Duelling Dynamics

Artificial Intelligence (AI) is revolutionising the global energy sector, presenting both transformative opportunities and significant challenges. On one hand, AI systems demand vast computational power, contributing to immense energy consumption. On the other hand, AI has the potential to optimise energy systems, facilitating renewable energy integration, improving grid stability, and enhancing efficiency. This article explores the regulatory, investment, and legal considerations shaping the complex AI-energy dynamic, providing seminal insights for corporate leaders traversing the AI-energy transition nexus.

The Double-Edged Nature of AI’s Energy Usage

AI presents a paradox for the energy sector. While it drives efficiency, AI’s deployment - particularly for generative models - requires enormous computational power. As a result, AI contributes to the rapid growth of data centre energy demand, which some industry analysts expect to triple by 2030. Technology companies are heavily investing in renewable and nuclear energy generation, including small modular reactor (SMR) technologies, to meet this demand. Meanwhile, legacy electricity grids - in regions such as the UK and EU - require extensive modernisation to support increasing and more complex energy flows.

Conversely, AI’s potential to optimise energy systems could dramatically reduce emissions and improve efficiency. It could facilitate renewable energy integration, enhance storage solutions, or improve operational decision making. Over the coming year, stricter regulatory oversight or voluntary initiatives may propel sustainable energy practices, with operators encouraged to minimise emissions and improve data centre efficiency.

Leveraging AI for the Global Energy Transition

The electricity sector accounts for approximately 30% of global greenhouse gas (GHG) emissions. Achieving international climate targets will require continued growth and decarbonisation of the electricity sector. In support of these ambitions, AI could offer solutions to energy security, affordability, and sustainability challenges.

Electricity Generation and Grid Management

More specifically, AI could optimise renewable energy project planning by using advanced algorithms to forecast complex weather patterns, geological conditions, and grid constraints. These algorithms might enhance operational efficiency and reduce costs. In addition, AI-driven preventative maintenance could minimise interruptions in energy infrastructure.

In transmission and distribution, AI might improve grid stability by using dynamic line rating, which increases the capacity of transmission lines by analysing real-time conditions (including real-time weather conditions and fluctuations in energy demand). Indeed, AI is already deployed in parts of Germany to dynamically adjust electricity flows to enhance efficiency and reliability. These applications may also support renewable energy integration and reduce the need for costly backup systems.

End-Use Energy Management

AI-driven systems can optimise the energy use of devices such as electric vehicles (EVs), lighting, and air conditioning by adjusting settings - based on demand pattern predictions - to improve energy performance, minimising emissions and costs. Similarly, AI algorithms could reduce data centre resource usage by dynamically calibrating thermal, water, battery, and server management systems to real-time demand. Virtual power plants also increasingly leverage AI for demand forecasting, enabling peer-to-peer energy trading and load balancing.

Energy Storage

AI’s ability to balance real-time supply and demand could enhance the efficiency of energy storage systems, extending the lifetime value of assets such as batteries, pumped hydro, chemical storage, and molten salt storage. It may also facilitate innovations in battery chemistry and EV charging technologies - including vehicle-to-grid (V2G) systems - by optimising charging and discharging schedules.

Barriers and Risks

Various impediments to higher AI adoption in the electricity sector. These include poor-quality data, cybersecurity and safety risks, and regional differences in governance regimes. For example, AI models trained on region-specific data may perform sub-optimally in other contexts, such as different weather patterns or economic conditions.

Inconsistent regulations across jurisdictions also create operational and compliance challenges. In multiple regions - such as the US, EU, and China - data centre energy demand is also eclipsing the growth of low-carbon electricity generation. Nevertheless, operators could address many of these barriers and risks by adopting best practices in AI governance, robust cybersecurity measures, and through close collaboration with governments and regulators.

Applying AI to the Global Energy Transition

Governing the AI-Energy Nexus

Infrastructure Investment and Corporate Structuring

Strategic regulation and investment are critical to aligning AI development with energy transition objectives. AI infrastructure - comprising hardware and energy assets - remains capital intensive. Traditionally powered by centralised electricity grids, data centres are now exploring decentralised energy models, including onsite renewable generation and battery storage. Proposals such as modular data centres and “Graphics Processing Units (GPUs)-as-a-service” also aim to address electricity and land scarcity, while improving energy efficiency.

Convergence between these assets and sectors is driving new corporate structuring and financing strategies, particularly in the UK, EU, and US. We observe vertical integration as a significant trend, with companies co-locating renewable energy assets with data centres to secure sustainable energy supplies. With this, strategic partnerships between technology firms, energy companies, and infrastructure developers are also increasingly enabling shared investment in large-scale renewable projects and advanced data centres.

Innovative financing models - such as YieldCos and DevCos - can attract investment by enabling companies to separate operational assets - that generate predictable revenues - from riskier developmental projects, to ensure both stability and continuous innovation. Sustainability-linked instruments - including green bonds - are also increasingly deployed to lower borrowing costs for companies achieving environmental performance targets.

Formal Regulation

Governments are introducing measures to align AI with energy transition objectives. In the EU, measures such as the Taxonomy Regulation, European Code of Conduct for Energy Efficiency in Data Centres, Energy Efficiency Directive, and Corporate Sustainability Reporting Directive (CSRD) establish reporting requirements for energy consumption, emissions, temperature, thermal recycling, and resource use. The Corporate Sustainability Due Diligence Directive (CS3D) also requires technology companies to evaluate and address risks deriving from algorithmic biases, data privacy, supply chain, energy, and water consumption. Moreover, the AI Act mandates risk assessments and data governance for high-risk applications, including critical digital or electricity infrastructure deploying AI for grid management or data centre operations.

The UK’s Streamlined Energy and Carbon Reporting (SECR) regime requires large companies to disclose energy use and emissions. At the same time, the Task Force on Climate-related Financial Disclosures (TCFD) mandates disclosure of climate-related risks and opportunities. The Building Regulations also prescribe energy efficiency standards for buildings. Furthermore, reforms to the UK’s Nationally Significant Infrastructure Project (NSIP) regime could impose more stringent environmental or reporting requirements on new UK data centres. Several US states - such as California - enforce stringent energy efficiency standards for data centres. These regulatory efforts promote transparency and encourage investment in sustainable AI infrastructure.

Voluntary Industry Initiatives

Industry-led initiatives are complementing formal regulations. For example, the Climate Neutral Data Centre Pact commits operators to achieve climate neutrality by 2030, focusing on energy efficiency, renewable energy, water usage, and thermal recycling targets. Similar sectoral initiatives include the Circular Economy for the Data Centre Industry project, the Green Grid initiative, and the iMasons Climate Accord. Adopting AI-driven energy monitoring systems could streamline reporting and improve transparency, which is increasingly valued by stakeholders and regulators alike. Carbon markets also offer opportunities for technology companies to neutralise emissions or generate carbon credits through AI-optimised operations.

Geopolitical and International Trade Considerations

Complex international trade challenges and opportunities may emerge over the next 12 months. Integrating AI into the energy transition will necessitate addressing geopolitical challenges. Export restrictions on AI hardware and tariffs on critical technologies - including energy-efficient GPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), and reduced instruction set computer (RISC) chips, which accelerate sequential processing and machine learning - could disrupt supply chains, and incentivise regional production. Simultaneously, data sovereignty regulations - including the EU Data Protection Act - may compel localised data storage, thereby driving demand for regional data centres and infrastructure investment. Aligning with emerging international standards can position companies to capitalise on new cross-border opportunities, while navigating regulatory complexities.

Conclusion

AI’s impact on the global energy transition relies on multiple interconnected factors. Ultimate success will hinge on unleashing AI’s ability to catalyse renewable electricity generation, transmission, storage, and end-use, while mitigating its environmental footprint (through energy-efficient hardware and software, and increasing renewable energy deployment). Corporate leaders and general counsel will remain architects of a future in which the AI and the energy transition mutually reinforce each other to drive sustainable progress. By structuring power purchase agreements, navigating data centre reforms, tracking transatlantic regulations, safeguarding intellectual property, or drafting sustainability reports, legal teams can holistically and strategically ensure that their organisations comply, and thrive, in this era of transformation.