Time to power is becoming the new measure of AI infrastructure readiness

As AI technology transitions from experimentation to large-scale deployment, a new infrastructure challenge has emerged: securing reliable, rapid power supply to support the massive computational demands of AI data centers. These centers differ significantly from traditional power customers, being larger, more concentrated, and requiring near-instantaneous, uninterrupted energy.

Consequently, the availability of energy is no longer just an operational consideration but can determine where AI infrastructure is built, how swiftly it comes online, and whether investments translate into business value.

Natural gas is emerging as a potential solution to bridge the power gap for AI infrastructure. While technology leaders focus on generating sufficient electricity, an equally crucial question arises: can dependable, dispatchable power reach the data center when and where it is needed? Natural gas can provide round-the-clock generation to meet large AI workloads, particularly when other non-intermittent power options face limitations.

However, having an adequate supply of natural gas does not guarantee timely access to the data center. Delivering the gas involves production, transportation, storage, and delivery at the appropriate pressure through connected infrastructure. The real challenge lies in the time factor rather than capital investment. Developers face numerous constraints, such as permitting, pipeline rights-of-way, grid interconnections, turbines, water, and skilled labor, all of which can significantly extend development timelines.

This situation reshapes the decision-making process around infrastructure, making the ability to secure reliable energy on the required timeline a critical factor in site selection.

Data center developers are increasingly turning to behind-the-meter generation, pairing on-site or nearby gas generation with a firm fuel supply to circumvent the traditional grid interconnection process. PwC estimates that over 30% of AI-related gas demand could be behind the meter by 2035. Other emerging models include dedicated pipeline laterals linked with generation and integrated arrangements connecting gas supply, transportation, storage, generation, and data center load.

The implications of this shift extend beyond individual data centers. Technology companies must engage with a broader ecosystem, including hyperscalers, data center developers, utilities, natural gas providers, and pipeline operators. These collaborations are essential for solving the entire path from energy supply to operational compute rather than optimizing individual components.

By securing energy infrastructure early and understanding regional constraints, technology executives can mitigate schedule risks before substantial investment is made in compute capabilities.

Ultimately, the race to scale AI infrastructure hinges on recognizing energy as a strategic capability. Organizations that prioritize time to power alongside time to value will be best positioned to capitalize on the opportunities presented by AI technology.