When we talk about the AI boom, we usually talk about:
ChatGPT
Claude
Gemini
AI agents
GPUs
LLMs
But underneath all of those technologies is something much more fundamental:
Semiconductors.
And a recent report about China's largest chip foundry, SMIC, shows just how strongly AI demand is reaching into the semiconductor supply chain.
SMIC says AI-related demand remains strong enough that it has raised prices for some of its most sought-after manufacturing capacity.
That's a big signal.
Because AI growth isn't happening only at the software layer.
It's creating enormous demand for physical computing infrastructure.
🏭 First: What Does SMIC Actually Do?
SMIC stands for Semiconductor Manufacturing International Corporation.
Think of a chip company as having several layers.
A simplified version looks like this:
Chip Design
↓
Chip Architecture
↓
Manufacturing
↓
Packaging
↓
Testing
↓
Computer / Server
Companies can design chips without actually manufacturing them.
A foundry manufactures chips designed by other companies.
SMIC is primarily a semiconductor foundry.
Its customers provide chip designs, and SMIC manufactures those designs on semiconductor wafers.
🧠 Why Does AI Need So Many Chips?
Modern AI models require enormous amounts of computation.
Training a large model might involve:
Massive Dataset
↓
Neural Network
↓
Millions/Billions of Parameters
↓
Repeated Computation
↓
Thousands of Accelerators
↓
Huge Energy Consumption
And training isn't the only cost.
Once a model is deployed, millions of users may interact with it.
Every request requires inference.
For example:
User
↓
AI Application
↓
API
↓
Model Server
↓
GPU / AI Accelerator
↓
Inference
↓
Response
Multiply that by millions of users and billions of requests.
The hardware requirements become enormous.
📈 SMIC's Numbers Tell an Interesting Story
According to the Reuters report, SMIC generated more than:
$3 billion in quarterly revenue
for the first time.
Its second-quarter performance was helped by strong AI-related demand.
Some other important numbers:
Metric Result
Q2 Revenue >$3B
Wafer shipments +14% QoQ
Average wafer selling price +5.7%
Monthly capacity 1.1M 8-inch-equivalent wafers
Capacity utilization 93.7%
Q2 shareholder profit $479.2M
H1 capital spending $3.4B
Expected 2026 amortization ~$5B
These numbers show something important:
Demand is strong, but semiconductor manufacturing is extremely capital-intensive.
💰 Why Are Chip Prices Going Up?
The simplest explanation is:
High AI Demand
↓
More Chip Orders
↓
Limited Manufacturing Capacity
↓
Higher Utilization
↓
Tighter Supply
↓
Higher Prices
SMIC's average selling price increased by 5.7% while wafer shipments increased by 14%.
And SMIC said it raised prices for its most sought-after capacity after negotiating with customers.
That's basic supply and demand.
But in semiconductors, increasing supply isn't as simple as:
“Let's build another factory.”
A semiconductor fabrication plant can require billions of dollars and years of planning, construction, equipment installation and qualification.
🔬 What Does “7nm” Mean?
This is another important concept for anyone interested in AI hardware.
SMIC is reported to be the only Chinese foundry currently capable of mass-producing logic chips such as CPUs and GPUs using a 7-nanometre process.
But what does 7nm mean?
Very roughly, it's a label associated with a particular semiconductor manufacturing technology generation.
Smaller process technologies generally aim to provide:
More transistors
Better performance
Improved power efficiency
Greater computational density
A simplified evolution might look like:
Older Process
↓
Larger Transistors
↓
Fewer Transistors / Area
↓
Newer Process
↓
Smaller Features
↓
More Computational Density
Modern AI accelerators depend heavily on advanced semiconductor processes.
🤖 AI Is More Than GPUs
One detail from the SMIC report is particularly interesting.
SMIC said much of the increase in shipments was driven by AI-related demand for chips other than CPUs and GPUs, particularly from Chinese customers.
That's important.
When people hear “AI hardware,” they usually think:
GPU
But an AI infrastructure stack can contain many different kinds of chips.
For example:
AI Data Center
│
┌──────────┼──────────┐
↓ ↓ ↓
CPU GPU Networking
│ │ │
└──────────┼──────────┘
↓
Storage
↓
Controllers
↓
Power Systems
AI demand can therefore affect an entire semiconductor ecosystem.
🌐 The AI Supply Chain Is Huge
A modern AI system isn't simply:
GPU + Model
It's more like:
Semiconductor Manufacturing
↓
AI Chips
↓
Servers
↓
Networking
↓
Data Centers
↓
Cloud Platforms
↓
AI Models
↓
Developer APIs
↓
AI Applications
↓
Users
A bottleneck at almost any layer can affect the layers above it.
This is why semiconductor manufacturing is becoming strategically important to the AI industry.
⚡ Why 93.7% Utilization Matters
SMIC reported capacity utilization of approximately:
93.7%
Capacity utilization tells us how much of the available manufacturing capacity is actually being used.
Imagine a factory can theoretically produce:
100 units
but is currently producing:
50 units
Its utilization is:
50 / 100 = 50%
If it produces:
94 / 100
utilization becomes:
94%
A utilization rate around 94% indicates the factory is operating at a very high level.
That's good for revenue.
But it also means there isn't an enormous amount of unused capacity available if demand suddenly increases.
🏗️ The Hidden Cost of AI: CapEx
There's another number developers should pay attention to:
Capital expenditure
SMIC spent approximately $3.4 billion during the first half of the year.
Why so much?
Because semiconductor manufacturing requires expensive infrastructure.
Think:
Fab
↓
Lithography
↓
Deposition
↓
Etching
↓
Ion implantation
↓
Metrology
↓
Packaging
↓
Testing
The equipment involved can be extremely expensive.
This is why semiconductor companies aren't like typical software startups.
A software company might scale by adding:
Servers + Engineers
A semiconductor manufacturer needs:
Factories
+
Specialized equipment
+
Clean rooms
+
Engineers
+
Materials
+
Energy
+
Years of investment
💡 The Interesting Business Lesson
This creates a fascinating economic relationship.
AI companies want:
More compute.
Chip designers want:
More manufacturing capacity.
Foundries want:
More equipment and factories.
Cloud providers want:
More data centers.
And everyone is trying to scale simultaneously.
So the AI boom creates a chain reaction:
AI adoption
↓
More model usage
↓
More compute
↓
More chips
↓
More semiconductor capacity
↓
More factories
↓
More capital investment
🇨🇳 Why SMIC Matters Beyond One Company
There is also a broader strategic dimension.
SMIC is China's largest chip foundry.
The Reuters report says approximately 90% of SMIC's second-quarter revenue came from China, while the United States contributed around 8%.
That tells us how strongly its business is tied to the domestic Chinese technology ecosystem.
And as AI becomes increasingly important to:
Cloud computing
Robotics
Autonomous systems
Defense
Software
Data centers
Consumer electronics
semiconductor manufacturing becomes a strategic capability.
🔥 AI Infrastructure Is Becoming a Competitive Advantage
For years, software developers could think mostly about:
Code
+
Servers
AI changes the equation.
Now the competitive stack increasingly looks like:
Algorithms
↓
Models
↓
Inference
↓
Accelerators
↓
Semiconductors
↓
Manufacturing
↓
Energy
A company can have an incredible AI model.
But if it doesn't have enough compute to serve that model efficiently, scaling becomes difficult.
This is why hardware availability can directly influence software and AI companies.
👨💻 What Does This Mean for Developers?
You might be wondering:
“I'm learning Python and AI engineering. Why should I care about semiconductor manufacturing?”
Because understanding the infrastructure beneath AI makes you a better engineer.
You don't necessarily need to become a chip designer.
But you should understand concepts like:
AI Compute
CPU
GPU
TPU
NPU
AI accelerators
Systems
Memory
Networking
Storage
Distributed computing
AI Infrastructure
Model serving
Batching
Quantization
Parallelism
Inference optimization
GPU utilization
Cloud
Containers
Kubernetes
Data centers
Load balancing
Observability
These concepts help you understand what actually happens when your Python code calls an AI model.
🧠 The Developer Perspective
Imagine you build an AI application.
Your code might look incredibly simple:
response = client.generate(
model="some-large-model",
prompt=user_prompt
)
But underneath that one line could be:
Your Python Application
↓
API Gateway
↓
Load Balancer
↓
Inference Server
↓
Model Runtime
↓
GPU Cluster
↓
Networking
↓
Data Center
↓
Semiconductor Hardware
That's the hidden infrastructure behind modern AI.
And understanding that stack is becoming increasingly valuable.
🚀 The Bigger Picture
The SMIC story shows that the AI revolution isn't happening only inside research labs.
It's happening inside:
Factories.
It's happening inside:
Data centers.
It's happening inside:
Cloud platforms.
It's happening inside:
Chip-design teams.
And of course, it's happening inside:
Software applications.
The AI industry is effectively building a new computing infrastructure layer.
🔮 What's Next?
If AI demand continues growing, we'll likely see increasing pressure across the entire compute ecosystem:
More AI applications
↓
More inference
↓
More accelerators
↓
More semiconductor demand
↓
More manufacturing investment
↓
More capacity
↓
Lower bottlenecks
But achieving that isn't easy.
Factories take years.
Equipment is expensive.
Advanced manufacturing is technically difficult.
Energy requirements are significant.
And geopolitical constraints can complicate the supply chain even further.
💭 Final Thought
When someone says:
“AI is growing rapidly.”
don't think only about ChatGPT, Claude or Gemini.
Think about everything underneath them.
AI Applications
↓
AI Models
↓
Inference
↓
GPU / Accelerators
↓
Servers
↓
Data Centers
↓
Semiconductors
↓
Manufacturing
SMIC's latest results are a reminder that the AI revolution is also a hardware revolution.
The companies building the models may get most of the attention.
But underneath every AI model is an enormous physical infrastructure of chips, factories, electricity, cooling systems, networks and engineers.
The future of AI will be written in software—but it will run on silicon. 🧠⚡
Source: Reuters, August 14, 2026. Financial and operational figures in this article are based on the Reuters report.
