The AI-Energy Nexus: Why Big Tech is Turning to Small Modular Reactors (SMRs) to Power the Next Generation of Data Centers

AI workloads are driving an energy problem at scale. Modern training runs, inference fleets, and real-time services push power and cooling requirements well beyond what conventional grid upgrades can reliably supply. Big tech firms are increasingly exploring small modular reactors (SMRs) as a strategic option: compact nuclear plants that promise steady, low-carbon baseload with a predictable cost profile.

This post breaks down the technical drivers behind the shift, how SMRs integrate with data center architecture, operational and regulatory realities, and a practical checklist for engineers evaluating SMRs as a power source.

Why data centers need a new power model

AI changes the shape of demand, not just the magnitude.

• Power density: High-performance racks can require tens to hundreds of kilowatts each, increasing site power density beyond traditional assumptions.
• Predictability: Long-running model training and latency-sensitive inference need deterministic power and cooling to avoid throttling or reduced availability.
• Grid limits: Many regions hit transmission and substation constraints; permitting and building new grid capacity is slow and uncertain.
• Carbon goals: Corporate net-zero targets force a shift away from fossil fuel peakers and grid mixing with fossil-heavy hours.

The combination means operators need a reliable, continuous, and low-carbon energy supply that can be sited near compute and scaled in predictable increments.

What are SMRs? Quick technical primer

SMRs are nuclear reactors designed to deliver up to a few hundred megawatts electric per unit. Key characteristics:

• Modular fabrication: Units are factory-built and shipped, reducing onsite construction time and cost.
• Standardized designs: Reuse of proven modules shortens licensing and commissioning across multiple sites.
• Passive safety systems: Many SMR designs emphasize passive heat removal and simplified safety logic.
• Small footprint: Compared with gigawatt-scale plants, SMRs fit on smaller sites and can be colocated with industrial loads.

SMR technology families include light-water SMRs, high-temperature gas-cooled reactors, and molten salt designs. Thermal output determines whether thermal reuse (district heating, absorption cooling) is viable.

Why Big Tech is betting on SMRs

Reliability and resilience

SMRs provide stable baseload power. For data centers, that reduces dependence on volatile grid conditions and expensive diesel or gas peakers. The predictable output helps maintain SLAs for latency and availability.

Carbon and sustainability

SMRs deliver low lifecycle carbon intensity. When paired with electrified cooling and heat recovery, the overall carbon footprint of compute-intensive workloads can drop substantially.

Proximity, latency, and land use

Because SMRs have a smaller footprint, operators can site power very near or even adjacent to compute campuses. That reduces transmission losses and can simplify microgrid and redundancy planning.

Cost predictability and hedging

Fuel and operating costs for SMRs are more deterministic than spot electricity markets. For large, continuous loads, locking in a stable cost basis can be economically attractive compared with fluctuating renewable + storage arbitrage.

Scalability and modular economics

The modular nature of SMRs maps well to phased data center expansion: add another module as capacity needs grow, rather than overbuilding upfront.

Integrating SMRs with data center design

Transitioning to SMR-supplied power is not plug-and-play. Below are core integration areas engineers must design for.

Electrical architecture and SPDs

SMR output couples to the data center via medium-voltage switchgear and redundant transformers. Plan for:

• N+1 transformer topology for critical loads.
• Synchronous condensers or power electronics to handle short-term transient response and reactive power.
• Surge protection and grounding strategies compatible with reactor electrical characteristics.

Load following and ramping

Most SMR designs favor steady output. For variable compute demand, combine SMR baseload with fast-response battery energy storage systems (BESS) or flexible load management. A hybrid control strategy works well:

• SMR provides the baseline power.
• BESS handles frequency-response and short ramps.
• Workload schedulers shift noncritical compute to low-cost windows.

Thermal reuse and cooling loops

If the SMR design offers usable thermal energy, integrate it into absorption chillers or district heat. Consider:

• Secondary loops for waste heat capture.
• Heat exchangers sized for steady-state thermal profiles.
• Controls to switch between heat recovery and direct cooling depending on workload.

Safety, security, and staffing

Colocating nuclear power requires additional site security, emergency planning, and trained operations staff. Design shared operations centers and automated diagnostics to minimize incremental staffing overhead.

Monitoring and control interfaces

Expose power and thermal telemetry to data center infrastructure management (DCIM) and site reliability engineering (SRE) tooling. Key signals:

• Plant output power, voltage, and frequency.
• Reactor trip and safety state indicators (abstracted for operator use).
• Thermal supply and return temperatures for heat reuse.

Example: simple capacity and reserve calculation

Below is a small example to estimate how many SMR modules are needed for a campus and what reserve margin to maintain. The code assumes each module has a fixed capacity and you want a given percent reserve.

def required_smr_modules(peak_load_mw, module_capacity_mw, reserve_pct):
    """Return integer number of modules needed to cover peak plus reserve."""
    reserve = peak_load_mw * (reserve_pct / 100.0)
    total_needed = peak_load_mw + reserve
    modules = int((total_needed + module_capacity_mw - 1e-9) // module_capacity_mw)
    if modules * module_capacity_mw < total_needed:
        modules += 1
    return modules

# Example: 90 MW peak, 50 MW SMR module, 20% reserve
# result = required_smr_modules(90, 50, 20)  # returns 3

This simple function is a starting point. In reality include derating, maintenance outages, and availability factors.

Operational, regulatory, and social considerations

SMR deployment is not just an engineering choice. Expect a multi-year program with stakeholder engagement.

• Licensing and permitting: Nuclear regulation varies by jurisdiction and can dominate the timeline. Pre-application engagement with regulators speeds approval.
• Community and political acceptance: Transparent plans for safety, emergency response, and decommissioning build public trust.
• Fuel supply and waste management: Contracts for fuel fabrication and long-term arrangements for spent fuel are nontrivial and must be included in TCO models.
• Insurance and liability: Understand operator and site liability models, and how insurers price nuclear risk versus conventional generation.

Cost modeling and procurement patterns

Key inputs for a financial model:

• Capital cost per SMR module and balance-of-plant integration costs.
• Fixed O&M and staffing increments.
• Fuel cost and scheduled outage downtime.
• Value of avoided grid upgrades and peak market exposure.
• Revenue from heat reuse or grid services (frequency, inertia emulation).

Procurement often follows a long-term power purchase agreement (PPA) or direct ownership with an operator partner. Evaluate both.

Checklist for engineers evaluating SMR options

• Assess site suitability: land, water, seismic, and grid interconnection constraints.
• Run capacity modeling with realistic availability and maintenance windows.
• Design hybrid electricity architecture with BESS for fast response.
• Plan thermal reuse paths early, including heat exchange and controls.
• Engage regulators and begin community outreach in parallel with technical design.
• Define monitoring and control interfaces between plant operators and DCIM/SRE teams.
• Include fuel supply, waste handling, and decommissioning in lifecycle cost models.
• Validate staffing and security plans and quantify incremental operational costs.

Summary

AI workloads create an energy profile that challenges conventional grid and data center paradigms. SMRs offer a compelling combination of steady, low-carbon power and modular scalability that maps well to the needs of hyperscale and edge AI deployments. They are not a drop-in replacement: electrical integration, hybrid control strategies, regulatory timelines, and public engagement all matter.

For engineering teams, start with rigorous capacity modeling, design for hybrid baseload-plus-storage architectures, and engage regulatory and community stakeholders early. Done right, SMRs can be a deterministic foundation for the next generation of AI infrastructure.

Checklist (copyable):

• Site feasibility study completed
• Capacity model with reserve and outage scenarios
• Hybrid power architecture (SMR + BESS) designed
• Thermal reuse pathways identified
• Regulatory engagement started
• Ops, security, and staffing plans defined
• Lifecycle cost model including fuel and decommissioning

If your team is evaluating SMRs, begin with a small pilot and a tight integration plan between power engineers and SREs. The technical upside is real, but execution requires discipline across engineering, policy, and operations.