The race to build artificial intelligence data centers is hitting a wall that isn't made of silicon. According to a recent Reuters column, the metals that go into the chips and infrastructure powering AI are becoming a critical bottleneck — and the problem may be most acute for obscure materials like gallium and germanium that sit deep in the semiconductor supply chain.
These elements are not household names, but they are essential for making the high-frequency chips and optical components that AI systems rely on. Gallium is used in compound semiconductors for radar, satellites and 5G, while germanium appears in fiber-optic cables and infrared optics. Both are produced in very small quantities, often as byproducts of mining other metals, which makes their supply chains fragile.
Why metals matter for AI
AI data centers are not just about software and cooling systems. They require enormous amounts of physical hardware — servers, networking gear, power infrastructure — all of which depend on a wide range of metals. Copper for wiring, aluminum for structures, and rare earths for magnets are all in demand. But the Reuters column highlights that the most overlooked constraints may be the niche metals that have tiny global markets.
Gallium and germanium are prime examples. Global production of gallium is measured in hundreds of tonnes per year, not millions. Germanium is similar. That means even a modest increase in demand from the AI sector can have an outsized effect on prices and availability. In 2023, China — which dominates gallium and germanium production — imposed export controls on these metals, a reminder of how geopolitical factors can tighten supply further.
The column also points to grid connections as a major hurdle. Data centers need vast amounts of electricity, and in many regions the power grid is not ready to deliver it. This is not a new issue, but it is becoming more pressing as tech giants announce ever-larger AI projects. Some companies are teaming up with energy firms to secure power outside traditional grid systems, but that approach has its own challenges.
Policy and public pushback
Beyond physical constraints, the build-out faces political and social friction. Local communities are increasingly pushing back against data center projects, citing noise, water use, and the strain on local power grids. This policy pushback can delay projects or force companies to change plans, adding another layer of uncertainty to the timeline.
The Reuters column suggests that these factors — grid connections, policy resistance, and tiny metals markets — could shape how quickly the AI build-out really happens. Investors who assume the expansion will proceed smoothly may be underestimating these bottlenecks.
What it means for investors
For everyday investors, this story is a reminder that the AI boom is not just a software story. It is also a physical, industrial story. Companies that mine or process these niche metals could see increased attention, though their markets are small and volatile. Junior miners are already looking to raise capital for metals projects, and base metals have been rallying in recent months.
But investors should be cautious. The metals that are most at risk of shortage are not the ones with large, liquid markets. Gallium and germanium are produced in such small quantities that even a small shift in supply or demand can cause big price swings. That makes them difficult to invest in directly, and any company that depends on them could face cost pressures.
For those holding tech stocks, the takeaway is that the AI build-out may be slower and more expensive than some optimistic forecasts suggest. Companies are betting big on data centers as a growth engine, but they will need to navigate these supply constraints.
Ultimately, the metals bottleneck is a reminder that the digital economy still runs on the physical world. As AI expands, the materials that make it possible will become increasingly strategic — and increasingly contested.
