The surge of AI and the data center boom have started to pose challenges to the global energy system amid soaring power demand, spiking energy bills, and a higher environmental footprint.
As much as AI is changing the world and the economy, it could also offer assistance to one of the energy sector’s most pressing needs in times of rising demand, uncertainty in fossil fuel supply, and inflationary and supply-chain pressures in the renewables industry—energy efficiency.
AI could be the tool to help unlock additional energy gains and accelerate progress in efficiency, which has been slow in recent years. The advance of AI itself could be a leap toward energy efficiency, as data center developers will have to offset the negative image of drawing power and water resources from communities.
With increasingly stronger NIMBY movements to oppose data center locations in rural America and rising energy bills, AI could partly redeem itself by becoming the key enabler of massive progress in energy efficiency.
AI-Assisted Energy Efficiency
“We could be at a moment where we could step up efficiency progress, particularly in industry, unlocked by AI,” Brian Motherway, head of energy efficiency at the International Energy Agency (IEA), told the Financial Times.
At the end of last year, Motherway said that “slow efficiency progress is a wasted opportunity” as the world remains off track for the goal of doubling efficiency improvements to 4% per year by 2030.
Per the latest data from the IEA, rather than increasing towards the 4% goal, global efficiency progress has slowed in recent years. The average annual improvement since 2019 has been only 1.3%, well below the 2% starting point for the doubling goal.
Related: U.S. EV Adoption Slows While Global Demand Accelerates
In some regions, accelerating electricity demand growth has led to an overall increase in less efficient power generation, while increased access to air conditioners has pushed up cooling-related electricity demand, not necessarily with the most efficient air conditioners, Motherway argued.
In addition, policies have lagged progress in technology, “leaving significant savings on the table,” the official said.
While improved energy efficiency remains one of the fastest and most cost-effective ways to strengthen energy security, lower costs, and reduce emissions, it hasn’t lived up to expectations or to the IEA’s goals.
No one questions AI’s capabilities to be much more efficient than humans in recognizing where the wasted energy is, especially in industrial applications.
For example, renewable energy companies that have invested in AI and digital twin solutions could see huge benefits in their efficient operations, a 2025 study published in Energy Reports showed. The use of digital twin technology in renewable energy systems, combined with AI, boosts predictive maintenance efficiency. This reduces unplanned downtime by 35%, raises energy production by 8.5%, and reduces energy costs by 26.2%, according to the study by researchers at French and Moroccan universities.
Still, challenges to making AI mainstream in energy production, distribution, and transmission remain, “including high implementation costs, cybersecurity risks, and the complexity of integration,” the scientists noted.
To have AI-enabled efficiency gains, companies would need to invest in upgrades of equipment that would be expensive and often tailor-made, according to analysts.
“Companies need to go factory by factory investing in bits of kit that are often bespoke,” Sam Kimmins, director of energy at the non-profit Climate Group, told FT.
Efficiencies Cannot Offset AI Power Demand Surge
If AI could help accelerate efficiency gains, it could partly compensate for the surge in global power demand. Last year, electricity demand from data centers rose by 17%, and that of AI-focused data centers jumped even faster, soaring by 50%, the IEA said in a report in April. These surges in AI-driven power demand vastly outpaced the 3% growth in global electricity demand.
With the exponential surge in power demand, the AI value chain has seen a scramble for electricity, grid connections, manufacturing capacity, chips, and capital, the agency notes.
AI is not only sucking up energy, but it also uses water and other natural resources, including land.
The United Nations University Institute for Water, Environment and Health (UNU-INWEH) warned in a report earlier this month that by 2030, AI’s water use will match the needs of 1.3 billion people.
The AI data centers are also projected to consume 945 terawatt-hours of electricity globally by the end of the decade. This would nearly triple the combined annual electricity use of Pakistan, Bangladesh, and Nigeria—countries collectively home to more than 650 million people, the UN scientists say.
“This report is not a case against artificial intelligence, a technological transformation that is improving the lives of billions of people around the world,” said Professor Kaveh Madani, Director of UNU-INWEH, who led the investigation team.
“We have a narrow window to ensure that the backbone of the technological revolution of our era develops within planetary limits, and that the communities who provide the critical minerals for advancing AI and the ones that host its infrastructure and e-waste are also among those who benefit from it.”
By Tsvetana Paraskova for Oilprice.com
