▶ Full Post Text
AI’s Next Bottleneck Might Not Be GPUs. It Might Be Copper, Silver, and Gallium.
I have been digging into the physical supply chain behind AI infrastructure, and my conclusion is pretty straightforward:
* AI is not just a software trade.
* It is not just a semiconductor trade.
* It is not even just a power-grid trade.
At scale, AI is a metals trade.
The market loves talking about GPUs, Nvidia, hyperscalers, power contracts, cooling systems, and data center capex. That is all real. But every one of those layers sits on top of a physical metals base that looks increasingly constrained.
The main metals I focused on are copper, silver, gold, zinc, gallium, rare earths, aluminum, lithium, nickel, cobalt, and a group of smaller critical minerals like germanium, indium, tantalum, arsenic, fluorspar, platinum, and palladium.
My read is that the AI metals problem breaks into two categories:
1. Volume bottlenecks: copper and silver.
2. Chokepoint bottlenecks: gallium and rare earths.
Copper is the biggest one.
A single 100 MW hyperscale data center can require roughly 27 to 47 tonnes of copper per megawatt. That works out to about 2,700 to 4,700 tonnes of copper per facility, before counting the extra grid infrastructure around it.
That copper goes into power cables, busbars, connectors, transformers, switchgear, grounding systems, heat exchangers, substations, transmission equipment, and cooling infrastructure.
Copper can represent up to 6% of data center capital expenditure.
Global copper demand was about 28 million tonnes in 2025 and is projected to reach 42 million tonnes by 2040. That is a roughly 50% increase.
AI data centers alone are forecast to consume an average of 400,000 tonnes of copper per year over the next decade, with demand peaking around 572,000 tonnes in 2028. Longer term, data centers could consume up to 3 million tonnes per year by 2050, raising their share of global copper consumption from about 1% today to as much as 7%.
The problem is supply.
The copper market is already tight. Forecasts for the 2025 refined copper deficit range from 124,000 tonnes to 304,000 tonnes, depending on the source. For 2026, analyst consensus also points to deficit conditions.
Longer term, the numbers get more serious. The IEA projects a possible 30% copper supply deficit by 2035, equal to roughly 6 million tonnes annually. S&P Global is even more aggressive, projecting a possible 10 million tonne shortfall by 2040.
This is not easy to fix.
New copper mines take an average of 17 years from discovery to first production. Chilean copper ore grades have fallen about 40% since 1991. Exchange warehouse inventories were only about 661,021 tonnes as of late 2025. That was up year-to-date, but still historically tight relative to demand.
Copper prices already touched about $11,952 per tonne in December 2025, up roughly 35% year-to-date. BloombergNEF forecasts a possible peak around $13,500 per tonne in 2028 as demand keeps outrunning supply.
My view: copper is the cleanest “AI infrastructure bottleneck” metal. Not because AI is the only demand driver, but because AI is arriving at the same time as EVs, grid upgrades, renewables, electrification, defense reshoring, and industrial power demand.
That is the problem. Too many megatrends are leaning on the same metal at the same time.
Silver is the second major volume issue.
Silver is the most electrically conductive metal. In AI infrastructure it shows up in switchgear, circuit breakers, silver-plated copper connectors, busbars, thermal interface materials, heat exchangers, and electronics.
There is also the solar angle. Each solar panel used to power data centers contains about 20 grams of silver. A 500 MW solar array for a hyperscale facility can require about 300 tonnes of silver.
Total silver demand reached 1.16 billion ounces in 2024.
Industrial fabrication hit a record 680.5 million ounces, which represented about 59% of total silver consumption. A decade ago, industrial use was closer to 50% of the market.
Electrical and electronics demand alone consumed 460.5 million ounces in 2024. Solar photovoltaic demand added another 197.6 million ounces.
The silver market has now been in structural deficit since 2021.
The 2024 deficit was 148.9 million ounces, or around 4,630 tonnes. The projected 2025 deficit is 117.6 million ounces, smaller than 2024 but still the fifth consecutive year of shortfall.
Cumulative deficits from 2021 through 2025 total nearly 800 million ounces, or around 25,000 tonnes.
Mine production in 2024 was only 819.7 million ounces, up just 0.9%, even with strong demand.
The important detail is that about 70% of silver is produced as a byproduct of copper, lead, and zinc mining. That means silver producers cannot simply ramp supply in response to higher prices the way a pure-play commodity market might.
Inventories are also not comfortable. COMEX silver inventories reportedly fell from around 150 million ounces to about 46 million ounces. LBMA vaults hold roughly 325 million ounces of available metal.
Silver prices traded above $80 per troy ounce in January 2026, up around 170% year-over-year.
My view: silver is not just a precious metal story anymore. Industrial demand is now the core variable. AI, solar, electronics, grid equipment, and electrification are pushing silver further into a structural deficit market.
Gold is different.
Gold is not a shortage story. It is more of a cost-pressure story.
AI processors use 2 to 3 times more gold per unit than traditional processors because advanced packaging requires better signal integrity and reliability. Gold is used in high-frequency interconnects, semiconductor packaging, bonding wire, via metallization, trace plating, and die attach materials.
Electronics-sector gold consumption reached about 270.4 tonnes in 2025, roughly flat versus 2024. Total technology and industrial gold demand was around 222.8 tonnes in 2025.
East Asia accounts for about 68% of electronics gold demand because semiconductor supply chains are concentrated in China, Taiwan, and South Korea.
Gold does not look like an acute bottleneck because total global gold demand was above 5,000 tonnes in 2025, with most of that going into investment and jewelry. But rising gold prices are pressuring component manufacturers and pushing more R&D into thrifting and substitution.
My view: gold does not stop the AI buildout, but it adds cost to the hardware stack.
Zinc is not the main AI bottleneck.
Zinc matters because it protects steel structures from corrosion and because zinc ores are a primary source of germanium. Germanium is important for fiber optics and high-speed transistors.
Global refined zinc demand rose 1.9% in 2025 to 13.86 million tonnes.
The zinc market posted a 33,000 tonne deficit in 2025, down from a 69,000 tonne deficit in 2024. Mine production increased 5.4% in 2025, led by Australia, China, India, Peru, and the DRC.
Inventories fell by 77,000 tonnes to about 739,000 tonnes by the end of 2025.
But 2026 is expected to swing to a 271,000 tonne surplus as Chinese and Norwegian smelting capacity expands and demand growth slows to around 1%.
My view: zinc itself is not a critical AI constraint. The more important zinc-linked issue is germanium, because zinc ores are a key source and China dominates germanium refining.
Gallium may be the most important small metal in the AI stack.
Gallium is critical for gallium nitride, or GaN. GaN power devices are used in high-efficiency AI data center power systems. They enable higher power density, less wasted energy, and more efficient 48V DC-DC conversion.
GaN devices are about 5x more conductive than silicon. GaN power ICs can achieve power densities above 137 W/in³ with efficiencies above 97%.
Without GaN, AI servers run hotter, consume more electricity, and need physically larger power supplies.
The power GaN device market is projected to grow from $126 million in 2021 to $2 billion by 2027, a 59% CAGR.
The IEA projects that data center buildout could increase global gallium demand by up to 11% by 2030.
The issue is not demand size. The issue is control.
China controls about 98% of global gallium production. Gallium is mainly produced as a byproduct of aluminum smelting.
After China imposed export restrictions on gallium, prices outside China reportedly doubled within five months.
USGS analysis suggests that a 30% disruption in gallium supply could cause a $600 billion reduction in US economic output, equal to more than 2% of GDP.
My view: gallium is not a volume bottleneck like copper. It is worse in a different way. It is a chokepoint metal. You do not need enormous tonnage for it to matter. You just need the wrong country to control the wrong step of the supply chain.
Rare earths are the other chokepoint.
Neodymium and dysprosium are used in high-performance permanent magnets for data center hard disk drives and cooling system motors. Hard drives can contain around 15 to 20 grams of neodymium per drive.
Cerium oxide is used in chemical mechanical polishing of semiconductor wafers at advanced nodes, including 5nm and below. Cerium oxide accounts for 40% to 50% of global cerium production.
Lanthanum and erbium are used in optical fiber amplifiers for high-speed data transmission between data centers.
The IEA projects that data center buildout could boost global rare earth demand by about 3% by 2030. A mid-scale data center may need less than 100 tonnes of rare earth oxides per year, which is not massive by tonnage, but the operational impact of supply disruption is large.
China produces about 60% to 70% of global rare earth oxides and controls about 85% of heavy rare earth separation and purification capacity.
In October 2025, China imposed new export licensing rules requiring foreign buyers to disclose end-use applications. By late 2025, China added five more rare earths to its export control list.
The US imported over 13,600 metric tons of rare earths in 2024.
MP Materials’ Mountain Pass mine produced about 45,000 tons, but around 80% was exported to China for refining because domestic processing capacity is still limited.
MP’s Texas magnet facility is projected to produce about 1,000 tonnes of NdFeB magnets annually by 2027. China produced around 300,000 tonnes of NdFeB magnets in 2024.
The US Department of Defense has invested about $439 million since 2020 into domestic rare earth supply chains, with a target of covering defense demand by 2027. But the US still has no heavy rare earth processing capability and only limited light rare earth processing capacity.
My view: rare earths are not about raw mine output alone. Processing is the choke point. The mine does not solve the problem if the refining step still runs through China.
Aluminum is important, but not a major constraint.
AI data centers use aluminum in server racks, cooling units, radiators, HVAC systems, and structural panels. But aluminum is not generally used for electrical cabling inside data centers because copper has better conductivity.
AI data centers are expected to need around 800,000 tonnes of aluminum by 2030. That is only a little over 1% of current global production in a roughly 75 million tonne market.
My view: aluminum demand from AI is real but manageable. It does not look like copper.
Nickel, cobalt, and lithium matter through batteries.
Data centers use lithium-ion battery systems for UPS backup power and grid stabilization.
The data center lithium-ion battery market is projected to reach $17.69 billion by 2034.
The 2024 chemistry split was approximately:
* LFP: 41.2%
* NMC: 28.4%
* LTO: 12.5%
* LCO: 10.3%
* Other: 7.6%
LFP dominates because it is safer and thermally stable. NMC is preferred where higher energy density is needed.
My view: lithium, nickel, and cobalt are relevant to AI, but data centers are still a marginal demand driver compared with EVs. The bigger issue is geographic concentration, especially DRC cobalt and China-linked lithium processing.
The smaller critical minerals are where US import dependence gets ugly.
Some examples:
Tantalum: used in capacitors for server boards. US import dependence: 100%.
Germanium: used in fiber optics and high-speed transistors. US import dependence: 100%. China controls over 60% of refining.
Indium: used in semiconductors and displays. US import dependence: 100%.
Arsenic: used in compound semiconductors like GaAs. US import dependence: 100%.
Fluorspar: used in chip manufacturing etching gases. US import dependence: 100%.
Platinum: used in hard disk drives and capacitors. US import dependence: 85%.
Palladium: used in similar electronics applications. US import dependence: 36%.
This is the part of the AI trade that looks under-discussed to me.
* The US can throw money at fabs.
* It can subsidize chips.
* It can build data centers.
* It can sign power contracts.
But if the upstream metal, refining, and processing chains are controlled elsewhere, then the bottleneck just moves upstream.
My overall conclusion:
AI infrastructure faces a two-front metals problem.
On the volume side, copper is the biggest constraint. Silver is also structurally tight and already in a multi-year deficit.
On the chokepoint side, gallium and rare earths are the highest-risk materials because China controls dominant production or processing capacity.
Gold adds cost pressure. Zinc is indirect through germanium. Aluminum is manageable. Lithium, nickel, and cobalt matter, but more through battery systems than core AI compute.
The market keeps treating AI like a clean digital story. I think that is incomplete.
At the user level, AI feels like software.
At the infrastructure level, AI is power plants, transmission lines, transformers, cooling systems, server racks, semiconductors, interconnects, batteries, magnets, and a lot of mined material.
My blunt take: the AI boom does not just need more GPUs. It needs more copper, more silver, more gallium, more rare earth processing, more refining capacity, and more secure supply chains.
That makes critical minerals one of the more obvious second-order AI trades.
Not every miner benefits. Not every explorer becomes valuable. A lot of junior mining names are garbage. But the macro setup is real.
If AI demand keeps scaling the way hyperscalers are projecting, the metals layer is not optional. It is the base layer.