It can seem like everyone wants to talk about artificial intelligence right now. Love it or hate it, the software’s growing capabilities are often a popular topic. But comparatively less attention is often paid to the physical infrastructure required to support increasingly compute-intensive AI systems. Growing demand for AI computing is putting pressure on GPU cloud providers and data-center developers to bring additional compute capacity online quickly.
As demand grows, conventional data-center development faces challenges related to construction timelines, costs, power availability and supply chains. One response is a greater emphasis on construction efficiency.
Data-center developers and equipment providers are exploring modular and factory-built systems that use repeatable designs and allow some construction to take place off-site and in parallel with site work.
The scale of projected AI infrastructure demand
To understand the amount of AI infrastructure that could be required in the coming years, it’s helpful to consider the demands of the industry as a whole. The development of AI has often been compared with earlier periods of technological change, including the Industrial Revolution.
Large technological shifts can create ripple effects throughout an economy. Industrialization, for example, transformed transportation through the expansion of railroads and later automobiles and aviation.
These shifts required corresponding physical infrastructure, including rail networks, paved roads, airports, traffic-control systems, fuel depots and gas stations.
AI is creating its own set of infrastructure requirements, particularly around data centers, power and specialized computing equipment. A recent Brookings Institution paper modeled the potential physical investment associated with AI infrastructure. Under its central scenario, investment in data centers, specialized computing equipment, power infrastructure and related systems could total about $10.3 trillion between 2025 and 2032.
Under the paper’s assumptions, that investment would average 3.63% of U.S. GDP per year during that period. As a share of GDP, the paper estimates that this level of investment would exceed the major U.S. railroad, highway and electrification buildouts. The paper also estimates that the projected buildout would double the electricity consumption of the U.S. residential sector.
Even if actual development falls short of these projections, the scale of anticipated AI infrastructure investment is prompting developers and suppliers to look for ways to build data-center capacity more efficiently.
Why conventional data-center construction faces scaling challenges
Conventional data-center construction can involve long development timelines at a moment when demand for AI compute capacity is growing rapidly. That has increased interest in construction approaches that can shorten schedules and make projects more repeatable.
Many of these are straightforward concerns. Complex projects can face constraints involving specialized labor, equipment availability, supply chains and site-specific engineering. Highly customized designs can also limit opportunities to replicate engineering and manufacturing processes across projects.
Some of these constraints can be addressed over time through workforce development, supplier diversification and expanded manufacturing capacity. But those measures do not necessarily address near-term demand for additional compute capacity. That has increased interest in standardization and parallel construction as additional ways to reduce project timelines.
Using standardization and off-site manufacturing to accelerate data-center development
Standardization can create efficiencies by allowing designs, components and construction processes to be repeated across projects. In data-center construction, standardized designs and prefabricated components can also allow portions of a project to be manufactured and tested off-site while site work proceeds in parallel.
Some data-center developers are applying these principles to their infrastructure designs. Giga Energy, for example, has developed its GigaBase system around standardized, modular infrastructure. In practice, this type of approach can move portions of data-center development closer to a manufacturing model through:
- Standardizing designs: Using repeatable structures and footprints can reduce the amount of project-specific engineering required for each facility.
- Factory-built modules: Some components can be manufactured while site preparation occurs, reducing reliance on strictly sequential construction. Standardized components may also simplify replacement and maintenance when interchangeable parts are available.
- Pre-engineered systems: Along with the modules themselves, designs, equipment and connections can be engineered and tested before being shipped to a site, potentially reducing the amount of engineering and testing that must take place on-site.
Giga Energy says its GigaBase system is designed to take projects from shovel-ready to rack-ready in nine months by combining standardized infrastructure with parallel site development and off-site manufacturing. The approach is intended to reduce some of the delays associated with sequential site construction.
Rather than waiting for site preparation to finish before beginning other stages of construction, pre-designed modules can be manufactured off-site in parallel. Those components can then be delivered for assembly once the site is prepared.
To learn more about Giga Energy’s approach to standardized AI data-center infrastructure, visit GigaBase.
