The Exchanges

Q more about the technology that you're using? Obviously you mentioned you're mixing sort of older school data, modern Uh, uh, image-based data, et cetera, and then you have to kind of data mine it or extrapolate where these potential deposits are. What sort of models are you using? What approaches are you using? How do you think about overall what you're building from a sort of AI and data perspective?

A For sure. So Cobalt's technology is a full stack system for guiding exploration decision making. So there are dozens of different products that work together. And they fit on three, three themes. The first one is sensors, hardware that we have developed that collects new kinds of data about the earth. The second is the data system for taking all of the, all the data that we're collecting, all the historic data in structured data from many different kinds and a huge corpus of unstructured data and getting this all in one system so that we can interact with it systematically. And rather than hunting and pecking through this, we can interact with the whole corpus of data at the same time. Uh, LOMs and other technologies are very powerful for being able to interact with all of these different types of information. And the third theme are models, dozens of different models for making better predictions about where and how to look. And so these models, uh, they, again, they operate at many different length scales. So there's models trained on satellite imagery, uh, or our proprietary hyperspectral airborne imagery. And you've got some rock samples on the ground. And so we can predict from imagery what types of rocks we're going to find at the surface and what the properties of those rocks are going to be. And then what's really exciting is that it's not just that we have a model or a…

AI assessment note: “Cobalt's technology is a full stack system for guiding exploration decision making.”

Q What sort of data are you using in order to actually identify a mine site or a potential site?

A Okay, so there's, there's a huge amount of data. Humans have been collecting data about the earth for, you know, for as long as humans have been looking at rocks, right? And there's an enormous amount of data Great deal of which is actually in the public domain, and the length scales are very different. Start with the global length scale. What can you know about the earth, the entire earth? Well, you can look at satellite imagery, uh, and you can look at satellite imagery in different colors, and so you can get a sense of, uh, of the rocks that are exposed at the surface, and there are data sets that tell you about the structure of, uh, of the continents, and the ancient continents that collided, and where the sort of 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. Uh, they are, most private companies ha…

AI assessment note: “satellite imagery”

Q Josh, can you give us a sense of just like the scale of the operation for Kobold today and like, you know, where you are looking, where you own land, where you're drilling, what, what you've discovered?

A Absolutely. So We, um, we operate exploration projects. So we have the, basically the company does two things. We, uh, we, we find places that are prospective for making discoveries. And then we, we go test our hypotheses by going and collecting data, collecting rock samples, flying airborne surveys, drilling holes to get samples of rock from below the ground. And we develop technology that we use for guiding our decision-making. So our exploration portfolio is more than 60 projects. And they're on four continents. They're in North America, Europe, Australia, and critically in Africa, targeting copper and lithium and nickel and cobalt and likely other commodities to come. And again, in all of these cases, we own the exploration rights, either ourselves or in combination with a joint venture partner, and we are operating the exploration programs. Almost all of these are pre-discovery opportunities. They're seeds we've planted. Any of them Could it become great ore deposits? And what we have in Zambia is, uh, is really an extraordinary deposit. It is the highest, uh, highest grade large copper deposit that is not yet a mine. The average of operating copper mines today, uh, is that the concentration of copper in the ore is, is about 0.6%. So if you mine a thousand kilograms of ore, not including the non-ore rocks all around it, There's six kilograms of copper in it that you can po…

AI assessment note: “our exploration portfolio is more than 60 projects. And they're on four continents.”

Q um, you went and did consulting, and you worked in oil and gas, you worked in private equity around it, and so that feels more relevant. Um, but this is like such a cool discovery of an interesting problem that you might go apply, uh, You know, decision making science and data too. Um, how did you decide that you wanted to go work on mining and like better exploration?

A So I've always been interested in the intersection of, of energy and technology. I, you know, I studied physics because just like grappling with hard questions. Uh, so I did a PhD in quantum computing. I've just been interested in physics because it's like, you know, I like working on hard problems. I like learning things. Um, but wanted to apply that to the most relevant things in our society today, which relate to our energy systems. And, and, you know, I went and worked with energy companies as a, as a management consultant, uh, with power companies and oil and gas companies and industrial companies who make power equipment, oil field equipment. And, and, uh, my co-founder Kurt House and I were doing private equity investment in oil and gas together. Uh, in the private equity firm whose, uh, whose leaders had sponsored his previous startup company. And we had become friends as graduate students at Harvard together. He was also studied physics and philosophy as an undergraduate and then applied math and earth sciences as a graduate student. And we did, uh, we, you know, we would read papers on energy topics and go to visit power plants and coal mines and things like that. And So we're already working the energy system and quite, quite interested in how the raw materials relate to the global economy. And we decided we didn't want to work on fossil fuels anymore. This is 2018. …

AI assessment note: “we decided we didn't want to work on fossil fuels anymore. This is 2018.”

Q Was there any tool or data set that was most crucial for that, uh, murky discovery you made in Zambia? Like, was there a piece of data that others had overlooked? Was it just Looking at that geography, was it a specific tool that?

A There is no one piece of data that enabled that. And that's really a critical theme. This is often new technologies are invented in this industry where people think, ah, this is going to be the silver bullet. It's going to help us find, it's going to help us find all the ore deposits, or this data set alone is going to let us do that. Actually, the data is very high dimensional. And when you can add dimensionality to the data, then you can have improved predictive power. Uh, and so that's the, That's the story there as it is everywhere else. It's a combination of, you know, new analytical methods, the ability to quantify uncertainty and understand the range of possibilities, and critical scientific insights about the way that these OR systems are formed, and all of those things in combination are what make it possible. There is no way to isolate the AI from the HI. There's no way to isolate one piece of data that's uniquely powerful, and that's one of the reasons that I think is limited innovation as well, is that we think, oh, You know, this new airborne gravity gradiometry invented in the 19 nineties was going to find all the ore deposits. It doesn't, but it's really powerful. We're really happy that when we can get that data, we go collect it ourselves, but these are, these are incremental improvements to predictive power, but it's only possible if you can work with all of t…

AI assessment note: “There is no one piece of data that enabled that.”

Q don't want to mine certain Uh, locations anymore or certain types of, we don't want to do certain types of mining and so it's a bit more of a regulatory issue in some cases versus can we find stuff? Is that a correct understanding or is it these things are rare enough and scarce enough that you, you really have to scour the ends of the earth to find them?

A Regulatory constraints are really important, but at the same time, you can't just narrow down. You, you, you can't be too narrow in your initial filter because these, because they are rare enough and that you're, you know, you, you want to put yourself in the place where you have the highest probability of success. You want to start with, you want to start with the best prior that you can, uh, and, uh, and that way you're, you know, your likelihood of success is going to be much higher. It isn't just a function of regulations, uh, you know, we consider Security of property rights. You know, if we find something, we have to be able to develop it into a mine that is going to produce for decades, or we have to be able to sell it to someone who would do that, and so you have to be able to rely on the fact that you can continue to own the property for that period, and that you will, you know, the tax rates and the royalty rates will be consistent over that period of time.

AI assessment note: “Regulatory constraints are really important, but at the same time... because they are rare enough”