The AI Energy Economy — Part 4 (Revised): Industrial Automation, Cooling & Controls

The AI Energy Economy — Part 4: Industrial Automation, Cooling & Controls

The Intelligence Layer of the AI Power System

Artificial intelligence has triggered the largest surge in electricity demand in modern history.

Unlike earlier technology waves — microchips, the internet, cloud computing — AI does not scale gently. Modern AI data centers require gigawatt-scale electricity, extremely stable voltage, and industrial-grade cooling. A single AI campus can now consume as much power as a mid-sized city, and it must do so continuously, without interruption.

Because of that scale, the AI power story cannot be understood through any single lens. It spans every layer of the electricity system, from generation and long-distance transmission to what happens inside substations, factories, and data centers themselves.

In Part 2, we examined the companies that build the physical backbone of AI electrification — turbines, transformers, substations, transmission lines, HVDC systems, grid-scale batteries, and the construction required to deploy them.

In Part 3, we focused on the connective and last-meter layers, where power density, heat, and reliability constraints begin to strain systems as electricity moves from the grid into facilities.

This section builds directly on that foundation. Where Part 3 explained where AI power systems begin to strain, Part 4 focuses on what prevents those strains from becoming failures.

This is the intelligence layer of the AI energy economy: the automation, controls, cooling intelligence, and real-time systems that allow AI-scale infrastructure to operate continuously, safely, and reliably.

The AI Energy Economy Framework

This series breaks the AI–electricity supercycle into five interconnected layers:

Part 1 — The Power Producers

Nuclear operators, regulated utilities, and merchant power producers that generate and sell electricity to meet massive new AI-driven loads. These companies capture value through power ownership, rate structures, and scarcity pricing.

Examples: NEE, VST, CEG, D, DUK

Part 2 — The Grid Builders

Companies that construct the large-scale physical infrastructure required to move electricity from generation to demand centers — including turbines, transformers, substations, transmission lines, HVDC systems, batteries, and grid equipment.

Examples: GE Vernova, Eaton, Quanta, ABB, Schneider, Siemens Energy, Fluence

Part 3 — The Connective and Last-Meter Layers

Companies that sit between the grid and the point of use, resolving physical constraints as power density rises. This includes systems that distribute electricity safely inside facilities and remove heat so power can actually be used at AI scale.

Examples: GE Vernova, Prysmian, nVent, Vertiv, Amphenol

Part 4 — Industrial Automation, Cooling & Controls

The intelligence layer of the AI energy system. These companies automate, monitor, stabilize, and control electricity flows, thermal loads, and industrial processes in real time — ensuring the physical infrastructure built in Parts 2 and 3 can operate continuously and reliably at AI scale.

Part 5 — Merchant Power, Nuclear Scarcity & AI Contracts

How electricity is sold, priced, and owned — and why ownership structure, contracts, and nuclear scarcity determine who ultimately captures the financial upside from AI-driven demand.

1. What the Intelligence Layer Does

Companies in this layer do not generate electricity and do not build power plants. Instead, they make the entire system function under AI-scale stress.

They ensure that:

• electricity flows where it is needed,

• voltage remains stable despite massive, concentrated loads,

• substations and transformers operate safely,

• data centers remain within tight thermal limits,

• factories can scale production of turbines, transformers, SMRs, batteries, and grid hardware,

• grid controls respond in real time to sudden demand shifts,

• backup systems activate instantly during peaks or faults.

AI does not just require more power. It requires far more precision, coordination, and automation in how power is managed. This layer supplies that precision.

If the physical grid is the skeleton of the AI energy system, this layer is its nervous system.

2. Technology Foundations of the AI Intelligence Layer

Before turning to individual companies, it helps to understand the core technologies that define this layer.

2.1 High-Voltage Direct Current (HVDC): The Electricity Superhighway

Most of today’s grid uses alternating current (AC), which works well locally but performs poorly over long distances. HVDC solves this problem.

HVDC lines:

• move power hundreds of miles with far lower losses,

• carry multi-gigawatt loads,

• stabilize weak or congested grids,

• allow precise, real-time control of power flows.

AI clusters are concentrated, multi-gigawatt loads that often sit far from generation. HVDC is increasingly the only practical way to move enough power, with enough control, to support them.

Key suppliers include Hitachi Energy, Siemens Energy, GE Vernova, and ABB.

2.2 Multi-Gigawatt Interconnects: Bulk Power Pipelines

A multi-gigawatt interconnect can move 2–5+ GW of electricity between regions — enough to power millions of homes.

Major AI clusters in Northern Virginia, Phoenix, Dallas, and Atlanta cannot rely on local generation alone. They must import vast amounts of power across regions, often through HVDC-based links.

AI demand is therefore inseparable from new interconnect construction.

2.3 SCADA: The Grid’s Brain and Nervous System

SCADA (Supervisory Control and Data Acquisition) systems run the grid in real time.

They monitor voltage, frequency, temperature, and power flows; control substations and switches; issue instant alerts; and automatically adjust operations to prevent cascading failures.

AI data centers introduce extreme load concentration and tighter tolerances. Legacy SCADA systems were not designed for this environment. AI forces utilities to deploy more advanced sensors, power electronics, and predictive control systems.

Key suppliers include Emerson, ABB, Hitachi Energy, Siemens Energy, and Mitsubishi Electric.

2.4 Factory Automation: Scaling the Energy Supply Chain

The AI energy boom is also a manufacturing boom.

The world must now build:

• more transformers,

• more turbines,

• more SMRs,

• more batteries,

• more substations,

• more grid equipment.

Factories producing this hardware must scale rapidly while maintaining quality and reliability. That requires robotics, automated assembly, industrial software, and energy-optimized production systems.

Foundational players include Rockwell Automation, Emerson, and Honeywell.

2.5 Thermal Cooling: Keeping AI From Melting Down

AI workloads generate extreme heat. Modern GPU racks require 10–30× the cooling of legacy servers.

At this point, cooling is no longer incremental. It is existential. Power that cannot be cooled cannot be used — no matter how abundant generation may be.

This reality sets the stage for companies like Vertiv, whose systems determine whether AI infrastructure can operate at all.

3. Emerson Electric (EMR)

Emerson is foundational to the AI power expansion. Its automation and control systems are embedded across:

• power plants and substations,

• nuclear instrumentation and safety systems,

• precision sensors and flow controls,

• data-center monitoring,

• industrial software for grid and plant operations.

As AI drives construction of power plants, SMRs, substations, battery farms, and data centers, each layer requires Emerson’s control and automation suites.

Valuation: ~23× earnings, ~20× forward

Rating: Buy

One of the most reliable, lower-volatility ways to participate in AI electrification.

4. Honeywell (HON)

Honeywell operates at the intersection of energy, buildings, and industrial systems, with strengths in:

• building automation,

• data-center cooling and heat management,

• microgrid controls,

• industrial cybersecurity,

• sensors and safety systems.

It is deeply embedded and high quality, but much of that strength is already reflected in valuation.

Rating: Hold (watch for pullbacks)

5. Rockwell Automation (ROK)

Rockwell is the backbone of industrial automation in the United States.

As demand surges for transformers, turbines, SMRs, batteries, and grid equipment, Rockwell’s systems automate the factories producing the physical infrastructure of electrification.

Rating: Hold (watch for pullbacks)

Excellent long-term positioning, but already priced for strong growth.

6. Vertiv (VRT)

In Part 3, Vertiv sits at the last-meter constraint, where cooling determines whether power can be used at all. In Part 4, its role becomes even clearer: Vertiv is also part of the intelligence layer that allows AI infrastructure to operate continuously.

Vertiv provides:

• liquid and immersion cooling,

• thermal management systems,

• power conditioning,

• UPS systems.

Cooling demand is growing faster than server demand. Once power density crosses certain thresholds, cooling becomes the binding constraint.

Valuation: ~40× trailing, ~30× forward

Rating: Speculative Buy

Structurally critical, but richly valued.

7. Baker Hughes (BKR)

Baker Hughes is emerging as a key provider of firm, flexible power through:

• aero-derivative turbines,

• hydrogen-ready turbines,

• grid-stabilization equipment.

AI data centers require instant-response backup power and frequency stabilization, creating new demand for these systems.

Rating: Buy

8. Rolls-Royce Holdings (RR)

Rolls-Royce is developing a government-backed SMR design targeting ~470 MW modular units.

Potential applications include AI campuses, industrial hubs, and advanced microgrids.

Rating: Speculative Buy

9. Hitachi Energy

A global leader in HVDC, transformers, grid automation, and power electronics.

Rating: Buy

One of the strongest pure plays on AI-driven transmission and control.

10. Mitsubishi Electric

Key strengths include SCADA systems, substation automation, grid stabilization, and power electronics.

Rating: Hold

High quality, but lower torque due to diversification.

11. SCADA Leaders: Real-Time Grid Intelligence

Core SCADA beneficiaries include Emerson, ABB, Hitachi Energy, and Siemens Energy. These companies provide the millisecond-level intelligence modern AI-driven grids require.

Conclusion: The Intelligence Layer That Makes Everything Work

This section highlights the companies that automate, cool, control, and stabilize the electricity system at AI scale.

They are generally more diversified and less cyclical than power producers, but they are indispensable. AI may accelerate demand for nuclear, gas, renewables, SMRs, batteries, or microgrids — but none of it works without the intelligence layer.

That makes Part 4 the most stable and broadly leveraged way to invest in the AI–electricity megacycle: the systems that function no matter how the energy mix evolves.

The AI Energy Economy Series:

• • The AI Energy Economy — Part 1: The Nuclear & Utility Winners of the AI Power Boom
  • The AI Energy Economy — Part 2: The Pick-and-Shovel Suppliers Powering AI Electrification
  • The AI Energy Economy — Part 3 (Revised): The Connective and Last-Meter Layers of AI Electrification
  • The AI Energy Economy — Part 4 (Revised): Industrial Automation, Cooling & Controls
  • The AI Energy Economy — Part 5 (Revised): Merchant Power, Nuclear Scarcity, and AI Contracts