Power availability and skilled-worker shortages are major pressures on AI data-center expansion, but the evidence does not show they are always the two biggest bottlenecks. In a 2026 survey about scaling compute, accelerator and GPU supply ranked first, narrowly ahead of power and cooling. Which constraint matters most depends on what is being built, where, and which group is being surveyed.
Why AI data-center expansion is running into limits
AI data centers need more than land and servers. Their expansion depends on securing power, cooling capacity and specialized equipment, then building and operating facilities with workers who understand electrical systems, thermal management and data-center operations. A shortage or delay in any one of those areas can constrain deployment.
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Demand is rising, too. A 2026 survey by BCS Consultancy, covered by Computer Weekly, found that 78% of respondents reported an AI-related uplift in demand in 2025. All surveyed developers and investors, and 91% of service providers, reported AI-driven demand. Those are survey responses from distinct groups, not a measurement of total data-center capacity or a forecast of future construction.
Power is a major constraint, but rankings depend on the question
Power can limit a project at several points: a site may lack sufficient available electricity, grid connections may not arrive on the required schedule, or power and cooling systems may not support the planned computing load. These are related issues, but they are not identical to a shortage of chips or a lack of capital.
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In its April 24, 2026 survey release, Futurum Group asked about the single largest constraint to scaling data-center compute. Accelerator or GPU supply was selected by 26% of respondents; power and cooling came next at 23%. Budget or capital-expenditure limits were selected by 15%, while talent or skills shortages and networking lead times each received 11%. This is a ranking within that survey and question, not a universal ordering for every data-center project.
Uptime Institute’s July 2026 announcement of its 16th Annual Global Data Center Survey also lists power availability among industry concerns, alongside supply-chain disruptions, high costs, capacity forecasting, and worsening recruiting and retention pressures. The range of concerns helps explain why solving a power problem alone may not remove other obstacles.
Talent pressure is visible in hiring signals and executive surveys
Data-center workforce pressure appears in several measures, but they should not be treated as interchangeable. Deloitte Insights’ 2026 analysis found that job postings for core roles grew from 2023 to 2025 by 64% in data centers, 20% in the power sector, and 4% across the broader economy for the roles compared. Posting growth signals stronger demand for those roles; it does not count unfilled jobs.
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In a separate measure, 63% of data-center executives in Deloitte’s 2025 AI Infrastructure Survey cited a shortage of data-center-related skilled labor as their leading obstacle to securing talent, as reported in Deloitte Insights’ 2026 analysis. That result describes executives’ reported hiring difficulty, not the number of workers missing from the sector.
DCD Intelligence and DCD Academy’s global workforce survey, conducted in June 2026, included 161 respondents across the data-center industry and targeted operators. Its scope is an industry sample, not a census of workers or all regions. Data Center Dynamics’ September 18, 2026 coverage of the survey says expansion is outpacing available talent, with demand for fluid-dynamics and power expertise among the drivers of workforce expansion. Respondents were also looking to utilities and oil and gas for workers.
What may help close the workforce gap
DCD’s coverage identifies structured training, standardized onboarding and upskilling as workforce approaches raised in connection with the survey. They are relevant responses to the skills challenge, but the coverage does not establish that any one approach has already solved it or quantify its results.
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- Build transferable skills: Utilities and oil and gas are potential sources of workers with relevant power experience, while data-center roles may also require facility-specific knowledge.
- Train for specialized needs: Fluid dynamics and power expertise are cited as important workforce areas as data-center expansion proceeds.
- Make onboarding repeatable: Structured training and standardized onboarding can give expanding teams a consistent way to prepare workers for their roles; these are reported approaches, not measured outcomes.
How to interpret claims about the “biggest” bottleneck
Different reports measure different things. A respondent choosing a single largest compute-scaling barrier is answering a different question from an executive describing recruiting difficulty or a survey tracking job-posting growth. Similarly, a power connection problem during site development is not the same as a staffing challenge in ongoing operations.
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- Check who answered: Operator-focused workforce surveys, compute decision-makers, developers, investors and service providers can describe different parts of the industry.
- Check the timeframe and stage: A constraint during construction or commissioning may differ from one affecting later operations.
The evidence supports describing power and talent as major pressures on AI data-center growth. It does not identify them as the top two constraints in every survey or establish a single ranking that applies across locations, projects and stages of deployment.
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