The Trump administration is moving to fast-track data center power hookups

The Trump administration is accelerating data center power hookups, reducing approval timelines from several years to just 60 days. Trump’s administration is pushing regulators to advance Trump’s AI and manufacturing goals. 

Energy Secretary Chris Wright has urged the Federal Energy Regulatory Commission (FERC) to adopt an expedited 60-day review process for grid connection requests from large data centers. If approved, the rule would provide rapid access to power for hyperscale data centers, which support AI infrastructure for providers such as Amazon and OpenAI. 

Wright urges the FERC to adopt the 60-day review process for Hyperscalers

According to a Bloomberg report, the 60-day proposal would shift from the existing regulatory model, where approvals often take multiple years. It would also help President Trump fulfil his campaign promise to scale AI capabilities as part of a broader domestic manufacturing revival.

“To usher in a new era of American prosperity, we must ensure all Americans and domestic industries have access to affordable, reliable and secure electricity,” 

–Chris Wright, Energy Secretary

Meanwhile, state energy regulators have raised concerns over potential risks of connecting data centers directly to power grids. Several states argue that they are already struggling with surging demand from EVs, factories, and data centers; the rule could only add strain and potentially translate to huge bills for small households and businesses. 

Wright’s proposal, however, contains conditions intended to mitigate such concerns. For instance, Data centers seeking fast-tracked approval times must either invest in new generation capacity or agree to scale down during grid peak times, such as summer heatwaves. 

Previous disputes, such as the FERC’s rejection of Talen Energy’s plan to directly power an Amazon facility from a Pennsylvania nuclear plant, may weigh in. The plan intended to expand behind-the-meter power sales from 300 MW to 480 MW. The regulator argued that PJM failed to prove that the deal was consistent with grid reliability standards. Such disputes have shown the escalation between federal ambitions and state-level oversight.

Energy expert argues AI data centers may stabilize U.S. power grid

Some power industry experts have argued against the growing public perception that AI infrastructures are driving up costs. Nate Mason, Energy expert at the Atlantic Council, argued that data centers can become part of the solution to drive down costs. He noted that many new facilities are collaborating with co-located generation plants, such as gas turbines and modular reactor systems, which may feed surplus electricity back to the grid. Such plans could drive down the cost of electricity, reduce grid congestion, and also create new private power-generating and storage facilities. 

Mason also advocated for time-of-use and value-based pricing models where data centers and small consumers can pay fluctuating rates depending on real-time demand. He believes the pricing model could flatten peak loads and make energy use more efficient across the U.S.

The emerging debate over grid reforms reflects the need for a key policy to maintain the U.S. leadership in the AI revolution. Both Hyperscalers and small consumers have acknowledged that AI, electrification, and even domestic manufacturing plants will demand a huge energy supply. However, they remain torn on who pays the price and who benefits. 

The Trump administration views the 60-day rule as a catalyst for modernization and innovation, aiming to fulfill Trump’s AI and manufacturing goals. On the flip side, some critics have argued that the rule may shift the burden from large corporations to ordinary citizens. 

Data centers may benefit from the proposed rule if they incorporate new generating streams or agree to reduce usage during peak demand hours. Also, feasibility studies may be required for cases such as the Talen Amazon proposal, which planned to connect to an existing power plant.

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