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AI and Data Centers Drive Innovation Using Retired Military Aircraft Engines for Energy

AI and Data Centers Drive Innovation Using Retired Military Aircraft Engines for Energy
The Growing Energy Demand of AI and Data Centers
The rapid expansion of artificial intelligence (AI) and data centers is reshaping the U.S. energy landscape, creating an unprecedented surge in electricity demand that is placing significant strain on traditional power grids and supply chains. As AI workloads intensify, the power consumption of data centers in the United States is projected to double or even triple by 2028, potentially reaching between 325 and 580 terawatt-hours annually. This dramatic increase is exacerbating a critical shortage of new gas turbines, with manufacturing delays pushing delivery timelines for large-scale units well into the next decade.
Data centers, which are essential for AI training and inference, are becoming increasingly voracious energy consumers. By 2035, U.S. AI data centers could require up to 123 gigawatts (GW) of power, representing more than a thirtyfold increase from current levels. Traditionally, natural gas plants powered by gas turbines have provided reliable, on-demand energy to meet such demands. However, the global surge in turbine orders has created severe backlogs. Siemens Energy reports that over 60% of its U.S. gas turbine orders are now linked to AI data centers, while GE Vernova anticipates an 80 GW backlog by the end of 2025. The shortage is particularly acute for large turbines used in combined-cycle systems, resulting in multi-year delays.
These supply constraints are already affecting the market. Data center stocks have experienced volatility as investors assess the risks posed by power shortages and rising infrastructure costs. As AI scaling encounters grid limitations, companies face mounting pressure to deliver greater computational output per watt and maximize megawatts per project to maintain competitiveness.
Repurposing Retired Aircraft Engines as a Strategic Response
In response to the widening energy gap, operators are increasingly turning to innovative and unconventional solutions, notably the repurposing of retired military and commercial aircraft engines into high-efficiency power generators. These aeroderivative gas turbines, originally designed for aviation, can be rapidly modified to operate on natural gas or distillate fuel oil. Unlike traditional turbines, these units are trailer-mounted and can be deployed within months, offering a flexible and swift response to surging energy demand.
The U.S. Air Force’s “Boneyard” at Davis-Monthan Air Force Base in Arizona, which stores over 4,000 retired military aircraft, exemplifies the scale of this opportunity. According to the U.S. Energy Information Administration, the engines housed there could theoretically generate up to 40,000 MW of electricity—exceeding 10% of Arizona’s current generation capacity. This potential includes approximately 32,000 MW from turbofan engines, 7,300 MW from turboprops, and 1,600 MW from turboshafts. Turbofans and similar engines share design characteristics with natural gas-fired turbines, making them particularly suitable for conversion.
Several companies are already capitalizing on this potential. Missouri-based ProEnergy specializes in converting CF6-80C2 engine cores—previously used in aircraft such as the Boeing 747-400 and Airbus A300—into 48 MW natural gas turbines. These units are equipped with generators and advanced control systems to ensure seamless integration into data center operations. To date, ProEnergy has sold 21 such turbines to two separate data center operators.
Future Implications
As AI continues to drive escalating energy demand, the industry confronts significant challenges stemming from infrastructure bottlenecks and rising costs. The innovative reuse of retired aircraft engines presents a promising and scalable solution to these challenges. However, the ability to deliver efficient and reliable power will remain a critical factor shaping the future growth of AI and data centers.

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