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

For much of the AI boom, the infrastructure conversation has centered on compute: chips, servers and the increasingly large data centers needed to support them.

<![CDATA[ <article> <p>For much of the AI boom, the <a href="https://www.techradar.com/best/best-infrastructure-management-service">infrastructure</a> conversation has centered on compute: chips, servers and the increasingly large <a href="https://www.techradar.com/best/best-data-migration-tools">data</a> centers needed to support them. But as AI moves from experimentation to deployment at scale, technology leaders face another infrastructure challenge that could be just as consequential: securing enough reliable power, quickly, to keep that compute running.</p><p>AI data centers are fundamentally different from traditional commercial and industrial power <a href="https://www.techradar.com/best/the-best-customer-database-software-of-year">customers</a>. They are larger, more concentrated and exceptionally time-sensitive, with near-zero tolerance for interruption. That makes energy availability more than an operating consideration. Increasingly, it can determine where AI infrastructure gets built, how quickly it comes online and whether organizations can turn enormous technology investments into business value.</p><h2 id="natural-gas-is-emerging-as-a-bridge-fuel-for-ai-39-s-power-challenge">Natural gas is emerging as a bridge fuel for AI's power challenge</h2><p>For technology leaders, the key question isn't simply whether enough electricity can ultimately be generated. It's whether firm, dispatchable power can reach a data center when and where it's needed.</p><p>That's where natural gas is playing an increasingly important role. Given constraints on other non-intermittent power alternatives, gas can provide the around-the-clock generation needed to support large AI workloads. PwC's scenario analysis shows the potential scale: even in our most conservative scenario, AI-linked gas demand reaches 5.2 billion cubic feet per day (Bcf/d) by 2030, compared with roughly 1.6 Bcf/d today. By 2035, our scenarios put demand between 7.6 and 11.5 Bcf/d.</p><p>For data center developers and technology companies, however, those numbers tell only part of the story. Having enough gas in the system doesn't mean it can necessarily reach a data center on the required timeline. It must be produced, transported, stored and delivered at the right pressure through connected infrastructure. In other words, AI's power challenge is increasingly becoming a deliverability challenge.</p><h2 id="the-scarce-resource-may-be-time-not-capital">The scarce resource may be time, not capital</h2><p>Technology companies are committing tens of billions of dollars to AI infrastructure, but money can't quickly solve many of the constraints standing between a planned data center and an operational one.</p><p>Permitting, pipeline rights-of-way, grid interconnections, turbines, water and skilled labor can all extend development timelines. Developers are simultaneously competing for critical equipment and industrial capacity while navigating local zoning and water constraints. That changes the calculus around infrastructure.</p><p>In a market defined by speed to deployment, an existing pipeline, permitted corridor, storage asset or available generation capacity can be more valuable than a theoretically lower-cost alternative that takes years to develop. For technology leaders making decisions about AI capacity, site selection therefore needs to account for much more than land, connectivity and eventual power availability. The ability to secure reliable energy on the required timeline should be considered much earlier in the process.</p><h2 id="energy-procurement-is-becoming-a-strategic-capability">Energy procurement is becoming a strategic capability</h2><p>We're already seeing data center developers respond differently. Behind-the-meter generation, for example, can allow a campus to pair on-site or nearby gas generation with firm fuel supply rather than relying solely on the traditional grid interconnection process. PwC estimates that more than 30% of AI-related gas demand could be behind the meter by 2035.</p><p>Other models are emerging as well, including dedicated pipeline laterals paired with generation and more integrated arrangements connecting gas supply, transportation, storage, generation and data center load. The larger lesson for <a href="https://www.techradar.com/best/best-business-cloud-storage-service">business</a> and technology leaders isn't that every data center should pursue the same energy strategy.</p><p>It's that energy procurement can no longer be treated as a back-office function that happens after the technology and real estate decisions have been made. It is becoming a strategic capability.</p><h2 id="building-ai-infrastructure-will-require-a-broader-ecosystem">Building AI infrastructure will require a broader ecosystem</h2><p>This shift also changes who technology companies need around the table. The next generation of AI infrastructure will require greater coordination among hyperscalers and data center developers with utilities, natural gas providers and pipeline operators. Increasingly, these parties will need to solve for the entire path from energy supply to operational compute rather than solely optimizing their individual piece of the equation.</p><p>For technology executives, that makes partnership strategy increasingly important. Securing energy infrastructure earlier, understanding regional constraints and developing relationships across the power ecosystem can help reduce schedule risk before billions of dollars of compute are waiting for power.</p><p>AI may be a technology revolution, but scaling it is quickly becoming a physical infrastructure challenge. The organizations best positioned for the next phase will be those that treat energy as a strategic capability and recognize that natural gas can play a critical role in providing the reliable, dispatchable power needed to bring AI capacity online and keep it running.</p><p>In the race to scale AI, time to power may ultimately determine time to value.</p><p><em></em><a href="https://www.techradar.com/pro/best-ai-chatbot-for-business"><em>We've featured the best AI chatbot for business.</em></a></p><p><em>This article was produced as part of </em><a href="https://www.techradar.com/pro/perspectives" target="_blank"><em>TechRadar Pro Perspectives</em></a><em>, our channel to feature the best and brightest minds in the technology industry today.</em></p><p><em>The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. 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