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OpenAI to Cover Energy Costs for ‘Stargate’ AI Infrastructure Project

Summarized by NextFin AI
  • OpenAI has committed to covering the full energy costs of its "Stargate" AI project, ensuring it won't increase local electricity prices.
  • The company aims to expand its AI infrastructure capacity to 10 gigawatts by 2029, with plans already exceeding half of that target.
  • Initial Stargate sites in Texas are operational, with additional facilities being developed in Texas, New Mexico, Wisconsin, and Michigan.
  • OpenAI's Stargate project addresses the growing demand for AI training, which is straining global power grids.

OpenAI said it will shoulder the full energy costs of its “Stargate” artificial intelligence infrastructure project, pledging that the initiative will not drive up local electricity prices as it scales up computing capacity across the United States.

In a statement released on Jan.21, the company said that, one year after announcing the Stargate project, it plans to expand U.S. AI infrastructure capacity to 10 gigawatts by 2029. OpenAI said it has already planned capacity well beyond half of that target.

The first batch of Stargate sites in Texas has begun training AI models and delivering services, the company said. Additional Stargate facilities are under development in Texas, New Mexico, Wisconsin and Michigan.

OpenAI said it has committed, across all Stargate community projects, to paying for energy costs itself to ensure that project operations do not place upward pressure on local power prices. The company acknowledged that each community and region has distinct energy needs and grid conditions, and said it will tailor its approach accordingly.

Depending on the location, those measures may include fully funding the construction of new dedicated power supply and energy storage facilities, or financing additional power generation and transmission resources, OpenAI said.

The Stargate project is part of OpenAI’s broader effort to secure large-scale computing and energy resources needed to support the rapid growth of advanced AI models, as rising demand for AI training and inference places increasing strain on power grids worldwide.

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