate than their predecessors, according to MIT Technology Review. These facilities require a massive amount of energy to support their computational needs.
The growth of metal-intensive data centers, electric cars, and renewable energy projects is rapidly increasing the demand for metals like nickel, copper, and rare earth elements, MIT Technology Review reported. However, producing these metals is becoming harder and more expensive as miners have already exploited the best resources. In Michigan's Upper Peninsula, the only active nickel mine in the US, Eagle Mine, is nearing the end of its life as nickel concentration falls. Biotechnology could potentially extract the metal needed for cleantech.
Meanwhile, European security officials believe that Russian space vehicles have intercepted the communications of at least a dozen key satellites over the continent, Ars Technica reported. These interceptions risk compromising sensitive information and could allow Moscow to manipulate the satellites' trajectories or even crash them. Russian space vehicles have shadowed European satellites more intensively over the past three years, amid high tension between the Kremlin and the West following Russia's invasion of Ukraine.
In other news, a Senate report released by Sen. Bernie Sanders (I-Vt.) concluded that the Trump administration was "destroying medical research" at the National Institutes of Health (NIH), Ars Technica reported. Jay Bhattacharya, director of the NIH under the Trump administration, testified before the Senate Committee on Health, Education, Labor, and Pensions (HELP) on Tuesday.
Additionally, the initial excitement around Generative and Agentic AI has shifted to a pragmatic, often frustrated, reality, according to VentureBeat. CIOs and technical leaders are questioning why their pilot programs aren't delivering the promised results. The issue is not the AI model's intelligence, but rather its lack of context, which is often trapped in a "Franken-stack" of disconnected point solutions and brittle APIs.
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