Kristen Smith
The U.S. government must leverage private sector research and development to achieve the goals of its AI Action Plan, Steve Orrin, Intel‘s federal chief technology officer and senior principal engineer, told Breaking Defense. The plan, released in July, calls for major investments in semiconductor manufacturing and infrastructure to supply the computing power needed to compete with China’s advances in artificial intelligence.
Orrin said the federal government’s needs often mirror those of the private sector, citing examples ranging from logistics and supply chain management to healthcare and finance. “The way to think about chip innovation for public sector is understanding that public sector writ large is almost a macro of the broader private sector industries,” he said.
Semiconductors and Supply Chains
The administration’s strategy emphasizes strengthening domestic semiconductor production, a critical safeguard against supply chain disruptions. The plan calls for expanding manufacturing capacity, streamlining regulations, and reviewing grant and research programs to accelerate production.
“When we talk about chip innovation specific for the public sector, it’s this notion of taking private sector technology solutions and capabilities and federalizing them for the specific needs of the U.S. government,” Orrin said.
AI Demands More From Power Grid
A central challenge to scaling AI is the power it consumes at every stage — from research to deployment. The United States faces an aging power grid that is vulnerable to disruption from natural disasters and increasing demand from vehicle electrification and personal devices. The AI Action Plan identifies priorities such as stabilizing today’s grid, optimizing existing resources, expanding reliable power sources like nuclear and geothermal, and creating a strategic blueprint for 21st century energy needs.
The Intel executive noted that pushing computing power closer to the point where data is generated could also help address energy and infrastructure constraints.
“Maybe it’s doing federated learning because there’s too much data to put it all in one place and it’s all from different sensors,” he said. “There’s actually benefits to pushing that compute closer to where the data is being generated and doing federated learning out at the edge.”
Military Applications of AI
While defense leaders emphasize the importance of human oversight, AI is already being used to support missions that do not require constant human input. The technology can sift through large volumes of reconnaissance data, flagging patterns and changes for analysts to review. It can also assist with predictive maintenance, detecting flaws in equipment before failures occur.
In logistics, AI can evaluate environmental and asset data to guide resource allocation. Operationally, it can track unknown objects, monitor troop movements and support the use of autonomous vehicles. These applications demand access to computing power in contested or remote environments, making cloud-based and edge solutions essential.
“Being able to take enterprise-level capabilities and move them into edge and theater operations where you don’t necessarily have large-scale cloud infrastructure or other network access means you have to be more self-contained, more mobile,” Orrin said. “It’s about innovations that address specific mission needs.”
