"SAN FRANCISCO (AP) Artificial intelligence company Anthropic announced a $50 billion investment in computing infrastructure on Wednesday that will include new data centers in Texas and New York. Anthropic, maker of the chatbot Claude, said it is working with London-based Fluidstack to build the new computing facilities to power its AI systems. It didn't disclose their exact locations or what source of electricity they will need."
"A report last month from TD Cowen said that the leading cloud computing providers leased a staggering amount of U.S. data center capacity in the third fiscal quarter of this year, amounting to more than 7.4 gigawatts of energy, more than all of last year combined. Oracle was securing the most capacity during that time, much of it supporting AI workloads for Anthropic's chief rival OpenAI, maker of ChatGPT."
"Anthropic said its projects will create about 800 permanent jobs and 2,400 construction jobs. It said in a statement that the scale of this investment is necessary to meet the growing demand for Claude from hundreds of thousands of businesses while keeping our research at the frontier. The tech industry's huge amount of spending on computing infrastructure for AI startups that aren't yet profitable has fueled concerns about an AI investment bubble."
Anthropic announced a $50 billion investment in computing infrastructure to build new data centers in Texas and New York in partnership with London-based Fluidstack. The company did not disclose the exact facility locations or electricity sources. A TD Cowen report found leading cloud providers leased over 7.4 gigawatts of U.S. data center capacity in the third fiscal quarter, exceeding all of last year; Oracle led capacity leasing, with Google second and Fluidstack third. Anthropic expects about 800 permanent jobs and 2,400 construction jobs from the projects. The investment aims to meet growing business demand for Claude while raising concerns about a potential AI investment bubble. Anthropic plans to prioritize cost-effective, capital-efficient scaling.
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