Context: Generative Artificial Intelligence (AI) has eased access to art and reduced the time and the effort required to complete certain tasks. But this ease comes at a significant energy cost. Small modular nuclear reactors could be the energy answer to support booming AI and data infrastructure.
Relevance of the Topic: Mains: Artificial Intelligence: Challenges
Energy cost of Artificial Intelligence:
- Data centres (the backbone of AI operations) consume enormous electricity and contribute 1% of global greenhouse gas emissions.
- A simple search request made through ChatGPT (an AI-based virtual assistant) consumes 10 times the electricity consumed by a Google Search.
- Training advanced AI models can emit up to 552 tonnes of carbon dioxide equivalent which is comparable to the annual emissions of dozens of cars.
- Projections indicate that these data centres could account for 10% of the world’s total electricity usage by 2030. The majority of this electricity comes from fossil fuel sources.
- India currently has sufficient capacity to generate electricity for its own domestic AI needs. Yet, with increasing adoption and ambitions, proactive planning is imperative.
Potential of SMRs is bridging the Energy demand
Leveraging nuclear energy, specifically Small Modular Reactors (SMR), could be a possible alternative to support booming AI and data infrastructure. This is possible as:
- SMRs are designed to be compact and scalable. Their flexibility allows them to be deployed closer to high-energy-demand facilities, such as data centres, which require consistent and reliable power to manage vast amounts of computational workloads. In contrast, traditional large-scale nuclear power plants demand extensive land, water, and infrastructure.
- SMR can provide 24X7, zero-carbon, baseload electricity making it an ideal alternative to renewable sources such as solar and wind by ensuring a stable energy supply regardless of weather conditions.
- Their modular construction reduces construction time and costs when compared to conventional nuclear plants, enabling faster deployment to meet the rapidly growing demands of AI and data-driven industries.
- SMRs offer enhanced safety features, with passive safety systems that rely on natural phenomena to cool the reactor core and safely shut down, reducing the risk of accidents. This makes them more acceptable and easier to integrate into regions where large-scale nuclear facilities would face opposition.
- SMR’s ability to operate in diverse environments, from urban areas to remote locations, also supports the decentralisation of energy production, reducing transmission losses and enhancing grid resilience.
In India's case, the cost of electricity from SMRs is predicted to fall from ₹10.3 to ₹5 per kWh after the reactors are functional, which is less than the average cost of electricity.
Challenges in adoption of SMRs:
- Significant policy shifts are required to create a robust regulatory framework that addresses safety, waste management and public perception.
- Adoption requires substantial upfront investment, as the technology is still maturing and may face issues of cost competitiveness when compared to established energy sources.
- Coordinating SMR deployment with existing renewable energy initiatives will require careful planning to maximise synergies while minimising redundancy.
Sustainable AI Adoption
- AI companies need to be transparent about their energy consumption, i.e., disclose their environmental impact.
- Such data would provide further insights on where energy is being used the most, and encourage R&D to create a more sustainable model of AI development.
Also Read: Environmental Impacts of Artificial Intelligence
The public-private partnership model presents a realistic solution to the challenges of sustainable AI development. By leveraging the strengths of both sectors, this model can facilitate the efficient development of SMRs alongside other forms of renewable energy to support advancements in AI.