India’s nuclear ambitions can be more fully realised by integrating AI across the plant lifecycle, transforming existing strengths into faster, more efficient, and future-ready energy deployment
As electricity demand surges with the expansion of artificial intelligence (AI) data centres and high-performance computing, nuclear energy is being seen as a key provider of reliable power. Much of the current discourse on the role of nuclear energy in powering AI infrastructure has focused on data centres. However, an equally important dimension is how AI itself can be used to transform and optimise nuclear power plants, especially as advanced technologies such as small modular reactors are being vetted worldwide to meet rising energy demands.
Through its Sustainable Harnessing and Advancement of Nuclear Energy for Transforming India (SHANTI) Act 2025, India recognises the need for expanded nuclear deployment to support AI, semiconductor manufacturing, and other data-driven sectors, and is expanding its capacities. Complementing this, the Nuclear Energy Mission for Viksit Bharat targets 100 GWe of nuclear capacity by 2047, signalling strong national intent to scale nuclear infrastructure.
Much of the current discourse on the role of nuclear energy in powering AI infrastructure has focused on data centres.
However, achieving these ambitions also requires addressing inefficiencies within its nuclear sector. As seen in countries such as the United States and China, AI is not only a beneficiary of nuclear expansion but also an important tool to optimise it. It can streamline regulatory processes, improve operational efficiency, and reduce delays. For India, integrating AI into nuclear systems is essential to align with the spirit of its new nuclear law, ensuring that capacity expansion is both efficient and future-ready.
As global energy demand rises alongside the rapid expansion of digital economies, nuclear power is re-emerging as a critical source of reliable and sustainable energy. AI is transforming how nuclear systems are designed, regulated, and operated. Leading tech powers, the United States and China, are leveraging AI to address longstanding inefficiencies, streamline regulatory processes, and enhance operational performance. Their approaches show how technological innovation, when coupled with policy support, can accelerate the development of safer and more efficient nuclear energy systems.
In the United States, the integration of AI into nuclear energy production is increasingly emerging as a necessary response to systemic inefficiencies that have constrained its expansion. Nuclear plant development has historically been slowed by complex and lengthy permitting processes, highly customised engineering, and fragmented data. Engineers often spend thousands of hours drafting, cross-referencing, and reviewing vast volumes of documentation—sometimes tens of thousands of pages—while identifying inconsistencies that can trigger costly delays. These documentation burdens, combined with multiple rounds of manual regulatory review, have made licensing and construction both time-intensive and expensive, with projects such as the Vogtle Unit 3 taking over a decade to complete.
For India, integrating AI into nuclear systems is essential to align with the spirit of its new nuclear law, ensuring that capacity expansion is both efficient and future-ready.
To mitigate these issues, AI is increasingly emerging as a viable solution. AI-driven collaborations between Microsoft and NVIDIA are addressing these bottlenecks by unifying fragmented workflows into standardised, repeatable, and traceable processes across plant design, permitting, construction, and operations. Through the integration of ‘digital twins,’ high-fidelity simulations, and genAI, these platforms reduce documentation delays and detect inconsistencies across large datasets, ensuring regulatory compliance. At the institutional level, initiatives led by the US Department of Energy (DOE), in partnership with Idaho National Laboratory and others, demonstrate AI’s ability to reduce licensing timelines. AI tools such as Everstar’s Gordian AI Solution can convert complex safety analyses into US Nuclear Regulatory Commission (NRC)-compliant documents in much less time, while also identifying missing or incomplete information. Complementary innovations, including AI-enabled ‘crosswalk systems,’ can bridge regulatory gaps between DOE and NRC frameworks, potentially accelerating the transition from reactor testing to commercial deployment.
Advanced simulation and permitting tools such as NVIDIA Omniverse and Microsoft’s generative AI permitting tools further enable 3D, 4D, and 5D modelling, allowing developers to virtually construct and optimise plants, thereby reducing costly delays and preventing rework. Early applications highlight tangible efficiency gains: companies such as Aalo Atomics report up to 92 percent reductions in permitting timelines, while deployments by Southern Nuclear and Idaho Labs, using AI copilots and automated safety tools, are standardising regulatory review and enhancing decision-making.
These developments come in the wake of an active US policy supporting AI integration in nuclear energy. Initiatives such as its ‘Genesis Mission’ promote the use of explainable AI, ‘digital twins,’ autonomous labs, and agentic workflows across the entire nuclear lifecycle, from design and licensing to construction and operations. It aims to double deployment speed and cut operational costs by over 50 percent. Complemented by US$ 293 million in funding, the policy fosters collaboration among national laboratories, industry, and academia to develop scalable AI solutions, reduce human error, strengthen energy security, and advance reliable, cost-effective nuclear power deployment.
In China, the integration of AI into nuclear plants shows a coordinated effort to address critical technical problems while enhancing efficiency, safety, and standardisation. One of the most significant challenges lies in sustaining and controlling plasma in fusion reactors, an ultra-hot, electrically charged gas at extremely high temperatures. Maintaining plasma stability is difficult and remains an obstacle to making fusion energy a reliable and scalable power source, as instability can lead to costly disruptions and equipment damage.
To address this and manage plasma instability, China has deployed AI-driven systems based on interpretable machine learning models that predict plasma disruptions with up to 94 percent accuracy and issue early warnings, while multitask learning models classify plasma states in real time with over 96 percent accuracy. These systems reduce reliance on multiple specialised monitoring tools and streamline reactor management.
Maintaining plasma stability is difficult and remains an obstacle to making fusion energy a reliable and scalable power source, as instability can lead to costly disruptions and equipment damage.
Beyond experimental reactors, AI is also being deployed in operational nuclear plants through industry solutions such as Huawei’s ‘AI + Electricity’ solution, which enables real-time monitoring, predictive maintenance, and intelligent scheduling of nuclear plants by analysing large-scale operational data, thereby minimising downtime and redundancy. Similarly, iFLYTEK uses natural language processing and data mining to analyse maintenance logs and operational records, improving decision-making and reducing manual administrative burdens.
These technological advancements are strongly supported by China’s policy frameworks issued by the National Energy Administration and the Ministry of Ecology and Environment, which, since 2020, have explicitly promoted the adoption of AI, big data, and digital technologies in nuclear construction and management. The 2023 ‘Guiding Opinions on Promoting the Digital Transformation and Development of Nuclear Power’ further reinforces this goal by calling for deeper integration of AI, IoT, and cloud computing across the nuclear sector. Together, these initiatives illustrate how China is systematically embedding AI into both experimental and commercial nuclear domains, reducing redundancies, automating complex processes, and advancing towards a more intelligent, efficient, and scalable nuclear energy infrastructure.
India’s nuclear power sector has made steady progress, as reflected in advancing reactor technologies and a clear long-term vision for capacity expansion, even as its current contribution remains about 3.1 percent of total electricity generation. Ongoing projects such as the Prototype Fast Breeder Reactor shed light on these efforts while also highlighting opportunities to strengthen planning and execution. Budgetary issues, as evidenced by discrepancies between initial and revised estimates, along with underutilisation of allocated funds, highlight the need for more precise forecasting, improved procurement readiness, and smoother project initiation. Similarly, a lengthy and rigorous regulatory environment, while necessary for safety, may benefit from greater efficiency in documentation and approval workflows. Furthermore, on the operational side, reducing extended shutdowns and improving maintenance cycles could enhance plant performance and overall productivity. These challenges, however, arise not from a lack of technical expertise or competency but from fragmented systems and insufficient integration across the plant lifecycle.
Integrating AI into India’s nuclear power sector is a strategic necessity—not only to secure future sustainable energy needs, but also to fuel its technological innovation ambitions.
In this context, AI offers a powerful opportunity to build on and optimise India’s present capacities through its data-driven, automated, and predictive capabilities. ‘Digital twins’ could be used to simulate reactor construction and operations, enabling more accurate planning, cost estimation, and real-time performance monitoring. AI can further enhance operational safety by enabling automated defect detection in nuclear fuel assemblies, ensuring structural integrity while reducing inspection time and costs. It can also support radiation dose prediction during nuclear emergencies, strengthening preparedness and response strategies. Additionally, AI-driven systems can detect fuel and component failures in advance, improving reliability and reducing unplanned downtime.
Furthermore, AI tools could streamline regulatory documentation, automate cross-referencing, and help reduce approval timelines. Predictive maintenance systems, powered by AI, could anticipate equipment failures, minimise downtime, and improve plant reliability. Additionally, data-driven AI financial tools could enhance budgeting accuracy and improve fund utilisation.
Integrating AI into India’s nuclear power sector is a strategic necessity—not only to secure future sustainable energy needs, but also to fuel its technological innovation ambitions. As global leaders such as the United States and China have demonstrated, AI has the potential to rapidly alter the current landscape and transform how nuclear energy plants are designed, regulated, and operated. For India, adopting these technologies offers a roadmap to overcoming persistent inefficiencies, increasing capacity, and meeting the growing energy demands of an AI-driven economy.
Debajyoti Chakravarty is a Research Assistant with the Centre for Digital Societies at the Observer Research Foundation.
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Debajyoti Chakravarty is a Research Assistant at ORF’s Center for New Economic Diplomacy (CNED) and is based at ORF Kolkata. His work focuses on the use ...
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