KoBold Metals
Canvas Category Software : Information Technology : Mining
KoBold is the first AI-powered mineral exploration company, innovating at the very upstream of the EV supply chain. We combine the world’s leading mineral explorers who collectively have made dozens of discoveries worth more than $20 billion with an outstanding team of data scientists and software engineers from top Silicon Valley software companies, to bring the most comprehensive and cutting-edge knowledge available to bear on battery mineral exploration.
Assembly Line
We need "more than $10T worth of newly discovered Lithium, Copper, Cobalt and Nickel.” @kurtzhouse on how mining startup @KoBold_Metals is utilizing #AI to pinpoint key metals for clean energy.
— Julia Chatterley (@jchatterleyCNN) July 26, 2023
Plus, challenges “It requires many, many years and a huge amount of investment.” pic.twitter.com/eCD0qvcSEb
🧠⛏️ KoBold Metals Is Silicon Valley’s Newest Unicorn
Berkeley, Calif.-based KoBold Metals, which explores for metals such as copper, lithium and cobalt using artificial intelligence, is raising around $200 million in a fundraising round, said co-founder and Chief Executive Kurt House. The capital injection values the company at more than $1 billion, he said. Part of that will be used to help it develop copper reserves it recently acquired in Zambia. A division of T. Rowe Price that manages client money led the round.
KoBold hadn’t planned on raising money so soon after its fundraising round last year, but stepped up plans in light of its copper project in Zambia. It also plans to use the fundraising proceeds for nickel and lithium exploration projects and software and hardware research and development, House said.
This AI Hunts for Hidden Hoards of Battery Metals
The mining industry’s rate of successful exploration—meaning the number of big deposit discoveries found per dollar invested—has been declining for decades. At KoBold, we sometimes talk about “Eroom’s law of mining.” As its reversed name suggests, it’s like the opposite of Moore’s law. In accordance with Eroom’s law of mining, the number of ore deposits discovered per dollar of capital invested has decreased by a factor of 8 over the last 30 years. (The original Eroom’s law refers to a similar trend in the cost of new pharmaceutical discoveries.)
Our exploration program in northern Quebec provides a good case study. We began by using machine learning to predict where we were most likely to find nickel in concentrations significant enough to be worth mining. We train our models using any available data on a region’s underlying physics and geology, and supplement the results with expert insights from our geologists. In Quebec, the models pointed us to land less than 20 km from currently operating mines.
Over the course of the summer in Quebec, we drilled 10 exploration holes, each more than a kilometer away from the last. Each drilling location was determined by combining the results from our predictive models with the expert judgment of our geologists. In each instance, the collected data indicated we’d find conductive bodies in the right geologic setting—possible minable ore deposits, in other words—below the surface. Ultimately, we hit nickel-sulfide mineralization in 8 of the 10 drill holes, which equates to easily 10 times better than the industry average for similarly isolated drill holes.
KoBold Metals Raises $192.5 Million to Use AI to Find Battery Minerals
KoBold aims to change the mind-set of an industry that has long relied heavily on sampling soil and sediment and drilling holes in the ground to determine whether areas contain valuable minerals. While the company still leans on those techniques, it hopes to limit the chances of failure by drawing on machine learning and other scientific computing techniques.
In September [2021], KoBold formed an exploration alliance with BHP, the world’s largest mining company by market value. It is one of a number of partnerships it has with resources companies world-wide.
Using AI to Find Essential Battery Materials
KoBold’s AI-driven approach begins with its data platform, which stores all available forms of information about a particular area, including soil samples, satellite-based hyperspectral imaging, and century-old handwritten drilling reports. The company then applies machine learning methods to make predictions about the location of compositional anomalies—that is, unusually high concentrations of ore bodies in the Earth’s subsurface.