Artificial Intelligence and the Energy Transition

1. Introduction

In recent years, the energy sector has entered a decisive phase of transformation, driven by mounting concerns regarding climate change and the recognized need to transition toward sustainable energy systems. This evolution, often characterized as the “energy transition”, is not only a matter of technology replacement, but constitutes a complex and interdisciplinary task that weaves together economic priorities, societal imperatives, and pressing environmental goals [1]. Within this context, Artificial Intelligence (AI) has emerged as a compelling driver of innovation, offering powerful tools for improving the reliability, efficiency, and overall feasibility of low-carbon energy systems [2].

Central to the energy transition is decarbonization, where fossil-based power is progressively replaced by renewables such as wind, solar, or hydropower [3]. Nonetheless, the challenge stretches beyond the substitution of fuel types with hydrogen, renewable synthetic fuels, and biofuels; it also demands the widespread adoption of energy-efficient practices and technologies. These can involve, for instance, the development of high-performance building envelopes in the buildings sector, advanced industrial control systems that minimize resource usage for the industrial sector, and policies that incentivize the more rational use of energy at the household level. Electrification forms another pillar: replacing combustion-based processes with electricity-based technologies can significantly reduce carbon footprints in sectors like transportation and heating. Meanwhile, grid modernization becomes indispensable, as distributed generation resources ranging from household photovoltaics to community-led wind farms increasingly complicate network operations [4].

AI addresses many of these complexities by detecting real-time shifts in supply and demand, guiding the optimal dispatch of renewable resources, and supporting predictive maintenance in power systems. Energy efficiency also receives a substantial boost: building controls can learn occupants’ routines to minimize waste, industrial setups can predict equipment degradation, and transport logistics can optimize routes and battery usage, reducing both emissions and operational costs. In parallel, demand-side management strategies benefit from advanced analytics that incentivize off-peak consumption and enable dynamic pricing. AI-driven oversight further enhances energy markets by detecting irregularities and predicting price fluctuations more accurately. On the research front, AI among other things, accelerates innovation by automating the search for new materials, optimizing clean energy technologies, and shedding light on the environmental impacts of carbon capture, storage, and utilization [5].

Advanced AI systems have already been utilized extensively in multiple domains accelerating research productivity [6]. Recent research has explored how digital “personalities” can be created that convincingly mimic human behavior in virtual worlds. The results showed that these characters can form relationships, coordinate, and react realistically [7]. Follow-up research investigated the possibility of creating simulations of people, using interviews and artificial intelligence to predict their behaviors and opinions. The results showed a stunning accuracy of 85% in predicting responses [8]. At the same time, it should be highlighted that frameworks for automated autonomous research have already been implemented and are continuing to evolve. These utilize frontier Large Language Models (LLMs) in multi-agent topologies, allowing for automated open-ended research [9]. While it is an undisputed fact that these AI tools can accelerate research and decrease the time requirements for new knowledge creation, research has highlighted that researcher fulfillment can decrease, no matter the increased scientific output. This fact creates further challenges regarding how to integrate AI in daily research [10].

Despite its promise, AI comes with challenges. Its large-scale computational needs can clash with sustainability goals, while data biases risk skewing outcomes. An analysis performed by Deloitte anticipates a significant surge in electricity consumption by data centers, projecting an increase from approximately 180 TWh to 290 TWh in 2024 to an estimated 515 TWh to 720 TWh by 2030. This represents a compound annual growth rate of 15% to 17% [11]. Ensuring equitable access to AI tools and establishing transparent frameworks will thus become vital [12]. In this context, collaboration among governments, industry, and civil society can cultivate best practices, ethical guidelines, and robust policy frameworks that preserve public trust.

Overall, AI holds significant potential to fast-track the decarbonization of energy systems and support global sustainability. However, fully realizing these benefits calls for coordinated efforts to align market structures, strengthen policy incentives, and guarantee transparency in AI-driven decisions. The sections that follow define key AI concepts, explore further applications in the energy sector, and address broader socioeconomic, security, and ethical considerations, including the growing energy demand and increased interest in nuclear power, as well as providing an overview of the challenges going forward.

2. Defining Artificial Intelligence

Defining Artificial Intelligence is inherently challenging, given the intricate tapestry of concepts it spans. Russell and Norvig’s well-known framework offers a structured view, illustrating how AI transcends mere programming to encompass cognition, logic, rational action, and practical adaptability [13]. Within this broad scope, early milestones such as Turing’s proposition—judging machine intelligence by whether its responses could pass for human—laid an influential foundation. However, that test focuses on external behavior rather than probing how machines derive their decisions [13].

From a cognitive standpoint, AI systems that “think humanly” draw inspiration from fields like neuroscience and psychology, striving to mimic the inner workings of the brain. Neural networks exemplify this strategy by emulating patterns of biological neurons to facilitate memory formation, pattern recognition, and self-improvement. Such models shed light on how humans reason, although they often have to confront the complexity of culture, emotion, and context—elements that defy straightforward computational replication [13].

A different perspective emerges from attempts to “think rationally”, drawing on formal logic to solve problems through clearly defined principles. Although logical frameworks can be powerful, they struggle with real-world uncertainty and large-scale complexity. In practice, AI systems often blend logic with probabilistic methods, thereby tackling ambiguous scenarios that would otherwise overwhelm purely rule-based reasoning [13].

The notion of “acting rationally” shifts the focus to goal-directed agents that evaluate their surroundings, weigh possible actions, and select the path that maximizes success. Self-driving vehicles and automated assistants typify this paradigm, as they adapt to changing conditions without strictly imitating human thought processes. This agent-based view supports scalable solutions, making it integral to AI’s mainstream applications [13].

In terms of scope, AI can be categorized as Narrow (task-specific), General (human-level versatility), or Superintelligent (surpassing human capabilities). Narrow AI, such as image recognition or language translation, currently dominates [14]. Artificial General Intelligence, still hypothetical, aims at broader cognition [15], while Artificial Superintelligence raises profound questions about self-improvement, ethical safeguards, and power dynamics in society [16]. Discussions of a “Singularity”, a turning point at which AI could surpass human intelligence and self-enhance at exponential rates, illustrate both the optimism and the caution that underscore cutting-edge research [17].

To simplify these varied concepts, many adopt the intelligent agent paradigm. By perceiving inputs, making decisions, and acting on its environment to fulfill predefined objectives, even a simple thermostat can be viewed as a basic form of AI. Between the thermostat-level simplicity of reactive control and the emergent complexities of LLMs, a diverse spectrum of methodologies captures the range of AI paradigms [18]. Soft computing approaches, such as fuzzy logic, fuzzy cognitive maps and neural networks, accommodate uncertainty and ambiguity, offering robustness in scenarios where classical binary logic fails. Evolutionary algorithms like genetic algorithms and particle swarm optimization —emulating natural selection—iterate through successive “generations” of candidate solutions, discovering optimized strategies for tasks like system tuning or multi-objective optimization. Knowledge-based systems, meanwhile, rely on curated rules or expert insights to facilitate automated reasoning in domains ranging from medical diagnosis to energy management. Machine learning in its varied forms—supervised, unsupervised, or reinforcement-based—focuses on extracting patterns from data, iteratively refining predictive models without explicit human instructions. Taken together, these AI paradigms underscore the field’s versatility—they fill the gap between elementary feedback loops on the one hand and the near-human-like linguistic agility of LLMs on the other. By integrating features like incremental learning, probabilistic reasoning, and heuristic searches, these intermediate forms of AI pave the way for increasingly autonomous systems, bridging the qualitative leap from simple automation to advanced cognitive technologies.

As the field of AI continues to evolve, its definitions and paradigms will likely expand and adapt, reflecting new discoveries and applications.

3. AI Applications in the Energy Sector

The electricity sector stands at the forefront of the global energy transition, connecting an array of sub-sectors—from policy and regulation to power generation, grid operations, and end-user services. As modern infrastructures grow more decentralized and data-intensive, Artificial Intelligence (AI) has emerged as a powerful catalyst to ensure efficiency, resilience, and sustainability across these interconnected domains.

In policy decision-making, AI equips stakeholders with data-driven tools to shape national and regional energy strategies [19]. By analyzing extensive records of consumption trends, renewable penetration, and demographic shifts, AI-powered simulations project future energy demand under different regulatory and economic pathways. This assists policymakers in formulating balanced strategies that secure reliable electricity supplies and decrease carbon emissions. In tandem, scenario-modeling platforms allow regulators to measure the impact of prospective regulations on grid operations and resource allocation before a policy is enacted.

Within electricity regulation and oversight, AI strengthens market integrity by detecting anomalies in market data. Clustering algorithms can spot abnormal pricing patterns or potential fraud, prompting swift corrective actions that maintain transparent and equitable competition. AI-driven automation also refines real-time pricing, aligning it more precisely with current supply, demand, and renewable availability [20].

On the transmission side, AI reinforces accurate grid load forecasting and early anomaly detection [21]. Robust time-series models help transmission system operators anticipate spikes in electricity demand or drops in renewable output. By adjusting operational settings in advance, grid managers prevent overloads and reduce blackout risks. Meanwhile, neural networks and diagnostic algorithms highlight subtle faults, such as local overheating in transmission lines, enabling immediate interventions.

In distribution operations, AI excels at balancing fluctuating loads and integrating distributed energy resources (DERs) [22]. Real-time analytics from smart meters inform demand-response strategies, where reinforcement learning algorithms fine-tune how and when to reduce or shift electricity usage. Such adaptability preserves grid stability and limits peak-load stress. Additionally, AI optimizes the coordination of rooftop photovoltaics, battery storage, and other DERs, enhancing the reliability of distributed generation.

Centralized power generation also benefits from AI. Predictive maintenance systems monitor plant components, detecting equipment wear before it escalates into outages [23]. In parallel, optimization software fine-tunes combustion processes to minimize fuel consumption and emissions. This confluence of predictive analytics and optimization can significantly decrease operating costs while meeting stringent environmental regulations.

At the retail level, AI-powered agents transform how end-users engage with electricity services [24]. Algorithms analyze historical consumption to suggest tailored ways of cutting energy use or shifting loads to off-peak hours. Automated support systems handle billing queries and outage notifications, delivering fast, accurate responses while freeing human agents to handle more complex tasks.

Although this discussion centers on electricity, parallel transformations are unfolding across other high-impact sectors—industry, buildings, transportation, and agriculture. In industrial settings, AI-driven maintenance and process optimization decrease downtime and energy waste. Smart building systems employ AI to regulate HVAC, lighting, and appliances in real time [25], while networked transport solutions leverage AI to optimize charging strategies for electric vehicles and orchestrate multi-modal transportation [26]. Meanwhile, in agriculture, machine learning supports precision agriculture applications and carbon-sequestering practices [27].

Taken as a whole, these AI applications highlight the rapidly evolving energy landscape. By capitalizing on AI’s ability to deliver actionable insights and adaptive strategies, stakeholders can develop robust, low-carbon systems that span every link in the energy chain—ensuring resilience, cost-effectiveness, and environmental stewardship for decades to come.

4. Socioeconomic, Security and Ethical Considerations for AI in the Energy Sector

AI’s integration into the energy sector carries wide-ranging socioeconomic implications that extend well beyond technical improvements. On one hand, automating tasks such as grid control or forecasting can displace skilled workers. On the other, new roles in AI development, data science, and energy research may emerge—opportunities that risk bypassing regions with inadequate education or infrastructure. Financial barriers similarly shape how AI is adopted: well-funded utilities can afford the initial investment costs, while utilities in developing nations may struggle to implement AI-driven upgrades, widening global inequalities.

Additionally, advanced pricing models and real-time tariffs can benefit households with flexible consumption patterns, but lower-income communities may lack the digital literacy, stable electricity, or even network access needed to participate. This digital divide can evolve into an energy divide if policies do not proactively address equity [28]. Nonetheless, transparent AI tools—like public dashboards that visualize consumption trends—can foster democratic engagement, encouraging communities to voice their concerns and guide local initiatives. In this way, careful governance, coupled with inclusive education and outreach, is essential to ensure that AI’s transformative potential remains broadly shared [29].

As large language models (LLMs) and multi-agent architectures gain traction in energy applications, data governance and cybersecurity emerge as key concerns [30]. AI solutions rely heavily on comprehensive, high-quality datasets—ranging from historical load profiles to current grid conditions. Compromised or manipulated data can invalidate AI predictions, undermine efficiency gains, and even disrupt critical energy operations.

In multi-agent systems, distributed AI modules communicate to coordinate tasks like demand response or renewable integration. This structure increases vulnerability: a single compromised channel could allow attackers to inject false signals or steal sensitive data. Thus, strict cryptographic safeguards, continual auditing, and robust anomaly detection measures are essential. Privacy regulations also come to the fore, especially if LLMs handle household consumption records or financial details. Compliance with data protection standards, alongside real-time threat monitoring and fail-safe protocols, reinforces both public trust and operational resilience. Collaboration among utilities, governments, and research institutions can further unify security practices, advancing industry-wide standards for protecting critical infrastructure [31].

The economic and security aspects intersect with the core ethical responsibilities in deploying AI in energy transitions. Fairness is paramount: biased datasets may marginalize vulnerable communities already grappling with energy poverty. Ensuring equitable data collection and model validation is crucial to avoid perpetuating existing disparities [32].

Transparency likewise underpins ethical AI. Stakeholders—ranging from community residents to policymakers—deserve clear explanations for AI-driven decisions, especially in areas like dynamic pricing or investment in new infrastructure. Black-box approaches risk eroding public confidence, whereas interpretable models and open consultations can secure broader acceptance. Finally, AI’s computational demands pose ecological dilemmas. Training advanced models can generate substantial carbon footprints, potentially offsetting the very sustainability goals the technology aims to champion. Ethical practice, therefore, mandates a balance between the benefits of sophisticated AI and its impacts, ensuring that innovation aligns with the imperative for social justice and environmental stewardship [33].

5. Energy Demands of LLMs and Nuclear Energy Resurgence

The rapid ascent of Large Language Models (LLMs)—often comprising hundreds of billions of parameters—has ignited pressing questions about the sustainability of digital infrastructure. Although these models expand capabilities in fields like healthcare, finance, and real-time data analytics, their escalating electricity needs complicate efforts to limit greenhouse gas (GHG) emissions [34]. Renewables such as wind and solar have advanced considerably, yet intermittency and storage bottlenecks remain. Under these constraints, nuclear power—long sidelined or phased out in certain regions—has reemerged as a potential baseload solution, despite its substantial capital costs, public skepticism, and the enduring issue of radioactive waste, along with the possibilities of an accident like Fukushima or Chernobyl. This section examines the delicate interplay among investment pressures, advanced AI demands, and decarbonization targets.

A core paradox for tech investors involves increasing the capacity of AI while reducing its carbon footprint. Many key players in LLM research, from tech conglomerates to innovative startups, publicly commit to carbon neutrality by purchasing renewable energy certificates or investing in green projects. However, the growth in data centers often outpaces the expansion of solar and wind capacity. Regulatory hurdles, land constraints, and grid limitations can stall the deployment of renewables, causing real-time energy demands to outstrip green supplies. Further complicating matters is the geographic spread of data centers, where site selection is driven by taxes and cooling costs rather than easy access to robust renewables. For instance, the combined corporate renewable energy procurements of Amazon, Apple, Google, Meta, and Microsoft exceed 45 GW, representing over half of the total global corporate renewable energy market [35]. As a result, maintaining around-the-clock clean power sometimes encourages investors to select nuclear options, which yield baseload electricity without direct CO₂ emissions. As an example, Microsoft has signed a 20-year agreement with America’s Three Mile Island nuclear energy plant to re-open upon improvements in order to provide clean energy for AI data centers [36].

Renewable energy’s inherent intermittency remains a fundamental barrier. Solar panels only generate power under adequate sunlight and wind turbines cease production if wind conditions fail. While battery storage, pumped hydro, and hydrogen-based solutions show promise, they still face cost barriers and limited infrastructure. Regions with abundant wind, hydro, biomass, or geothermal resources can approach full renewable penetration, but these cases are not universal. Many grids—especially those aiming to power energy-intensive data centers—require consistent baseload electricity. This is the reason for the renewed focus on nuclear, even if it introduces long-term waste-management risks and substantial financial obligations.

Nuclear power’s ability to produce zero-carbon baseload electricity has prompted some policymakers to reconsider reactors that were previously scheduled for decommissioning. In principle, restarting or refurbishing such plants can quickly provide significant emissions-free power. However, the use of nuclear technology entails well-documented challenges. High-level radioactive waste must be contained for millennia, necessitating both technical precision and long-term public trust. Constructing or refitting a nuclear site demands substantial upfront capital and intricate licensing, locking societies into decades-spanning commitments. Although nuclear power might counterbalance renewables’ intermittency, any safety incident or unresolved waste-management dilemma could undermine support for decades [37].

AI ventures attract substantial capital from venture funds, private equity, and public markets, many of which apply the Environmental, Social, and Governance (ESG) criteria. In principle, the ESG metrics discourage reliance on high-carbon energy but they often fail to capture the long-term complexities of nuclear waste or safety. Since nuclear operations have near-zero direct CO₂ emissions, some ESG frameworks may favor nuclear, despite its unresolved waste challenges [38]. Consequently, nuclear-powered data centers can appeal to investors seeking immediate emission reductions. However, others remain cautious about the immense costs, the potential for overruns, and the legacy of past nuclear incidents. This divergence of investor opinions—some championing nuclear, others favoring deeper renewable integration—underscores a fragmented global landscape, in which funding and policy directions hinge on both financial optimism and technological practicality [39].

Furthermore, recent advancements in nuclear small modular reactors (up to 20 MWe) have further increased the interest in using nuclear energy to power AI. On the one hand, they are small and simple and can be deployed much faster than traditional nuclear reactors. On the hand, there are many challenges involved, including fuel availability and security issues [40]. Still, they have become a potential choice for powering data centers. For example, Google announced the purchase of a number of small nuclear reactors to support its AI datacenters [41].

By blending stable, low-carbon baseload capacity with the growing electricity needs of AI, nuclear power presents clear advantages as well as crucial dilemmas. Although nuclear facilities can help offset renewables’ intermittency and meet escalating digital demands, they often entail massive up-front investments, intricate regulatory approvals, and waste-management obligations extending far beyond the mid-century. This raises legitimate fears that diverting capital toward nuclear may slow critical innovation in energy storage, grid upgrades, and renewable breakthroughs. This is especially the case for small modular reactors, which appear promising, but still face challenges regarding their commercialization [42]. Moreover, such projects largely benefit nations with plentiful financial resources, potentially intensifying global inequities where underserved regions struggle even to secure basic electrification. Thus, integrating nuclear into AI’s energy matrix demands sensible policy frameworks, rigorous governance, and the balanced prioritization of both near-term efficiency and broader sustainability goals.

6. Discussion

The intersection of AI with energy systems, as outlined in the previous sections, underscores both the outstanding opportunities for decarbonization and the complex challenges that accompany large-scale technological shifts. On the one hand, AI facilitates unprecedented precision in forecasting, enabling systems to accommodate renewables more effectively and reduce carbon intensity; on the other, advanced computational tools, aside from their actual power consumption, raise legitimate questions about equity, data governance, and long-term resource allocation. By automating complex generation, transmission, and distribution tasks, AI can reduce operational inefficiencies and support real-time adaptation to fluctuations in wind or solar output. However, these capabilities require massive datasets, sophisticated multi-agent architectures, and continuous updates, all of which demand secure, high-capacity power supplies, implying that technology choices in the electricity sector are deeply connected with AI’s evolution.

Equally pressing are the socioeconomic dimensions, where uneven access to capital, digital infrastructure, and skilled labor threatens to deepen existing global disparities. Although some nations can seamlessly integrate AI-driven solutions into their grids, others lack even the basic frameworks for universal electrification. Meanwhile, the development of new data centers in emerging markets may stimulate local job creation but can also stress limited grid assets. Without careful governance, new digital demands risk displacing more fundamental needs, particularly in communities that remain largely disconnected from modern energy services. AI’s role thus transcends operational optimization; it intersects with social justice objectives and broader policy considerations. Transparent initiatives—such as public dashboards or incentive programs grounded in inclusive policy—offer a path forward, ensuring that advanced analytics foster communal benefits rather than perpetuating exclusion.

Security and ethics concerns further complicate this landscape. Multi-agent LLMs that are capable of handling operational data in real time open the door to both greater system resilience and heightened vulnerabilities. Cyber threats could compromise essential infrastructures, with cascading consequences for entire regions. Implementing robust encryption, anomaly detection, and clear data management standards can mitigate these dangers but requires cross-sectoral collaboration among regulators, utilities, and AI developers. Beyond technical safeguards, ethical questions persist: AI models must be audited for biases that could marginalize underserved populations, while the computational footprint of AI itself demands scrutiny in an era defined by decarbonization targets.

In parallel, the debate over nuclear power highlights the need to balance near-term reliability with energy transition goals to advance renewable technologies. While nuclear offers a baseload capacity that could sustain AI’s around-the-clock energy requirements, its high capital costs, waste-management obligations, and lengthy project timelines pose formidable obstacles. These complexities underscore the broader principle that no single resource—whether nuclear, solar, wind or sustainable biomass—uniquely solves the deep-seated challenges of a rapidly changing energy landscape. Instead, deliberate planning and interdisciplinary cooperation are needed to chart a course that remains responsive to local contexts, global equity commitments, and ambitious climate objectives. AI’s scaling-up, with its inherent promises and demands, demonstrates how emergent technologies can support or hinder these pathways. Consequently, the success of this energy transition relies not just on which tools are adopted, but also on how effectively governance structures, policymaking, and social values are aligned to harness AI’s transformative potential without sacrificing inclusivity or ecological balance.

7. Conclusions

The findings presented in this paper highlight a rapidly transforming energy landscape where AI offers compelling solutions yet simultaneously creates new complexities. Renewables are still destined to meet much of the world’s growing energy demand, facilitated by AI paradigms. Nonetheless, the accelerating use of LLMs and data-intensive technologies, which are realized in high-power data centers, underscores the need for a dependable, round-the-clock baseload capacity—an imperative that can draw renewed attention to nuclear power, despite its associated social and environmental challenges.

From policy design to on-the-ground implementation, stakeholders face a delicate task: accelerate technological advancements without compromising equity, inclusivity, and ecological integrity. This involves recognizing that AI-related gains must be woven into broader social objectives, including universal electrification and transparent governance. Equitable data access, robust cybersecurity protocols, and progressive ESG frameworks can further ensure that AI’s computational burden does not exacerbate climate or societal vulnerabilities.

Ultimately, the success of AI-driven energy transitions will depend on robust alliances among governments, industry, civil society, and scientific communities. By integrating diverse expertise and perspectives, technological progress can be realized while safeguarding the long-term needs of both present and future generations.