Beyond GPUs

Fixing the Server Manufacturing Crisis That Could Derail America’s AI Future

There’s a growing crisis in AI infrastructure manufacturing

The global landscape of artificial intelligence is evolving rapidly, presenting both unprecedented opportunities and urgent infrastructure challenges. As nations around the world invest heavily in AI to drive innovation and economic growth, the United States and its companies must do more than put up the dollars to build data centers — we must ensure we can scale the manufacturing of the equipment that goes inside them. That means solving a growing crisis in AI infrastructure manufacturing.

The 3X Manufacturing Challenge

In 2025 alone, the collective investment by the U.S. government and the private sector has exceeded $1.32T dollars.

These investments are catalyzing one of the fastest industrial buildouts in U.S. history. Based on current forecasts, the number of fully-loaded server racks deployed in the U.S. will need to triple in 2025 compared to 2024, and continue to scale in subsequent years.

AI racks today are far more complex than traditional compute infrastructure. They are exponentially more power-hungry, integrated, liquid-cooled, and expensive. A fully loaded inference rack costs $400,000, while cutting-edge systems can be $3M.

Given national security concerns around tampering with this equipment, much of the assembly for racks destined for United States data centers is built in the United States or Mexico. These manufacturing lines are stretched

The Unseen Bottleneck: Manufacturing Yields & Process Limits

There are multiple critical bottlenecks in the manufacturing process for these advanced racks:

  • Functional testing – the limit at nearly every rack-level assembly factory is its power draw. Substantial power is needed to energize servers and racks for tests that take between 8 and 24 hours – and just like in data centers, increasing power is a municipal challenge.
  • First-pass yield (FPY) is low – FPY measures the percentage of units that can be manufactured without repair and retest. Server yields, particularly with advanced AI or liquid-cooling functions, can be as low as 20%, with 50-70% being more typical. For reference high-volume consumer electronics typically drive towards 90%+ FPYs. Low FPY means units have to go through the test several times before they pass, reducing the limited test capacity.
  • Manpower — Humans primarily assemble servers and racks. Factories struggle to onboard and retain human operators. Some factories have resorted to importing temporary labor from other facilities around the world to keep production lines going in the United States. Scaling the labor with the 3X increased demand will be a huge challenge.


To make things even more challenging, servers are manufactured in many different variants, even within the same product family. This high-mix makes it difficult for technologies from conventional high-volume manufacturing to be effective without significant integration efforts. In order to win the AI race, we need not only data center buildings and power hookups but also reliable servers and racks to plug into them.

We Need Bold, Cross-Sector Collaboration

This is not just a technology issue — it’s a national capability issue. Solving it requires a coalition:

  • Federal and municipal governments must align on supportive policies, incentives, and infrastructure (permitting, power, tariffs) that accelerate server manufacturing.
  • Technologists must prioritize inventing and rapidly deploying new technologies suited to solve these problems. Investors must support ambitious technologies beyond incremental improvements.
  • Manufacturing leaders from adjacent sectors like consumer electronics, automotive, and semiconductors must help reimagine high-yield, high-throughput production systems for AI infrastructure.
  • Hyperscalers and C-suite leaders must cooperate to build shared standards, tooling, and transparency across the value chain.

We Don’t Have Time to Wait

Other countries have demonstrated remarkable proficiency in scaling electronics manufacturing. The United States is at a critical disadvantage. Relying solely on market forces may not suffice; strategic collaboration and coordinated action are imperative to ensure that we not only compete but lead in the AI revolution. 

We invite government officials, technologists, investors, and industry leaders to share their perspectives on this issue, and join the conversation. The AI race will not be won by models alone. It will be won by those who can build, ship, and deploy infrastructure at scale.
Let’s make sure that’s us.

About the Author: Anna-Katrina Shedletsky


Anna-Katrina Shedletsky is the CEO and cofounder of Instrumental, Inc., helping manufacturers solve problems faster by closing feedback loops in development and production.

A former Apple system product design engineer and Apple Watch Series 1 lead, she specializes in mechanical design for mass production, in-factory implementation, and data-driven decision making. Anna also founded the Women in STEM Mentorship Program and holds BS and MS degrees in Mechanical Engineering from Stanford.

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