Analyst Dylan Patel argues the biggest risk to the multi-trillion dollar AI infrastructure build-out is the lack of skilled blue-collar labor to construct and maintain data centers, as their wages are skyrocketing.
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While AI chips represent the bulk of a data center's cost ($20-25M/MW), the remaining $10 million per megawatt for essentials like powered land, construction, and capital goods is where real bottlenecks lie. This 'picks and shovels' segment faces significant supply shortages and is considered a less speculative investment area with no bubble.
The huge scale of AI data center construction, requiring thousands of skilled laborers in one location, creates a 'crowding out' effect. Local businesses in places like Abilene, Texas, cannot compete for labor like HVAC technicians, leading to shortages and potential inflationary pressures on regional economies.
Despite a massive contract with OpenAI, Oracle is pushing back data center completion dates due to labor and material shortages. This shows that the AI infrastructure boom is constrained by physical-world limitations, making hyper-aggressive timelines from tech giants challenging to execute in practice.
AI will primarily threaten purely cognitive jobs, but roles combining thought with physical dexterity—like master electricians or plumbers—will thrive. The AI-driven infrastructure boom is increasing demand and pushing their salaries above even those of some Silicon Valley engineers.
Developed nations are building massive infrastructure projects like data centers, yet the construction workforce is aging and shrinking. This creates a critical bottleneck, as every project fundamentally relies on excavator operators—a role younger generations are avoiding.
AI is rapidly automating knowledge work, making white-collar jobs precarious. In contrast, physical trades requiring dexterity and on-site problem-solving (e.g., plumbing, painting) are much harder to automate. This will increase the value and demand for skilled blue-collar professionals.
The race to build AI data centers has created a severe labor shortage for specialized engineers. The demand is so high that companies are flying teams of engineers on private jets between construction sites, a practice typically reserved for C-suite executives, highlighting a critical bottleneck in the AI supply chain.
Most AI applications are designed to make white-collar work more productive or redundant (e.g., data collation). However, the most pressing labor shortages in advanced economies like the U.S. are in blue-collar fields like welding and electrical work, where current AI has little impact and is not being focused.
The primary constraint on the AI boom is not chips or capital, but aging physical infrastructure. In Santa Clara, NVIDIA's hometown, fully constructed data centers are sitting empty for years simply because the local utility cannot supply enough electricity. This highlights how the pace of AI development is ultimately tethered to the physical world's limitations.
Automation is hollowing out the labor market from both ends. Robots are replacing low-skill manufacturing jobs, while AI is automating high-skill knowledge work. For now, the most resilient jobs are skilled trades requiring high physical dexterity in unpredictable environments, like plumbing or electrical work.
