i-suite

AI readiness: Why Nuclear Energy for AI is gaining ground

insight
June 17, 2025
9 min read

Joteep_Mahato


Joteep Mahato is the Director and Global Head of the Energy & Utilities Line of Business at Nagarro. With over two decades of experience in digital transformation and engineering leadership, he drives strategic growth and innovation for global utility clients. 

Executive summary

A business’s AI readiness now depends on its energy readiness. As AI workloads increase, power instead of computing power becomes the real bottleneck. Nuclear power for AI infrastructures is gaining attention as it can provide stable, carbon-free power around the clock. This article explores the increasing investment in nuclear power for AI, the role of Big Tech and what this means for businesses planning to scale AI sustainably.

A global power play is unfolding: inside and outside the boardroom

Few weeks back, President Donald Trump signed an executive order to quadruple America’s nuclear power capacity over the next 25 years. While experts debate the feasibility of this goal, one thing is clear: energy policy is no longer just a national strategy, it’s a business strategy.

At the same time, tech giants like Meta, Microsoft and Google are boldly embracing nuclear energy to drive their AI ambitions. These moves signal something that every business leader needs to internalize: AI readiness now depends on energy readiness. And nuclear power is quickly becoming a strategic differentiator.

AI is driving up the demand for electricity

Artificial intelligence is driving an unprecedented increase in electricity demand, with data center consumption in the US projected to double by 2030 (IEA). As models become more powerful and computing loads increase, algorithms are no longer the bottleneck; it is energy. This shift is putting a strain on grid infrastructure, complicating sustainability efforts and forcing a new type of strategic planning.

The new strategic planning can't function on efficiency improvements and renewable energy contracts. For businesses scaling AI, the challenge now is to ensure access to stable, scalable, and clean power, as energy resilience has become an indispensable factor in the race for AI leadership. To counteract this, an increasing number of organizations are exploring nuclear power as a strategic enabler.

Energy is the new IT bottleneck for your business

As AI is central to product innovation and customer experience, energy is moving from a back-end problem to a boardroom issue. An uninterrupted, scalable energy supply will increasingly determine who gets ahead quickly and who gets left behind.

Energy constraints will not only slow down cloud adoption or model training, they will also impact AI roadmaps, infrastructure investments and sustainability reporting. If your energy strategy lags behind, so will your AI ambitions. It’s no longer enough to leave energy planning to facilities or procurement. Energy has become a major factor in IT planning. Availability, reliability and carbon intensity have a direct impact on where and how your systems are scaled.

Powering AI at scale: Which energy sources will win the next decade?

As AI accelerates, technology leaders must solve a critical energy problem: Which energy sources can provide reliable 24/7 power, scale quickly, remain cost-effective, and align with sustainability goals? The main contenders: nuclear, renewables (wind/solar), and natural gas all have trade-offs. Below is a high-level comparison tailored to the unique requirements of AI-driven data centers.

Nuclear Energy Powering AI
Nuclear energy takes the lead

 

With a capacity factor of 92–93%, nuclear energy delivers unrivalled power around the clock thar are critical for AI workloads. Renewables are unsteady; gas is flexible but volatile and less ESG-friendly.

Baseload reliability
Renewables win, but with caveats

 

Wind and solar are the cheapest with subsidies, but storage and backup increase overall costs. Nuclear is competitive with $30–40/MWh for existing plants; $90–120/MWh expected for SMRs when the technology matures. (source: sseb.org, nwsolar.com, aei.org, inldigitallibrary.inl.gov)

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Nuclear energy is compact and powerful

 

Nuclear power generates more electricity in less space. SMR plants can be built close to data centers, unlike renewables, which require large footprints and grid expansions. Gas can be scaled fast but is not profitable in the long term.

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Plan for the AI energy clock

 

Renewables are fastest (1–3 years). Gas takes 2–4 years but faces resistance. Conventional nuclear power plants take 8–12 years, but SMRs could reduce this to 3–5 years. Leading engineers are turning to existing nuclear power in the meantime. (source: ethz.ch, power-eng.com, nrc.gov, reuters.com)

Deployment timelines

Is nuclear energy for AI the answer to surging power needs?

AI is changing both business and energy demand. An AI query can consume up to ten times more energy than a standard web search (UtilityDive), and usage continues to increase. 
Renewable energy alone cannot meet the demand. Next-generation nuclear power, such as small modular reactors, offer the only scalable, emissions-free solution designed to meet the constant demands of AI. 

Nuclear Power is the missing link in AI infrastructure

To achieve true AI readiness, organizations need not only clean but also consistent, scalable and resilient power. Nuclear power for AI provides reliable, emission-free power around the clock, making it ideal for powering large, always-on workloads. As AI infrastructure expands, a constant power supply will become increasingly important to avoid downtime and performance issues. New technologies like Small Modular Reactors (SMRs) are making nuclear power more flexible and easier to deploy near data centers. It brings businesses greater control over their energy supply and enhances long-term resilience. For technology leaders, nuclear power is rapidly becoming a fundamental component of AI infrastructure strategy.

Nuclear power for AI

How can SMBs prepare their infrastructure for AI energy needs? 

While SMBs aren’t building reactors, they are adopting AI, and doing it fast. But without access to Big Tech’s custom infrastructure, smaller companies need to be just as strategic about power.    

Choose cloud providers wisely

Ask about their energy sources. Prioritize partners investing in nuclear or hybrid clean energy portfolios. 

Avoid energy bottlenecks

Evaluate data center or colocation sites based on local grid readiness and clean energy access.

Build lean AI workloads

Energy-efficient models and smart scheduling can cut usage and cost.

Include energy in your tech roadmap

Cross-functional planning across IT, Ops, and Procurement is key for long-term scale. 

Are big tech companies investing in nuclear energy for AI? 

Yes. Here is a look at their investments and projects.

The urgency to ensure a reliable energy supply on a large scale for artificial intelligence has prompted major US tech companies such as Microsoft, Google, Amazon and Meta to get into nuclear energy and make headlines. These companies, known for their ambitious renewable energy goals, are now turning to advanced nuclear reactors (including SMRs) to provide carbon-free power to keep their data centers running 24/7. Below, we look at each company's current and planned investments:
Meta secures first Nuclear deal to power AI growth

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In June 2025, Meta signed its first nuclear energy agreement with Constellation Energy to supply carbon-free power from the Clinton Clean Energy Center in Illinois. The 20-year deal boosts the plant’s capacity by 30 MW and supports its relicensing, ensuring reliable power for Meta’s growing AI and data center needs.

Microsoft bets on nuclear to power AI at scale

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AI brings energy strategy to the fore. Microsoft plans to bring the 819 MW Three Mile Island Unit 1 reactor back online by 2028 and has signed a fusion energy deal with Helion Energy. The company is also securing clean energy credits from international nuclear projects, laying the foundation for a resilient, low-carbon AI infrastructure.

Google is one step ahead of the curve
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Google is the first technology company to build its own reactors. Through Kairos Power, it has pre-ordered 6–7 SMR reactors, the first of which (50–100 MW) are expected to be operational by 2030. These reactors, located near data centers, will provide clean power around the clock and ensure control over future energy needs.

Amazon switches the cloud for nuclear energy
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AWS secures direct access to nuclear energy buying land near the 2.5 GW Susquehanna power plant and investing in four X-Energy reactors (320 MW Phase 1, by ~2032). AWS is also exploring a 300+ MW SMR project in Virginia and is targeting 5+ GW of future capacity to support AI growth.

Implications: Big tech is quietly rewiring the power grid 

As Big Tech invests directly in nuclear power, the energy landscape is shifting. These companies are no longer just buying clean power; they are building it and reserving control. As energy becomes a competitive advantage, smaller businesses may find themselves without access to reliable, 24/7 power or dependent on an increasingly constrained grid. There is also a risk of the digital divide widening, as large companies can scale AI faster, while small and medium-sized enterprises struggle with cost, reliability, or availability. New energy partnerships, smarter efficiency strategies, or even shared infrastructure measures may be needed to remain competitive. 

These moves aren’t just about scale they’re about control. As Big Tech takes energy strategy into its own hands, it’s a signal to all businesses: energy planning is now part of digital planning. Even smaller players will need to align AI strategy with clean, reliable infrastructure. 

How can businesses future-proof their AI infrastructure today?

Three questions every business leader should ask

 

1

Do we know how much energy our AI ambitions will require in the next 5 years?

2

Can our current infrastructure and our power contracts meet AI needs around the clock?

3

Ask your provider where their power comes from?

What’s the upside of moving now?

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Secure predictable energy prices for 10–20 years. 

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Reduce dependence on overloaded regional grids.

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Meet AI scaling schedules with minimal energy delays. 

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Increase brand value as a market leader in clean innovation.

Why the future of AI infrastructure may run on nuclear power

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