Wednesday, September 2, 2026
Artificial intelligence is usually discussed as a technology story.
Chips. Models. Data centers. Software. Automation. Productivity.
But the next phase of AI may be shaped just as much by two resources technology companies cannot manufacture overnight:
Skilled people and electricity.
That combination deserves much more attention from business leaders.
New U.S. employment projections suggest the workforce of 2035 will look materially different from the workforce organizations depend on today. At the same time, rapid expansion of AI and data-center infrastructure is creating substantial new electricity requirements and increasing pressure on power generation, grids, transformers, construction capacity, permitting, and specialized technical talent.
In other words, the AI economy is increasingly becoming a people + power + infrastructure story.
And that has implications far beyond the technology sector.
The Workforce of 2035 Is Already Taking Shape
On August 27, 2026, the U.S. Bureau of Labor Statistics released its new 2025–2035 Employment Projections.
The U.S. economy is projected to add approximately 5.9 million jobs, increasing total employment from about 170.3 million in 2025 to 176.2 million in 2035.
But total employment is projected to grow only 3.5%, considerably slower than the 10.9% growth recorded during 2015–2025.
Source: U.S. Bureau of Labor Statistics — Employment Projections 2025–2035
That does not mean opportunity is disappearing.
It means the composition of opportunity is changing.
Some capabilities are projected to grow much faster than the overall labor market.
For example, BLS projects employment of data scientists to grow 35% between 2025 and 2035, with approximately 24,800 openings per year on average during the decade. BLS specifically points to greater demand for data-driven decision-making and continued integration of AI-based systems as contributors to that growth.
Source: BLS — Data Scientists Occupational Outlook
The transformation is not limited to software.
BLS projects employment in computing infrastructure providers, data processing, web hosting, and related services to increase from approximately 479,900 jobs in 2025 to 600,300 in 2035—about 25.1% growth.
Interestingly, other electrical equipment and component manufacturing is projected to grow employment by approximately 25.9% during the same period.
Source: BLS — Fastest-Growing Industries, 2025–2035
That tells us something important.
The workforce of the future is not simply becoming “more digital.”
It is becoming increasingly integrated across digital, technical, industrial, infrastructure, analytical, and leadership capabilities.
AI Is Creating Demand Far Beyond AI
When people discuss AI employment, the conversation often begins and ends with data scientists, software engineers, and programmers.
That view is too narrow.
A rapidly expanding AI infrastructure ecosystem also requires semiconductor manufacturing, advanced packaging, networking equipment, transformers, switchgear, electrical systems, cooling systems, construction, data-center operations, maintenance technicians, cybersecurity, supply-chain professionals, energy infrastructure, project managers, and operations leaders.
AI may be digital at the point of use.
But the infrastructure supporting it is extremely physical.
That means organizations should ask a different question:
Which capabilities become more valuable because AI is expanding—not simply which jobs might AI automate?
The Other Constraint: Electricity
The second issue may become even more significant.
AI needs computing capacity.
Computing capacity needs electricity.
The International Energy Agency projects global data-center electricity consumption to more than double to approximately 945 terawatt-hours by 2030, up from roughly 415 TWh in 2024.
AI is expected to be the most important driver of that increase.
Source: International Energy Agency — Energy and AI
The United States is particularly important.
The IEA projects that data centers will account for nearly half of U.S. electricity-demand growth through 2030.
Source: IEA — Energy and AI Executive Summary
Think about that for a moment.
We frequently discuss AI investment in terms of processors, models, cloud capacity, and software.
But behind every AI workload sits a physical infrastructure chain:
Power generation. Transmission. Substations. Transformers. Cooling. Water. Land. Construction. Permitting. Grid connections. Backup generation.
And the skilled people required to design, build, maintain, and operate all of it.
Digital Speed Is Colliding With Infrastructure Speed
Technology can move incredibly fast.
Infrastructure usually cannot.
The IEA notes that a data center can potentially become operational within two to three years, while the broader energy system frequently requires substantially longer lead times because infrastructure involves planning, construction, regulatory approvals, major capital investment, and long equipment lead times.
Source: IEA — Energy Demand From AI
That mismatch creates an important business constraint.
Technology companies can make investment decisions quickly.
The power system cannot necessarily respond at the same speed.
Transformers cannot instantly appear. Transmission infrastructure cannot be built overnight. New generation requires capital, equipment, permitting, labor, and time.
And signs of those constraints are already visible.
The IEA reported that global data-center electricity demand increased by approximately 17% in 2025, while AI-focused data centers grew even faster. It also identified tightening supply conditions involving transformers, gas turbines, advanced chips, IT components, grid connections, and regulatory approvals.
Source: IEA — Data Centre Electricity Use Surged in 2025
That means one of AI’s next competitive questions may not simply be:
“Who has the best model?”
It may increasingly become:
“Who can secure the infrastructure required to scale?”
Geography May Become Strategic Again
For years, many digital businesses could operate with relatively limited concern about physical location.
AI infrastructure changes that.
Location increasingly matters because electricity availability is not uniform.
Neither are grid reliability, energy cost, water availability, land, permitting, tax incentives, construction labor, technical talent, fiber connectivity, renewable-energy resources, natural-gas infrastructure, or proximity to customers.
The IEA projects particularly strong data-center electricity growth in the United States and China and notes that data centers tend to be geographically concentrated, which can make their integration into local power grids considerably more challenging than their global share of electricity demand might suggest.
That creates opportunity for regions capable of combining several ingredients:
Energy + infrastructure + talent + speed of execution.
The winners may not simply be traditional technology centers.
They may also be regions capable of building the physical ecosystem the AI economy requires.
This Is Also a Manufacturing Story
Manufacturing leaders should pay close attention.
AI infrastructure growth creates demand across numerous industrial categories: electrical equipment, cooling technologies, semiconductors, power-management systems, transformers, switchgear, networking hardware, steel, copper, construction materials, automation equipment, backup generation, data-center infrastructure, and industrial services.
The latest BLS projections are particularly interesting here. Other electrical equipment and component manufacturing is among the fastest-growing industries for employment through 2035, with projected growth of 25.9%. Computing infrastructure and data-processing employment is projected to rise 25.1%.
At the same time, BLS projects computing infrastructure, data processing, web hosting, and related services to be among the fastest-growing industries for economic output through 2035.
Source: BLS — Industries With the Fastest-Growing Output
The AI economy has a very large physical supply chain.
For industrial companies, the strategic opportunity may therefore be much larger than simply using AI internally.
There may also be enormous opportunity in participating in the infrastructure required to build the AI economy itself.
The Talent Competition Will Become More Specific
A slower-growing overall workforce does not necessarily mean recruiting becomes easier.
The real issue is capability availability.
There may be plenty of candidates in one category and severe shortages in another.
Organizations therefore need to move beyond traditional headcount planning.
The better question is:
What capabilities will our strategy require?
Not simply:
How many employees will we need?
That distinction becomes increasingly important when demand concentrates around specialized capabilities such as data, cybersecurity, electrical engineering, industrial maintenance, power systems, automation, AI implementation, advanced manufacturing, project management, supply-chain analytics, and technical leadership.
Some of those capabilities take years to develop.
Waiting until the position becomes urgent may be too late.
Build Capabilities Before You Need Them
Companies routinely develop three-year and five-year capital plans.
They should consider applying similar discipline to critical capabilities.
What technology will the business be using? What processes will become automated? What decisions will require better data? What technical expertise will become harder to find? What leadership capabilities will be needed? What knowledge currently sits with employees approaching retirement? What skills should be developed internally rather than purchased through recruiting?
Those discussions need to begin before the shortage appears.
Because the workforce of 2035 is not something that suddenly arrives in 2035.
We are developing—or failing to develop—it today.
AI Strategy Is Becoming Infrastructure Strategy
This may be one of the most important changes executives need to recognize.
AI strategy cannot remain isolated inside the IT organization.
It increasingly touches operations, finance, human resources, supply chain, facilities, energy procurement, real estate, cybersecurity, capital planning, engineering, risk management, and corporate strategy.
A significant AI initiative may eventually create questions about electricity contracts, facility location, workforce capability, infrastructure investment, data governance, supplier availability, and business continuity.
That is why executives should resist treating AI as simply another software implementation.
AI is increasingly an operating-model decision.
A Wednesday Business Environment Check
Leadership teams should consider six questions:
- What capabilities will our business need five years from now that we are not deliberately developing today?
- Which technical roles in our organization would be hardest to replace?
- Where could AI create new demand for our products or services—not simply reduce our costs?
- Could electricity, infrastructure, or data-center capacity become a constraint on our technology strategy?
- Are our location and capital-investment decisions considering future energy and talent availability?
- Are we managing headcount—or are we managing capabilities?
The answers may reveal strategic risks and opportunities that are not currently visible in the annual operating plan.
Our Perspective
At EGBS — ENLOSA: Global Business Solutions, we believe understanding the business environment requires looking beyond today’s numbers.
Leaders need to understand where demand, technology, infrastructure, capital, and talent are moving.
The AI transformation illustrates why.
The technology may be extraordinary.
But technology still depends on physical reality.
People have to design it. People have to manufacture it. People have to install it. People have to operate it. And somebody has to generate and deliver the electricity that powers it.
The companies that understand those dependencies early will have more options.
They can build capabilities before shortages become critical. They can position facilities where infrastructure exists. They can develop suppliers before capacity disappears. They can invest before competitors create bottlenecks. And they can identify business opportunities created by the physical expansion of AI—not just its digital applications.
So perhaps the most important AI question for leaders today is not:
“How are we going to use AI?”
It may be:
“Do we have the people, power, infrastructure, and capabilities required to compete in the economy AI is creating?”
Because the AI revolution will not be built by algorithms alone.
It will be built by people—and powered by infrastructure.
ENLOSA: Global Business Solutions
Strategy. Leadership. Execution.
Primary Sources
- U.S. Bureau of Labor Statistics — Employment Projections 2025–2035
- U.S. Bureau of Labor Statistics — Data Scientists
- U.S. Bureau of Labor Statistics — Fastest-Growing Industries
- U.S. Bureau of Labor Statistics — Fastest-Growing Output
- International Energy Agency — Energy and AI
- International Energy Agency — Data Centre Electricity Use Surged in 2025
#ArtificialIntelligence, #FutureOfWork, #Workforce2035, #Leadership, #BusinessEnvironment, #Energy, #DataCenters, #Manufacturing, #TalentDevelopment, #Infrastructure, #Strategy, #EGBS
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