The 10 Commodities Powering the AI Race

Artificial intelligence may be virtual — but its infrastructure is anything but.
Behind every ChatGPT query, self-driving algorithm, and cloud training run lies a vast physical network of semiconductors, data centers, and power grids. All of them run on metals, minerals, and energy resources that are fast becoming the real battleground of the AI economy.

As Big Tech races to expand compute power, these 10 commodities have become the “core inputs” to win the AI race — shaping everything from chip fabrication and data center design to national industrial strategy.

Silicon remains the foundation of every semiconductor chip — from GPUs to TPUs to DRAM memory. While abundant in nature, the production of ultra-high-purity silicon wafers is highly concentrated among a few producers in East Asia. As AI workloads scale, silicon demand is shifting toward advanced nodes below 5nm, which require immense energy, ultrapure materials, and highly specialized photolithography systems. Without silicon, there’s no compute — and without compute, there’s no AI.

Every data center rack, power line, and cooling loop is laced with copper. AI training clusters draw massive amounts of electricity — and copper is essential for power transmission, server wiring, and heat dissipation.

According to Trafigura, AI-driven infrastructure could add 1 million tons of copper demand by 2030 — equivalent to nearly 5% of current global production. As AI and electrification converge, copper may quietly become the metal that throttles digital growth.

Often overshadowed by lithium, graphite is vital for energy storage and thermal management — critical in AI servers, GPUs, and power backup systems. Natural and synthetic graphite are used in lithium-ion batteries that keep AI data centers running during outages. Moreover, its high thermal conductivity makes it ideal for heat sinks and cooling materials in processors. With China controlling over 70% of global graphite processing, the AI supply chain inherits a new layer of geopolitical vulnerability.

AI is an energy hog. Training a large model consumes more power than some small countries. Lithium-ion systems underpin UPS (uninterruptible power supply) units in data centers, edge AI systems, and mobile AI-enabled devices. As AI and renewable energy infrastructure grow in tandem, lithium’s dual role — powering grids and data centers — cements its place as a critical enabler of digital resilience.

AI depends on the precision movement of electrons — and that depends on magnets. Rare earths like neodymium, dysprosium, and praseodymium are used in high-performance electric motors, hard drives, cooling fans, and sensors. China’s dominance — refining over 85% of global REEs — gives it strategic leverage over the hardware foundation of AI. The world’s drive to “de-China” critical minerals makes rare earths a diplomatic and industrial flashpoint in the AI era.

Gallium and its alloys (like gallium nitride and gallium arsenide) are increasingly used in high-speed semiconductors, power amplifiers, and 5G/AI communication hardware. These materials outperform silicon in certain high-frequency applications, making them key for AI inference chips and defense-grade electronics. China’s export restrictions on gallium in 2023 were an early warning shot — showing how niche materials can ripple across the entire chip industry.

Cobalt enhances battery energy density and longevity, ensuring steady performance for both EVs and backup energy systems that power AI data centers. Around 70% of the world’s cobalt comes from the Democratic Republic of Congo, often refined in China — a dependency that mirrors the lithium supply chain. While cobalt-free chemistries are emerging, demand remains strong as AI infrastructure scales globally.

Aluminium’s lightweight, high conductivity, and cooling efficiency make it indispensable in server chassis, chip packaging, and data center structures. Its refined cousin, High-Purity Alumina (HPA), is critical for sapphire substrates in LEDs, laser optics, and chip manufacturing. As AI systems push the limits of thermal management, aluminium-based cooling and HPA’s heat-resistance properties are becoming non-negotiable industrial inputs.

Training AI models consumes enormous power — and much of that electricity still comes from fossil-fueled grids. In the short term, natural gas is the balancing fuel behind many new data centers, especially in the U.S. and Asia. While not a “metal,” its role as the transitional energy source for AI infrastructure is undeniable. AI’s carbon footprint, therefore, is tied as much to gas supply chains as it is to chip innovation.

While silicon powers intelligence, steel builds the environment that houses it. Every data center, chip fab, power substation, and transmission tower rests on vast quantities of structural and stainless steel. Each hyperscale data center uses 30,000–50,000 tons of steel in framing, racks, cooling enclosures, and backup generators. The expansion of AI infrastructure — thousands of new data centers worldwide — is quietly fueling a global steel demand boom.

In 2024, steel producers like ArcelorMittal and Nucor reported rising orders from the “digital infrastructure segment”, a category once dominated by telecoms but now led by cloud and AI operators.