AI Energy: Powering Sustainable Digital Future

AI energy, exploring how small modular reactors can meet the surging power demand of AI, reduce carbon emissions, enable hybrid energy systems, support India’s digital, sustainable energy goals.

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AI Energy Future of Sustainable Energy Introduction

A recent report by the International Energy Agency (IEA) highlighted that electricity consumption by data centres is expected to more than double by 2030 due to the rising demand for artificial intelligence applications. The report warns that this surge in power usage will pose new challenges to global energy security and efforts to meet CO₂ emission reduction targets.
Electricity usage by data centres has grown significantly in recent years. According to the report, these facilities accounted for approximately 1.5 per cent of global electricity consumption in 2024, marking an average annual growth of 12 per cent over the past five years. Generative AI, in particular, demands substantial computing resources to process data from massive databases. At present, the United States, Europe, and China together represent around 85 per cent of global electricity consumption by data centres.
As countries confront the climate crisis and strive to meet net-zero targets, nuclear energy—especially through the adoption of Small Modular Reactors (SMRs)—emerges as a cleaner, scalable, and dependable solution for powering the future of digital infrastructure.

Why Does AI Require So Much Energy?

- AI technologies, particularly advanced models like GPT-4, require massive computing power throughout their lifecycle. The computational requirements of AI models during training and post-deployment operations are immense, and here’s why:

Continuous Energy Use After Deployment:

- - Once AI models like ChatGPT or Midjourney are deployed, they require constant energy to operate across global server networks 24/7. These systems are designed to handle requests from millions of users simultaneously, continuously consuming power to serve these demands.
  - For example, Midjourney and DALL·E rely on high-resolution image synthesis, putting additional strain on data centers that are already energy-intensive. The ongoing operation of AI models demands a steady power supply and constant server uptime.

Data Storage and Management:

- - AI systems rely on massive datasets, often spanning petabytes of information, which need to be stored and managed in high-performance data centers. These data centers consume significant energy not only for processing but also for cooling systems to maintain optimal operating conditions.
  - Cooling typically accounts for 40-50% of the total energy consumption in data centers. With the growing scale of AI-driven services, the energy costs of maintaining data storage are becoming a major concern, especially as more data is generated every second.

High Computational Demands of AI Models:

- - AI models, such as GPT-4, demand enormous amounts of energy during both their training and inference stages. These models are powered by thousands of graphics processing units (GPUs) working in tandem, each cycle consuming significant energy.
  - For context, training a large-scale AI model can emit as much CO₂ as five cars over their entire lifecycle. In fact, a study by MIT Technology Review revealed that the lifecycle emissions of AI models are comparable to those of some small nations’ per capita CO₂ emissions. The scale of these emissions has raised serious concerns about AI’s environmental impact.

Energy-Hungry GPUs for AI Processing:

- - Another major factor contributing to AI’s high energy demand is the reliance on power-intensive GPUs for processing data. These GPUs, which power the training and inference stages of AI, consume large amounts of electricity and generate significant amounts of heat, adding to operational inefficiencies.
  - OpenAI’s CEO has publicly noted that their GPUs are “melting,” underscoring the thermal inefficiencies and energy challenges associated with AI systems.

Edge AI and Real-Time Analytics:

- As AI integrates with the Internet of Things (IoT) and real-time applications, Edge AI is becoming more prevalent. Edge AI involves decentralized processing closer to where data is generated, which increases the overall energy demand for AI services globally.
- Major tech companies like Amazon, Google, and Microsoft rely on redundant global data hubs, powered primarily by fossil fuel-heavy grids. These data hubs not only consume energy for processing but also require constant cooling and maintenance, exacerbating their environmental footprint.

What is an SMR and How Can It Meet AI’s Energy Demands?

- Small Modular Reactors (SMRs) are nuclear reactors with a smaller capacity than traditional plants, typically generating between 50-300 MW of electricity. Their compact design and modular construction make them ideal for applications requiring a flexible, reliable power supply, such as AI data centers. Let’s dive deeper into why SMRs are an ideal energy solution for the digital future.

Low Carbon Emissions: SMRs are zero-emission during operation, making them an excellent choice for industries striving to meet net-zero targets. By using SMRs to power AI systems, countries can reduce their carbon footprints while still meeting the ever-growing energy demand driven by AI.

Scalable and Modular Design: SMRs are inherently scalable. They can be modularly deployed to meet growing energy demands as AI systems expand. The NuScale Power SMR design, approved by the U.S. Nuclear Regulatory Commission (NRC), is an excellent example of modular nuclear construction that can be gradually scaled up as required by evolving infrastructure needs.

Space Efficiency: SMRs require significantly less land compared to traditional energy sources like solar or wind farms, making them ideal for densely populated or land-scarce areas where AI data centers are located. The reduced land footprint is particularly beneficial in urban environments where real estate costs are high.

Faster Deployment: Unlike traditional nuclear plants, which often take 10+ years to construct, SMRs can be operational in 3 to 5 years, making them a faster solution for energy deployment in regions that need quick infrastructure upgrades, especially to meet the increasing power demands of AI systems.

Enhanced Safety Features: SMRs are designed with passive safety systems that do not require active intervention for cooling, reducing the risk of meltdown and enhancing operational safety. For example, Rolls Royce‘s SMRs utilize natural convection to cool the reactors, minimizing the need for external power or mechanical pumps.

On-Site Integration with AI Clusters: Co-locating SMRs near AI data centers can significantly reduce transmission losses, providing a more efficient power supply. For instance, Microsoft plans to use SMR-generated power at the former Three Mile Island nuclear site to supply nearby infrastructure, including AI services.

Hydrogen and Heat Co-Production: SMRs can generate both industrial heat and hydrogen, which can be used to power AI applications and other green industries. High-temperature SMRs can produce clean hydrogen, offering a dual advantage—clean energy production and support for the hydrogen economy.

Water Neutral Design: Modern SMRs are designed to require significantly less water for cooling, with some even using recycled water. This is especially important in regions with water scarcity, where conventional nuclear plants and other power sources may place additional strain on local water resources.

Economic Competitiveness: According to NITI Aayog, the cost of electricity generated by SMRs is expected to fall dramatically in India, from ₹10.3 per kWh to ₹5 per kWh once these reactors are fully operational. This reduction in electricity costs makes SMRs an attractive option for powering energy-hungry AI systems while ensuring cost-efficiency in the long run.

24/7 Baseload Energy: Unlike intermittent sources like solar or wind, SMRs can provide continuous baseload energy, ensuring that AI infrastructure receives reliable power around the clock. This is critical for applications like AI models and cloud computing which require 24/7 uptime. For instance, Google signed an agreement in 2023 to power its AI operations using nuclear energy, acknowledging the role of SMRs in maintaining consistent power delivery.

The Significance of SMRs

Industrial Decarbonization: Industries like steel, cement, and chemicals require consistent baseload power, which is difficult to achieve with renewable energy alone. SMRs offer an effective solution for these sectors to decarbonize while maintaining production capacity.

Water Desalination: Countries such as the UAE are exploring the use of SMRs for desalination of seawater, turning nuclear power into a solution for clean drinking water in water-scarce regions.

Space Exploration: NASA is investigating SMRs for space colonies, with projects like Kilopower aiming to provide reliable, small-scale nuclear energy for missions to Mars and beyond.

Remote Power Supply: SMRs are ideal for providing off-grid power to remote regions, such as the Arctic and island communities. These microreactors can offer independent, secure power in areas where traditional infrastructure is not feasible.

Climate Change Mitigation: SMRs offer a zero-carbon power solution that can help countries meet their climate goals, as outlined by the IPCC and the Paris Climate Agreement. By replacing fossil fuels with clean nuclear energy, SMRs could contribute to the global push toward carbon neutrality.

Key Indian & International Initiatives to Support SMR and AI Energy Needs

BARC & NPCIL Research on Indian-Designed SMRs: India’s Bhabha Atomic Research Centre (BARC) and NPCIL are actively researching the design of 100 MW SMRs to meet India’s growing energy needs. These efforts aim to make India a key player in modular nuclear energy by developing indigenous SMR technology.

International Atomic Energy Agency (IAEA) Collaboration: India is an active participant in the SMR Safety Working Group under the International Atomic Energy Agency (IAEA). This collaboration aims to harmonize SMR safety regulations globally, ensuring safe deployment of SMRs worldwide.

India-U.S. Civil Nuclear Pact: Under the India-U.S. Civil Nuclear Pact (2008), there are ongoing discussions about SMR technology cooperation. The partnership aims to leverage advanced nuclear technologies, including SMRs, to address both energy and sustainability challenges.

Paris AI Action Summit: At the Paris AI Action Summit, India pledged to make AI development energy-efficient and sustainable. This commitment includes exploring SMRs as a clean energy source for powering AI infrastructure, aligning with global net-zero emissions goals.

Quad Clean Energy Program: The Quad countries—India, the U.S., Japan, and Australia—are collaborating on SMR research and deployment under the Quad Clean Energy Program. This initiative is focused on advancing SMR technology and ensuring clean energy solutions for the Indo-Pacific region.

Act East & Arctic Engagement: India’s Act East Policy has integrated SMRs into its Arctic infrastructure diplomacy, with potential partnerships with countries like Norway and Russia. These collaborations aim to explore the use of SMRs for remote power supply in cold, arid, and isolated regions.

IndiaAI Mission: ₹10,300 Crore Investment for AI Development: India has launched the IndiaAI Mission, with a budget of ₹10,300 crore, to develop public computing infrastructure for AI. However, this mission requires a sustainable power solution, and SMRs could play a pivotal role in ensuring a low-carbon, reliable energy supply to fuel India’s AI ambitions.

NITI Aayog – SMR Roadmap (2022): The NITI Aayog has recognized the strategic importance of SMRs in India’s low-carbon energy transition. In 2022, NITI Aayog outlined a roadmap to incorporate SMRs into India’s energy mix, positioning them as a critical part of the nation’s green energy strategy.

India-France Nuclear Cooperation: India and France are exploring potential synergies in deploying SMRs alongside AI-integrated renewable hubs, particularly in regions like Rajasthan and Ladakh, where solar energy can be complemented by nuclear power for grid stability.

Public-Private Pilot Projects: Discussions are underway between Indian tech companies and nuclear energy startups, such as NuScale Power and TerraPower, to launch pilot projects exploring the potential of SMRs to power AI data centers in public-private partnership (PPP) mode.

Challenges Facing SMRs in India and Globally

Policy and Regulatory Hurdles: India’s Atomic Energy Act of 1962 needs significant updating to accommodate SMR-specific regulations. The lack of a comprehensive policy framework for SMRs is a significant barrier to their deployment.

Public Perception and Nuclear Anxiety: Nuclear accidents like Chernobyl and Fukushima have left a lasting impression, and there is public nuclear anxiety surrounding the safety of SMRs. Even Microsoft’s project at Three Mile Island faced public scrutiny, highlighting the need for effective public awareness campaigns.

High Upfront Investment: SMR projects require significant upfront investment, with estimated costs ranging from ₹3,000 crore to ₹5,000 crore per unit. This high cost often deters private sector involvement and poses financial challenges.

Long Approval Timelines: Nuclear projects in India face delays due to challenges related to environmental approvals, land acquisition, and regulatory hurdles. Streamlining the approval process is critical for timely SMR deployment.

Skilled Workforce Gap: India faces a shortage of skilled nuclear engineers and professionals capable of integrating AI with nuclear power. Addressing this skills gap is essential to support the growth of SMR technologies.

Waste Management: SMRs generate radioactive waste, and although the volume is smaller than traditional reactors, long-term disposal solutions for SMR waste remain a challenge in India.

Security Concerns: Smaller nuclear units may be at a higher risk of sabotage or theft if not adequately safeguarded. Ensuring robust security protocols around SMRs is crucial.

Renewable Coordination: Integrating SMRs with solar and wind power in a hybrid grid requires smart policy integration to balance baseload nuclear power with intermittent renewable sources.

Land and Water Use: AI data centers consume vast amounts of land and water for cooling and operation. Balancing the land and water use of SMRs with AI infrastructure is crucial to minimizing environmental impact.

Electronic Waste and Effluents: AI hardware manufacturing results in toxic e-waste, including chips and circuit boards, which adds to environmental concerns, especially when AI hardware is powered by nuclear energy.

Way Forward

Updating Nuclear Policy: India should amend the Atomic Energy Act to facilitate private sector investments and provide a clear regulatory framework for SMR deployment.

Green Energy Mandates for AI: Implement energy audits for AI firms and establish green energy mandates to ensure sustainable AI development.

Public Awareness Campaigns: Educate the public about SMR safety through initiatives like Vigyan Samagam, India’s national science expo.

Fast-track Pilot SMRs: Launch pilot SMR projects in AI clusters like Chennai under PPP models, similar to Tamil Nadu’s nuclear corridor.

Hybrid SMR-Renewable Projects: Pair SMRs with solar farms in high solar irradiance areas like Rajasthan and Ladakh for a hybrid energy solution.

Global R&D Collaborations: Collaborate with global partners like the U.S. Department of Energy and Canadian Nuclear Labs for SMR technology transfer and safety training.

Green Data Center Policy: Encourage data centers to adopt green energy practices under the Energy Conservation (Amendment) Act, 2022.

AI for SMR Optimization: Use AI to optimize energy efficiency in SMRs and hybrid microgrids, as demonstrated in Finland and Japan.