The world is building AI infrastructure at breakneck speed, but the tools that could make that infrastructure energy-efficient are being deployed far too slowly. According to a new report from the International Energy Agency (IEA), electricity demand from AI-focused data centers surged 50 percent in 2025 alone, yet the energy sector remains fragmented and slow in adopting AI solutions that could reduce emissions and improve grid reliability.

This mismatch reveals a troubling paradox: while tech companies and investors tout artificial intelligence as essential to solving the climate crisis, the deployment of AI tools to curb the energy intensity of the technology itself is not keeping pace with the sector's voracious appetite for power. The IEA's report, "Key Questions on Energy and AI," found that capital expenditure by the world's largest tech firms exceeded $400 billion in 2025 and is expected to rise by another 75 percent in 2026, with spending by just five tech companies now exceeding total global investment in oil and gas production.

Why Is the Energy Sector Struggling to Adopt AI Efficiency Tools?

Despite AI's potential to improve energy security and sustainability, the energy industry faces significant barriers to adoption. A survey of energy companies by the IEA identified multiple obstacles preventing faster deployment of AI solutions:

• Digital Skills Gap: Energy companies lack the workforce expertise needed to implement and manage AI systems effectively.
• Fragmented Data Systems: Legacy infrastructure and incompatible data platforms make it difficult to integrate AI tools across operations.
• Cybersecurity Concerns: Energy companies worry about vulnerabilities that could expose critical infrastructure to attacks.
• Weak Policy Support: Less than half of global energy demand is covered by policy frameworks designed to promote AI adoption in the energy sector.

The policy gap is particularly striking: only 10 percent of global electricity consumption falls under open electricity data policies, which are essential for AI systems to optimize grid performance and energy distribution.

How Rapidly Is Data Center Energy Demand Growing?

The numbers paint a stark picture of accelerating demand. The IEA estimates that global electricity demand from data centers will nearly double from 485 terawatt-hours (TWh) in 2025 to 950 TWh by 2030, equivalent to around 3 percent of global electricity demand. More concerning, electricity consumption from AI-focused data centers, which are designed to support much higher computational loads than traditional facilities, is expected to triple over the same period.

Using satellite tracking, the IEA found that "AI factories," which are specialized data centers designed for training and running advanced AI models, have more than tripled in capacity over the last 18 months. This explosive growth is driven by energy-intensive applications such as video generation, AI reasoning, and autonomous "agentic" systems that can consume hundreds or even thousands of times more electricity per query than simple chatbot interactions.

By 2027, an individual server rack in an advanced data center could have peak power demand equivalent to 65 households, according to the IEA's projections.

What Supply Chain Bottlenecks Are Emerging?

The physical energy infrastructure and supply chains supporting this boom are struggling to keep pace. Grid connection delays, shortages of transformers and power electronics, limited supplies of high-bandwidth memory chips, and surging demand for gas turbines are all creating critical bottlenecks.

Orders for gas turbines to power data centers rose 70 percent in 2025, highlighting mounting pressure on energy equipment supply chains. In the United States, some developers are moving ahead with onsite natural gas generation because of slow grid connection timelines. The IEA estimates that between 15 and 27 gigawatts of onsite gas-fired power capacity could be supplying data centers globally by 2030.

Data centers are also driving growth in battery storage; the IEA projects that data centers could host between 20 and 25 gigawatts of battery storage capacity globally by 2030, potentially allowing them to act as flexible grid assets if the right market incentives are introduced.

Could AI Still Help Solve the Energy Crisis?

Despite the mounting environmental and societal concerns, the IEA argues that AI could still play a key role in accelerating the energy transition, but only if adoption barriers are addressed. AI technologies are already being used to monitor transformers and electricity grids, optimize industrial processes, and improve energy efficiency in ways that could yield significant savings.

The agency estimates that existing AI applications could save more than 13 exajoules of energy by 2035, equivalent to around 3 percent of global final energy consumption, if deployment scales up. In energy-intensive industries, AI-enabled optimization could reduce energy costs by between 3 and 10 percentage points.

The challenge is clear: the energy sector must accelerate its adoption of AI tools while simultaneously managing the explosive growth in data center infrastructure. Without faster policy support, investment in digital skills, and standardization of data systems, the world risks building an AI infrastructure that consumes more energy than the efficiency gains it could provide.

What Are the Broader Implications for Communities and Grids?

As data center construction accelerates, public opposition is growing in some jurisdictions over electricity prices, water consumption, environmental impacts, and land use. In February, communities in Johor, one of the world's largest hubs for data center infrastructure, protested against the water intensity of AI-based IT infrastructure.

Data centers have become a "highly visible flashpoint" for anxieties about AI's impact on jobs, affordability, and sustainability. Emissions from data centers are projected to double by 2035 to around 350 million tonnes, although the IEA estimated that they would still account for only around 2 percent of global electricity-sector emissions.

Poor planning could increase electricity prices in some regions if grid investments and data center demand become misaligned. The IEA warned that "data centres are large, concentrated, rapidly developed infrastructure that are likely to trigger a need for new generation and grid investments in systems that host them." The uncertainty surrounding future AI demand further complicates planning, as some data center projects may never be completed while investment pipelines continue to expand rapidly.