GenAI’s Human Infrastructure

As America’s AI Action Plan moves into implementation, the real test begins. The CSIS Economic Security and Technology Department has identified critical areas—skilled trade labor needs, the geographic distribution of data centers, the design of the AI Export Package, and more—where deeper analysis can strengthen program design and execution. In this four-part series, we will deliver actionable insights to help policymakers and industry leaders ensure that U.S. AI leadership translates into national strength, economic resilience, and enduring alliances.

To build the infrastructure needed to power America’s AI future, we must also invest in the workforce that will build, operate, and maintain it—including roles such as electricians, advanced HVAC technicians, and a host of other high-paying occupations.

— America’s AI Action Plan, July 2025

GenAI’s Human Infrastructure Challenge

America’s AI Action Plan, released in July 2025, heralds generative AI (genAI) infrastructure as “an industrial revolution, an information revolution, and a renaissance—all at once.” That phrase captures both the scale of investment underway and the challenge of ensuring its success. Until now, debate around AI infrastructure has primarily focused on three inputs: cutting-edge graphics processing units (GPUs), the financial capital needed to deploy them at scale, and the electric power required to run vast new data centers. These factors were identified in early 2025 by CSIS’s The AI Power Surge study as binding constraints on U.S. genAI expansion. The action plan has now added a fourth constraint—skilled labor—and the focus on workforce demand could not have come sooner.

Over the past 18 months, hyperscale cloud and technology companies have mobilized capital at historic levels and secured unprecedented volumes of semiconductors. Utilities are adding new generation capacity at a pace not seen in decades. Yet data centers will not be wired, cooled, or commissioned without electricians, pipefitters, HVAC technicians, welders, and laborers.

Industry leaders increasingly recognize that labor, as much as chips and power, is a binding constraint. For instance, Google announced a $10 million grant to train electricians in April 2025. Microsoft, Amazon, and TSMC have launched community college partnerships. The International Brotherhood of Electrical Workers explicitly targets bringing “more than 100,000 sorely needed electricians into the trade” for AI-driven data centers. At an AI Labor Summit in 2024, Microsoft pledged to incorporate feedback from labor unions into the development of AI. While promising, are these efforts scaled to the size of the challenge?

This paper models the labor requirements of the AI infrastructure build-out, compares them to baseline projections from the Bureau of Labor Statistics (BLS), and evaluates the institutional capacity of U.S. workforce development and apprenticeship systems to deliver. Based on our modeling, we find that:

• Even conservative AI adoption scenarios create demand shocks that the current workforce pipeline cannot absorb without decisive action. Demand shocks will be particularly acute in certain regions that are experiencing congestion effects.

• By 2030, under our high-case scenario analogous to the Second Industrial Revolution, the United States will require roughly 140,000 incremental skilled trades positions beyond current capacity.

• While specialized labor for data centers may be relatively mobile through time, over shorter periods of time, the lack of skilled labor in geographies with anchor AI infrastructure investments could result in schedule delays and idle capital.

• The Trump administration’s AI Action Plan commits to several actions related to the workforce. But our analysis shows that additional measures are urgently needed. For instance, under our high-case scenario, the United States will need to expand the scope of apprenticeship programs as well as their scale by at least 50 percent by 2030. These in turn will require measures to reduce instructor shortages, as well as demand- and supply-side measures to finance training facilities and apprentices. To accelerate these efforts, and galvanize the public and private sectors, the administration should establish a National AI Infrastructure Workforce Consortium.

• Growing a pipeline of homegrown skilled trade talent should be the country’s highest priority. However, even the above-mentioned efforts will take time. In the shorter run, the United States will need to consider reasonable immigration solutions to avoid falling behind in the genAI infrastructure race.

National Labor Demand Projections—Scenario Results and Implications

The scale of workforce needs depends on the pace and breadth of AI adoption and investment duration. Still, the relationship between technology deployment patterns and workforce demands involves complex, nonlinear interactions between project timing, regional concentration, skill specificity, and productivity trends. To better understand these relationships, we refined our model—first used in the CSIS AI Power Surge Report—to elaborate scenarios for skilled-labor demand in person-years (see Appendix 1 for model specifications).

Scenarios. We constructed scenarios to reflect archetypical technology adoption pathways based on historical analogues. In the high case, genAI triggers transformation on the scale of the Second Industrial Revolution, with sustained investment through 2030 as it becomes as foundational as electricity or telecommunications. The mid case mirrors the personal computer revolution of the 1980s, revolutionizing office work and services but leaving broader economic structures intact, with adoption concentrated in software, media, and professional services before stabilizing around 2028. The low case reflects a dot-com-style boom-and-bust cycle, where investment peaks in 2025 before moderating, as real capabilities hit practical limits and revised market expectations.

Given the current uncertainty around monetizing AI and the evolving chip technology, forecasting can be challenging. It is important to keep in mind that these scenarios are not predictions but sensitivity tests. They acknowledge that past technology booms often fell short of expectations, even as today’s genAI investments rest on transformational assumptions. They also adjust for power utilization: Rather than near-perfect GPU usage, real-world thermal, bandwidth, and demand spikes cut GPU uptime to ~60 percent, lowering projected power needs from 80 gigawatts (GW) to ~50 GW by 2030 (still a massive build concentrated in a few metropolitan areas). Finally, they translate infrastructure into labor using craft-hour coefficients: about 5 person-years per megawatt of new data center capacity, 6 for generation, and 0.05 for transmission. Occupational shares from the National Renewable Energy Laboratory (NREL) and BLS were consolidated into 10 core crafts. Each megawatt of AI data center capacity requires ~1,800 electrician-hours, well above traditional facilities, because of the precision wiring and redundancy these systems demand.

The results are summarized in Table 1.

▲ Table 1: Three Scenarios for Skilled Labor Demand

Aggregate Skilled Labor Needs. Even the conservative low-case scenario requires roughly 63,000 additional skilled workers beyond baseline growth; that is more than the population of a mid-sized U.S. city. The “PC revolution” scenario doubles that demand to more than 100,000, while a “Second Industrial” trajectory pushes the total to above 140,000. These figures matter because skilled trades are not easily fungible. Electricians cannot be trained overnight, HVAC technicians cannot be repurposed from other sectors, and welders or equipment operators require years of preparation. Labor needs of this magnitude cannot be met by reallocating the existing workforce; they require an expansion of the pipeline itself.

To appreciate the challenge, it is essential to compare these projections with baseline labor trends. According to the BLS, electrician employment is expected to grow by 9 percent from 2024 to 2034, creating 81,000 openings annually. HVAC mechanics and installers are projected to grow by 8 percent, construction laborers by 7 percent, and welders by 2 percent. These projections reflect economy-wide demand, but genAI infrastructure adds additional strain to systems already approaching capacity.

▲ Figure 1: Demand for Skilled Trade Workers Expected to Soar by 2030 Under High-Growth Scenario

Layering genAI requirements on top of baseline growth compounds skilled labor needs. What makes this more concerning is the demographic profile of the workforce. Nearly one-fifth of construction workers are 55 or older, with a median age of 42. Many retire in their late 50s due to the physical demands of their work. This retirement wave will hit just as AI infrastructure projects peak. The result is a timing mismatch: The workers needed most urgently are the very ones leaving the workforce.

Developing Homegrown Talent—Apprenticeship Requirements and Gaps

Expanding labor supply requires proportional apprenticeship program growth, but success depends as much on institutional capacity development, instructor availability, and employer coordination as on financial resources for program expansion. Current capacity analysis reveals significant shortfalls across trades that require systemic changes in how training programs are funded, operated, and coordinated with industry demand patterns.

▲ Table 2: Apprenticeship Programs—Requirements and Capacity Gaps

Instructor Shortage as Primary Constraint. The instructor shortage represents arguably the most acute constraint on training expansion, as qualified instructors must combine journeyman-level technical skills with teaching abilities, safety expertise, and knowledge of rapidly evolving genAI infrastructure requirements that few existing instructors currently possess. Traditional apprenticeship programs rely heavily on experienced tradespersons who transition into teaching roles, but many potential instructors earn significantly more in field work than in teaching positions.

Instructor compensation in most apprenticeship programs reflects public education funding models that fail to compete with private sector wages for skilled trades workers, particularly in metropolitan areas experiencing rapid technology infrastructure development. Electricians with data center experience may earn $80,000–$120,000 annually in field work, while instructor positions often offer $50,000–$70,000 with limited opportunities for overtime or project-based premium compensation.

The technical knowledge requirements for genAI infrastructure instruction exceed those of traditional electrical or HVAC training, requiring an understanding of high-density power systems, electromagnetic compatibility requirements, precision cooling technologies, and integration with digital monitoring and control systems. Many experienced tradespeople lack exposure to these advanced systems, requiring additional training and certification for instructors before they can effectively teach others.

Facility and Equipment Requirements. Training facilities for genAI-focused programs require specialized equipment and infrastructure that significantly exceed traditional apprenticeship program requirements. Electrical training must include high-voltage systems that simulate data center power distribution, sophisticated protective equipment for working with dangerous voltages, and classroom space for theoretical instruction in electromagnetic compatibility and system integration.

HVAC training facilities need mock-ups of liquid cooling systems, precision air handling equipment, and integration with building automation systems that represent the complexity of modern data center cooling requirements. These specialized training environments often cost $5-$10 million per facility, requiring federal and state cost-sharing programs or innovative financing arrangements that many traditional training providers cannot access independently.

Student Support and Completion Rate Challenges. Completion rates determine the effectiveness of apprenticeship program expansion more than enrollment numbers, as high dropout rates can negate the benefits of increased program capacity while creating economic inefficiency and personal disappointment for workers who invest time and effort without achieving career success.

Starting wage levels in apprenticeship programs often fall below living wage requirements in expensive metropolitan areas where genAI infrastructure development is concentrated, forcing apprentices to choose between program completion and meeting immediate family financial needs. Wage progression policies, income support during training, and coordination with other public benefit programs become essential for apprenticeship success in high-cost regions.

Housing affordability in metropolitan areas with active genAI infrastructure development may exceed the financial capacity of apprentices, even with wage progression during training programs. Workforce housing programs, shared housing arrangements, or housing vouchers specifically for apprentices can address these barriers while supporting regional workforce development objectives.

Quality Assurance and Standards Maintenance. Rapid program expansion poses risks to training quality and safety standards, which must be mitigated through quality assurance systems, accreditation processes, and ongoing oversight to ensure program effectiveness while achieving scale increases. The pressure to increase enrollment and completion rates would need to be balanced against the need to maintain technical competency and safety standards essential for complex technology infrastructure work.

Curriculum development for genAI infrastructure may require ongoing updates as technology evolves, industry practices change, and safety requirements are updated based on operational experience. Traditional apprenticeship curricula evolved slowly over decades, but genAI infrastructure requirements may change significantly within individual apprenticeship cycles, requiring more dynamic curriculum management and instructor training systems.

Assessment and certification standards must ensure that program graduates possess the skills needed for safe and practical work in genAI infrastructure environments while maintaining consistency across different training providers and regional programs. Standardized competency assessments, practical skill demonstrations, and ongoing professional development requirements ensure that apprenticeship completion represents a genuine qualification for complex technical work.

Demographic Attrition. Workforce aging amplifies capacity constraints that extend beyond simple retirement numbers. It impacts knowledge transfer, productivity patterns, and career development pipelines that take decades to mature. With 25 percent of construction workers over 55 and a median age around 42, retirement waves will remove experienced personnel during peak demand periods.

In addition to the challenge of replacing retiring workers, the demographic transition poses challenges for the transfer of institutional knowledge, safety practices, and technical expertise that experienced workers have accumulated over decades of career development. Master electricians who understand the integration of traditional electrical systems with modern digital controls, HVAC technicians who can troubleshoot complex interactions between cooling systems and building automation, and welders who can adapt techniques for new materials and applications represent irreplaceable knowledge assets.

The apprenticeship pipeline that produces replacement workers requires four to five years for completion, meaning that workers who begin training today will not reach full productivity until 2029–2030, exactly when demographic retirements reach peak levels. This timing mismatch creates a period of maximum vulnerability where workforce exit rates exceed entry rates while infrastructure construction demands reach historic highs.

Retirement patterns in construction trades differ significantly from office-based occupations due to the physical demands of field work, exposure to workplace hazards, and the accumulated wear on bodies that have performed manual labor for decades.1 Many skilled trades workers retire earlier than the general population, with significant numbers leaving the workforce in their late 50s rather than working into their 60s, as is increasingly common in other sectors.

The knowledge transfer challenge extends to safety practices and workplace culture, which are transmitted through informal mentoring relationships between experienced and new workers. Safety consciousness, hazard recognition, and emergency response capabilities are often learned through observation and guidance from senior workers rather than formal training programs. The rapid departure of experienced workers can disrupt these knowledge transfer mechanisms and increase accident risks for newer workers.

Strengthening the Workforce Dimensions of the AI Action Plan

The AI Action Plan appropriately emphasizes the labor needs of the genAI infrastructure build-out. While the capital cost of labor in data centers is relatively small compared to hardware and energy, the cost of delay is enormous. A data center sitting idle because of a shortage of electricians represents hundreds of millions in stranded investment and, more importantly, a lost window in the global race for AI leadership.

The AI Action Plan tasks the Department of Labor to take the lead in helping build the backbone of the United States’ AI workforce. The Department of Labor has committed to prioritizing AI skill development across funding streams, launching a national initiative to identify priority AI infrastructure occupations, scaling registered apprenticeships and pre-apprenticeship programs, standing up an AI Workforce Research Hub to provide recurring forecasts, and using Workforce Innovation and Opportunity Act (WIOA) funding to expand AI literacy and training programs. These initiatives appropriately bridge classrooms and construction sites, from technical skilling to system-wide data and programs. But our analysis shows that additional measures are urgently needed.

Under the AI Action Plan, the Department of Labor, in collaboration with the Department of Commerce, should establish a National AI Infrastructure Workforce Consortium—modeled after the Department of Energy’s Foundation for Energy Security and Innovation (FESI) and the National Semiconductor Technology Center’s Workforce Center of Excellence—to bring together industry, federal and state governments, unions, and other stakeholders around the singular mission of meeting genAI trade labor needs. The consortium should help mobilize public, private, and philanthropic resources, as well as coordinate national and subnational efforts among public and private actors to deliver on five priorities:

• Expanding Apprenticeships: The consortium should coordinate with states and industry to establish standardized apprenticeship profiles for data center electricians, HVAC technicians, and commissioning specialists, aligning them with federally recognized Registered Apprenticeship slots tied directly to AI infrastructure projects.

• Building an Instructor Corps: The Instructor Corps should be tasked with developing fast-track instructor certifications, liability coverage templates, and mentorship playbooks while leveraging stipends and wage-matching funds to support rotations of experienced craft workers and early retirees into instructional and mentoring roles.

• Upgrading Training Facilities: The consortium should publish standardized reference designs for high-voltage systems, liquid cooling setups, automation controls, and commissioning labs, coordinating vendor donations and matching state and federal funds to ensure all hubs feature interoperable, industry-current equipment.

• Improving Retention in High-Cost Markets: The consortium should standardize retention-support packages—including housing vouchers, tool stipends, and structured mentorship—and broker funding arrangements among WIOA, state programs, employers, and philanthropic partners for consistent implementation.

• Streamlining Licensure and Immigration: The consortium should facilitate collaboration among states, credentialing boards, and industry stakeholders to draft reciprocity agreements and skill verification protocols, while also creating bridge curricula enabling internationally trained tradespeople to swiftly transition into licensed, U.S.-based roles.

The estimated demand of 140,000 new skilled jobs by 2030 cannot be met without expanding instructional capacity, modernizing facilities, improving retention, and addressing immigration and licensure gaps. Without a well-coordinated effort across industry as well as federal and state governments, the genAI infrastructure agenda risks delays, wasted investment, and lost leadership in the AI era.

The AI build-out has more in common with the Industrial Revolution, rural electrification, and the interstate highway system than with the PC or internet revolutions. Those eras succeeded not just because capital and technology were available, but because the United States mobilized vast armies of skilled workers to wire grids, pour concrete, and weld steel. Similarly, the genAI revolution now depends on the electricians, welders, and technicians who will build its backbone. If the United States can mobilize labor as effectively as it did in those past industrial surges, it will not only secure “an industrial revolution, an information revolution, and a renaissance—all at once,” but also create durable middle-class careers and cement its leadership in the AI era.

Appendices

Appendix I: Model Specifications

• Scenarios for Technology Adoption

We use three adoption scenarios corresponding to historical technological transformations that reshaped labor markets and infrastructure requirements:

Second Industrial Revolution (8+ years): Modeled on 1870–1914 electrification, steel, and internal combustion adoption. During this period, the United States witnessed the construction of electrical power grids in every major city, the laying of thousands of miles of railroad track, and the erection of steel-framed skyscrapers that required entirely new categories of skilled workers. If genAI enables comparable economic transformation, fundamentally altering how businesses operate, how consumers interact with information, and how government delivers services, current investment rates are likely to continue through 2030 and beyond. This scenario assumes that generative AI becomes as foundational to economic activity as electricity itself, requiring sustained infrastructure development at unprecedented scale.

PC Explosion (5 years): Reflects personal computer adoption patterns that revolutionized business operations and clerical work without fundamentally transforming broader economic structures. The PC revolution, spanning from 1981 to 1986, created entirely new categories of jobs while eliminating others, but its infrastructure requirements were relatively modest: office space, local area networks, and electrical upgrades rather than massive construction projects. Under this scenario, genAI is heavily adopted in some service sectors, such as software development, media production, and professional services, while leaving manufacturing, agriculture, distribution, and construction essentially unchanged. The investment surge continues until 2028 before stabilizing at more moderate levels.

Dot-Com Boom (2 years): Despite internet company interest beginning in 1995, concentrated venture investment occurred between late 1999 and mid-2001, followed by rapid collapse and consolidation. This scenario acknowledges that technological revolutions often follow boom-bust cycles, with initial euphoria giving way to more measured deployment. The dot-com parallel suggests that while genAI capabilities are real, market expectations may have run ahead of practical applications. Under this trajectory, the massive investment surge that began in late 2023 peaks in 2025 before moderating as companies focus on profitability rather than growth at any cost.

• Power Demand Recalibration and Utilization Constraints

Initial modeling predicted 80 GW of new generation capacity by 2030 under our aggressive scenario, based on theoretical GPU utilization rates and straightforward scaling from current data center power consumption. However, genAI data centers face persistent utilization challenges that significantly modify these projections and, consequently, the associated labor requirements.

Engineering constraints limit theoretical GPU utilization to 85–90 percent under optimal conditions, a ceiling imposed by memory bandwidth limitations, thermal management requirements, and the need for system maintenance windows. Training large language models requires sustained, coordinated compute across thousands of GPUs for weeks or months. At the same time, inference workloads fluctuate dramatically based on user demand patterns that remain difficult to predict.

The rapid expansion in inference-time computing has further complicated utilization optimization. Training compute follows predictable schedules determined by model development timelines and research priorities. Inference compute, by contrast, depends on real-time user demand that varies by time of day, geography, application type, and external events. A viral social media trend or breaking news event can trigger inference demand spikes that exceed baseline consumption by orders of magnitude within minutes.

These demand patterns, combined with the massive capital investments in GPU hardware, have pushed operators toward conservative capacity planning. Rather than risk service degradation during demand spikes, most genAI data centers maintain substantial spare capacity, reducing actual utilization to approximately 60 percent, well below the aspirational 90 percent target that informed early projections.

Each 10-point utilization decline increases the required GPU inventory by 6–8 percent to maintain service reliability, proportionally increasing capital costs. Since our model uses capital expenditure as baseline input, higher capital costs translate to slower growth in both compute deployment and power utilization.

Revised projections estimate that 50 GW of new generation capacity will be needed by 2030 under the high-growth scenario—still substantial, but more conservative than earlier forecasts. This adjustment reflects both utilization realities and the likelihood that efficiency improvements in chip design, cooling systems, and power management will partially offset raw compute growth. However, the labor requirements for 50 GW of new capacity remain massive, particularly when concentrated in specific metropolitan areas with limited existing workforce capacity.

• Craft-Hour Coefficients and Labor Translation Methodology

Our craft-hour coefficients were developed as follows: We set three envelope intensities that translate capacity into total onsite person-years: Data Center build = 5 PY/MW, Generation build = 6 PY/MW, and Transmission build = 0.05 PY/MW. We then derived occupation shares from two public baselines: the NREL JEDI models for generation and transmission (using the On‑Site Construction impacts), and BLS OEWS (NAICS 236200) to proxy data center construction’s occupational mix. These shares were mapped into our 10‑craft cluster and scaled to the envelopes.

We consolidated the Standard Occupational Classification system’s detailed occupations into 10 craft clusters: electricians, line workers; pipefitters/HVAC; construction equipment operators; structural steel and welders; construction laborers; foremen/supervisors; project management; engineers; and office/admin; chosen to cover ≈90 percent of onsite hours while keeping the model transparent for policy readers.

For each build area and craft, we computed intensity based on the high-level envelope from above (5/6/0.05 PY per MW, respectively) and craft shares drawn from JEDI (Power Generation, Power Transmission) and OEWS 236200 (Data Centers) models. For example, each megawatt of genAI data center construction requires approximately 1,800 electrician‑hours (~0.9 electrician person‑years), reflecting the specialized nature of high-density server installations. Unlike traditional office buildings that might require 200–400 electrician-hours per megawatt of connected load, genAI facilities demand precision electrical work for server racks drawing 40–50 kilowatts each, sophisticated power distribution systems with multiple redundancy levels, and integration with advanced cooling systems that blur traditional boundaries between electrical and mechanical trades.

• Key Trades

Our analysis focused on five trades critical to the genAI build-out—two of which are mentioned in the administration’s AI Action Plan, electricians, and HVAC technicians. Understanding the importance of these trades and how to alleviate potential shortages is the first step in answering the administration’s call.

Electricians represent the quintessential tradecraft for genAI infrastructure development. Modern genAI facilities operate at power densities that would have been unimaginable in conventional data centers, with individual server racks consuming 40–50 kilowatts compared to 5–10 kilowatts in traditional installations. This power density creates unique challenges in circuit design, load balancing, and fault protection that require specialized training beyond standard electrical apprenticeships.

The inspection and maintenance of transformers and circuit breakers in genAI facilities require the consideration of harmonic distortion, power factor correction, and electromagnetic interference, which can affect computational accuracy. System-wide failure diagnosis requires understanding the interaction between electrical infrastructure and computational workloads, as power anomalies can cascade through computational systems.

Electricians learn their trade through four- to five-year apprenticeships that require around 2,000 hours of on‑the‑job training yearly, but genAI facilities often demand additional specialized training in high-density power systems, liquid cooling integration, and automated monitoring systems. Licensing ensures that only fully trained journeymen perform energized work in environments where electrical faults can result in millions of dollars in computational hardware damage and service interruptions affecting global AI services.

Pipefitters and HVAC technicians face unique thermal management challenges in genAI facilities, where computational workloads can generate heat densities approaching those found in industrial furnaces. Traditional air-cooling approaches prove inadequate for the 40–50 kilowatt server racks standard in genAI installations, necessitating sophisticated liquid cooling systems that blur the boundaries between plumbing, electrical, and mechanical engineering.

These specialists fabricate and weld chilled-water piping systems designed to handle thermal loads that fluctuate rapidly based on computational demand. Unlike traditional HVAC systems with predictable heating and cooling cycles, genAI facilities experience thermal spikes that can occur within seconds as AI training jobs launch or inference demand surges. Cooling systems must respond with equal speed while maintaining precise temperature and humidity control to protect sensitive computational hardware.

Connection and maintenance of cooling systems involve specialized refrigerant handling for high-density applications, integration with waste heat recovery systems that capture thermal energy for building heating or industrial processes, and coordination with electrical systems for pump control and monitoring. Many genAI facilities employ liquid cooling systems that deliver chilled water or specialized coolants directly to individual server components, requiring installation and maintenance skills that combine traditional plumbing with precision engineering.

The mastery of thermodynamics, refrigerant handling, and building codes for genAI applications requires an understanding of heat transfer rates, fluid dynamics, and system controls that exceed traditional HVAC training. Apprenticeships typically last four to five years, but genAI-specific applications may require additional specialized training in high-density cooling, liquid cooling systems, and integration with computational infrastructure.

Heavy‑equipment operators in genAI facility construction face unique challenges related to the massive scale and precision requirements of AI infrastructure development. Site preparation involves grading land for facilities that may span hundreds of acres while meeting precision tolerances for foundations that will support equipment worth hundreds of millions of dollars. Excavation work includes trenching for utility infrastructure, including high-voltage electrical feeds, fiber optic networks, and cooling water systems that must be coordinated with millimeter-level precision.

Foundation pouring for genAI facilities requires handling concrete pours that may continue for days while maintaining structural specifications designed to support equipment loads and vibration isolation requirements that exceed most industrial construction. The specialized nature of AI server equipment creates unique foundation requirements, including vibration-dampening systems, precision leveling, and integration with cooling and electrical infrastructure that traditional construction rarely encounters.

Crane operation for positioning large mechanical equipment involves handling transformers that may weigh hundreds of tons, backup generator systems rated in the tens of megawatts, and cooling equipment with precision placement requirements measured in fractions of inches. The coordination required between multiple crane operators, electrical crews, and facility engineers exceeds the complexity of most construction projects due to the interdependence of systems and the high cost of errors.

Construction laborers and helpers provide essential support services that enable electricians, HVAC technicians, and other specialists to focus on skilled work while ensuring efficient material flows and site preparation in facilities where security requirements and equipment sensitivity create unique challenges. Concrete finishing work requires an understanding of specialized foundation requirements for equipment that generates vibration, heat, and electromagnetic fields while supporting loads that may shift based on computational demand patterns.

Cleaning and material handling must comply with contamination control requirements that exceed those of traditional construction, as dust and debris can damage computational equipment worth millions of dollars per server rack.

While structured apprenticeships are less common for construction laborers than for other trades, formal programs specific to technology infrastructure construction improve both productivity and safety outcomes.

Structural steel workers and welders face unique challenges in genAI facility construction due to the specialized mounting systems required for equipment that combine extreme density, high value, and rapid technological evolution. Traditional data center racks might support 5–10 kilowatts of equipment with relatively straightforward structural requirements; genAI server configurations can exceed 50 kilowatts per rack while requiring mounting systems that accommodate thermal expansion, vibration isolation, and rapid equipment replacement as AI hardware evolves.

Steel framework installation must accommodate not only structural loads but also electromagnetic shielding requirements, thermal management integration, and cable management systems that traditional industrial construction rarely encounters. Cable trays and conduits require precision installation that coordinates with electrical, cooling, and monitoring systems while allowing for equipment upgrades and reconfigurations that occur much more frequently than in traditional data centers.

Welding tolerances must accommodate the thermal expansion patterns of equipment that cycles between high and low power states based on computational demands, creating structural stress patterns that differ significantly from traditional industrial applications.

Navin Girishankar is the president of the Economic Security and Technology Department at the Center for Strategic and International Studies (CSIS) in Washington, D.C.

Karl Smith is an economist providing AI-related analysis for the Economic Security and Technology Department at CSIS.