AI: The Paradox of the Energy Transition

Revising the EU’s Energy Goals

The annual EU Energy Week this month brought together experts to discuss Europe’s mobilisation towards sustainable energy — one session presented plans to accelerate Europe’s grid modernisation and redesign through the incorporation of Artificial Intelligence (AI).

The talk signals the burgeoning demand for electricity and clean energy; with renewable energy consumption across the EU almost doubling between 2009 and 2023, to 24.6%, the Renewable Energy Directive has set a hopeful target to reach 42.5% by 2030.

But as digitalisation and AI integration accelerate, a central paradox underscores this contemporary energy transition: electricity demand is being driven up by the very technologies expected to reduce it.

Policy Officer at the European Commission, Stavros Stamatoukos, mentioned in the session a planned revision to the October 2022 report “Digitalising the energy grid.” The ascent of AI soon after the plan’s publication quickly outdated it, requiring a forthcoming “Strategic Roadmap for digitalisation and Artificial Intelligence in energy.”

Expanding beyond the grid to explore AI’s potential across the energy sector, Stamatoukos addressed the Commission’s ambition to identify and support AI innovators across Europe. The goals are to accelerate their arrival on the market, enhance their global competitiveness, and mobilize them towards cleaner energy.

But as AI’s appetite for energy grows, the question remains when its promise as a solution will begin to outweigh its role as a stressor on energy demand.

Hopeful Projections for the “Age of Electricity”

AI’s high and increasing energy usage breeds worry about the efficacy of implementing it in energy systems. Publishing a flagship report on Energy and AI this April, the International Energy Agency (IEA) estimated a quadrupling of energy consumption of AI-optimised data centres by 2030 – a staggering surge that risks derailing the energy transition.

The report nevertheless concludes that it is necessary to implement AI solutions to achieve significant progress in energy efficiency within the energy sector.

The IEA remains optimistic that the multi-trillion-dollar AI transition will be a worthy investment. Looking toward its current and prospective transformation of the energy sector, the organisation outlined projections about what the alliance between the two sectors would bring.

Significant hopes for operational gains and efficiency — lowered costs, reduced emissions, and competitiveness — called for prescriptions on how to accelerate the energy revolution:

Large-scale investment in and ubiquitous adoption of AI in clean energy technology innovation to massively reduce development time.
Enhancing collaboration to overcome adoption barriers will help navigate uncertainties, overcome risks, and allow the full impact of research in decision-making.
Making data centres significantly more productive, otherwise energy usage and carbon contribution will offset AI’s benefits.
Commitment to grid redevelopment to meet energy demands, which otherwise can threaten larger energy and climate policy goals.
Local AI developments can facilitate broader digital and economic development.

These hopes still come with a tacit acknowledgment of the paradox: AI as both the driver of soaring electricity demand and a necessary tool to tame it.

Intelligence in the Grid and the Microgrid

At the panel session, European Commission representative Antonio de Paola highlighted another report scheduled for September 2025, which will focus on grid-enhancing technologies powered by AI solutions. The publication will be under the Joint Programme Smart Grids, which was established in 2010 by the European Energy Research Alliance (EERA) — an organisation fostering collaborative research efforts across the continent to help achieve climate neutrality.

Increases in demand to develop outdated one-way power flow grids have been prompted by both the growing complexity and demand of renewables and AI’s proliferation.

Smart grids enabled by AI and digital technologies are being trialled as the next-generation framework to manage the complexity that their predecessors struggled to meet. Real-world pilots, such as the National Grid ESO’s “nowcasting” solar forecasts using Open Climate Fix (co‑founded by ex‑DeepMind researcher Jack Kelly), demonstrate significant gains in grid balance and reduced reserves.

But scaling from local pilots to continent-wide deployment requires overcoming current challenges that remain largely unresolved: interoperability, data sharing, and regulatory fragmentation.

Complementing centralised efforts, microgrids are emerging as flexible building blocks.

These self-sufficient, localised energy systems are used for campuses, hospitals, or remote areas. They are developed to ensure energy security in the event of an outage.

Forecasts anticipate the microgrid market to surge by over USD  41 billion between 2025 and 2029, reflecting their growing appeal for energy resilience, sustainability, and sovereignty. When paired with AI-driven controls and embedded cybersecurity, microgrids enhance overall system agility.

AI Integration: A Double-Edged Sword

However, a massive dependency on hardware such as sensors, converters, and batteries, which all rely on critical raw materials, limits the expansion of smart and micro grid development.

Besides environmental concerns, geopolitical issues also arise with the growth in demand among competing affluent nations for these critical minerals essential to many future-oriented technologies.

Without stronger supply-chain transparency and sourcing strategies, hardware shortages or mining risks could hinder the rollouts of AI-powered grids.

This infrastructural contradiction — where technology that promises clean energy depends on resource-intensive supply chains — underscores the complexity of the energy transition. And the famous breakdown that recently occurred in Spain and Portugal, plunging both countries into a prolonged blackout, underscores the danger of not setting up a solid infrastructure in advance.

The importance of adopting standardised datasets, interoperability protocols, and lifecycle planning must come in tandem with technical innovation. Other issues bridge the challenges for the grid and renewables:

Large quantities of clean and high-quality data that machine learning models require will make integration into the grid or solar and wind systems.
Fragmentation of the grid across regions and differences in infrastructure inhibit data collection and immediate mass implementation of AI.
The variability of meteorological energy – including solar and wind inputs – makes predictive modeling difficult, and further complicating this are larger issues regarding the transparency and comparability of data collections.

With potentially huge risks involved in AI’s integration into critical energy structures, even including nuclear stations, its reliability, understanding, and human monitoring are paramount.

A more unified approach to dataset collection across energy infrastructures is seen as an essential step forward to enable meaningful cross-comparability and progress in expanding AI’s usage in energy management environments. For unity to be achieved, collaboration is essential.

While this article focuses on grid-level infrastructure and policy tensions, the consumer edge of AI-driven energy tools — from smart thermostats to AI-managed appliances — represents another emerging frontier. Though these smarter technologies can help reduce consumer energy consumption, their positive impact remains dwarfed and risks being thwarted by growing industrial and infrastructural energy demands.

Global Energy Investment and Direction Towards AI Implementation

Despite, or equally in light of, huge geopolitical and economic uncertainty, global investment in the energy sector is on a persistent rise.

Surging electricity demands have been hugely driven by the pervasion and application of AI across the globe. The 10th annual edition of the IEA’s World Energy Investment report, released this month, demonstrates a strong tendency towards clean energy investment: at $2.2 trillion, it is double what is spent on fossil fuels.

However, investment in fossil fuels continues to rise. Approvals for coal-fired plants and other fossil fuels are still increasingly demanded by various regions globally. Reports reveal that dozens of large banks have given their financial backing to fossil fuel companies, with total investment skyrocketing by 20% to $869 billion between 2023 and 2024.

Nevertheless, the great disparity between clean and fossil fuel energy investment heralds change.

Commitments such as the EU Commission’s goal to phase out Russian oil and gas importation, agreed in the Versailles Declaration of March 2022, was re-iterated on June 17. To mitigate the risks associated with relying on Russian energy sources, the gradual phase-out will initially seek alternative global gas markets in pursuit of a longer-term vision.

Such aspirations for greater energy independence in the EU and clean energy are now being reviewed and revised to consider how AI can facilitate these and bring forth a more energy-efficient future.

This month’s VivaTech conference in Paris underlined how the private sector is fast pivoting towards AI-driven and clean energy solutions.

With financing flooding ventures across every level of the energy system, AI is heralded as the most powerful enabler of the energy transition — transforming old, uneconomical infrastructure into new, optimized systems.

Yet legacy grids, rising fossil fuel investments, and uneven digital readiness continue to slow this evolution.

The critical question remains: will AI’s soaring energy demand be met swiftly enough by its ability to optimise and decarbonise energy systems?

If left unchecked, this imbalance risks turning AI from a saviour into a brake on the energy transition. To truly deliver on the promise of optimised grids, faster renewable rollouts, and resilient systems, ambition and experimentation alone will not suffice.

Strategic public investment, coordinated international cooperation, and rigorous testing are essential to bridge the gap between aspiration and reality. Ensuring AI’s reliability, understanding its energy impact, and fostering cross-sector collaboration between developers, utilities, and regulators is not just useful — it’s imperative for fully achieving all the benefits of the “Age of Electricity.”

Editor’s Note: The opinions expressed here by the authors are their own, not those of impakter.com — Cover Photo Credit: