How Small Modular Reactors Reduce Environmental Cost of AI

Source: The post How Small Modular Reactors Reduce Environmental Cost of AI has been created, based on the article “Redrawing the not-so-pretty energy footprint of AI” published in “The Hindu” on 6 May 2025. How Small Modular Reactors Reduce Environmental Cost of AI.

UPSC Syllabus Topic: GS Paper2-Infrastructure: Energy, Ports, Roads, Airports, Railways etc.

Context: Generative AI tools like ChatGPT provide rapid access to creative content. However, they consume vast energy from data centres powered mostly by fossil fuels. Their increasing use strains global power supplies. This article examines the environmental cost of AI and investigates whether Small Modular Reactors (SMRs) can offer a sustainable energy alternative.

For detailed information on Small Modular Reactors (SMRs): Potential and Challenges read this article here

The Energy Footprint of AI

1. Rising Electricity Consumption: Every AI use requires energy from data centres. Most are powered by fossil fuels. By 2030, data centres may consume 10% of global electricity. While India currently meets AI energy needs, growing demand calls for future planning.
2. High Carbon Emissions from Training: Training AI models like ChatGPT or Midjourney produces CO₂ emissions equal to five cars running over their lifetimes. Even after deployment, these tools need continuous power, worsening the environmental impact.
3. Need for Transparency: AI companies must disclose energy usage, sources, and reduction measures. Like data privacy rules, such transparency can identify major consumption points and guide efforts to build sustainable AI infrastructure.

Small Modular Reactors as an Energy Solution

1. Stable, Zero-Carbon Power Supply: SMRs offer 24×7 zero-carbon electricity. Unlike large nuclear plants, they are compact and scalable. Unlike solar or wind, they can function regardless of weather and be located near data centres.
2. Faster and Safer Deployment: SMRs are quicker and cheaper to construct due to their modular design. They feature passive safety systems that cool the core naturally and reduce accident risks. This improves public acceptance.
3. Decentralised and Reliable Energy: SMRs can be placed close to high-demand areas, reducing transmission losses. Their flexibility suits both urban and remote locations, supporting grid resilience and decentralised energy production.

Challenges to SMR Adoption

1. Policy and Public Perception: Governments must develop regulations for safety, waste management, and public trust. Without a strong framework, scaling SMRs will remain difficult.
2. High Initial Costs: SMRs require large upfront investments. However, in India, costs could fall from ₹10.3 to ₹5 per kWh, lower than the average cost, once functional.
3. Integration with Renewables: SMRs must align with existing solar and wind strategies. Careful planning is needed to avoid duplication and optimise resources.

Way forward

1. Collaborative Support for Innovation: A public-private partnership model can combine government oversight with private innovation. This joint effort can accelerate the deployment of SMRs and other renewable energy sources to meet AI’s rising power demands.
2. Aligning Energy Goals with AI Needs: Through collaboration, both sectors can align sustainable energy practices with the rapid growth of AI. This synergy ensures that energy supplies keep pace with technological advances.
3. Ensuring Long-Term Sustainability: Public-private models offer a realistic path for long-term, sustainable AI development. They can drive efficient research and development, making sustainable energy for AI a future-ready solution.

Question for practice:

What are the potential benefits and challenges of using Small Modular Reactors (SMRs) to power AI data centres?