A sheet of silicon steel, thinner than two A4 papers, is quietly holding back global AI giants.
While engineers in Silicon Valley are still struggling to push the parameter limits of next-generation large models, an unexpected bottleneck has already emerged—not chips, not algorithms, but electricity.
More precisely, it is the “heart” that delivers electricity safely and stably to every data center: the transformer.

The core material used to manufacture this heart is a special type of steel called silicon steel.
The ultimate battlefield of the global AI competition is shifting from the virtual world of code to these massive, grey industrial machines weighing dozens of tons.
Power Black Hole: AI Is “Consuming” the World’s Electricity
You may not know this: when you ask ChatGPT to write a poem or generate an image, the electricity consumed behind the scenes is about 10 times that of a traditional Google search.
And this is only the tip of the iceberg.
Elon Musk built a super computing center called “Colossus” in Memphis, Tennessee, to train his xAI large model.

This facility alone consumes 150 megawatts of electricity per day, equivalent to the daily power usage of 100,000 American households.
A single data center is consuming the energy supply of a mid-sized city.
The International Energy Agency has warned that by 2030, global data center electricity demand will more than double, reaching approximately 945 TWh—equivalent to Japan’s total annual electricity consumption today.

AI data centers are no longer just server rooms; they are evolving into terrifying “power black holes.”
The power demand of a single AI server rack has surged from the traditional 5–10 kW to 100–150 kW, and may even reach 500 kW in the future.

This means that within the same physical space, power demand is dozens of times higher than before.
More critically, AI’s “appetite” is not only large but also extremely “picky.”
Its power usage fluctuates violently within milliseconds, generating intense and frequent power shocks and high-frequency harmonics.
This type of load presents unprecedented challenges to traditional power grids and equipment, easily leading to overheating, increased losses, and even failures.
Therefore, supplying power to AI is no longer as simple as connecting a cable.
It requires a highly robust, stable, and intelligent power transmission and regulation system.
And the starting point and hub of all this is the transformer.
Transformer Shortage: Global Computing Power Is Waiting for Its “Power Heart”
Demand has exploded, but supply is constrained.
Energy consulting firm Wood Mackenzie estimates that the global shortage of power transformers has reached 30%, while distribution transformers face a 10% gap.
In the United States, delivery cycles for transformers have extended from 50 weeks to 127 weeks (more than two years), with prices rising sharply.
Why has a global “transformer shortage” occurred?
Three major forces have intertwined into a deadlock:
First, the “power hunger” of AI computing centers.
Every newly built data center requires hundreds or thousands of large, specialized, high-reliability transformers.
Without them, billion-dollar GPU clusters are nothing more than scrap metal.

Second, the “sweet burden” of the global green energy transition.
Renewable energy sources such as wind and solar are not located near cities like traditional thermal power plants.
Electricity generated in remote areas must be integrated into the grid, requiring a massive number of substations, each centered around transformers.
For the same power generation capacity, solar plants require 1.8 times more transformers than traditional thermal plants.
Third, the concentrated outbreak of aging power grids in Europe and the United States.

The average age of the U.S. power grid exceeds 30 years, with about 70% of large transformers already operating beyond their intended service life.
Some transformers in Europe have been in operation for 40 to 50 years.
These aging systems are extremely vulnerable under extreme weather conditions and AI-driven loads, making replacement urgent.
However, transformer manufacturing is a slow and capital-intensive industry.
Building new production lines and training skilled workers takes 3 to 5 years.
Western manufacturers have experienced industrial hollowing-out and cannot respond effectively.
As a result, a somewhat ironic situation has emerged:
Global technology giants and power companies are waiting for industrial equipment from China—transformers.
China’s Speed: 60% Global Capacity and Factories Running at Full Load
When Western customers are told to wait 2–4 years, Chinese transformer factories provide a different answer:
“The machines are almost smoking from overwork.”

This is the real situation in transformer factories across Zhejiang, Jiangsu, and Guangdong after the 2026 Spring Festival.
Production lines are running at full capacity, workers are operating in three shifts, and orders are already scheduled through 2027 or even later.
China has become the undisputed “heart” of global transformer manufacturing.
According to customs data, China’s transformer exports reached a record RMB 64.6 billion in 2025, a year-on-year increase of nearly 36%.
The average price per transformer rose to RMB 205,000, up by about one-third.

Data from the China Electricity Council shows that China has built the world’s most complete transformer manufacturing system, accounting for approximately 60% of global capacity.
From Southeast Asia to Europe, the Middle East, and Africa, Chinese transformers are powering the world.
China’s advantages are overwhelming:
Delivery speed: Western lead times of 18–48 months can be reduced to 10–12 months in China, or even just weeks for standard models.
Cost advantage: Products with similar performance are 20–30% cheaper.
Technological strength: In ultra-high voltage fields (±1100 kV), China leads globally, with transformer losses about 15% lower than Western products.
Chinese manufacturers are now core suppliers for global technology companies.
Silicon Steel: The Crown Jewel of Steel and China’s Strategic Advantage
At the core of transformer voltage conversion lies a magnetic core made of thousands of laminated silicon steel sheets.
The performance of silicon steel directly determines transformer efficiency, losses, and size.
You can think of silicon steel as a “highway” for electricity.
Its mission is to allow magnetic flux to flow smoothly, quickly, and with minimal loss.
Ordinary steel is full of “potholes” (high losses), while high-quality grain-oriented silicon steel is like a perfectly smooth magnetic track.
This process is known as the “crown jewel of the steel industry.”
Its core secret lies in aligning all crystal grains in one direction through extremely complex processes.
China has achieved leadership in this advanced technology.

Chinese companies can now mass-produce ultra-thin, high magnetic induction grain-oriented silicon steel.
These products significantly improve transformer efficiency and lead globally in reducing core losses and noise.
China’s 0.18 mm and 0.20 mm ultra-thin CRGO steel—thinner than two sheets of A4 paper—has become the core material for high-end energy-efficient transformers.
China’s annual production of grain-oriented silicon steel is five times that of Japan and eight times that of the United States, accounting for a significant share of the global market.
This means that the core material for high-end transformers is firmly in China’s hands.
The Future Battle: Solid-State Transformers and China’s Leap Forward
Traditional transformers are based on electromagnetic induction principles developed over 100 years ago and are becoming bulky and rigid in the AI era.
The next-generation solution is clear: solid-state transformers.

They are no longer just “iron blocks” but integrated systems combining power electronics, third-generation semiconductors, and intelligent control.
They can be understood as “intelligent power routers”:
Size and weight: only 1/4 to 1/6 of traditional transformers
Efficiency: up to 98.8%, far exceeding traditional 92–94%
Functions: voltage regulation, harmonic suppression, AC/DC conversion
Adaptability: perfectly matches the 800V high-voltage DC architecture required by NVIDIA’s computing clusters

NVIDIA has clearly stated that next-generation AI data centers must be equipped with solid-state transformers.

Tesla has also announced that all megawatt-level supercharging stations will adopt this technology.

Jensen Huang and Elon Musk are both betting on solid-state transformers.
In this future competition, China is leading for the first time.
China has already achieved large-scale commercial deployment of 2.4 MW solid-state transformers and built a complete industrial ecosystem.
In 2025, China’s solid-state transformer market grew by 217%.
This time, China is not catching up—it is defining the standard.
The Ultimate Answer: Industrial Strength Supports Digital Civilization
This AI-driven competition ultimately comes down to electricity, transformers, and silicon steel.
It reveals a simple truth:
All advanced digital civilization is built on heavy industrial foundations.
Elon Musk once predicted:
The bottleneck of AI will shift from chips to transformers, and ultimately to electricity.
Today, this prediction is becoming reality.
China and the United States face different bottlenecks:
China is working to solve chip shortages, while the U.S. struggles with power constraints and reliance on Chinese transformers.
Behind this is decades of industrial investment:
From large-scale power infrastructure to ultra-high voltage networks and smart grids.
When you interact with AI next time, remember:
Behind it are not only algorithms, but also factories, machines, workers, and a thin yet critical sheet of steel.
The end of AI is electricity, the core of electricity is transformers, and the heart of transformers is silicon steel—and China is holding this global lifeline.
This silent industrial revolution is far more important than we imagine.
At MOOPEC, we support flexible small-batch supply of transformer-grade silicon steel to help customers respond quickly to real project demands.