Artificial Intelligence (AI) is rapidly transforming the global energy landscape—both as a growing consumer of electricity and as a powerful force for system optimization. According to the International Energy Agency (IEA), AI’s exponential growth is reshaping how energy is produced, distributed, and consumed across the world.
Massive Energy Demands from AI Infrastructure
The IEA reports that training and deploying AI models demands vast computational resources, which are typically housed in enormous data centers. A single AI-focused data center can consume as much electricity as 100,000 homes. As more facilities come online, some of the newest centers could require 20 times that amount.
Since 2022, global investments in data centers have nearly doubled, reaching $500 billion by 2024. This surge has sparked concerns about surging electricity demand. In 2024 alone, data centers accounted for 1.5% of total global electricity use—roughly 415 terawatt-hours—while consuming even more in concentrated regions like the U.S., China, and Europe.
Future Projections: AI and Electricity Usage by 2030
Looking ahead, the IEA forecasts that electricity usage by data centers will more than double by 2030, reaching around 945 TWh. For perspective, that’s just above Japan’s entire national electricity consumption today. The United States is expected to lead this trend, with data centers potentially accounting for nearly 50% of the country’s electricity demand growth by the end of the decade.
By 2035, electricity demands could soar to 1,200 TWh globally under the IEA’s “Base Case” scenario. However, this figure could vary significantly, ranging from 700 TWh in a conservative “Headwinds Case” to 1,700 TWh in an aggressive “Lift-Off Case,” depending on how quickly AI adoption advances and how efficiently systems evolve.
The Energy Mix: What Will Power the AI Boom?
Meeting AI’s growing energy appetite will require a balanced mix of sources. The IEA identifies renewable energy and natural gas as leading contributors through 2035. Renewable sources, supported by energy storage and modern grid infrastructure, are projected to meet half of the global increase in data center demand.
Natural gas will remain crucial in markets like the U.S., expected to expand by 175 TWh in the coming decade. Nuclear energy, especially through innovations like small modular reactors (SMRs), is also anticipated to play a key role in countries like China, Japan, and the United States.
However, generation alone won’t suffice. Existing grid infrastructure must be significantly upgraded to support the load. Delays in grid upgrades have already jeopardized 20% of planned data center projects worldwide, often due to lengthy approval processes and bottlenecks like transformer shortages.
AI as a Catalyst for Energy Optimization
While AI demands a great deal of power, it also holds enormous potential for optimizing energy systems. In the oil and gas sector, AI is already enhancing exploration, maintenance, and safety—while reducing methane emissions. It’s also aiding in the discovery of critical minerals essential for clean tech development.
In the electricity sector, AI improves forecasting for renewables, boosts grid stability, and shortens outage durations by 30–50%. Smarter grid management could unlock 175 GW of transmission capacity without building new infrastructure.
End-use sectors also stand to benefit. AI can streamline industrial processes to the tune of Mexico’s entire energy use. In transportation, intelligent traffic systems could save energy equivalent to taking 120 million cars off the road. Buildings, though slower to adopt digital tools, offer significant untapped potential.
AI’s power extends to innovation, too. It can accelerate research in areas like next-gen battery tech, synthetic fuels, and carbon capture—though the energy sector still underutilizes AI compared to industries like healthcare or biotech. AI-driven platforms in manufacturing are already showing how automation can unlock new efficiencies across sectors.
Key Barriers to AI Adoption in Energy
Despite its promise, AI’s integration into the energy sector faces several hurdles. These include limited access to high-quality data, lack of digital infrastructure, and a shortage of AI talent in energy-focused roles. Regulatory issues and cybersecurity threats also present major challenges.
Cyberattacks on utility providers have tripled over the past four years. While AI enhances cyber defense capabilities, it also arms hackers with advanced tools. Additionally, securing supply chains for critical minerals like gallium—essential for AI hardware—remains a pressing concern.
Shaping the Future Together
To navigate these challenges, the IEA emphasizes the need for stronger collaboration between tech leaders, energy providers, and policymakers. Smarter data center locations, streamlined permitting, and flexible grid operations are key strategies to ensure infrastructure keeps pace with AI’s explosive growth.
While AI has the potential to offset its own energy demands through systemic efficiencies, these environmental benefits depend on thoughtful implementation. As Fatih Birol of the IEA puts it, “AI is a tool, potentially an incredibly powerful one, but it is up to us—our societies, governments, and companies—how we use it.”
The IEA remains committed to providing the data and insights needed to guide energy and tech stakeholders into a future where AI and sustainable energy can thrive together.
