In a stark warning to lawmakers, technology leaders from the nation’s biggest AI companies will testify Thursday that the US’ aging infrastructure is unprepared for the massive energy demands of AI, potentially jeopardizing US competitiveness in the rapidly evolving technology race.
US Senator Ted Cruz, who will lead the hearing, in a statement, emphasized the strategic importance of removing barriers to AI development, “Growth and development of new AI technologies will bolster our national security, create new jobs, and stimulate economic growth. This hearing will help us find ways to remove restraints on the AI supply chain and unleash American dominance in machine learning and next-generation computing.”
Executives from Microsoft, OpenAI, CoreWeave, and AMD will highlight significant challenges in the nation’s readiness to power the next generation of AI technology at an upcoming US Senate Commerce Committee hearing titled “Winning the AI Race,” while offering solutions that could help maintain the US’ technological edge, reported Reuters.
“America’s advanced economy relies on 50-year-old infrastructure that cannot meet the increasing electricity demands driven by AI, reshoring of manufacturing, and increased electrification,” Microsoft President Brad Smith wrote in his prepared testimony.
The energy requirements for AI systems are unprecedented and growing rapidly. CoreWeave CEO Michael Intrator, in his testimony, highlighted Department of Energy estimates showing data center electricity consumption could nearly triple from 4.4% of all US electricity in 2023 to approximately 12% by 2028.
“Millions of hours of training, billions of inference queries, trillions of model parameters, and continuous dynamic scaling are all driving an insatiable hunger for compute and energy that borders on exponential,” Intrator wrote in his prepared remarks.
The implications are clear: without dramatic improvements to the US energy infrastructure, the nation’s AI ambitions could be significantly constrained by simple physical limitations – the inability to power the massive computing clusters necessary for advanced AI development and deployment.
Streamlining permitting processes
The tech executives have offered specific recommendations to address these challenges, with several focusing on the need to dramatically accelerate permitting processes for both energy generation and the transmission infrastructure needed to deliver that power to AI facilities, the report added.
Intrator specifically called for efforts “to streamline the permitting process to enable the addition of new sources of generation and the transmission infrastructure to deliver it,” noting that current regulatory frameworks were not designed with the urgent timelines of the AI race in mind.
This acceleration would help technology companies build and power the massive data centers needed for AI training and inference, which require enormous amounts of electricity delivered reliably and consistently.
Beyond the cloud: bringing AI to everyday devices
While much of the testimony focused on large-scale infrastructure needs, AMD CEO Lisa Su emphasized that true AI leadership requires “rapidly building data centers at scale and powering them with reliable, affordable, and clean energy sources.”
Su also highlighted the importance of democratizing access to AI technologies: “Moving faster also means moving AI beyond the cloud. To ensure every American benefits, AI must be built into the devices we use every day and made as accessible and dependable as electricity.”
This vision of AI as a ubiquitous utility – available everywhere and to everyone – will require not just centralized computing power but also dramatic improvements in energy efficiency for AI systems that can run on consumer devices with limited power budgets.
Unlocking government data for AI development
In a notable policy recommendation, Microsoft’s Smith called for opening US government data sets for AI training, pointing to similar actions already taken by China and the UK.
“The federal government remains one of the largest untapped sources of high-quality and high-volume data,” Smith wrote. “By making government data readily available for AI training, the United States can significantly accelerate the advancement of AI capabilities.”
Access to diverse, high-quality training data remains a critical limitation for AI development, and government datasets could provide valuable resources for training next-generation models while keeping that development within US borders rather than offshore.
Building a ‘brain for the world’
Perhaps the most ambitious vision comes from OpenAI CEO Sam Altman, who noted that his company aims to “build a brain for the world and make it super easy for people to use it, with common-sense restrictions to prevent harm.”
Altman emphasized that as AI systems improve, demand will naturally increase, requiring more chips, training data, energy, and supercomputers. This virtuous cycle of improvement and adoption drives the urgency behind infrastructure improvements.
As the Senate prepares to hear this testimony and weigh potential legislative responses to these challenges, technology leaders across industries will be watching closely to see how America plans to power its AI future.
Source:: Network World