AI is racing ahead, but the hidden bottleneck is copper. Every data‑center, every transmission line, every AI‑driven device leans on this reddish metal, and the world’s demand is skyrocketing. One hyperscale facility can swallow tens of thousands of tonnes, and analysts predict global copper demand could hit about 37 million tonnes annually by 2031. That’s a massive surge, and the supply side is already strained – more than 70 percent of known reserves sit in low‑grade ores that traditional mining can’t process efficiently.
Because copper is the lifeblood of electrical infrastructure, a shortfall ripples through every AI project. Delays in transformers, lagging transmission builds, and stalled data‑center expansions become the new normal when the metal isn’t there. In short, the AI hype can’t outrun the physics of copper availability.
A startup called Endolith is tackling the problem with biology. Instead of grinding rock and smelting it at high temperatures, they harness specially selected microbes that “eat” copper from complex ores like chalcopyrite and enargite. These microbes work in leaching heaps, pulling the metal out faster, using far less energy, and leaving a smaller environmental footprint. Machine‑learning models guide which strains to deploy, how to mix them, and how to adapt over time, turning bioleaching from a trial‑and‑error experiment into a scalable, AI‑driven system.
If microbial mining can unlock copper from previously uneconomic deposits, it could ease the supply crunch and keep AI’s power‑hungry infrastructure humming. The technology is already being piloted in Colorado and has attracted interest from major producers like BHP. In other words, the same AI that fuels data centers is now helping to harvest the copper that keeps those data centers alive. This could be the key to preventing a copper‑induced stall in the AI boom.
