Nuclear-powered AI factories are moving from concept to construction as tech giants race to solve the energy crisis threatening artificial intelligence expansion. Canadian engineering firm AtkinsRéalis has partnered with Nvidia to develop large-scale AI data centers powered by on-site nuclear reactors, fundamentally changing how companies design and build the infrastructure that trains and runs AI systems . Unlike renewable energy sources that depend on weather conditions, nuclear power provides the constant, reliable electricity that AI data centers demand 24 hours a day, 365 days a year.

Why Are Tech Giants Turning to Nuclear Power for AI?

The explosion in AI computing has created an unprecedented energy demand that existing power grids cannot meet. Major hyperscalers including Meta, Microsoft, Amazon Web Services (AWS), and Google are all investing billions in new data center capacity, but they face a critical bottleneck: reliable power supply . Data centers running large language models and training AI systems consume enormous amounts of electricity continuously, making them fundamentally different from traditional computing infrastructure that can tolerate intermittent power sources.

Nuclear energy solves this problem in ways that renewables cannot.

"We see nuclear energy providing secure energy supplies for the AI infrastructure. AI helps manage and operate the energy supplies to optimise performance, and can help transform delivery processes to provide that energy faster and with greater certainty," explained Sam Stephens, head of digital for AtkinsRéalis' nuclear arm.

Sam Stephens, Head of Digital, AtkinsRéalis Nuclear

The company supports CANDU reactor technology, which uses unenriched uranium from stable regions and can be refueled while operating, making it uniquely suited for long-term, uninterrupted power generation .

Projects of this kind are already underway. Energy North West is deploying X-Energy's small modular reactor (SMR) with AWS, with AtkinsRéalis supporting as owner's engineer . Oklo, a nuclear technology company, has secured a growing list of potential data center customers and expects its first plant to come online in late 2027 . These aren't theoretical exercises; they represent real capital commitments from the world's largest technology companies.

How Does Co-Locating Nuclear Power Change Data Center Design?

Integrating a nuclear power plant on the same site as an AI data center requires significant changes to traditional data center architecture. According to Nvidia's reference blueprint for a 1-gigawatt AI factory, accommodating on-site power generation and related infrastructure increases the overall land footprint by approximately 20 to 30 percent compared to conventional data center designs . This is a substantial increase, but the benefits justify the additional space requirements.

The design integration creates multiple efficiency advantages that wouldn't exist with separate power and computing facilities:

• Waste Heat Recovery: Modern data centers use closed-loop water cooling systems instead of air cooling. Nuclear plants generate significant waste heat that can be captured and used with absorption chillers, dramatically reducing cooling costs and improving overall sustainability.
• Grid Infrastructure Sharing: Nuclear power plants connect to the electrical grid to serve local communities and essential services beyond just the AI facility. This shared infrastructure reduces redundancy and allows data centers to benefit from grid stability while supporting broader regional power needs.
• Integrated Design: When power generation, cooling systems, and grid connectivity are designed together from the start rather than as separate components, the entire facility operates more efficiently and reliably than traditional data centers.

This integrated approach represents a fundamental shift in how companies think about data center infrastructure. Rather than treating power as a utility to be purchased from external sources, hyperscalers are now building their own generation capacity directly adjacent to computing facilities .

Steps to Accelerate Nuclear-Powered AI Infrastructure Deployment

• Digital Twin Simulation: AtkinsRéalis and Nvidia are using AI and digital twins on Nvidia's Omniverse platform to simulate and optimize plant designs from construction through decommissioning, reducing design risk and accelerating timelines for nuclear permitting and construction.
• AI-Driven Project Management: Machine learning models can better predict project risks and delivery outcomes, helping teams manage schedules more effectively. This is critical because speed to market is a primary concern for Nvidia and the broader AI ecosystem.
• Information Reuse Across Projects: AI helps designers reuse information and data from previous nuclear and data center projects, eliminating redundant work and allowing teams to simulate environmental conditions to reduce risk to the lowest reasonably practicable levels.

These acceleration strategies matter because scaling nuclear power quickly enough to meet AI demand requires fundamentally new ways of working. Digitization and AI-assisted design promise to provide the breakthrough needed to compress timelines from years to months .

Will Nuclear-Powered Data Centers Become the Industry Standard?

Industry experts believe the answer is yes.

"We expect nuclear power to become the preferred approach as hyperscalers and neoclouds seek to avoid volatile fossil fuel prices and seek long term cost certainty, with further support from investors to meet ESG and CSR targets," stated Sam Stephens.

Sam Stephens, Head of Digital, AtkinsRéalis Nuclear

The economics are compelling: nuclear power provides cost certainty over decades, insulating companies from fossil fuel price volatility while meeting environmental, social, and governance (ESG) commitments that investors increasingly demand .

The geographic distribution of AI infrastructure is also shifting. While most AI infrastructure investment has concentrated in the United States to date, companies are now building data centers closer to where people access AI services, driven by the need for faster inference from pre-trained models and by countries seeking "sovereign AI" to protect their information . This geographic spread will require multiple nuclear-powered facilities across different regions, not just a handful of mega-facilities in the US.

Looking further ahead, emerging AI paradigms like agentic AI and physical AI will drive even more infrastructure demand. Agentic AI systems that can autonomously plan and execute tasks, and physical AI that controls robots and autonomous vehicles, both require massive computational resources and therefore massive amounts of reliable power . Nuclear energy is positioned to become as essential to AI infrastructure as the electrical grid is to modern civilization.

The convergence of nuclear power and AI represents more than just a technical solution to an energy problem. It signals a fundamental transformation in how the world's largest technology companies think about infrastructure, sustainability, and long-term planning. By the end of this decade, nuclear-powered AI data centers may no longer be novel; they may simply be how hyperscale AI infrastructure gets built.