Predicting the Earth with Josh Goldman: How KoBold Uses AI to Find Critical Minerals

**SPEAKER_1** (0:05)
Hi, listeners, and welcome back to No Priors. Today, we're speaking with Josh Goldman, co-founder of KoBold Metals. KoBold is building the world's largest collection of geoscience data and using their AI tools to better identify mineral deposits like lithium and copper to be a better explorer. KoBold invests over $100 million annually across 70 projects on four continents today. Josh, welcome to No Priors.

**Josh Goldman** (0:28)
It's a pleasure, thanks so much for having me.

**SPEAKER_1** (0:30)
This is a super interesting real world business. You run an intelligent mining company. What does that mean? What does KoBold do?

**Josh Goldman** (0:38)
We explore for minerals. We're looking for lithium and copper and the other metals that we need to build other businesses that are powered by batteries and AI. We develop AI technologies and we combine AI with human intelligence to be better explorers, more successful at finding the sources of minerals that we need for these businesses. Are you both finding them as well as actually going to do the mining or is it only a tool to find these sorts of assets or resources? That's a central question.
Our business is focused on exploration and it's focused on exploration for a couple of reasons. One is because there's way more value to be created there, and the second is that's where technology can be really differentiating. The economics of exploration are really quite extraordinary. With a few million dollars of capital, you can create 100 to 1,000 times return. Exploration is a very old business. Think about gold miners back in the middle of the 19th century. If you can get the right claims, you can strike it rich if you can dig in the right places. It's about where you look and how effectively you can look. The unit economics of discovery are really extraordinary. The problem with exploration as a business is that the success rates are really low. You have to try many, many different places before you can find something, and the problem keeps getting harder. But that's also the reason why technology is so differentiating. We're looking for things that are harder and harder to find. It used to be that you could find minerals literally with your eyeballs by walking across the ground and prospecting, and a lot of the copper ore minerals that form at the surface, that are modified by the air and the water in the surface environment turned blue and green, like the patina and the Statue of Liberty. Anything you can find by traipsing across the ground with your eyes has been found by now, and we need more intelligent ways of looking for minerals in places that are concealed. They're literally underground and concealed by the rocks. Technology is a way to create differentiation to be a much better explorer. Once we find things that there's a continuum from, you had a good idea and you collected some rock samples, you found something underground, you have many different holes and you've established that you've got something continuous, to, oh, it's going to be economic to mineness, to we're designing the mine, to we're building the mine. There's a whole spectrum and the technology that we use to find resources and define those resources helps set a project up to be a more economical mine as well. So we continue to contribute technology and stay involved in projects as they evolve. What sort of data are you using in order to actually identify a mine site or a potential site? Okay. There's a huge amount of data. Humans have been collecting data about the Earth for as long as humans have been looking at rocks. There's an enormous amount of data, a great deal of which is actually in the public domain. The length scales are very different. Start with the global length scale. What can you know about the entire Earth? Well, you can look at satellite imagery and you can look at satellite imagery in different colors and so you can get a sense of the rocks that are exposed at the surface. There are data sets that tell you about the structure of the continents and the ancient continents that collided and where the ancient continental proto-continents were and where those crashed into each other a long time ago and formed mountain ranges. You zoom in and you go to another length scale and you can fly airborne surveys with sensors on them that can detect the magnetic properties and the density and the electrical conductivity of the rocks. Go out and collect rock samples and measure what they're made out of, all the concentrations of different chemical elements and likewise for soil samples. And these are standard types of data that are used in the industry. And there's a huge number of these old data sets that are in the public domain. Most private companies have to disclose their data to regulators any place you look. Typically, a number of other companies have looked there before and haven't yet found anything. But this data is, even when it's in structured form, it is spread out over tens of thousands of different repositories. There's no where you can go where this is all aggregated in one place. You both have to do a lot of really hard technical work to get it together. And you have to do a lot of scientific work to use judgment about what this data actually means and whether or not it's fit for purpose. There's all kinds of messy problems with the data. But a lot of this data is unstructured as well. And geologists use a lot of words. There's a very rich lexicon of geological vocabulary for rocks and time periods.