Originally published by Data Center Dynamics (DCD) as an Industry View.

By Eric Reaman, President & CEO, Cashman Preload Cryogenics (CPC) 

The data center industry is solving for a variable the power sector has not confronted in decades: speed at scale. AI campuses are racing toward 500 MW and 1 GW footprints. Grid interconnection queues stretch five to seven years. To regain schedule control, developers are moving behind the meter, deploying onsite natural gas generation to guarantee power delivery. 

On paper, this solves the electricity problem. In practice, it exposes a constraint that does not appear in capacity models: fuel deliverability. 

A contract is a legal instrument. Fuel reliability is a physical condition. At gigawatt scale, that distinction is no longer academic. It is foundational. 

The Firm Contract Fallacy 

The prevailing solution to gas reliability is the firm transportation contract; a premium paid for priority pipeline access. But “firm” means first right to molecules if they are flowing. It does not create molecules when the system is physically constrained. Facilities engineered for five-nines availability cannot rely on financial priority alone. They require physical certainty. 

During Winter Storm Elliott in December 2022, U.S. gas production fell roughly 16 Bcf/d within 48 hours. FERC and NERC data attributed nearly a quarter of generation outages to fuel supply failures. Turbines did not malfunction. The fuel simply was not there. 

Weather events make this vulnerability visible. But storms are only one expression of a broader structural reality. Gigawatt AI load is leaning on a gas system optimized for diversified, seasonal demand, not synchronized, nonstop industrial clusters. The system is strong, but it was not designed for this. 

Six Structural Vectors of Fuel Risk 

Fuel deliverability can be reduced through multiple independent pathways. None require a hurricane. 

Planned Pipeline Maintenance 

Compressor overhauls, hydrostatic testing, integrity digs, and pigging runs routinely reduce available capacity. These are scheduled events, not emergencies. During maintenance windows, operating pressures are derated and interruptible service is curtailed. AI campuses operate continuously. Maintenance schedules do not adjust to uptime guarantees. 

Regulatory Curtailment Priority 

In severe cold events, regulators prioritize: residential heating, hospitals, public safety, power generation, then large industrial loads. A 1 GW AI campus will almost certainly be categorized as industrial. During Winter Storm Uri, firm industrial contracts were curtailed to protect residential pressure. A campus consuming the gas equivalent of hundreds of thousands of homes will not win that allocation debate. 

Gas–Electric Cascade Risk 

Gas plants generate electricity. Electricity powers compressor stations. Compressors maintain pipeline pressure. When this loop destabilizes, failure cascades. During Uri, grid outages disabled compressors, which dropped pipeline pressure, which triggered more generation failures, which disabled more compressors. Each failure amplified the next. The vulnerability was infrastructure interdependence, not weather alone. 

Linepack Depletion 

Pipelines store energy as compressed gas within the system itself. When regional demand spikes simultaneously, linepack is drawn down and local delivery pressure can collapse even if upstream supply exists elsewhere. This dynamic is particularly acute in constrained markets such as ISO-NE, the Mid-Atlantic, the Ohio River Valley, the Carolinas and the southwestern US, precisely the regions where AI campuses are clustering. 

Upstream Production Variability 

Freeze-offs are well known. But production also declines due to basin maintenance, processing outages, water cut increases, and regulatory constraints. National abundance does not guarantee regional deliverability at a specific hour on a constrained segment. 

 Political and Social License Risk 

Energy infrastructure is not insulated from politics. As AI campuses become system-significant loads, regulators will evaluate their impact during stress events. A facility that competes with residential heating during a cold snap invites scrutiny. A facility that decouples from the pipeline during stress, reducing system load rather than competing for it, changes that narrative entirely. 

These vectors are structural and independent of storms. At gigawatt scale, they compound.    

The Duration Gap 

Traditional data centers relied on diesel backup. At 20 MW, that model worked. At 1 GW, the physics change. A gigawatt-scale dual-fuel facility can consume more than one million gallons of diesel per day, requiring roughly one tanker arrival every ten minutes around the clock. During regional emergencies, that logistics chain fails precisely when it is most needed. 

Batteries bridge minutes. Diesel bridges hours. Pipeline stress across any of the vectors above can last days. That is a design gap, not a contingency gap. 

Treating Fuel as Infrastructure 

From the 1960s through the 1980s, utilities built LNG peak-shaving facilities to manage demand exceeding pipeline capacity. Gas was liquefied during off-peak periods, stored locally, and vaporized during system stress. More than 170 such facilities remain in operation in the United States today. The principle: shift gas in time. 

For gigawatt AI campuses, onsite LNG storage reintroduces that buffering layer. Physical inventory behind the meter creates operational separation between generation assets and upstream constraints. When the pipeline flows normally, operations proceed as usual. When the system is stressed, stored fuel provides independent runtime measured in days, without contracts or trucking convoys. 

This is not backup fuel. It is engineered fuel certainty. 

The New Diligence Standard 

The industry’s focus on generation capacity and interconnection timelines is appropriate. Without power, there is no campus. But at gigawatt scale, the underwriting question must evolve. 

It is no longer sufficient to ask whether a firm gas contract exists. The relevant question is: what physical mechanism keeps the plant running when the pipeline is stressed for 120 hours? 

If the answer depends on priority language or road access, the risk is unpriced. Energy abundance in the United States is demonstrable. Deliverability under coincident peak conditions is conditional. As AI load becomes system-significant, fuel assurance must transition from contractual allocation to engineered resilience. 

Physical storage introduces deterministic runtime where pipeline deliverability remains probabilistic. In gigawatt AI infrastructure, reliability is not achieved through priority language. It is achieved through molecules physically secured, stored, and controlled at the point of use. 

About the Author 

Eric Reaman is President & CEO of Cashman Preload Cryogenics (CPC), an EPC contractor specializing in cryogenic infrastructure and fuel assurance for energy and industrial markets. With over 30 years in heavy civil and energy construction, he works with utilities, developers, and technology companies to engineer resilience solutions for constrained gas-electric markets.