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Fixing the Future from IEEE Spectrum magazine is a biweekly look at the cultural, business, and environmental consequences of technological solutions to hard problems like sustainability, climate change, and the ethics and scientific challenges posed by AI. IEEE Spectrum is the flagship magazine of IEEE, the world’s largest professional organization devoted to engineering and the applied sciences.
Eliza Strickland: Hi, I'm Eliza Strickland for IEEE Spectrum's Fixing the Future podcast. Before we start, I want to tell you that you can get the latest coverage from some of Spectrum's most important beats, including AI, climate change, and robotics, by signing up for one of our free newsletters. Just go to spectrum.ieee.org/newsletters to subscribe.
In 2022, more than 10 million electric cars were sold around the world, up 55 percent over sales in 2021. For this trend to continue, though, mining companies need to find a lot more of the metals used to build electric cars and their batteries. Today I'm talking with Josh Goldman. He's the co-founder and president of KoBold Metals, an AI-powered mineral exploration company working to discover the materials for electric vehicle batteries. Josh, thanks so much for joining me on Fixing the Future.
Josh Goldman: It's a pleasure to be here, Eliza. Thank you.
Strickland: So let's first talk about what minerals and metals we're discussing here. What metals do we need for electric vehicle batteries and how much do we need of them?
Goldman: So there's a whole suite of different metals that we need, and they each play different roles in the renewable energy system. For a battery that you want to pick up and move around like you want to put in an electric vehicle, lithium-ion batteries are by far the winning technology and will remain there for a long time. And to make a lithium-ion battery, you need lithium ions. We need a great deal of lithium, of course. For the cathode of the battery, we need a layered metal oxide. That's performance cathode structure. And the highest energy density and the greatest cycle life, the greatest durability of a battery as it undergoes many charge and discharge cycles as you fill it up with energy and drive it and recharge it come from batteries that are rich in cobalt and nickel. And then for electrical systems broadly, we need electrically conductive materials. And the workhorse electrical conductor, the kind of perfect blend of conductivity and abundance and cost to extract is copper. And so we use copper to move electric power around the vehicle, to move electric power around the energy system in the transmission grid. And then of course we use copper windings in the electric motors as well.
Those are the four that we are focused on because we think that the supply gap is the greatest and your estimate may vary depending upon your forecast of electric vehicle adoption. But it is almost universally agreed that the supply gap across those four metals to get to a fully electrified vehicle fleet is more than $10 trillion worth of those metals. So the scale of the problem is extraordinary. And the way that we fill that supply gap is by finding new deposits, new sources of those metals around the world.
Strickland: So why is there a challenge here? There are a lot of mining companies out there. You'd think that they'd be on top of this business opportunity. What am I missing?
Goldman: Yeah, there're hundreds of companies that are out there looking for metals. And the fundamental problem is that it's a really difficult problem. What we're looking for are unusual rocks and we're looking for them under the ground where we can't see them. And what do we mean by unusual rocks? What is an ore deposit? An ore deposit is a place where the rocks are unusually enriched in the metals that we're looking for. All of these metals, copper, for example, copper is present in basically at some quantity, at some concentration, copper is present in every rock. Some rocks that are very abundant are naturally a little bit higher in copper, but nowhere near high enough that you can economically extract the copper. There's copper in your driveway, but it's not a great source of copper. It's too dilute. And so what we're looking for are the places where natural geological processes have scavenged the copper out of a very large volume of rocks, they've concentrated it in a much smaller volume of rocks. And so the natural abundance of copper, think like 50 parts per million, 60 parts per million in the upper continental crust. And an ore deposit containing copper is more like 10,000 parts per million. So the natural processes needed to do that much. And once we've got to about 10,000 parts per million, we can do the rest with industrial processes at reasonable cost.
And so we're looking for these rocks that are unusual and these are places that occur very infrequently in the crust. We've found many such places historically, and those have been the sources of these metals in industry and for the electric vehicles built so far and for other industrial uses of some of these metals. But the places where they're relatively easy to find, where they're exposed at the surface or more easily detectable at the surface, we've found most of those sources already. And so the parts of the Earth's crust that are well endowed with these metals, they're deeper below the surface, they're concealed, and there are overlying rocks. And so we're trying to detect rocks that are somewhat different from the rocks around them, and we're trying to see through tens to hundreds of meters of other rocks that are concealing them. And so that's just a really difficult problem.
And this is what we do as scientists all the time. We make inferences about things that we can't see. And it's a very noisy problem. Any rock that you look at, you pick up off, you can see the heterogeneity of the rock. When you drive through a road cut on a highway, you can see how all the layers are dipping and folding and intersecting each other. And so you're dealing with this incredibly heterogeneous system and that creates a lot of noise. And the more rocks that you have to see through, the more weathering processes that have occurred or geologic alteration processes that have occurred, the more different ways the rock can have been modified. And so we're trying to detect through all of these degrees of complexity.
And the other kind of fundamental reason why this is so hard is because we live on the surface. And the places that we can easily get around to more or less easily-- sometimes we have to go to quite remote locations. You may have to take a helicopter or a snowcat to get somewhere. But even once you get there, you're still standing on the surface and so you're making a measurement of something. It might be you're making a measurement of the angle at which the rock beds are dipping. You might be making a measurement of the composition of a rock sample that you take at surface or a soil sample. It might be a measurement of the gravitational field at that location, or it might be from an airborne measurement from a helicopter, a fixed-wing aircraft or a drone or even a satellite. All of those are things we can get to constrain our model of what's under the subsurface, but the data sets that we get are really sparse in general because we can't sample the whole planet and they're especially sparse in 3D because the number of places where we actually have samples from underground is really quite small. So that's what makes the problem really hard.
And so lots of clever people are working on this problem. There's the resources that go into exploration. But the success rate in the industry starts from the fact that we're trying to do something really difficult. And it's compounded by the increasing difficulty of the problem and the fact that the exploration methodology is just not keeping up with the increased difficulty. There's been an underinvestment in innovation in exploration for these mineral resources. We are still using methods that were largely developed for and applied to problems where you can detect things closer to the surface. We have conceptual models of how ore deposits form that can be sometimes limiting because we're looking for things that match the last discovery and not imagining the things that could be the next discovery. And where the sparsity of the data makes it difficult to apply some of these quantitative methods, but that means we just have to work harder to do so.
Strickland: Yeah, and I know you are doing fieldwork now in several locations, but let's talk first about how you decided on those targets, how you decided where you would go. What kind of data sources were you drawing on as you tried to figure out where you'd try and explore first?
Goldman: Yeah. So it's a surprise to many to learn that there's actually a great deal of geoscience information in the public domain. Most of the information ever collected about the Earth's crust actually is accessible. It's just not accessible in any sort of compact format. It's widely fragmented, tens and hundreds of thousands of geological maps, different geochemical and geophysical surveys. And you can find these things in databases that are kept by the different states and provinces, both of data that was collected at public expense of geologists with a geological survey going out and making maps and taking samples of the chemistry and the sediments at the bottom of lakes and so on. And then also data sets of historic exploration activities that have been conducted by other companies. In some jurisdictions, when you go do work, you have to write a detailed technical report and provide the data and that data becomes public. And this is really good policy because most discoveries are made on ground that many different companies have held. And what's important is that when one company runs out of steam and they've exhausted their ideas, that the next company who picks up the ground picks up where the last one left off and uses all the same information and all the learnings rather than just collecting the same data all over again.
So we actually know a great deal and we know it at very different length scales and it's patchy as we talked about. And so we're starting from a combination of a kind of deep geological understanding and large-length scale data sets that allow us to make models to augment our geological understanding. We're not starting with a completely blank slate about the world. The fact that these ore deposits are so unusual means they only occur where certain processes were happening and we know enough about the large-scale structure of the Earth's crust to know that what are some of the broad regions where we either know some of those processes were occurring or where they might be occurring and we can hypothesize that we can find evidence of that.
And so there's a kind of initial filtering both on sort of the largest length scale geologic prospectivity and also by where we think we can do business effectively. It has to be a place where you can access it. There's enough infrastructure to be able to work. And where there's a good rule of law and where we can operate a business to the highest ethical standards, which is really important to us in everything that we do. We have to know that given that we are never going to engage in corrupt activity, we have to be able to do work and we have to be able to retain interests that we acquire. When we put a lot of capital to work, we have to plausibly be able to earn a return on that. And that means being able to sort of be there--still be in the project when it is realized.
Strickland: Excellent. So let's talk about a real example here. Can you tell me what's been going on in Quebec for the past few summers?
Goldman: I'd be delighted to. So in Quebec, we're exploring in a province called the Cape Smith Belt in the far north of Quebec in Nunavik. And this is an area where, in particular, we're looking for a type of deposit called a magmatic sulfide. And magmatic sulfides typically are rich in nickel, often have cobalt and copper, and sometimes some platinum group elements in them as well. And we have a very large area of claims there, more than 250,000 acres. So it's a vast expanse in a really difficult location to get to. It's more than an hour's helicopter ride from the nearest airport to get to the places where we're working. To get gear in there requires putting it on a boat in September for the following summer. At times, to get our camp supplied this summer, we had some tractors on skids pulling sleds across the tundra in the wintertime so that the camp was well supplied rather than doing a heavy lift operation to get things in.
So this is a very remote part of the world, and there's a lot of rock exposure, and it's a district that has actually a lot of nickel that we know about, but there's very large expanses of this district that have seen much, much less exploration. And so we're using a whole suite of different technologies to guide our exploration decisions. We have a team on the ground, who are walking and observing the rocks at the surface and going to places where we have predicted there are interesting rocks that are exposed at surface, where we might be able to see either evidence of the right kind of rocks, the right kind of mineralizing processes, or the mineralization itself in particular. We want to see the nickel and the copper ore minerals there in exposure at the surface. And they're going to places that we predict, and they're also going to places where the model is struggling to make a prediction and there's a very high degree of uncertainty.
We've conducted several generations of airborne surveys to collect information about the conductivity and the magnetic properties of the rocks in the subsurface. And then we're using those and other pieces of information, like satellite imagery, to make decisions about where there are very specific regions, what we call a target, where there's evidence of all of the right mineralizing processes and a specific thesis about something that could be there in the subsurface. And then we're drilling holes in order to see what's down there and test our hypotheses and constrain our models in 3D at that kind of length scale. And the way that we're guiding those models in particular is based on all that kind of larger-scale information. And then we're doing much more localized exploration around those as well. One of the great features about this type of deposit is that it often has a contrast in the conductivity of the rocks in the deposit from the rocks that surround it. And so we can be looking for those anomalies and using electromagnetic methods to probe the conductivity of the subsurface. So one of the things we'll do is we'll lay a loop on the ground and pulse it and listen for the echoes from the conductive materials on the subsurface. And then when we drill a hole, we'll also stick a probe down the hole and pulse that loop on the surface and use the detector at different places down the hole to be able to directly probe the volumes there as well.
So we have a suite of technologies that we call stochastic inversions that don't just build one estimate of the subsurface they don't build our sort of best understanding of the volume that we're probing with these electromagnetic surveys. They build a whole ensemble of different possibilities that are all consistent with the data. There are many, many configurations of rocks in the subsurface that are equally consistent with the data. And what we need to do instead of kind of coming up with our best one based on what we think the geology is, we need to come up with many of those possibilities. And we need to understand the whole range of different possibilities. We need to understand the probability distribution of the things that matter, like what is the conductivity of this anomaly, and how deep is it, and how large is it, and what direction is it dipping? And we use that to make a decision about how to most effectively test all those possibilities with sequence of holes or another after that.
And so not only are we deploying this technology, but we're deploying it in very short cycles. When a hole finishes, we'll run the probe in the hole and pulse the loop on the surface, and collect these electromagnetic measurements. And then we need to turn around and do something with that information in a very short period of time. The rig is sitting there. It's waiting to be redeployed. The geologist is standing there on the rig, trying to decide what to do. And the data scientist is kind of furiously trying to get some information out of this data that has just been collected and delivered. And this is a kind of unprecedented cycle time and speed here. It is typical to collect data in a much larger batch. It's typical to have some time to think about it and process it. It's also typical for these types of inversions where you get some data on the geophysical response and you use it to predict the physical properties of the rock--it's typical for those things to take a really long time. You're trying to do a large 3D finite element model. This is a hard problem. And it's very computationally expensive.
And what we're not just trying to do, but actually doing is turning these things around in hours to a day. It's like we get the data and then data scientists using the system that our technology team and software engineers have built is producing this whole probability distribution of possible subsurface. And it’s not a fully automated process. It requires scientific context and scientific judgment to get this right. And then is producing this and putting it in context with what we understand about the geology of the region and then using it to make a decision about what to do with that drill rig that's sitting there. Does it drill another hole at a different angle from the same surface location? Do we need to move the rig a couple hundred meters that way and drill back the opposite direction because now we have a better constraint on which direction the beds are dipping? Or do we need to move it entirely and we've learned what there is to learn here and it's sort of good enough for now and if there's something really good well it's not impossible that it's there, it's just very unlikely and it doesn't compete anymore with the whole inventory of other targets that we've got. And what's amazing is that this is working. It's actually working really well. We're turning these decisions around in this really short period of time and the results that we're getting from it are incredibly encouraging.
Strickland: Okay, and so you mentioned that you are finding the auras that you were hoping to find in Quebec. What's the end game there? I mean, do you imagine extracting them yourself, or what happens next?
Goldman: Yeah, it's a great question. And I guess, to clarify, there are sort of many steps along the way from finding evidence that you've got mineralization to sort of extraordinary intersections to 3D continuity of those intersections that you can establish to provide a mineral resource then on to the sort of economic viability of a resource. And across our portfolio, we're in kind of very different stages in very different projects. And our Mingomba project in Zambia is by far the furthest along.
And where do we go from there? Our goal is to get these projects all the way into production so that they're actually producing the minerals that we need in order to build electric vehicles, in order to build the electrical systems, the batteries, and all the things that we need. And in our projects, we're in them for the long term because that's the way to create the most value. We want to ensure the long-term success of the project. We're a long-term partner in the communities where we operate. We may need to augment our capabilities by working with the right partners in order to get projects very effectively into production. And we have relationships with large companies who could be potential partners on any of our projects. So exactly how that works kind of project by project. We'll be making judgment calls on that. But we have long-term interest in projects.
Strickland: Is there anything else? Is there anything else you think it's important for listeners to understand about cobalt and what you're doing?
Goldman: I mentioned it very briefly in terms of our selection about where do we work in terms of being able to run a really ethical business. And that's not limited to a choice about do we explore in this country or that country. That extends to everything about the way that we operate as a business. We want to create social value in the communities where we operate. We want to be a good long-term partner. We're committed to environmental protection and high standards of labor practices wherever we work. And there are many decisions that we've made already and many decisions that we will make in the future that reflect all of these. And it's not enough to say we're looking for these materials because they're going to help us avoid climate change. It really behooves us to work in really responsible ways in all of the projects that we're working on and to do so really at every stage. These are not commitments that only matter once you start mining. They're things that matter a lot from the earliest phases of actually getting on the ground in a community.
Strickland: Thank you, Josh, so much for joining us. I really appreciate it.
Goldman: Very glad to. Really appreciate it. Thank you, Eliza.
Strickland: That was Josh Goldman speaking to me about his company, KoBold Metals, which uses AI to search for the ore deposits needed to build electric vehicles. If you want to learn more, we've linked Goldman's IEEE Spectrum feature article in the show notes. I'm Eliza Strickland, and I hope you'll join us next time on Fixing the Future.