AI Doesn’t Have to Be the Enemy of the Energy Transition – It Could Be Its Greatest Engineer

As artificial intelligence emerges as a defining technological force of our time, many of the trailing political issues remain as-yet overlooked or misunderstood – even though these policies will ultimately fuel or choke off broader adoption. Nowhere is this dynamic more apparent than in the discussion of utility prices, energy consumption, and the national buildout of energy-intensive AI data centers.  

Companies operating at the intersection of AI and energy face a dual, immediate challenge: they must manage the reality of growing demand for energy resources while credibly educating the public on their role as positive drivers of change. 

In just the last two years, elections in Virginia, New Jersey, and Georgia have been closely tracking the public’s shifting views on energy issues and the development of AI infrastructure. Concerns about rising prices, consumption, and the reliability of local grids are no longer just technical issues; they are now political flashpoints that businesses can’t afford to ignore. GE Vernova CEO Scott Straznik described the source of this anxiety at the Axios Climate House at the most recent Climate Week NYC when he said, “Demand is going to be a lot higher for a lot longer. … We haven’t seen this much load growth since 1945.” And just last week at USEA’s Energy Industry Forum, American Clean Power’s Jason Grumet and others confirmed that “demand is soaring.” 

With midterm elections on the horizon, companies can expect elected officials and political candidates to press the issues of energy consumption, production, and affordability on both the state and national stages. While energy issues become mainstream, some perceive AI as a threat to affordability and reliability. But companies navigating these challenges are well positioned not only to build the next iteration of the U.S. economy but also to help engineer an energy transition decades in the making. Whether they communicate that story clearly and authentically – and how it’s perceived – could determine how their reputations emerge from the upcoming election seasons.  

When the nation’s energy and business leaders met in New York this past fall, several of them pointed to AI-engineered efficiencies that are already making a positive impact on energy use and delivery. The technology’s ability to solve complex problems – and see solutions humans operating alone might miss – has already transformed supply chains, logistics, and siting decisions. As Amazon’s Chief Sustainability Officer, Kara Hurst, described it, “Applied across products globally, AI can eliminate the first barrier of climate action—the prohibitive cost of knowing simply where to begin.” 

AI-driven efficiency doesn’t just apply to companies relying on the grid – it applies to the energy grid itself. Currently, the U.S. grid operates at an average efficiency of only 40%. Increasing the grid's efficiency would have transformative effects on the overall resiliency of our infrastructure and on the adoption and deployment of additional energy resources. With a better understanding of when and where to direct resources, AI can help stabilize grids, better forecast energy demand, and minimize waste. It can also model grid improvements, identify resilience vulnerabilities from cyber attacks and extreme weather, and enhance grid maintenance.  

AI is part of the future – and energy stakeholders, policymakers, and innovators should embrace its potential for energy systems. The very technology driving new energy demands can also deliver the solutions needed for a resilient, efficient grid. Companies leveraging AI should articulate their vision for the technology as an optimizer – for energy, logistics, or consumers – with clarity. 
 
To do that, companies should prioritize trust and authenticity as central to their mission through proactive planning and transparent communication. In an age of rapid transformation and cost-driven economic anxiety, the public is skeptical of promises about long-term gains that come with upfront costs – especially since affordability continues to top the list of voter concerns.   

Companies should highlight the tangible benefits of their AI investments, especially from the perspective of workers and local communities. AI already promises a brighter future for business and the economy, but those wins need to be translated to voters, as well. To build trust, companies should provide real examples of how investments in AI and energy infrastructure are – or will be – directly benefiting local communities and workers. For example, what efficiencies have been made in logistical supply chains or energy loads? How are companies reducing transportation and fuel costs or energy demand as a result? Will these lower operational costs help flatten or reduce the prices of consumer goods? As companies answer these questions, they should provide a clear tie back to consumer benefits, especially in communities where rising power bills are perceptually being laid at AI’s feet. 

It’s imperative that companies communicate this transition transparently, position themselves as proactive partners, and be clear about how they are delivering wins with widespread impact. If consumers increase their trust and comfort with the technology’s newer applications, AI’s benefits can compound rather than collide with the grid.