Hyperscaling’s Hidden Challenge: The Hardware Between Power, Processors And People

Rush LaSelle is the CEO of Fathom Manufacturing.

gettyChatGPT recently reached 1 billion monthly app users, showing just how quickly demand for AI can scale. But the physical infrastructure behind that growth cannot move at the same pace. Every increase in computing demand eventually has to show up in the hardware, manufacturing capacity, power systems, cooling and the equipment that keep data centers running. ​

The AI infrastructure race is most frequently described in terms of models, chips, power and data center capacity. Those are important measures, but they only tell part of the story. Behind an eerie AI deployment lies a layer of physical infrastructure that must be designed, manufactured, qualified and installed. Investors and policymakers must look beyond these traditional signs of success and shift their focus to the hardware that powers, processes and transmits computational data.​

That infrastructure includes racks, enclosures, cabinets, power distribution systems, cooling equipment, containment structures, cable management and other mechanical assemblies. These components and manufacturing systems rarely receive the attention that chips and AI models do, but they can determine how quickly new capacity actually comes online. AI may feel almost intangible to the people using it, but underneath the model layer and the cloud, it is intensely physical. ​

With hyperscalers projected to pour trillions of dollars into AI infrastructure, the question is no longer simply how much computing power they want to build. It’s whether the manufacturing ecosystem can build fast enough to keep pace. The companies, factories and supply chains behind that effort may attract a fraction of the attention given to chipmakers and AI developers, but they ultimately help determine whether massive capital spending translates into actual operating capacity. ​

The scale of today’s data center buildout is historic. Bank of America Global Research forecasts that AI-related capital spending by hyperscalers could approach $800 billion in 2026 and surpass $1 trillion in 2027. That spending is translating into extraordinary physical demand. Goldman Sachs Research estimates global data center power demand will surge from roughly 62 gigawatts to 92 gigawatts by 2027, with AI consuming an increasing share. ​

The scale of that investment has put chips, power and data center capacity at the center of the conversation. But capital does not become capacity until the equipment is designed, manufactured, qualified and installed. That leaves hyperscalers confronting a fundamental challenge: AI demand can grow at the speed of software, but the physical infrastructure required to support it cannot. ​

AI infrastructure is being called on to scale while the underlying technologies continue to mature. Higher densities, new cooling requirements and increasingly complex power systems are driving more frequent engineering changes across the mechanical components that support them. These supply chains were largely designed around relatively predictable product releases. Now, they must adapt to frequent engineering changes while simultaneously scaling production.

As AI infrastructure evolves, engineering changes ripple through the mechanical supply chain at an increasing rate. Hyperscalers can commit billions to new capacity, but those commitments still have to move through the manufacturing chain, from design and prototype to NPI and eventually production. When demand is accelerating while designs continue to evolve, the ability to move through these stages becomes a constraint in its own right.

The question then becomes not just how much capital is being committed to AI infrastructure but also whether the manufacturing ecosystem can convert that capital into qualified equipment quickly enough. ​

Large-scale manufacturing works best when designs have stabilized. AI infrastructure has not reached that point, with much of it still in a period of rapid development. This has forced OEMs and infrastructure suppliers to manage more engineering changes while production is already underway. ​

That puts greater value on manufacturing partners that can move between engineering and production without creating delays. Capabilities ranging from design and engineering feedback to machining, inspection and assembly become a part of keeping projects on schedule.​

The same pressure extends to the workforce. The Manufacturing Institute and Deloitte estimate U.S. manufacturing may need as many as 3.8 million additional employees between 2024 and 2033, with as many as 1.9 million positions potentially going unfilled. Meeting the demands of AI infrastructure requires machinists, welders, programmers, engineers and supply chain professionals who can work through changing technical requirements. ​

For investors, this makes the manufacturing companies supporting AI infrastructure worth closer attention. They may sit well below the chip and cloud layers in visibility, but they provide the capacity and expertise required to turn engineered designs into deployable systems. ​

​For investors evaluating the next phase of AI infrastructure spending, the relevant question is not simply which companies have exposure to data center growth; it’s whether their manufacturing operations can keep pace with the speed and complexity of that growth. ​

That means evaluating how early a manufacturer is brought into the engineering process, how quickly it can respond to design changes and how effectively it can move a part from prototype through qualification into production. ​

Investors should also consider whether the company has the inspection, documentation, supplier relationships and workforce required to maintain quality as volumes increase. These capabilities become particularly important when production cannot wait for every design decision to be finalized. ​

The winners in the coming wave of AI infrastructure will be the companies that can convert innovation into deployable capacity with speed and discipline. As AI moves from a race for computing power to a race to build it, the manufacturing layer may prove just as important as the technologies it supports.​

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