Amazon’s Chief Executive, Andy Jassy, recently told investors that the company could significantly increase its sales if it had more data centers. Jassy explained that electricity is critical to the company’s success, and that “the single biggest constraint is power.”
It’s Artificial Intelligence (AI) that is driving this need for more power, propelling computing demand to levels not seen since the advent of cloud computing. Training foundation models, deploying inference at scale, and supporting AI-powered applications require massive levels of compute, storage, and power capacity that have never been experienced. However, the task of scaling data centers creates a range of structural challenges, including power availability, supply chain fragility, security, and geographic constraints.
Power as the Ultimate Bottleneck
One of the primary challenges for building data centers is power. These data centers require megawatts of power delivered to racks designed for densities exceeding 50 kilowatts per square meter. Securing this kind of power can be difficult, with interconnection queues for new generation and transmission projects often extending over a decade.
Gas power plants may not be the solution. These kinds of energy plants, which have not already contracted equipment, are unlikely to be available until the 2030s. In addition to the negative environmental impacts that can agitate communities, there’s a fear that investments in gas-fired infrastructure could become “stranded” as the world transitions to cleaner energy sources. And yet, renewable energy build-outs can be constrained by transmission bottlenecks and land availability.
This conundrum posed by gas power and renewable energy sources highlights a problem between the current speed of AI workloads, which tend to occur within six-month time frames, and the multi-year timelines of energy infrastructure. This mismatch highlights how power availability is becoming a significant constraint in the AI era.
Supply Chain Fragility
Supply chains are the next most significant challenge after power. Delays in infrastructure components, such as transformers, uninterruptible power supply (UPS) systems, switchgear, generators, and cooling distribution units, are stalling and complicating projects. According to Deloitte, 65% of companies identified supply chain disruptions as a significant issue for data center build-outs.
Critical equipment now carries 12–18-month lead times, and global logistics remain susceptible to geopolitical instability. Trade restrictions, material shortages, and regional conflicts all impact procurement schedules, creating challenges for developers as they strive to align construction timelines with delivery schedules. With speed to market being the key to competitiveness, a one-year delay in equipment delivery could result in a significant and potentially fatal lag. The ability to pre-plan procurement, diversify suppliers, and stage modular components is quickly becoming a competitive differentiator.
Security and Reliability Pressures
With AI playing a critical role in economic and national competitiveness, security becomes an all-important concern. Sixty-four percent of data center executives surveyed by Deloitte ranked security as one of the biggest challenges. Vulnerabilities in AI data centers pose not only a threat to business profitability but also impact the healthcare, finance, and national defense sectors.
Modern operators must think about resilience in layered terms: physical hardening, advanced cyber protection, and compliance adherence, all while delivering at hyperscale speed. Building secure, resilient AI centers is no longer just an IT issue; it’s a national infrastructure imperative.
Spatial and Infrastructure Constraints
Geography presents the next biggest hurdle. Appropriate locations that have accessibility to load centers, available land, and access to water for cooling are not easy to find. Reliable power delivery is hindered by space limitations that make colocating data centers next to transmission infrastructure a challenging task. As for legacy infrastructure, it fails to meet the rack densities or dynamic load profiles required by modern AI. This inability forces operators to weigh the costs of retrofits against the benefits of greenfield development.
The Timeline Paradox
Traditional data center builds typically take 18 to 24 months. However, AI technology is evolving much more quickly. Model architectures and hardware accelerators change every six months. By the time a facility comes online, its design assumptions may already be outdated in relation to the latest AI requirements.
This paradox is forcing developers to reimagine delivery, turning to modular builds, pre-fabricated components, and “power-first” design strategies that bring capacity online in phases. The goal is no longer a perfect data center, but one that can evolve in lockstep with AI’s breakneck pace.
Conclusion
Industry leaders are reimagining procurement to ensure that critical components can be delivered earlier; they’re also diversifying supplier bases to lessen geopolitical risk and adopting modular construction to speed up deployment. Some organizations are partnering with utilities to co-plan grid upgrades, and others are exploring on-site generation and storage to bypass interconnection queues.
Treating supply chain resilience as a competitive differentiator is the ticket to a prosperous future for AI infrastructure. Organizations that can strike a balance between speed and reliability will keep pace with AI innovation.
The AI revolution is redesigning the structure of the digital economy. The challenges, ranging from strained power grids and fragile supply chains to evolving security demands and spatial constraints, are significant. Organizations that successfully navigate these challenges will set the standard for resilient digital infrastructure in the decades to come.
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About the Author
Scott Embley is an Associate at hi-tequity, supporting sales operations, business development, and client relationships to drive company growth. He manages the full sales cycle, identifies new opportunities through market research, and ensures client success through proactive communication. Scott holds a B.S. in Business Administration and Management from Liberty University, where he graduated summa cum laude.
