No Priors: Artificial Intelligence | Technology | Startups - Predicting the Earth with Josh Goldman: How KoBold Uses AI to Find Critical Minerals

Episode Date: April 17, 2025

This week on No Priors, Sarah and Elad are joined by Josh Goldman, cofounder and president of KoBold Metals. KoBold is using AI to transform how we discover critical minerals like lithium and cobalt, making the exploration process faster, more precise, and more scalable than traditional methods. In this episode, Josh explains how KoBold is rethinking the fundamentals of mineral exploration by combining unique datasets, scientific modeling, and predictive algorithms. They dive into the company’s driving philosophy and technical approach, how they validate underground hypotheses, and why regulatory knowledge and a localized approach are crucial. Josh also discusses what success looks like in exploration today and the scarcity of world-class deposits. Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @KoBold_Metals Show Notes: 0:00 Introduction 0:29 KoBold Metals 3:14 Using unique datasets 6:20 Traditional methods of lithium exploration 8:38 Regulatory vs. rarity constraints 13:40 Technical approach 16:25 Validating hypotheses 23:56 Redefining success in mineral exploration 25:44 Scarcity of good projects and deposits 32:44 Philosophy behind prediction 36:46 KoBold’s origin story

Hi, listeners, and welcome back to No Pryors. Today, we're speaking with Josh Goldman, co-founder of Cobold Metals. Cobled 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. Cobold invests over $100 million annually across 70 projects on four continents today. Josh, welcome to NoPriars. It's a pleasure. Thanks so much for having me.

This is a super interesting real-world business. You run an intelligent mining company. What does that mean? What does cobalt do? 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.

And we develop AI technologies and we combine AI with human intelligence to be better explorers, more successful at finding the sources of mineral 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.

So 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 a hundred to, a thousand times return. Exploration is a very old business. Think about, you know, gold miners back in, you know, 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. And so the unit economics of discovery are really extraordinary. The problem with exploration as a business is that the success rates really low. You have to try many, many different places before you can find something and a problem keeps getting harder. And but that's all 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 are modified by the air and the water and 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. we need more intelligent ways of looking for minerals in places that are concealed. They're literally underground and concealed by the rocks. And so technology is a way to create differentiation to be a much better explorer. And 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 mine to, 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, so 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, right? And there's an enormous amount of data, a 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 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.

And there are data sets that tell you about the structure of the continents and the ancient continents that collided and where the sort of ancient continental protocontinence 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. They are, 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 nowhere 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. Geologists use a lot of words. There's a very rich lexicon of geological vocabulary for rocks and time periods. There's a lot of text data and reports that are filed by companies, often with regulators, that become public after a period of time. And there's an enormous amount of data in maps of various kinds. One of my favorite data sets that we use around the world, a set of maps from Zambia from almost 100 years ago, the originals are hand-painted on linen. And we got a tip from an elderly geologist on which drawer in the state archives to look in that had this particular collection of maps. And you could never collect data like this again. It's incredibly labor intensive. And now there's lots of farms

and people living there. You can't go traipsing across their ground looking at the rocks. But these observations were made by skilled geologists, and the rocks haven't moved. So there's no expiration date on the data. And so you can take data sets like this that provide ground truth and use it for training machine learning models based on modern airborne geophysical surveys and modern satellite imagery. And it's the combination of all these many different data sets of different types of data and the systematic use of structured and unstructured data that's really powerful. In a pre-Cobald world, or just with, like, I mean, maybe you can just tell us who the largest couple other explorers are out there. Like, how do you go look for lithium?

Oh, yeah. Okay. So, again, you've got this different set of length scales, right? You start with the Earth. And you say, okay, I'm interested in lithium. What's the recipe for making a lithium deposit? There's a, what is an ore deposit in the first place? So there's an enormous amount of lithium in the Earth's crust.

The central problem is that the lithium that's in your driveway is in very low concentration. The lithium that's in, you know, the granites that you can see out your window is not economical to extract. It's too dilute. A lot of the minerals that we're looking for, or the metals we're looking for, their concentration in the crust is a few tens of parts per million. The crust is really big, so there's a lot of metals. So what we're looking for is those places in the Earth's crust where natural processes, and geologic processes in Earth's history have gathered up

a bunch of metals from a really large volume of rock and then they've moved them and they have concentrated them and then redeposited them in a much more concentrated form more like 1% copper or 1% lithium or even more than that

and then you can take it the rest of the way to 100% with industry. So that's what an ore deposit is and there not only are is there lots of lithium and lots of copper in the crust but there are actually many, many places where those geological processes have happened, even though they're rare in the earth as a whole.

And so the problem is, where are those special places where these natural processes happened? And how can we find those? And this is, we talk about exploration as an information problem because the scarce resource is not lithium or copper metal in the ground. It's actually information. The scarce resource are not the ore deposits, and the scarce resource is the information about where the ore deposits are located. So you have to first understand, well, how is an ore deposit form?

You have to know the recipe, and you have to have some ideas about where those processes might have been occurring on the earth and how they're going to be expressed in the data sets. Then you can marshal the data, and you can start asking questions of the data. You can make hypotheses, and then you can narrow down on some specific portion of the earth, And then you actually, what you want to do is you want to go acquire the land. I guess another overlay may be sort of the geography relative to governance of the country, regulatory ability to actually mine things.

Like my sense is, for example, the U.S. has a pretty diverse range of deposits. We just kind of don't want to mine certain 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 really have to scour the ends of the earth to find them? Regulatory constraints are really important, but at the same time, you can't just narrow down, you can't be too narrow in your initial filter because these, because they are rare enough, and that you're, you know, 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. And, and that way, you know, your likelihood of success is going to be. be much higher. It isn't just a function of regulations. 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. Development is challenging because you don't just have regulators. You have lots of different community interests. And these things are extremely local. The U.S. is not monolithic. You have state regulators. And within a state, you have many different communities, many different indigenous groups. And this is true the world over. It's true in Zambia.

There are 50 different chiefdoms. And so you have traditional leaders everywhere that you work. Technical success is not very helpful. Success is you find something that is really economic to develop that either we can develop or we can sell to somebody who can develop it. And so if we don't actually have the so-called social license to operate. If we haven't invested in the relationships with the community to be able to build and we haven't started in a place where that's possible, then we're not going to be successful. But these are hyper-local problems for sure. Josh, can you give us a sense of just like the scale of the operation for Cobold today and like, you know, where you are looking, where you own land, where you're drilling,

what you've discovered? Absolutely. So we operate exploration project. So we, we We have, basically the company does two things. We find places that are prospective for making discoveries, and then 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.

Africa, targeting copper and lithium and nickel and cobalt and likely other commodities to come. And again, in all 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. There are seeds we've planted. Any of them could become great ore deposits. And what we have in Zambia is really an extraordinary deposit, it is the highest grade large copper deposit that is not yet a mine. The average of operating copper mines today is that the concentration of copper in the ore is about 0.6%. So if you mine 1,000 kilograms of ore, not including the non-or rocks all around it, there's six kilograms of

copper in it that you can potentially extract. And the Mingomba deposit in Zambia, the core of it is over 5% copper and it's very large. And that's extraordinary. That means the economics are much better because if you compare a high grade and a low grade deposit, a 5% and a 0.5% deposit, if they're producing the same amount of copper, they have the same revenue. But the high grade deposit, if you have 10 times the grade, it means you are producing 10 times less rock, at least. You have 10 times less stuff to haul out of the ground, 10 times less waste, 10 times smaller plant, So that means the economics are far better. The capital intensity is lower.

The operating costs are lower. And it means the environmental footprint is smaller. So those are the things that we are looking for. We're looking for, you know, in a commodity business, everybody sells copper for the same price. It's a global commodity market. And our ability to make money depends on what our margin is. That means we need to be a low cost producer and we want low capital intensity assets. And so that is the definition of the exploration problem is finding the highest quality assets.

And in Zambia, it went so far, we have a quite extraordinary, really world-class copper deposit. Can you tell us a little bit more about the technology that you're using? Obviously, you mentioned you're mixing sort of older-school data, modern 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 a purchase are you using? How do you think about overall what you're building from a sort of AI and data perspective? For sure.

So, Cobold'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 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 data that we're collecting, all the historic data, 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. 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, again, they operate at, different length scales. So there's models trained on satellite imagery 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 with 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 model for lithium pegmatites or a model for mafic to ultramathic rocks, that might host nickel deposits.

It's that we make a prediction and develop an initial set of hypotheses on that. And then when our team gets on the ground, every day that they're in the field, they are collecting new training data. And they're not just going to places where we have high confidence in what the rocks are, because we're not going to learn anything.

We're going to places where the models are highly uncertain. And the new training data, small amount of additional ground truth, can dramatically improve the predictive power of our models. And so what happens is you have geoscientists in the field making observations. And using those observations, we are retraining those models every day and serving new predictions out to the team. You've got this duet of data scientists or technologists and geologists working together

on the same problem. Of like a hypothesis and then like validation or invalidation? Is it like, I'm imagining like, okay, at this set of spots in Zambia, I am going to go 20 feet below the surface or whatever it is, and I'm going to find this concentration of something. Absolutely. Yeah. So a whole bunch, let me give you a whole bunch of examples, right? So one example is, I'm going to go this location, and I'm in a pegmatites of a container rocks for lithium deposits. And we predict that there's, it's just a name for a rock, like a granite or something like that. Okay, we're going to predict that we have these special rocks, pegmatites,

that might contain lithium. We're going to predict that there's one in this location. And we're going to go then land on it and we're going to sample those rocks and we're going to look at it and see. Okay, that's a prediction we're making at the surface. And we're making predictions in 3D and we're saying, okay, here. Now, I think there is a layer of conductive rocks here. And I think those conductive rocks are prospective for hosting nickel and copper and cobalt. And I think this rock layer is, we're going to intersect this rock layer, you know, between 200 and 300 meters below surface. And it's going to be highly conductive. It's going to have, some distribution for how much sulfur and how much nickel and copper in it. And more than that,

we're going to say the best place to test this set of hypotheses is by putting a hole at this location and by drilling it in this direction. And other times there's a known layer of rock and we're saying, okay, we think this layer continues out in this direction. And here is a surface where we're predicting this layer is going to be at this depth and it's going to be this thick and it's going to have this much copper in it. And you're going to get a probability distribution for all of these at any given point. Those are the kinds of predictions that we're making.

And we go collect a piece of information and then condition the model on the new data and serve out a new prediction. And on the third theme of sensors, we use everything that's available today that we can get from a service provider. But most mining companies are not as not as keen to use new data types and as keen to invest in new kinds of technologies.

And sometimes we need to go build our own. And so an example of this is our hyper-spectoral imaging technology. There were new imaging chips available that were not yet deployed in the service market. And mining industry was adopting them too slowly. We built our own hyper-spectoral imaging system. In less than a year, we had it flying on a light aircraft, and we're surveying areas that we're interested in and we're using it for getting data in 600 colors at dramatically lower cost of acquisition and much, much faster time to deliver processed images. So that we're

using, we're integrating that information with other types of data and using that to make decisions while we're where to go in the first place and then how to change our, how to change our exploration plans while we're in the field. Is there any tool or data set that was most crucial for that marquee discovery made in Zambia? Like, was there a piece of data that others had overlooked? Was it just looking in that geography? Was it a specific tool? 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. And, And so that's the story there as it is everywhere else. It's a combination of new analytical methods, the ability to quantify uncertainty and understand the range of possibilities, and critical scientific insights about the way that these ORF 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 AI.

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 radiometry invented in the 1990s was going to find all the ore deposits. It doesn't, but it's really powerful. We're really happy when we can get that data. We go collect it ourselves. But these are incremental improvements to predictive power, but it's only possible if you can work with all of these different data sets together in a unified way. How does a project like this get valued? Like if you sell it to somebody else or you develop it. It sounds like, well, copper is whatever price it is and you know, take some risk on that

over time. And then there's, you know, cost of operation based on basically how concentrated the deposit is. And then like how large it is. And then those kind of give you some sort of cash flow model for the business. That's exactly right. Yeah. This is, it's actually really easy to value a natural resource asset like this. They all trade on their present value of future production, which is very knowable. It is much, it is much easier to know what a mine is going to produce 20 years from now, then it is to know what a SaaS company's sales volume is going to be 20 years from now and how it's going to be priced, right? I feel attacked. It is. No, but they're very different kinds of businesses. You think a goal of mine that can move, you know, say 10 million tons of

ore per year. And then what you're going to do is you're going to dig 10 million tons of ore per per year. And you're going to dig the highest grade part first than the next highest grade. And on average, it's going to produce whatever percent coffer it's going to produce. And so that, you know, it's very simple, right? The cash flow is, the revenue is what the commodity price is. The volume is based on the size of the mine you build and how you cost it. The cost is very knowable because you need to know, well, how many trucks do you need to move and how much water do you need to pump? And what does it cost to pump the water? And that's all straightforward stuff. And then you need the capital costs, which is like, okay, I'm going to build a plant. And these things are, you know, they're big

vessels. Like, you got a tank and you got a crusher and the crusher's got some steel balls or the mill have some steel balls in it. And these are knowable things. They're typically built. So you can figure out what the margin is going to be. You can see what the capital profile is going to be. You have to assess what fraction of the copper can you recover. And then those are your sensitivities. Like, I think we can get 90% of a copper. If we can get 92, the economics are juicier. If we only get 88, it's a little dilutive. But those are the uncertainties. And then you discount that according to, well, what is the risk profile of the asset? What stage is it? How close are you to production? and, you know, you might demand a higher rate of return if you are in a less stable jurisdiction.

And so it's quite straightforward. And actually, we know, you know, we know with high confidence what the sales volume will be from Mingomba 20 years from today. And that's amazing. There's potential, you know, upside if we find more and more resource. And that's one of the things about these deposits is once you get underground and you start mining, then you learn more and more about the geology. and you keep finding extensions. And so mines are often designed.

You underwrite an investment based on the first 20 years, and then actually many of these mines operate for decades, especially many decades longer, 50, 60, 70 years because the resource keeps going and you can keep adding to it as you go. So it's actually pretty straightforward to understand how these are valued, and these are hard assets. This is a, you know, there's a property interest, and the market values these accordingly.

They all trade on their present value of future. How successful are exploration companies in general today? Like, if I look at, if I'm, start, I don't know how to ask this question, but if I start sampling 100 sites, I have 100 DECs, like, do I find one? Do it find zero? Do it find 10? And like, how much better do you think Cobold can be? Yeah, this is the key question, right?

Which is, like, what is the success rate in the industry and how much better hope we can do? So in the industry, it's gotten 10x worse in the last 30 years because the problem has gotten harder and the industry is slow to innovate. The way to think about it is not the number of successes. You can go look at there. There are studies that will say, you know, a half a percent success rate or something like that.

But what actually counts as an attempt is ambiguous. And the way that we think about it is that, you know, the key resource input is you have to invest some, capital to run an exploration program. You have to put a geologist on a helicopter and go out and take samples. You have to drill holes. If you take a portfolio of exploration projects that costs some money, say a billion dollars, industry-wide, how many successes will you have? And then, you know, industry-wide, a billion successes will have hundreds of failures, but eight discoveries as of 30 years ago. And today, less than one. Less than one like high-quality

economic deposit. And so that is why that's why exploration in the aggregate is not a great business. So Cobold, we target $50 to $100 million per discovery. That's the goal. That is how well we want to do. And so far, we have, you know, we now have an extraordinary copper deposit and we have succeeded. Now we need to do it again and again and again. One thing I've heard on the capital side, which may have been going to be true. So it'd be great to get your sense of this is that a lot of the people who used to buy out and run some of these assets in terms of mining assets or things like that, at least in the Western world, have run into more and more capital constraints because, you know, the funders have sort of dried up in part due to ESG or other

programs. Has that had all been a case or something that's impacted your perception of the sorts of players that are in this business these days? Or do you think that really doesn't matter? And there's plenty of capital availability. And it's just hard to find these deposits. Yeah. I think that the real scarcity is good quality or deposits. Like great projects don't have problems getting funded. Whoever owns them. Great, great projects have lots of suitors of people who want to buy them. The problem is there just aren't very many great projects. And so that's what we need to do. We need to go find more really high quality deposits. Are there parts of the world that you feel are dramatically under-export relative to that?

It varies a lot by commodity. Copper has been an exploration target for a long time, and so people have been looking for copper in South America and Central Africa, yet there are still parts of these places that are quite under-explored. We're very active in Zambia, where, of course, Mingomba is, along with a number of other exploration projects, there are—and the parts of Zambia, like, where Mingomba lies, is deeper underground where you don't have surface expression.

The deeper parts of the basins in Zambia that host copper deposits are quite under-explored. You know, you have a jurisdiction like Congo that has had a number of challenges, the exploration potential remains great across many commodities. There has been a lot of activity, but there could be dramatically more activity. And lithium, much of the world is under-explored for lithium.

and lithium hasn't been a primary exploration target until very recently, until the, you know, growth of lithium ion batteries for big devices like EVs and drones and whatnot, not just personal devices. You know, the big lithium deposits in production today, at least the hard rock lithium deposits, were found like people looking for tantalum, for capacitors, for the electronics industry, you know, in the 1980s. And so the science of how lithium or deposits, form is incipient. That's really exciting, because a little bit of increased scientific understanding can be a really potent differentiator. So, potential for big breakthroughs. Are there any commodities that you think are overstated in terms of their scarcity? So an example that I've heard

as like rare earth minerals may not be as rare as people say, and there's deposits, you know, more broadly than just in China where it's often spoken about, or like, what are, what are the things that you think are actually not that scarce that people talk about as scarce. That's the top of the list. Rare Earth, a lot of the noise about rare earth is because it has the word rare in its name. Good branding. Not that rare. Also, lithium and copper and nickel and cobalts are not rare earth elements.

The rare earth elements are, it's a well-defined term, not just things that are rare, but neodymium and dysprosium, which are important for permanent magnets, which are important for electric motors and so on. They are important. The reason that rare earths get noise besides the name rare is that a concentration of downstream processing capacity in China. And so spurred by Chinese incentives, there's been a lot of processing. You have to, you know, you extract the minerals from the ground, and then you have to refine

them into a metal that you can put into a product. And there's been a huge built out of that, not just for rare earth processing, but also for lithium and now copper smelters as well. And that does a couple of things. One is it means it's really hard for somebody else to go build a processing facility because you are, you know, you are competing for feedstock. You want to take copper concentrate from somewhere and you want to, you know, you want to smelt it into copper metal. We have to go buy your copper concentrate. If a Chinese party is willing to buy it for more than you because they will accept less margin, that makes it much harder. It's much harder to underwrite a project like that. And so that

has, it has a, it's had a, you know, deterring effect on just private commercial actors willing to put the capital to work to invest in processing capacity. It makes it hard for another private actor to do the same without guarantees or subsidies or something, which we don't have any of that as a business. That's a strength of Cobold is that we just have great assets and rather than, you know, a subsidy. And the second is that it actually, so because there's so much downstream processing capacity in China, then you have the raw materials going to China, and then you have a concentration of the downstream supply chain from there. Then you make products from that. And so it's a big strength for Chinese manufacturing capacity, as you have all of these materials landed there already.

And, you know, if you think about that on an integrated economic basis, it can be very powerful. And so that's one of the reasons that rare earths are in the news a lot, too. Is there anything that's the other way around where you actually worry about some commodity and material not being able to meet demand for something that's industrially important for us? You know, the ones that I listed for us are the ones where we think both there's, you know, there's a lot out there to find that demand tailwinds are really strong. There's going to be some depth to those commodity markets, so you don't have to you don't have to have a really well-dialed view on commodity prices, which we don't.

Again, our goal is like, we want to be the low-cost producers, and so surprise it's in that market, not great. We are looking at other commodities that could be those, you know, could be those unusual ones, but there isn't one today that stands out that we're tackling. So it's not that important that we buy Greenland. Not going to go there. My joke version of this is to take over Baja because it's already called California. California. It's nice and beachy and sandy. That seems like a really great place to annex if you were to annex somewhere. I'm happy to go to these places regardless of which flag. Right enough. Yeah, me too.

It actually sounds nice. Only if the algorithms tell you, the algorithms and the initial rock samples tell you that's going to be efficient to get the lithium out, I suppose. Yeah, we need more lithium out Baja. So let me get in there before. A lot will go overseas on the beach. Josh, when we last saw each other, we had like a really interesting discussion about how important you felt like philosophy was to the business and the investments you'd made about just how the company operates. Can you talk about this a little bit? Yeah, Coble is a, it's kind of an epistemic project, really. Our business is about making better predictions. That's what we're doing, right? This is the thing we lack is information about where the ore deposits are and the thing we do,

like the actual business activity, we make a prediction, make a hypothesis. We go out and we deploy capital and we spend time testing our hypotheses. And so we are successful as a business depending upon how good our predictions are. So what the models are meant to do. We're making predictions about what the rocks are at the surface and what the rocks are below the surface and what their properties are, like their density and how much copper and nickel and other things they contain. So how good are we at doing that? Well, we have to think, we have to think hard about on what basis are we making those predictions? What things do we know about the world? And one of the critical elements of this is dealing with uncertainty. When you have sparse data, then you make a prediction

about everything, you know, in between your data points. There are many possible geologies that are consistent with the data. When you make a prediction based on data that you have from the surface or from an aircraft, and you're making a prediction about what the properties of the rocks are underground. There are many possible geologies that are consistent with the data. Standard practice in the industry is to choose just one, and make your one

best model, because, well, what else are you going to do? It's hard. You can't work with 10,000 different models. It's very difficult to keep multiple inconsistent hypotheses in your mind at the same time. But it's what we have to do. That is how we become better is by embracing that uncertainty

and recognizing that our job is to judiciously reduce that, uncertainty. That's what we do when we go out and collect data, and the data is useful in as much as it reduces uncertainty. The way that we think about this is informs our practice for how we actually explore. What is it that our teams are doing every day? And the scientific culture is one of the critical aspects of the business. So we have some unusual things. We have a document in the company called Cobold's epistemology of exploration. And it really has only, you know, that's a small number of core ideas in it, that actually epistemology is important

for reasons that I talked about, that we have to make really definite predictions, and that means they have to be falsifiable. It has to be, you have to go on record before you go collect the data about what could you observe that would cause you to abandon this hypothesis. This is how we avoid confirmation bias, which is very susceptible to it in this business. You come up with an idea and then you collect some data and you figure out how to modify your hypothesis to accommodate it and then you justify going out and spending more time

and more money. And really, the third idea is that you have to work with multiple alternative hypotheses, not just one hypothesis, but what are the other possibilities and the point of data collection is to distinguish between them. And at least one of those hypotheses

has to be economically relevant. We are a business, not a science project, right? But this is the careful thinking about what you're doing is really important. So the epistemology of exploration, there's a lot of vocabulary about this. It feels like philosophical vocabulary that it's really important. Oddly, we have a chief philosopher who is an epistemologist. This is Michael Trevins. He wrote a wonderful book called The Knowledge Machine about what is science and how it is different

from other ways of knowing. And so this really, really guides exploration practice and technology development. So lots of the technologies are designed to quantify uncertainty and then given a set of possibilities to determine what information can we collect that will most effectively reduce that uncertainty. For those of us who don't have an in-house philosopher epistemology is the study of, you know, what we know, like what is knowledge and how we know it is knowledge and justifiable understanding, Right. Maybe one last thing on this. Like, how do you, you're a math and physics guy originally, right? Yes. And 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. But this is like such a cool discovery of an interesting problem that you might go apply, you know, decision making science and data too.

So how did you decide that you wanted to go work on mining and it's like better exploration? So I've always been interested in the intersection of energy and technology. I studied physics because just like grappling with hard questions. So I did a PhD in quantum computing. I've just been interested in physics because it's like working on hard problems. I like learning things. But I wanted to apply that to the most relevant things in our society today. which relate to our energy systems.

And I went and worked with energy companies as a management consultant with power companies and oil and gas companies and industrial companies who make power equipment, oil field equipment. And my co-founder, Kurt House and I were doing private equity investment in oil and gas together

in the private equity firm whose leaders had sponsored his previous startup company. And we had become friends as graduate students at Harvard together. He was also, he studied physics and philosophy as an undergraduate and then applied math and earth sciences as a graduate student. And 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 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. And we thought from first principles about what raw materials the future economy will need. Where are those going to come from? And think about all the raw materials that look around you at your desk and your house. And everything, every one of these products ultimately originated from agriculture, we grew it, or from rocks. We mined it. And so what materials are we going to need?

Well, think about the big trends in the global economy. Batteries, AI. Batteries, whether it's cars and trucks or drones and. aircrafts and robots, you know, to make a vehicle that has a long range and is durable, battery needs lithium, which is different than fuel-burning vehicles, which have no lithium in them at all. And AI, trend, I don't have to explain to anyone who's listening to this, a huge build out of data centers, and then the electricity to power those data centers require an enormous

amount of copper. And the scale we're talking about here is gigantic, right? to build a future that is powered by batteries and AI, by mid-century, we will need to mine over the next 25 years more copper than has been mined so far in all of human history. To get to a high penetration of battery-powered devices, we need a tenfold increase in lithium production relative to today. So where are these materials going to come from? We have to go find more of these. And recognizing that the problem is getting much, much harder because innovation has been slowing down. It's this perfect application where we can use technology to create a

differentiated business and it does something really important for our society. And that's really personally motivating to me and to other people who join COBOL. Very exciting. It's so cool you're doing. So, no, congrats. I hope you find others. Okay, we'll keep you posted. Thanks, Josh. find us on Twitter at no priors pod subscribe to our YouTube channel if you want to see our faces follow the show on apple podcasts Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts for every episode at no dash priors.com