The Raw Materials Behind AI
Much of the discussion around artificial intelligence focuses on models, computing power, and software capability.
But AI is not just software.
It is physical.
Behind every AI system is a vast amount of infrastructure, and that infrastructure depends on raw materials.
Metals. Minerals. Energy inputs.
The question is not just how we build AI systems.
It is what they are built from.
AI Is Built on Physical Infrastructure
AI systems rely on multiple layers of infrastructure:
datacentres
semiconductor manufacturing
electricity generation
fibre networks
cooling systems
Each of these requires significant material inputs.
From silicon wafers to copper cabling, from rare earth elements to structural steel, AI is deeply dependent on global supply chains.
Semiconductors and Critical Materials
At the centre of AI are semiconductors.
Advanced chips used in AI systems require:
ultra-pure silicon
copper interconnects
cobalt and nickel (in supporting systems and batteries)
rare earth elements for specialised components
Manufacturing these components is both:
resource intensive
geographically concentrated
Production depends heavily on global supply chains, with key stages located in specific regions.
This introduces a structural dependency.
Copper: The Backbone of Connectivity
Copper is one of the most critical materials in AI infrastructure.
It is used in:
power distribution
data transmission
datacentre systems
network infrastructure
As AI systems scale, so does demand for copper.
The International Energy Agency has highlighted copper as a key material in the energy transition, with demand expected to increase significantly as electrification and digital infrastructure expand.
AI accelerates this demand.
Rare Earths and Specialty Materials
Rare earth elements are essential in many advanced technologies.
They are used in:
electronic components
cooling systems
power systems
specialised manufacturing processes
Supply of rare earths is highly concentrated globally, creating potential risk in:
availability
pricing
geopolitical stability
For AI infrastructure, this introduces another layer of dependency.
Steel, Concrete, and Scale
Datacentres are often discussed in terms of computing power.
But they are also large scale industrial facilities.
They require:
steel structures
concrete foundations
cooling infrastructure
electrical systems
The physical footprint of AI infrastructure is expanding rapidly.
This means increased demand not just for advanced materials, but for traditional construction inputs as well.
Water and Cooling
Cooling is a critical part of AI infrastructure.
High performance computing generates significant heat.
To manage this, datacentres require:
water cooling systems
heat exchange infrastructure
energy-intensive cooling processes
This introduces additional resource dependencies:
water availability
energy consumption
environmental constraints
Energy and Materials Are Linked
Material extraction, processing, and transport require energy.
At the same time, AI systems require increasing amounts of electricity.
This creates a feedback loop:
more AI = more infrastructure
more infrastructure = more material demand
more materials = more energy required
AI is not just a digital system.
It is an energy and materials system.
Supply Chains and Strategic Risk
AI infrastructure depends on global supply chains that are:
complex
interconnected
geographically concentrated
This creates exposure to:
supply disruptions
geopolitical tensions
resource competition
Countries that control or secure access to key materials will have an advantage in building and scaling AI infrastructure.
Australia’s Position
Australia is one of the world’s largest producers of:
iron ore
lithium
bauxite
rare earth elements
These are all critical inputs into modern infrastructure and technology systems.
This creates a strategic opportunity.
Australia is not just a consumer of AI infrastructure.
It is a supplier of the materials that enable it.
The Strategic Question
As AI becomes a defining technology of the next decade, the focus often remains on software and computing capability.
But the underlying question is broader.
Who controls the materials?
Who controls the supply chains?
And who has the capability to build the infrastructure?
Closing Thought
AI may be described as a digital revolution.
But it is built on physical systems.
And those systems depend on raw materials.
Understanding AI is not just about understanding algorithms.
It is about understanding the infrastructure, and the resources, that make it possible.
Footnote
This article is part of a series exploring the physical infrastructure behind the AI economy.
References
International Energy Agency – Critical minerals and energy transition reports
Geoscience Australia – Mineral resources and production data
CSIRO – Materials and energy systems research
US Geological Survey – Global mineral supply data
Disclaimer
The views expressed in this article are my own and are intended for general information and discussion purposes only. They do not represent the views of any employer, organisation, or client.
© 2026 Rodney Terry – Digital Backbone. All rights reserved