Pairing small modular reactors with AI data centers
September 11, 2025
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SMRs can help us to sustainably power AI data centers. AI can help us to design the SMRs. It¡¯s a symbiotic relationship that we¡¯re excited about.
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The demand for data centers is going nowhere but up. This is especially true with the emergence of artificial intelligence (AI) and machine learning (ML). But there¡¯s a challenge. AI data centers are incredibly energy intensive. They are mission critical facilities and require a significant amount of power. And they must operate 24 hours a day, 7 days a week.
How can we possibly provide the energy AI data centers need? And how can we power them without relying on traditional fossil fuels?
The AI data center industry needs sustainable and efficient energy solutions. Small modular reactors (SMRs) and nuclear power have emerged as a promising technology. For decades, nuclear power was almost out of the question in many places around the world. This was in part due to cost, environmental, and safety concerns. But the energy transition is driving us to rethink our priorities. We must now look at alternative sources of stable and reliable energy like nuclear. And advancements in safety are allowing us to do that.
In fact, just last year Microsoft announced the reopening of Three Mile Island in Pennsylvania. Amazon also said it is investing half a billion dollars in SMRs. And Meta said it is interested in both. Clearly, the tech industry is moving towards SMRs and nuclear power to solve its energy problems. All while committing to emissions reduction.
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The demand for data centers is going nowhere but up. This is especially true with the emergence of artificial intelligence and machine learning.
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But it turns out that while SMRs can support AI data centers, AI can support the development of SMRs, too. It¡¯s a relationship that can help us to reliably power our AI data centers¡ªwithout the greenhouse gas (GHG) emissions from typical energy generation.
In this blog, we¡¯ll explore how AI and ML can play a pivotal role in the design, construction, and operation of SMRs.
A brief background on SMRs
SMRs are advanced nuclear reactors that can help us generate reliable, consistent power. As the ¡®S¡¯ in their name suggests, SMRs are much smaller than traditional nuclear plants. They have a power capacity of up to 300 megawatts (MW) per unit. Given their size, SMRs are perfect to deploy with AI data centers where space may be limited. They can also reduce transmission losses and dependency on external energy grids?while taking advantage of ¡°behind the meter¡± cost efficiencies.
AI data centers require a stable, baseload energy supply. A great fit for SMRs. Why? Because they provide a constant, stable output of electricity. SMRs are also not affected by weather conditions, unlike solar or wind power. This reliability limits downtime risks. It also allows for uninterrupted data processing for critical digital infrastructure.
Unlike traditional reactors, SMRs are designed to be assembled in a factory and transported to their operational sites. This represents the ¡®M¡¯. The modular approach allows for scalability. One site can use a single SMR, whereas a larger site can make use of several. This approach also allows clients to start small with one and then build incrementally, adding units as energy demand grows. This matches AI data center scaling needs. It makes nuclear energy more accessible and adaptable to various applications.
SMRs generate electricity with near-zero carbon emissions during operation. This is another reason why they are an ideal power source. They can meet increasing AI data center demands while reducing impacts on the environment. This helps AI data centers to reduce their carbon footprint and align with sustainability goals.
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The role of AI and ML in SMR construction
It¡¯s pretty clear that SMRs can help us to sustainably and reliably power AI data centers. But as we said earlier, AI and ML technologies can play a key role in the design, construction, and operation of SMRs. Here are several ways these technologies can contribute:
Optimized design and simulation. AI-driven design tools can analyze vast amounts of data to help optimize reactor designs. ML algorithms can simulate various scenarios to predict how different designs will perform under specific conditions. This helps to select the most efficient and safe configurations.
Smart fuel management and monitoring. Fuel is a critical component of a nuclear reactor. It requires precise handling and strategic planning for replacement. AI can significantly enhance these processes. The International Energy Agency notes that . The result? Greater efficiency and reduced waste in a SMR.
- Predictive maintenance. AI and ML can in real time. This predicts potential failures before they occur. It¡¯s a proactive approach that limits downtime and extends the lifespan of critical infrastructure.
- Enhanced safety measures. AI models can analyze operational data to detect anomalies and potential safety threats. ML can help in developing advanced safety protocols that adapt to changing conditions. This allows the reactor to operate within safe parameters.
- Energy management. AI-driven systems can within data centers powered by SMRs. By adjusting power distribution based on real-time demand, these systems can reduce energy waste and improve overall efficiency.
- Public transparency and trust. AI can also play a role in improving the public perception of SMRs. By enabling real-time, transparent reporting of reactor performance, safety metrics, and environmental impact, AI systems can help build public trust. Interactive dashboards¡ªpowered by AI¡ªcan make complex nuclear data accessible and understandable to nonexperts. This helps to address concerns and foster informed community engagement.
As you can see, AI has plenty to offer in the design, construction, operation, and maintenance of SMRs. Pairing the two together results in many mutual benefits.?
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AI and ML technologies can play a pivotal role in the design, construction, and operation of SMRs.
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Benefits for AI data centers
AI data centers are critical infrastructure in the digital age. They require reliable, scalable, and sustainable power sources. Integrating SMRs with AI and ML offers a powerful solution with several key advantages. Let¡¯s look at some below:
- Scalability. SMRs can be deployed incrementally to match the growing energy demands of data centers. This approach allows operators to start with a single unit and expand as needed. It also doesn¡¯t require the massive upfront investment needed for traditional nuclear plants. Several conventional nuclear power plants have cost tens of billions of dollars. Plus, AI can further boost scalability by forecasting future energy needs based on usage trends and automating deployment planning.
- Sustainability. Nuclear energy is a low-carbon power source. That¡¯s why SMRs are a strong candidate for reducing the environmental footprint of data centers. Unlike fossil fuels, SMRs produce minimal GHG emissions. In fact, that by 2050 ¡°nuclear energy could displace 5 gigatonnes of emissions per year, which is more than what the entire US economy emits annually today.¡± Furthermore, when paired with AI, data centers can balance loads between SMRs and renewable sources. This helps both sustainability and performance.
- Cost efficiency. AI and ML can optimize SMR operations. How? By needs, adjusting output based on real-time demand, and improving thermal efficiency. This helps reduce operational costs, minimize downtime, and extend the lifespan of reactor components. Over time, these efficiencies can translate into huge savings for data center operators. AI-driven analytics can reduce maintenance costs by up to 30 percent and increase equipment availability by as much as 20 percent. Overall, this improves the economics and reliability.
- Resilience and reliability. SMRs offer a stable, 24/7 power supply. And the power is less vulnerable to weather disruptions compared with solar or wind. AI can monitor grid conditions and automatically reroute power or adjust reactor output to maintain uptime. This allows the data center operations to continue without interruption.
- Security and safety. AI-enhanced monitoring systems are ready to support security and safety. They can detect anomalies in reactor performance or cybersecurity threats in real time. This enables rapid response to both physical and digital risks. It reinforces the safety of SMR-powered data centers. It also addresses public concerns about nuclear energy.
- Smaller footprint and siting flexibility. SMRs require less land and infrastructure than traditional nuclear plants¡ªoftentimes only around 25 percent of the space. This makes them suitable for co-location with data centers¡ªeven in urban or remote areas. AI can assist in site selection in several ways. It can analyze environmental, logistical, and regulatory factors to help identify optimal locations for SMRs.
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AI data centers require a stable, baseload energy supply. And that makes them a great fit for SMRs.
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Challenges and future prospects for AI data centers
The integration of AI and ML in SMR construction holds great promise. But it also faces challenges. These can include regulatory hurdles, mostly around the transparency of AI models. These issues must be addressed for safety and compliance. However, ongoing research and development in explainable AI are paving the way for broader adoption in the nuclear sector.
Heading forward, AI and ML have the potential to revolutionize the construction and operation of SMRs. This can lead to them being a viable and sustainable power source for AI data centers. As these technologies continue to evolve, they will play an more critical role in shaping the future of nuclear energy.
As we see it, this is a win-win-win scenario. Together, we can store the incredible amount of data for AI development. We can produce a copious amount of stable, reliable energy. And we can still reduce GHG emissions and be responsible stewards of the environment for the people that will come after us.
It is our hope that we see more and more companies pairing their AI data centers with SMRs.
A version of this blog was published in .
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