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AI is becoming an electricity-infrastructure race, not just a software race. Andreas Schierenbeck, Hitachi Energy’s CEO, says rapidly expanding data centers, long equipment lead times and a wider threat environment are forcing utilities and governments to build and protect grids before demand arrives.
Why AI is driving a new electricity build-out
AI data centers have unusually demanding electrical behavior. Their GPUs can start and stop in parallel, creating very short power bursts. Hitachi Energy’s Investor Day leadership team says these dynamics distinguish AI facilities from conventional data centers and can stress both the facility’s internal electrical system and the external grid.
“AI is a race for speed, which means a race for energy, and that’s what the US is having,” Schierenbeck said in 2026. The competitive issue is therefore not only access to computing hardware or models. It is whether generation, transmission and distribution can be planned, permitted and connected quickly enough.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Schierenbeck argues that infrastructure must be built ahead of demand. He describes the United States as moving quickly on AI data-center development, while Europe faces greater uncertainty over regulation and grid connections. A project can have financing and computing equipment ready yet remain stalled while it waits for a grid connection, substation or transformer.
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The delivery-time mismatch
AI load can be added in months, but major electrical assets are ordered on much longer cycles. Schierenbeck describes a 10–15-year market outlook for grid equipment, while acknowledging that some customers plan around 15–20 years. S&P Global Energy/Platts reported in 2026 that large-transformer waiting times had reached up to 40 months.
| Pressure | Reported figure or outlook | Practical consequence |
|---|---|---|
| AI load growth | Short, parallel GPU power bursts | Power-quality, protection and capacity planning must account for rapid changes rather than only average demand. |
| Large-transformer supply | Waiting times of up to 40 months, reported by S&P Global Energy/Platts in 2026 | New data centers and network upgrades can be delayed even after a project is approved. |
| Equipment demand | Hitachi Energy describes a 10–15-year outlook; some customers expect 15–20 years | Utilities and developers need long-range procurement and permitting plans. |
| Manufacturing response | Hitachi Energy plans $6 billion of manufacturing-capacity expansion by 2027 and says it has committed more than $9 billion globally to shortage relief, including $1.5 billion for transformer production | Additional factories can ease supply, but they do not eliminate near-term lead times or permitting constraints. |
Why transformers have become a binding constraint
Transformers convert voltage between generators, transmission lines, distribution networks and customer facilities. Large units are specialized, heavy and difficult to replace quickly. Manufacturing capacity, testing, transport, specialist labor and materials all limit how fast supply can expand.
The shortage is especially consequential for AI campuses because a single site may require a new substation, multiple transformers, protection equipment and network reinforcements. Ordering a transformer late can move the entire connection schedule, regardless of how quickly servers can be installed.
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Hitachi Energy says it estimates a $350 billion global market for its products by 2030. That is a company estimate, not an independent forecast, and it signals how the vendor views the scale of the grid-equipment cycle. The company profile published in 2026 says it operates in more than 140 countries, has more than 50,000 employees in 60 countries and generates around $16 billion in revenue.
Energy security now includes more than fuel supply
Schierenbeck frames the shift as an “Electricity Era” in which economies move from molecules to electrons. “Power grids now hold the same, if not greater, strategic importance as oil and gas pipelines once did; what pipelines represented in the past, resilient electric networks represent today,” he said.
That resilience requirement covers four connected exposures:
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- Physical attack: substations, control buildings, lines and transformers can be damaged deliberately or accidentally.
- Cyber threat: greater digital connectivity increases the consequences of compromised operational technology and information systems.
- Supply-chain vulnerability: dependence on specialized factories, components, materials and transport can prolong recovery when equipment is unavailable.
- Targeted disruption: an adversary can seek a high-impact interruption rather than a broad, conventional outage.
“As electricity becomes the backbone of modern economies, these grids face new risks: physical attacks, cyber threats, supply-chain vulnerabilities, and targeted disruption,” Schierenbeck said. Security planning therefore has to include spare-equipment strategy, operational visibility, recovery procedures and coordination across physical and digital teams—not only perimeter protection.
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Anthony Allard, Hitachi Energy’s executive vice president and head of North America, describes a progression in utility AI use:
- Assistance for manual work: AI helps staff find information, prepare records and complete repetitive tasks.
- Forecasting and planning: models support demand, maintenance and network-development decisions.
- Predictive capabilities: systems identify abnormal behavior or signs of deterioration before a failure.
- Real-time decision support: operators receive data-driven recommendations while managing changing system conditions.
Generative and agentic AI can optimize discrete enterprise workflows, Allard says, but utilities need an AI-ready strategy across the operational-technology and information-technology stack. That means reliable asset data, governed access, integration with control and maintenance systems, and clear human responsibility for consequential decisions. AI does not remove the need for engineers, operators or protection specialists; it changes where their attention is applied.
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What HMAX Energy is designed to do
Hitachi Energy launched HMAX Energy on March 23, 2026. The suite organizes AI-enabled asset management into Plan, Predict and Prevent, covering transformers, switchgear, substations, HVDC systems and power-quality equipment.
| HMAX function | Purpose | Typical action |
|---|---|---|
| Plan | Asset-lifecycle and operational recommendations | Prioritize investment, maintenance and operating choices across an equipment portfolio. |
| Predict | Monitoring for early wear and unusual behavior | Detect developing problems before they become forced outages. |
| Prevent | Health checks, simulation and proactive intervention | Test scenarios, schedule action and reduce the chance or impact of failure. |
Hitachi Energy cites the following results from its reference cases. They are vendor-reported figures, not guarantees for every installation:
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- Up to 60% lower revenue loss from equipment breakdowns.
- 50% fewer transformer failures through early detection.
- Up to 75% lower repair costs.
- Up to 90% faster HVDC incident response when using digital twins such as IdentiQ.
Wolf Mueller, Hitachi Energy’s managing director of business-unit service, says the aging grid faces soaring demand, increasing complexity, supply-chain constraints and workforce shortages simultaneously. He describes HMAX Energy as a way to safeguard critical infrastructure, anticipate failures earlier and keep electricity reliable. The results above should be read in that reference-case context: performance depends on asset condition, sensor coverage, data quality, operating practice and the specific failure mode.
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Storage and equipment choices in the transition
More generation and wires are necessary, but they are not the only tools. Schierenbeck supports locating green-hydrogen production and electrolyzers near renewable generation and pipelines. Batteries are advancing quickly, yet he says longer-duration storage remains necessary for periods when renewable output is low for an extended time.
Digital twins can help operators understand complex assets and rehearse interventions without experimenting on live equipment. Solid-state transformer concepts may eventually provide different control and power-quality capabilities, but the immediate bottleneck remains the availability and delivery of proven high-voltage equipment. The technology choice has to match the grid layer, required duration, protection scheme, physical site and time available for deployment.
What this means for data-center and grid planners
- Model peaks, not only averages: include synchronized GPU ramping and power-quality effects in connection studies.
- Order long-lead assets early: transformer and substation procurement can take years, so it should begin alongside site and generation planning.
- Plan across the full grid layer: evaluate the data-center electrical system, transformers, switchgear, substations, HVDC links and wider transmission network together.
- Design for disruption: combine physical protection, cyber controls, supply-chain alternatives, spares and tested restoration procedures.
- Make AI operationally usable: establish trustworthy OT-IT data, governance and human escalation paths before deploying generative or agentic tools.
- Match storage to the problem: batteries can address fast or shorter-duration needs, while hydrogen and other options may be relevant when longer-duration storage is required.
The central message from Schierenbeck is that AI deployment and grid resilience are now the same planning conversation. Computing demand can arrive faster than heavy electrical infrastructure, and an electricity system that is not secure, maintainable and expandable can become the limiting factor for AI growth.
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