AI may soon dwarf small nations in power usage as new projections reveal

AI’s growing appetite for energy

Artificial intelligence has been expanding at a pace that few industries can match, but this growth comes with a rising cost in electricity.

Recent IEA projections show electricity use from data centres could more than double by 2030, to roughly 945 TWh in a central scenario, a scale that, in high-growth cases, is comparable to the electricity use of entire countries.

This shift highlights not only the transformative impact of AI but also its heavy dependence on infrastructure.

Why training AI models consumes so much?

Training an advanced AI model requires massive amounts of computation. Thousands of specialized chips run complex calculations for weeks, consuming large amounts of electricity. Each new generation of models tends to be larger, requiring more data and compute power.

While some companies try to improve efficiency, the trend is that newer models use more energy than their predecessors. This pattern makes the growth of AI’s energy footprint hard to control without major changes in hardware or design.

Data centers as the backbone

Data centers are the physical backbone of AI. These massive facilities host racks of processors and rely on constant cooling to prevent overheating. Projections show data centers alone could account for a significant share of global electricity demand in the next decade.

AI workloads add another layer on top of existing cloud services, stretching power grids further. This makes their energy efficiency a critical factor in how sustainable AI adoption can become in the long term.

Comparisons with small nations

Experts note that AI’s energy use is on track to surpass the electricity needs of some smaller countries.

This is already visible in places such as Ireland, where official statistics show data centres used 22% of metered electricity in 2024, and in countries like Denmark, which energy authorities and analysts identify as a likely hotspot for rising data-centre demand.

If AI growth continues, its electricity footprint could rival that of entire economies. This comparison underscores the scale of the challenge, where a single industry could compete with national demand, putting stress on energy policies and international climate goals.

The role of GPUs in demand

The surge in power use is tied to the graphics processing units (GPUs) that drive AI development. These chips excel at parallel computations but are extremely power hungry.

Running thousands of GPUs around the clock requires vast amounts of electricity, both for the processors themselves and the cooling systems supporting them.

Even though new GPUs claim higher efficiency, the overall scale of use means energy consumption keeps climbing. The chip race directly translates into energy demand growth.

Generative AI’s heavy footprint

Generative AI tools, including chatbots and image creators, are some of the biggest drivers of energy use. Every interaction requires multiple computations across distributed servers. With millions of users engaging daily, the cumulative electricity consumption adds up quickly.

Unlike one-time model training, inference happens continuously as people use these tools. This means generative AI not only requires immense energy to build but also keeps drawing power constantly in its operation, making its footprint especially significant.

Water use in cooling systems

Electricity isn’t the only resource AI impacts. Many data centers use water for cooling, and reports show billions of liters consumed annually. This raises concerns in regions already facing drought or limited water supplies.

As AI workloads expand, the pressure on both power and water infrastructure grows. This environmental footprint extends beyond carbon emissions, touching local ecosystems.

Communities near large data facilities are beginning to voice concerns about sustainability and resource fairness as AI infrastructure expands.

Industry promises of efficiency

Tech companies are aware of the criticism and have made pledges to improve efficiency. Some firms commit to running on renewable energy or designing more efficient chips. Others aim to recycle heat from data centers into local grids.

While these efforts show progress, experts argue they often don’t keep up with the pace of AI growth. Efficiency gains may reduce the per-operation cost, but overall energy use still rises as demand for AI services skyrockets.

The renewable energy debate

One solution presented is powering AI infrastructure entirely with renewable energy. Solar and wind can offset emissions from large data centers, but these sources are intermittent. Matching 24/7 demand from GPUs with variable power supply is a challenge.

Some companies invest in battery storage or contracts with clean energy providers. Still, critics warn that relying solely on renewables may not be enough, as the overall demand curve continues to steeply rise beyond current supply capabilities.

Global hotspots of AI demand

AI energy use is not spread evenly across the globe. The largest concentrations of data centers are in the United States, Europe, and parts of Asia. Countries with cheap power and cooler climates often attract new facilities.

Iceland and Sweden, for instance, are considered prime spots for energy-intensive computing. Meanwhile, regions with less robust grids may face blackouts or instability if large AI workloads expand too quickly without adequate planning or infrastructure development.

Climate goals under pressure

The growing power needs of AI could complicate global climate commitments. Countries have pledged to cut emissions, but the electricity demand from AI could offset gains made in other sectors.

Analysts caution that unchecked growth might make it harder to meet targets under agreements such as the Paris Accord. This places policymakers in a difficult position, balancing economic incentives from AI with the environmental responsibility to keep emissions within agreed-upon limits worldwide.

Projections for 2030 and beyond

Mainstream IEA analysis places data-centre electricity at just under 3% of total global electricity in a central 2030 scenario, while higher-growth stress scenarios can push the share higher.

Researchers stress the importance of scenario assumptions, and careful reviews put plausible ranges rather than single-digit or double-digit certainties.

These projections are not guarantees but highlight the urgent need for planning. Without innovation in hardware, software, and energy supply, AI’s power use could become one of the defining infrastructure challenges of the next decade for governments and businesses alike.

Can regulation make a difference?

Governments may step in to regulate AI’s environmental impact. Policies could require energy reporting, efficiency standards, or caps on consumption for large data facilities. Similar rules already exist in industries such as aviation and manufacturing.

However, regulation of digital services is tricky, especially when companies operate across borders. Effective oversight will require international cooperation, ensuring AI growth doesn’t outpace the ability of countries to manage electricity and carbon budgets responsibly for future generations.

Hardware breakthroughs on the horizon

Innovation in chip design may provide relief. Researchers are exploring neuromorphic computing, optical chips, and specialized accelerators that promise far lower power use than current GPUs.

These technologies are still in early development but could drastically reduce the energy required for both training and inference.

If widely adopted, hardware breakthroughs could reshape the trajectory of AI’s footprint, allowing growth without such steep increases in electricity demand. The industry is watching closely for these breakthroughs to materialize.

The role of user behavior

AI energy use is not only shaped by industry decisions but also by how people use these systems. Frequent prompts, image generations, or large-scale use of chatbots multiply the power drawn by data centers.

Encouraging efficient use and building awareness could play a role in reducing demand. While individuals have less influence than providers, consumer habits can push companies to design more energy-aware services. Awareness campaigns may become part of the broader sustainability effort.

The push for sustainability aligns with regulatory steps, as the FTC orders AI firms to reveal safeguards for teens and kids using AI companions, reflecting how consumer trust depends on transparency and protection.

A crossroads for AI and energy

The future of AI rests on balancing innovation with sustainability. The technology holds promise for solving major problems but may also become one of the largest strains on global power grids. Industry leaders, policymakers, and users must all navigate this trade-off.

Projections showing AI surpassing small nations in energy use are a warning sign. Whether growth can continue without overwhelming the planet’s resources remains one of the biggest challenges in tech today.

Sustainability questions add weight to the discussion, even as bold prediction says AI stock may outshine Nvidia and Palantir by 2030, showing the mix of risk and reward shaping the sector’s future.

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This slideshow was made with AI assistance and human editing.