What Is the Intelligence Economy?

How artificial intelligence is creating a new market for power, compute, infrastructure and economic growth

Every major economic era is shaped by a resource that becomes easier to produce, distribute and use.

The Industrial Economy scaled physical production.

The Digital Economy scaled access to information.

The Intelligence Economy is scaling the ability to analyze, predict, generate, decide and automate.

Artificial intelligence is the technology making that possible. But the market forming around it is much larger than models, applications and software.

Every unit of machine intelligence begins with processors. Those processors operate inside data centers. The data centers require electricity, fiber, cooling systems, land, water, workers, permits and capital.

As demand for AI grows, demand for those underlying systems grows with it.

This is the Intelligence Economy: an emerging economic system built around the production, infrastructure, distribution and application of machine intelligence.

It may begin with a chip.

It does not end there.

The Short Answer

The Intelligence Economy is the network of companies, infrastructure, workers, investors and communities involved in creating, powering, distributing and applying artificial intelligence.

It Includes AI Models and Software, but It Also Includes:

• Semiconductors and computing equipment

• Data centers and cloud platforms

• Power generation and electrical infrastructure

• Fiber networks and interconnection systems

• Cooling and water infrastructure

• Land and construction

• Skilled labor and technical services

• Public policy and permitting

• Infrastructure capital

• Sustainability and resource management

At Infrastructure of Intelligence™, we organize this physical foundation around Eight Pillars: Power, Connectivity, Land, Water, Workforce, Policy, Capital and Sustainability.

Together, these systems determine how quickly AI can scale, where it can be built and who participates in the economic value it creates.

What Does “Intelligence” Mean in This Context?

Intelligence does not refer to information alone.

It refers to the ability of computing systems to perform tasks that previously required human judgment or specialized analysis.

These systems can recognize patterns, generate content, interpret documents, write software, predict equipment failures, optimize supply chains, assist medical research and automate business processes.

As these capabilities become embedded in products, companies and institutions, intelligence begins to function like an economic input.

• Businesses will purchase it.

• Governments will deploy it.

• Workers will use it.

• Industries will build around it.

Just as electricity became a general-purpose resource for the Industrial Economy and connectivity became essential to the Digital Economy, computing intelligence is becoming a resource that can be delivered across many parts of the economy.

But intelligence is not created from nothing.

It must be produced.

The Intelligence Supply Chain

A simple AI interaction may appear to happen instantly, but it relies on a much larger production system.

1. Data and Models

AI systems begin with data, algorithms and model development.

Researchers and technology companies design architectures, train models and improve their ability to understand or generate information.

This is the layer most people see.

2. Compute

Models require processors to train and operate.

Advanced GPUs, CPUs, networking components, memory and storage systems work together to perform enormous volumes of calculations.

This is where intelligence becomes a computing workload.

3. Data Centers

Processors must be installed inside secure, reliable facilities.

Data centers provide electrical distribution, networking, cooling, fire protection, backup systems and the physical environment required to keep computing equipment operating continuously.

This is where intelligence becomes an industrial operation.

4. Infrastructure

Data centers depend on systems extending far beyond their walls.

Power plants generate electricity. Transmission lines move it. Substations transform it. Fiber routes carry information. Water and cooling systems manage heat. Construction companies build the facilities. Utilities, regulators and communities coordinate their arrival.

This is where intelligence becomes an infrastructure market.

5. Applications

Companies turn computing capacity into products and services.

AI is deployed across healthcare, manufacturing, logistics, finance, defense, education, government, media, agriculture and consumer technology.

This is where intelligence enters the broader economy.

6. Economic Outcomes

Organizations use AI to improve productivity, create new products, reduce costs, accelerate research and automate work.

Those changes can influence business formation, employment, investment and regional growth.

This is where intelligence becomes an economic force.

How Is the Intelligence Economy Different From the Digital Economy?

The Digital Economy was built around creating, storing and moving information.

The Intelligence Economy builds on that foundation but adds another capability: machines can now interpret information and produce useful outputs from it.

A search engine helps someone find a document.

An AI system may read the document, summarize it, compare it with other information and recommend an action.

A traditional software platform follows rules written in advance.

An AI system can respond to new information, identify patterns and generate an answer that was not individually programmed.

The Intelligence Economy does not replace the Digital Economy.

It expands it.

The internet made information widely accessible. Cloud computing made software and storage available on demand. Artificial intelligence is making analysis, generation and decision support increasingly available as a service.

That transition changes what companies can build.

It also changes the infrastructure required to build it.

The Market Signal

The scale of investment provides the clearest signal that AI is becoming an infrastructure economy.

McKinsey estimates that global data centers could require approximately $6.7 trillion in capital investment through 2030. Around $5.2 trillion of that total is associated with facilities and computing equipment needed to support AI workloads. McKinsey & Company

That capital will not remain inside technology companies.

It Will Travel Through a Much Larger Supply Chain:

• Chip manufacturers

• Server companies

• Utilities

• Power producers

• Electrical-equipment manufacturers

• Data-center developers

• Engineering and construction firms

• Cooling providers

• Fiber manufacturers

• Network operators

• Real-estate owners

• Infrastructure funds

• Municipalities

• Workforce-development organizations

The electricity requirement is growing with the investment.

A 2025 update from Lawrence Berkeley National Laboratory estimates that data centers could account for approximately 11.8% of total US electricity consumption by 2030, with scenarios ranging from 9.5% to 15.3%. Lawrence Berkeley National Laboratory

This is no longer ordinary enterprise technology spending.

It is an industrial buildout.

The Three Layers of the Intelligence Economy

The market can be understood through three connected layers.

The Intelligence Layer

This includes the models, applications and platforms people use directly.

Companies in this layer develop foundation models, AI assistants, business software, autonomous systems and industry-specific applications.

This is where intelligence is packaged and sold.

The Compute Layer

This includes semiconductors, servers, cloud platforms, storage systems and high-speed networking.

Companies in this layer create and operate the computing environments required to train and run AI.

This is where intelligence is produced.

The Infrastructure Layer

This includes power, fiber, land, cooling, water, construction, capital, policy and workforce.

These systems determine how much computing capacity can be built, where it can operate and how quickly it can reach the market.

This is where intelligence becomes possible.

The three layers depend on one another.

Software demand creates compute demand.

Compute demand creates infrastructure demand.

Infrastructure availability determines how much intelligence can be produced.

The Eight Pillars of the Intelligence Economy

The physical foundation of the Intelligence Economy can be organized into eight connected pillars.

1. Power

AI requires large amounts of reliable electricity.

The Power pillar includes generation, transmission, substations, transformers, switchgear, backup systems, batteries, microgrids and behind-the-meter energy.

A data-center project may have land and financing, but without a credible path to electricity, it does not have an operating business.

Power is becoming the first test of AI infrastructure readiness.

2. Connectivity

AI also requires information to move quickly and securely.

The Connectivity pillar includes long-haul fiber, metro networks, data-center interconnections, cloud on-ramps, subsea cables, internet exchanges and the high-speed networks connecting processors.

The fastest chip is less useful when the network around it cannot keep up.

Connectivity determines how far and how quickly intelligence can travel.

3. Land

AI infrastructure must exist somewhere.

The Land pillar includes data-center sites, utility corridors, industrial campuses, zoning, environmental conditions, access roads and proximity to power and fiber.

The most valuable property is not simply inexpensive land.

It is land that can be permitted, connected, powered and developed on schedule.

4. Water

Computing equipment produces heat, and that heat must be managed.

The Water pillar includes cooling systems, reclaimed water, municipal supply, treatment infrastructure, watershed conditions and the tradeoffs between water and energy use.

Not every data center consumes the same amount of water. Cooling architecture, climate, water source and operating practices can significantly change the outcome.

Water readiness is becoming part of site readiness.

5. Workforce

Infrastructure does not build itself.

The Workforce pillar includes electricians, engineers, fiber technicians, construction workers, equipment operators, data-center technicians, water specialists and cybersecurity professionals.

Regions may attract projects with power and land, but they will need enough skilled people to build and operate them.

Workforce capacity will influence development timelines.

6. Policy

AI infrastructure moves through a public decision-making system.

The Policy pillar includes permitting, zoning, utility regulation, energy policy, tax incentives, water rules, environmental review and community engagement.

Policy can accelerate a project, delay it or stop it entirely.

In the Intelligence Economy, speed to permit can become as important as speed to power.

7. Capital

AI infrastructure is expensive.

The Capital pillar includes hyperscaler spending, project finance, private equity, private credit, infrastructure funds, utility investment, municipal financing and public-private partnerships.

Capital determines which projects move from announcement to construction.

It also determines who owns the infrastructure and where the financial returns travel.

8. Sustainability

AI growth must operate within environmental and community constraints.

The Sustainability pillar includes energy efficiency, emissions, water stewardship, heat reuse, grid impact, equipment sourcing and long-term resource resilience.

Sustainability is not separate from infrastructure performance.

It affects operating costs, permitting, community support and the long-term viability of a project.

Where Does the Money Go?

The first wave of attention goes to model developers and semiconductor companies.

That makes sense. They are closest to the visible technology.

But the capital does not stop there.

A New AI Campus Can Require Investments In:

• Power plants

• Transmission upgrades

• Utility substations

• Transformers and switchgear

• Backup generation

• Battery storage

• Fiber routes

• Cooling plants

• Water-treatment systems

• Roads and construction

• Warehouses and manufacturing

• Workforce training

• Housing and community services

This creates several layers of potential economic beneficiaries.

First-Order Beneficiaries

These are the companies selling AI directly.

They include model developers, cloud providers, semiconductor designers and enterprise software platforms.

Second-Order Beneficiaries

These are the companies the AI builders need.

They include utilities, power producers, data-center operators, electrical-equipment manufacturers, cooling companies, engineering firms, construction contractors and fiber providers.

Third-Order Beneficiaries

These are the regions and businesses affected by the physical buildout.

They may include landowners, local contractors, industrial suppliers, colleges, workforce programs, municipal utilities and service businesses located near major infrastructure projects.

This is one of the defining features of the Intelligence Economy.

The value chain expands far beyond the technology sector.

The Builders Behind the Builders

Some of the most important companies in the Intelligence Economy may never develop an AI model.

They make the systems the model companies need.

• Eaton produces electrical-distribution and power-management equipment.

• Vertiv supplies power and cooling systems for data centers.

• GE Vernova produces generation and grid equipment.

• Quanta Services builds transmission, substation and utility infrastructure.

• Corning manufactures optical fiber and connectivity products.

• Schneider Electric provides electrical, automation and cooling systems.

• Equinix and Digital Realty operate data-center and interconnection platforms.

• Constellation, Vistra, NextEra Energy and other energy companies participate in the power supply supporting large computing loads.

These businesses sit below the software layer, but they are becoming increasingly important to AI growth.

They are the builders behind the builders.

Company Watch: Eaton

Everyone is watching processing power.

I’m also watching how that power reaches the processor.

Eaton operates across several parts of the electrical chain supporting AI data centers, including switchgear, uninterruptible power systems, busway, circuit protection, power-quality equipment and modular electrical infrastructure.

In April 2026, Eaton announced a new 370,000-square-foot manufacturing facility in Nebraska intended to increase switchgear production amid growing data-center demand. The company expects the project to create more than 200 engineering, manufacturing and production jobs. Eaton

That announcement captures how the Intelligence Economy spreads.

AI demand creates data-center demand.

Data-center demand creates electrical-equipment demand.

Equipment demand creates factories, supply-chain investment and workforce demand in another community.

The economic impact travels.

Geography Is Returning to Technology

The internet created the impression that digital businesses could operate from anywhere.

AI is bringing geography back into the conversation.

Data centers need to be built near usable infrastructure. That gives certain regions an advantage.

A Strong Location May Offer:

• Available and expandable power

• Multiple fiber routes

• Developable land

• Reliable water or low-water cooling options

• Skilled labor

• Predictable permitting

• Supportive utilities

• Access to capital

• Community acceptance

• A credible sustainability strategy

Regions where these systems converge can develop into Intelligence Corridors connected markets where data centers, power infrastructure, fiber networks, manufacturers, universities and technical workforces begin to cluster.

These clusters can create economic gravity.

Once infrastructure is established, the next project becomes easier to justify. Suppliers move closer. Training programs expand. Utilities gain experience. Investors become more comfortable with the market.

AI may be delivered globally.

Its infrastructure is built locally.

The AI-Ready Community

The Intelligence Economy changes how communities compete for investment.

For decades, economic-development strategies emphasized affordable land, highway access, taxes and available workers.

Those factors still matter.

But an AI-Ready Community Must Answer a More Demanding Set of Questions:

• How much electricity is available?

• When can it be delivered?

• Can the grid support additional growth?

• Where are the fiber routes?

• Is the land near usable infrastructure?

• What cooling systems can the region support?

• Is the workforce deep enough?

• How long will permitting take?

• Is the community prepared for the project?

• Who will pay for the required upgrades?

A community does not become AI-ready by announcing that it wants a data center.

It becomes AI-ready by coordinating the infrastructure a serious project will require.

The New Competitive Advantage

During the early internet era, access to technology created an advantage.

During the cloud era, access to scalable computing created an advantage.

In the Intelligence Economy, access to infrastructure may determine who can expand.

A company with capital but no power can still be delayed.

A region with land but no fiber may remain disconnected.

A project with electricity but no permitting path may sit idle.

A data center with processors but insufficient cooling cannot operate at full capacity.

The competitive advantage will come from coordination.

The regions and companies that can align power, connectivity, land, water, workforce, policy, capital and sustainability will be able to bring computing capacity online faster and with greater certainty.

Infrastructure readiness becomes market readiness.

What Market Intelligence Should Track

Traditional AI coverage focuses on models, product releases, benchmark scores and semiconductor sales.

Those signals matter.

They do not tell the whole story.

To Understand Where the Intelligence Economy Is Moving, Leaders Should Also Track:

• Hyperscaler capital expenditures

• Data-center project announcements

• Utility load forecasts

• Interconnection requests

• Generation contracts

• Transmission and substation projects

• Transformer and switchgear manufacturing

• Fiber construction and dark-fiber leases

• Data-center land acquisitions

• Water and cooling strategies

• Workforce shortages

• Permitting changes

• Infrastructure financing

• Community opposition or support

• Supply-chain expansion

These signals can reveal where future capacity is likely to be built before a finished data center appears.

A transformer order can be market intelligence.

A utility filing can be market intelligence.

A new fiber route can be market intelligence.

The physical economy often moves before the public announcement.

What Leaders Should Start Asking

Business Leaders Should Ask:

• How much computing capacity will our AI strategy require?

• Where will that capacity come from?

• What infrastructure risks could affect cost or availability?

• Which suppliers sit beneath our AI providers?

Investors Should Ask:

• Where is AI capital moving after it leaves the hyperscaler?

• Which companies control bottleneck infrastructure?

• Which businesses have manufacturing capacity and credible delivery timelines?

• Where are second-order opportunities forming?

Communities Should Ask:

• Which parts of the eight-pillar system are already strong?

• Where are the infrastructure gaps?

• What type of AI investment fits our resources?

• What must be built before the market arrives?

Utilities Should Ask:

• How much of the project pipeline is credible?

• When will new loads connect?

• Who funds the required generation and grid upgrades?

• How can large customers support reliability rather than weaken it?

These are not separate conversations.

They are different views of the same emerging economy.

Frequently Asked Questions

What Is the Intelligence Economy?

The Intelligence Economy is the network of technologies, companies, infrastructure, workers, investors and regions involved in producing, distributing and applying artificial intelligence.

How is the Intelligence Economy Different From the Digital Economy?

The Digital Economy primarily scaled the creation and movement of information. The Intelligence Economy builds on that foundation by using computing systems to interpret information, generate outputs, make predictions and support decisions.

What Infrastructure Supports the Intelligence Economy?

Its physical foundation includes power, connectivity, land, water, workforce, policy, capital and sustainability—the eight pillars of the Infrastructure of Intelligence™.

Which Industries Participate In the Intelligence Economy?

The market includes technology, semiconductors, cloud computing, data centers, utilities, energy, telecommunications, construction, engineering, manufacturing, real estate, finance, education and government.

Who Benefits From the Growth of AI Infrastructure?

Potential beneficiaries include model developers and chip companies, but also utilities, power producers, electrical-equipment manufacturers, data-center operators, cooling providers, fiber companies, engineering firms, construction businesses and infrastructure-ready regions.

Where Will the Intelligence Economy Be Built?

It will grow in regions able to coordinate reliable power, scalable connectivity, developable land, responsible cooling, skilled workers, predictable policy, sufficient capital and long-term community support.

The Golden Nugget

The Digital Economy was built around moving information. The Intelligence Economy is being built around producing, powering and distributing intelligence.

That difference changes where investment goes.

The IOI Take

The Intelligence Economy is not one industry.

It is a connected market forming across technology, energy, infrastructure, finance and regional development.

AI companies may create the demand, but they cannot build the next era alone.

• They will need utilities to deliver power.

• They will need manufacturers to produce equipment.

• They will need fiber companies to move information.

• They will need developers and construction firms to build facilities.

• They will need workers to operate them.

• They will need governments and communities to provide a credible path forward.

This is why artificial intelligence is becoming much more than a software story.

It is becoming an infrastructure story.

And increasingly, an economic-development story.

One Last Thought

The most valuable resource of the next economy may be intelligence.

But intelligence still needs somewhere to live.

It needs power.

It needs networks.

It needs people.

It needs infrastructure.

Artificial intelligence may be digital.

The Intelligence Economy is being built in the physical world.

Workforce

Policy

Capital

Sustainability