AI and Data Centres Surge in Global Electricity Consumption Projected to Reach 1,300 TWh by 2035, AI to Save USD 110 Billion in Power Plant Operations

According to data published by Precedence Research, the growing investment in data centres, driven by artificial intelligence (AI), has already led to data centres consuming around 1% of global electricity. As AI adoption continues to rise, data centre electricity consumption is projected to nearly triple by 2035, reaching 1,300 TWh. Furthermore, AI’s potential to optimise energy systems could save USD 110 billion annually in power plant operations and maintenance costs by 2035. With this rapid increase in demand, AI also promises to unlock 175 GW of additional transmission capacity through enhanced grid integration of renewable energy.

Ottawa, Feb. 10, 2026 (GLOBE NEWSWIRE) -- AI and data centres are becoming key drivers of global electricity demand, with AI models contributing to a significant rise in energy consumption. As data centres already account for around 1% of global electricity use, their share is expected to surge to 3% by 2030, with a total of 945 TWh. AI’s role in energy optimisation will play a critical part in balancing the growing demand, unlocking 175 GW of transmission capacity, and saving USD 110 billion annually in operational costs, according to data published by Precedence Research.

A wave of investment in data centres is raising concerns about the surge in electricity demand and its potential impacts on the energy sector. Over the past two years, as artificial intelligence (AI) has become a driving force behind digitalisation, data centre investments have skyrocketed. This rise in demand, coupled with the massive energy consumption associated with AI models and data centre operations, is expected to continue growing.

In 2023, overall capital investment by major companies such as Google, Microsoft, and Amazon in data centres surpassed the total investment of the U.S. oil and gas industry, representing approximately 0.5% of the U.S. GDP.

As this investment grows, it’s important to assess the energy implications for the future. Data centres already account for around 1% of global electricity consumption and are expected to play a significant role in the energy demand landscape, with AI applications poised to further escalate this demand.

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The Growth of Data Centres: Impact on Global Energy Demand

The rise of hyperscale data centres, which require up to 100 MW of electricity (equivalent to the demand of 350,000–400,000 electric cars annually), highlights the sheer scale of energy consumption associated with modern computing infrastructure.

Electricity Demand and Projected Growth

The electricity demand from data centres is set to double by 2030, with an annual growth rate of 12%. This surge is driven by the increased adoption of AI and the rapid expansion of cloud services and data storage.

While data centres represent a relatively small part of global electricity demand, the spatial concentration of these centres, especially in major economies like the U.S., China, and the EU, can create localised energy challenges. In Ireland, for instance, data centres already account for over 20% of total electricity consumption.

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Localised Impact on Energy Networks

The spatial concentration index shows how energy demand from data centres can disproportionately affect certain regions:

Large data centres, especially in urban areas, can put strain on local power grids. Regions experiencing rapid data centre construction, such as the U.S., China, and Ireland, may face difficulties in meeting climate targets due to the higher electricity demand required to power these centres. Furthermore, the pace of data centre construction often outpaces the growth and reinforcement of local power infrastructure.

AI’s Role in Energy Optimisation: Potential and Challenges

AI can serve as a powerful tool for optimising energy systems. As energy systems become more electrified, decentralised, and digitalised, AI can optimise electricity generation, distribution, and consumption. This optimisation extends to energy storage, grid management, and even the integration of renewable energy sources.

In the Widespread Adoption Case by 2035, AI is projected to:

• Save USD 110 billion annually in power plant operations and maintenance costs.
• Unlock 175 GW of additional transmission capacity by enhancing the integration of renewables into the grid.

Energy Savings by Sector in 2035: Widespread AI Adoption

AI’s ability to optimise production processes across industries, transportation, and buildings will be critical in reducing overall energy consumption, with an 8% energy savings anticipated in light industry by 2035.

Barriers to AI Adoption in the Energy Sector

Despite the significant potential of AI, there are barriers to sector-wide adoption:

1. Regulatory challenges: Existing regulations may not fully accommodate the rapid advancements of AI.
2. Data access issues: Limited availability of shared, high-quality data hampers AI implementation.
3. Interoperability concerns: The lack of integration between various energy systems complicates the adoption of AI.
4. Skills gap: A shortage of AI expertise in the energy sector limits effective deployment.
5. Inadequate digital infrastructure: Some regions still lack the necessary infrastructure to support AI-based energy solutions.
   
   

AI’s Role in Accelerating Energy Innovation

AI is critical for speeding up energy innovation, particularly in areas like clean energy technologies and electrification. For example:

• AI is used to optimise battery gigafactories, predicting performance and identifying potential faults.
• AI is accelerating the development of more efficient solar technologies, such as perovskite solar cells, by enabling the testing of thousands of materials in a fraction of the time it would take through traditional methods.

However, AI-first innovation in energy is still underrepresented, with only 1% of energy-related patents referencing AI and 2.3% of energy startups leveraging AI. More effort is needed to integrate AI into the energy innovation pipeline.

The future of AI in the energy sector is promising but requires careful management of the energy demands driven by data centres and AI applications. While renewables play a pivotal role in meeting the growing demand, fossil fuels such as natural gas and coal will continue to be key for addressing short-term energy needs.

As AI adoption grows across sectors, it will be crucial for policymakers, energy operators, and technology companies to collaborate to ensure a balance between technological growth and sustainable energy practices. The IEA Global Conference on Energy & AI, scheduled for 4-5 December in Paris, will provide a platform for high-level discussions on how AI can transform energy systems globally.

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