What's Your Problem?Moving from fossil fuels to renewable energy will require huge amounts of copper, lithium, and other metals. Kurt House is the co-founder and CEO of KoBold Metals. The company recently made a huge copper discovery in Zambia, and is looking for other metals in other places. Kurt's problem is this: How do you use AI – machine learning, data science – to find the metals we'll need for the energy transition?
Pushkin. Digging up metal from out of the ground is a business that is literally thousands of years old, but mining suddenly has new importance. The energy transition, going from fossil fuels to renewable energy is going to take a ridiculous amount of metal, metal like copper and lithium. The need is so great and so urgent that we're gonna have to come up with new ways to find metal buried in the earth. And as it happens, a new kind of mining company, a mining company you might call an AI driven startup, just made the biggest copper discovery in over a decade. It's worth tens of billions of dollars. I'm Jacob Goldstein, and this is What's Your Problem, the show where I talk to people who are trying to make technological progress. My guest today is Kurt House, the founder and CEO of Cobald Metals. Kurt's problem is this, how do you use AI machine learning data science to find the metals we need for the energy transition. As you'll hear, my conversation with Kurt goes beyond mining and AI to cover Kurt's really compelling way of just thinking about making decisions in an uncertain world. We started though, by talking about how he came up with the idea.
For his company.
So if you go back about eight years ago, my co founders and I were looking at the trends in the energy transition, seeing the electric vehicle and renewable energy sort of revolutions coming, and it's quite easy to convince yourself that the material requirements for the energy transition will be tremendous. The amount of very specific materials that the world needs copper, lithium, cobalt, nickel, graphite, others.
Uh, this is basically stuff to build, like batteries and wires.
Tony right.
This is motors electrification.
Just an electric motor is a bundle of copper wire surrounded by surrounding a permanent magnet. Every battery require, every mobile battery requires lithium and nickel and cobalt.
These are all that These are.
These are the key materials for which which in some cases, like the humanity has been using lots of copper for a long time, there's a big copper market, but it needs to at least double from a very large base lithium. Humanity has not been using much lithium for very long and now the lithium market needs to grow by well more than a factor of ten uh to to UH fully electrify just just the transportation sector. So the the sort of macro needs were very obvious. So that's observation one. Observation two say, okay, well, maybe the incumbents are really good at finding new materials, and as prices rise a little bit, they'll find new materials and the market will just be well supplied. And that turns out to be definitely wrong. And it's actually really easy to verify that it's wrong because the large, well resourced mining companies basically don't even do exploration. Actually, the big mining companies out of they spend sixty sixty five billion dollars a year on dividends and share buybacks and less than half a billion on exploration activities.
But that half a billion, that's the.
Deployment of conventional exploration technologies, right, things that would be natural to most geologists from the nineteen sixties or seventies, right, almost, so you can round to zero how much money they're spending on research and development for new techniques and new technologies to improve.
The exploration process.
So it was basically those sets of observations, those two sets of observations.
We need metals, and nobody's really looking.
They're not looking for them, but they're certainly not getting better at it, they're getting worse at We call this, we call that that that that trend and the increasing cost of discovery, we call that e Rooms law of mining.
Moore's law back.
Good, Yeah, I'm impressed.
Yeah, they talk about that in biotech as meaning, I'm where whereas microchips get cheaper and better every year, mining gets worse and slower and more more expensive.
And specifically exploration, exploit discovery specifically. Okay, yeah, so those were the those were the major needs. So then you say, okay, what what can we do, how can we do? What can we do differently? How can we help? And And the answer is that exploration is fundamentally an information problem, fundamentally right, if you we know, for deep physical reasons which I can explain in a minute, we know there's there's gobs and gob of undiscovered uh Rich deposits out there. We don't know where they are. So so the gap is the knowledge of where they are. Right If if God gave you a perfect model of the Earth's crust, right the location and form of every atom.
You'd be a perfect explorer.
You'd know where all the where all the high grade high concentration anomalies were.
You'd also be a perfect miner.
The miner's religious vision, right, is the gift from God of perfect information.
Yes, perfect exactly, But it's not that it's a so we don't have that. So we have we have a huge amount of uncertainty. But the sort of managing the data that you have and then making probabilistic inferences on that data, uh is fundamentally an information problem. We look at it as this is this is kind of a perfect tailored application for data science and modern scientific computing. It has it's it's it's a little different, it has some some sort of unique, really cool attributes to it. But it is fundamentally an information problem and fundamentally a search problem. And so the thing that could be massively different would be a company built from the ground up, a sort of a Silicon Valley company built from the ground up that combines the best existing knowledge of geoscientists with world class data scientists and software engineers coming out of the major tech monopolies Google, Apple, Facebook, you name it, who have never worked in the metals and mining business before, right.
So it's fundamentally sort of bringing the tools of data science, machine learning AI to bear on geoscience.
Absolutely, if I'm going to reduce totally totally.
Yeah, Yeah, it's amazing that nobody got to it before you did. There are these giant billion dollar mining companies and it was right there for them, but they didn't do it. I mean, why didn't somebody do it before you?
What you will definitely hear is, oh, we use data science like we we we use scientist, right, And it's like and it's like not totally wrong. But what is what is definitely unambiguously different, if not unique to Cobold, is that we're we're a full stack explorer.
We were started, we were started and built.
On the concept that that applying uh vanguard scientific competing techniques to these problems would improve the efficacy and efficiency of exploration, right, that that is the goal. So we have our technical staff is about sixty percent data scientists or software engineers, about forty percent geo scientists, right, so we're roughly equal equal numbers across the three disciplines.
And that's that's completely Uh, that's that's unique.
Let's let's talk about data, right. I feel like, uh, discussions about AI, for me tend to get more interesting when we get into data and and it seems like that's where a lot of the action is and and from what I understand about the story of your company, kind of building the data set is a big part of the story and a big part of what has differentiated you. So you have all these data scientists, what they need is data. How do you go about building this data set to find these metals?
Yeah, it's incredibly good question.
So most of the data we use was collected by other people at other times. Humans have been collecting information about the physics and the chemistry of the Ear's crust for a very very long time. Right, They've been doing it for well, I mean, in some sense for millennia, but certainly over the last century they've been doing it in ever more sophisticated ways, and for reasons I can explain. Most almost all of that data is actually in the public domain. The problem is it is a utter mess It is like an end member hard messy data problem. Think of different humans in different decades, speaking different languages and different places of the world.
Collecting different types of.
Data, and I'll get into the types of data in a moment, with different measurement techniques based on the vintage of their of the era, and then storing it in all manner of storage media, everything from literally hand written geologic notes or handwritten drilling notes all the way to cloud native data data structures right and everything in between.
And so it is this incredible mess of data.
Give me some specific examples. What are specific like did you find stuff in a drawer or something sort like, give me some specific examples.
So I'll give you examples of there's geologic libraries archives right with carefully carefully constructed geologic maps that were that might be one hundred years old, and there they were a smart skilled geologist just make it a doing field mapping, which basically means deserving and recording the observations of outcrops and describing the rock, the rock types and those outcrops right and locating them in space. And the Earth's crust changes very slowly. So provided that was provided that was like a well done one hundred years ago, it's still valid. It's just that it's it's it's you know, literally in drawers piled on top of each other in you know, and uh and.
And basically not used.
It would only be used by a very industrious human being who spent who spent countless hours sort.
Of searching through the old archives.
Right, so we go, so we go to various archives, and we make an arrangement to digitize the information at our expense, and we give the.
Owners all full digital copy.
It's almost always public domain data, and so we we have a right to use it, or we negotiate a specific use right. So digitizing a geologic map is like is the very very beginning.
Then then you need to extract the information from from the.
Digital copy of the map, uh and, and you have many different types of information there.
You can have in the paper records.
You might have you might have chemical assays, so measurements of the concentrations of the elemental concentrations of samples taken from different locations on the map, and that could be a.
Part of a part of the record.
And so that's tabular data because it'll say, well, this sample sample whatever had had x percent calcium and y percent magnesium and z percent silica, and et cetera, et cetera, et cetera. That's all valuable information. So that's tabular information that then gets extracted by by our systems and populated into into what we call our universal schema, which just means that every data type is stored in a in a consistent format.
You're standardizing this wildly messy heterogeneous test.
That's exactly right, And we should talk about more about what the data is because it's really fascinating, right, So I gave you, I give you two examples. I give you the sort of qualitative almost like drawn geologic map, which is incredibly useful information, but qualitative and continuous in nature. Then there's the sort of tabular data that would be any kind of any kind of assay data, measurements of composition. But then you have a whole different classes of data, like geophysical data, which tells you something.
About the physics of the Earth's crust.
So, for example, you probably know that the Earth's gravitational field changes from place to place as you move around. It changes because you can go up or down in elevation. Well, that's easy to adjust for because you know the elevation. It also changes because the density of the rocks below you change. And so if you're standing over over a or body that has twice the density of whose rocks are twice as dense as the surrounding rocks that'll pull on you slightly more.
Okay, and you can measure that that I did not know.
And this is let's go down this rabbit hole actually because it's super interesting. Okay, because imagine, so imagine you make this measurement. What are you actually measuring. You're measuring the force of gravity in a particular location, and you can measure Okay, I've adjusted for elevation and the force of gravity is a little bit higher here. Okay, that's all you actually know at this moment. So what is that telling you? Is it telling you you have a modestly more dense object like just below the surface, or is it telling you you have a massively more dense object deeper. Well, it turns out that this is a fundamentally degenerate problem or non unique problem would be the way to describe it.
Mathematically.
There are many ways to solve that problem. There are many different configurations.
You don't know the answer, you know the ant, but.
You know, what you do know is that there's a there's a very large class of in of invalid solutions, and then there's a smaller, uh but still very large class of valid solutions.
Ok there's a lot of things that it is not, and there's some things that it could be.
Could exactly incredibly well said, that's that's that's exactly right. So here's a there's a really cool application of our of our our technology and our approach. So the industry standard approach to this is basically, what what does a does a normal conventional company do with this gravitational noma. Well, they do one of two things. Most of the time, maybe ninety percent of the time. They'll just look at it and say, okay, here, here is a gravitational anomaly. This is hot, it's higher here than here. That's interesting, and they just see it on a two D map.
Okay. So that doesn't do anything for the non uniqueness. It just tells you. It just tells you what the measurement is, okay. So that now they would just use it.
They would just say that, well, it's interesting because it's higher, Okay. So what Cobold does is very very different and sort of impossible to have done even ten years ago, maybe even five years ago. What we do is we solve a conditioned on a set of geologic hypotheses that we find interesting. We solve for the full set of possible subsurfaces. So we might actually test like a billion subsurfaces and say nine hundred and nine d nine nine nine million, nine hundred thousand of those don't match the data we tried.
They don't match it.
So there they've been rejected, But we still have one hundred thousand now that do. So now we have all of the good we've rejected the bad possibilities and we've narrowed on the good possibilities. But it's still an incredibly large search space.
Yes, impractical, impractically, you've got to get a lot smaller for you to do anything right.
But there's there's a lot of information in the uncertainty that we've now quantified. We've now quantified the uncertainty, and so we apply something that we call efficacy of information, which is is a phrase that we coined and you can read scientific papers about it. It's it's a very general and really cool concept, and it's also kind of obvious actually, although it's hard to formalize.
But you look very happy right now. This is an audio medium, Like your face is full of delight right now. I don't want to interrupt to you. Keep going good.
Yeah, I'm excited. I love talking about this stuff because it's super cool. So we say, okay, the basic idea behind EOI is you're going to collect some information next, okay, And that's really what every exploration process always is.
You're going to go try and get more information to figure out what is going on in the rocks of the surface.
So the question you obviously want to want to ask is what next piece of information will I will tell me the most given sort of uh per unit, per unit of dollar that I expend, what am I going to learn the most from?
What has what has the highest return on investments?
Yeah?
And from a from a knowledge an information perspective, what am I going to learn the most from? And so here's a way to think about that. It is the piece of information that decreases your uncertainty the most. And because we solved, we solved the we have the one hundred thousand possible answers, right. Yeah, The piece of information that will decrease our uncertainty the most is in fact, the piece of information that tests the most number that falsifies the highest number of those of those one hundred thousand. So, for instance, say we're gonna we're gonna drill in different in different directions. Okay, yeah, So we're gonna drill and we're gonna intersect the sort of various predictions of concentration. If we can do one drill hole and it would test one hundred of the one hundred thousands, yeah yeah.
Okay, that's only.
That's only, we're only testing zero point one percent of the possible answers.
Don't don't drill there, Yeah, don't do that.
We're gonna learn very little.
We're gonna end up with basically the same amount of uncertainty. But if we could drill a different hole, a different core that tests fifty thousand, say half.
Of them, we massively reduce our search space.
We we we falsify fifty fifty percent of the possible answers, right, and sometimes we can actually falsify like eighty and ninety percent of the possible answers. So we massively reduce our search space. And we fundamentally it's the most most possible information you can get per unit dollar. So every time we go to collect any information, we try we try to form a formally quantify the uncertainty, and then calculate this EOI cont of ter, which is which is the piece of information that we have the greatest expectation will reduce our uncertainty the most compelling.
I'm glad you think so.
It would be nice to be able to do that in life. More generally, it's.
Super super hard, and it comes. The really hard part is quantifying the uncertainty correctly. Once you have that quantified correctly, then calculating the EOI, like if you know for sure you correctly quantified the uncertainty, optimize the calculating the EI is kind of an engineering optimization.
It's kind of it's kind of straightforward.
In this instance, quantifying the uncertainty is basically how many how many ways could the rock under the surface.
Be exactly Yeah, that's that's exactly right.
So you've been talking about sort of gathering this very old school data and making it useful to you and then what you do with it. There is another piece of your data gathering operation, or another set of pieces that are more high tech and that involve going out into the world and getting new data that the then already exist. And some of those are kind of fun and so I want to talk about that a little bit. Tell me about detecting muons.
Yeah, so this is this is this is kind of one of our frontier of R and D projects within the company.
Something we would the opposite of a hundred year old map.
Yeah, exactly exactly, And we have a lot of a lot of physicists at the company, so they love the stuff. So what is a muan. Let's start with that and then I'll get to the why why they can be useful. So in the so cosmic grays are hitting are hitting air molecules in the in the upper atmosphere all the time, and when when they collide, sometimes they produce they produce muons in the in the in the reaction. So it's a it's a it's a sub atomic uh particle, a muon, and it's it travels very very very fast. It's a sort of relativistic particle, travels near the near the speed of light.
So right now muons.
Are showering through us, you and you in your studio and me and my home. Newons are coming through us, and they I think about like if you put your hand flat, you can expect about one muon per second to be going through you through your hand. You don't notice. They mostly go right through you. Uh, it doesn't cause you any harm. They do interact with electrons, and it turns out that when they every time they interact with electron, they they.
Slow down a little bit.
Uh.
And when they eventually when when they slow down, eventually they slow down enough that they decay into other things.
Okay, so they they disappear.
So if you if you are measured, Let's say you have a muon detector and it's sitting at the surface and you're listening. You know, you like listen to its detection. So it's like click click, click click. That's just telling you, okay, mwan's going through means going through me's going through right.
Okay.
Now I drill a borehole and I start lowering the muon detector into the borehole, and you know the rate it was at the surface.
Then as I get to say one hundred meters, now it'll be like.
Click, click click, And then as I go to like five hundred meters, it'll be like click.
Okay.
What's happening is you're getting fewer and fewer muons are hitting that location. And the reason is because you've got so many more, so many more atoms between you and the at and the top of the atmosphere that the muon that the muons aren't surviving, they're they're hit.
They're hitting the.
R're hitting the rock.
And so now you think of think of the journey of a specific muon as it's going through the rock. Okay, the the muon that interacts with the fewest electrons is the most likely to hit you, and the interro and the muon that interacts with the most atoms will say is is less likely to hit you because it's likely to to decay, because it's likely to lose its energy. You know, think of a think of a of a of a ball bouncing hitting a bunch of other bals.
Right.
Yeah, So, now if you're sitting in the you're sitting at the muon detector that's been lowered into into a location underground, and you're looking up in kind of a cone and you can look in three dimensions. Yeah, you're seeing muons from stay say, they're coming from the right a lot, but they're not coming from the left very much.
Yes, Yeah, that's.
Telling you something about the number of atoms, the relative number of atoms to the left, which tells you about the density.
That tells you the rocks to the.
Up and above you, into the left of you right are denser than the rocks up into the right of you. And denser might mean an ore body, it might mean a high concentration set of metal in the rock.
Uh.
And so that is it allows us to probe in really sophisticated ways the density of the Earth in the same way we were talking about the gravitational force changes around the Earth.
It's the same thing, but it's a much higher precision measurement.
UH.
So we've designed our our own novel muon detector.
We did it in collaboration with Occidental College and it's working, and it's it's in a pilot hole, uh, collecting muons. And then we have a bunch of new ideas about how to use this this you this uh you kind of new data data type. There's there's a few other companies doing this, but it's very new, very new concept.
After the break from Fury to Practice, Kurt talks about Cobold's huge copper discovery in Zambia. Let's talk about Zambia. I want to talk about Zambia because it suggests that your hypothesis for the company is worked right, Yeah, to.
A large degree.
Yes.
Let me first sEH, why are we in Zambia in the first place.
So we look around the world and we evaluate jurisdictions on four dimensions. Okay, our physical prospectivity or how we perceive the physical prospect the likelihood that there's going to be something new to discover a particular location.
Yeah, that's thing one. Thing two is the rule of law.
Right if we if we make it discovery, we have a property right.
How robust is that property? Right? That's thing two. Thing three is access to markets infrastructure.
Right. If you find something in the middle of Antarctica, you're not going to be able to get it into the market, no matter how great it is. And then thing thing four is how much resistance or slash support will there be to building a new industrial project in that location?
Right?
If you find something in Palo Alto, no one's going to allow you to build it, right, So it just doesn't.
You can't even build an apartment building there, much less a lithium exactly exactly.
So those are the four dimensions we look at, and we looked around the world early on Zambia rose to the very top on all four of those. It's a fantastic jurisdiction. It's the most consistent and stable democracy in Southern Africa. The physical prospectivity is tremendous because there's been mining there for one hundred years and so well you might look at that and say, well, sure it was a good place to look a hundred years ago, but isn't it all picked over?
And the answer is definitively not.
This is easy to verify because the uh, basically all the all the deposits that were mined over the last one hundred years were actually sticking out of the surface. They were they were known about a century ago, and there's been almost no exploration into the deep parts of the basins. Uh, the what we'd call blind exploration. Right, this is not directly directly evident, like you can see it at the surface. There's there's been almost none. It was this kind of perfect perfect location in that sense. It's right on the Central African copper belt, which provides a significant majority of the world's copper, so it's easy to get it to market, right, and it's a legacy mining country. That's that's very supportive of development, right, and so it's like perfect it was. It rose to the top across the board and we'd love it, and we love our Zambian colleagues, uh, and we think it's just it's one of the best jurisdictions in the world for us, for us to operate.
So that that's why we were there in the first instance.
And we we we we started we started exploring in twenty twenty there in very modest fashion, loosely, and we but we were exploring in areas right in the heart of in the heart of active mining basins because again there were sort of active minds, but if you went out into the deeper parts of the basin, deeper than five hundred six hundred meters, there were areas that just had never been probed or explored.
Yeah, So like briefly, what did you find and how did you find it?
Looking at all the data we had, some of our geoscientists had really really clever ideas about how the mineralogy was changing and how we actually might have very very distinct mineralogy, so we might have areas where it looks like it's all this kind of one distribution, one sort of set of statistics, but actually, as you cross this boundary, it's a totally different geochemical system. Different sets of geochemical reactions occurred. So if you're able to draw that boundary and then only go and explore within that comple CAD three dimensional boundary, then you'd consistently have high grade and thick right.
That was that was the sort of assertion.
What you have in a in the in the reservoir, we have nine in the legacy data. Ninety five percent of the data is this low, you know, kind of modest grade thin stuff. And then there's five percent of the data is this higher grade stuff. And if we and if we drill there and within that boundary and only drill there, it's gonna be.
Good whole, good hohole, good hold, good hoole, good hole. Right, And that's and now.
That's the hypothesis. Yes, you test the hypothesis and does it happen, right.
Yeah, exactly, So you found it, Yes, and we proved it, and it's and it's there, and it's it's it's very it's it's gone from marginally economic to very economic. For the same unit of rock that we move, we we we sell ten times as much copper as the average copper mind today.
Yes, okay, so you found it correct. The hypothesis was true, the system worked. You found it correct. Now you are like gonna become a why different company. It seems to me right, Like you are in Silicon Valley, you are working with data scientists, you have a technical background. You have been running essentially a high tech startup. You are about to be running a mining company. Where like your problems are not just being very clever and hiring the right AI people. They're like, you know, getting the US government to finance a railroad in Zambia and making sure that the Zambians like you. Like that seems entirely different than what you have been doing so far.
Partially true, there's it's it's a little more continuous than you might have implied.
Yeah, I'm just trying to sight but but it's not. It's not a regular startup anymore, correct, Like maybe it never was. But like it's a super different job. It's a super different skill set. I mean, I'm sure to some extent you've been dealing with this, but it's really different than what we have been talking about. It's a whole different universe of problems and hard things to deal with and a very different domain.
Sure, right, No, you're you're, you're these are these are excellent questions. So you can think about think about the company now as there is the discovery machine. The discovery machine is everything we've been talking about, right, the and the discovery the unit economics of exploration are great, right because the you can make a hundred times your money or even more on proving the deposit because it's worth so much once it's clearly clearly economic.
Right, so you could just sell the rights in some fashion. Right, you don't have to be a mining company. You could be a discovery company.
Correct, And it's correct, so so so the most important part of them, the heart and soul of the company is the discovery machine. Now we have we have at least one deposit that is sort of unambiguously going to be a mine.
Uh.
And and the question is what happens from here with that mind and the odds are very high that we're going to bring in a partner uh with complimentary capabilities to sort of help bring it to production and bring in a partner.
You know.
It s a simple way to think of this is we own eighty percent of it.
Now the Zambian Parastatal Mining company owns twenty percent of it. It's worth a certain amount. You could imagine what it's someone would pay to own it entirely, and.
That order of magnitude is billions of dollars.
It's like, right, yeah, yeah, yep.
And so someone could come in and pay and contribute to capital and capabilities to bring it into a mine and we would still have and we can principle spend no more money and we still have a large share of all the future cash flows.
It's worth a lot to have the steak you have in this thing. Yeah, you have a set of choices about what to do with it and how much sort of money to take and how much of your interest to sell.
Correct, That's that's exactly right.
So is the answer you're trying to figure out how much you're gonna be a mining company versus a discovery company.
I mean, it's that yeah, And we know, we know for sure that the most important thing is to not not weaken the discovery engine, right, that's the most most of them. And there's a lot of culture around the discovery engine. There's a lot of attention. That's the most important thing. We also know that it's it's very very important that this that the value potential gets realized in the Zambian deposit.
Is there about to be some deal if this show comes out in two weeks?
Actually no, and I can I can confirm that we're not actually looking for a partner for a couple of years. Actually we're not. We won't formally partner with anyone for a couple of years. And the reason they're actually really obvious. It's just that the the what what we have discovered sits on about four square kilometers and there's another one hundred and fifty square kilometers on the license that we own that are totally untouched, totally unexplored, right, completely unexplored. So we are going to fully explore that whole area and totally know what we have before we formally do any partnership. But we're also building the capabilities to take to take the project as far as we we want to.
Right, so we're hired. We recently hired.
We stood up an amazing Zambian leadership team of people of project developers and engineers, metallurgists, et cetera, hydrologists to continue to do the engineering optimization of what a mind will look like in this location, right, And so we're doing all of that because that that has to be done anyway. It's it adds a ton of value to figure out exactly how you'll optimize the operations and and it just moves the project forward.
And our our goal, our our stated intention.
Is to start construction on the mine within two years, uh and and to have.
It in production in the early part of the next decade.
So I know that we need a lot of copper, and it's great that you just found more. It is also the case that minds have often been bad for the places where the minds were and for the people who worked in the minds, Like how do you deal with that, you know, harming people and the world.
What's super ex about this particular deposit, and in general the deposits we're looking for is super high grade, right, ten times higher grade than the average.
Copper mine around the world. That means ten times less waste for the same amount of copper. Huh Okay. It's also an underground mine as opposed to an open pit mine. So when you consider the overburden that open pit minds big holes in the ground would otherwise have it actually ends up being about thirty times less waste, and we're going to take almost all of that waste. And as we mind, as we excavate the locations underground, we take we take the waste and we put it back in and we backfill is what it's called. We we we stuff it back into the area.
So at any given time, there's only a modest volume, you know, modest cavity that's open. And then in terms of the we are passionate and obsessed with skills transfer and so it's the reason that we are really building a Zambian mining company to develop this project. Our CEO, vi Kay Mackay is uh the CEO of Cobold Africa. We have ninety percent of the employees in country, our Zambian chief metallurgist, chief mind engineer, chief project director, right, a psych geologist. All of them are our Zambian and they're extraordinary and we're investing tremendous amounts in helping them get be the best professionals they can be because we're going to be there for fifty years, right, and we want to be there for fifty years.
So what's the next big discovery?
So I'm predicted I will predict nickel and Canada, lithium in Australia, and another and another copper discovery in Zambia.
I appreciate the specificity that I love falsifiable prediction.
God bless you. So something I make.
I make wagers all the time, weird, dis weird wagers. But it's not it's not because I'm a gambling man. I've never played a spin of roulette in my life, right, But what I love to do is people make vague predictions about the future, and I try to I try to pin them down on something that's clearly testable, and then we and then we usually bet, like a nice bottle of wine or a dinner that we enjoy together. So it's you know, it's it's fun. But the loser pay is obviously uh and yeah, So this is so we have the in Fact company. A big part of our company culture is what we call the culture of falsification. Right, So when you go out to test hypothesis, your job is not to collect information to confirm that hypothesis, because you can always do that. You could always paint a new story. Right, that's inductive reasoning, it's invalid, yeah, right, What your job is to go out is to tell me how you're gonna test it, how you're gonna prove it wrong, and go and go falsify it.
And you either one of two things happens.
You either successfully falsify it, in which we move on and we celebrate that, we celebrate falsification, or you fail to falsify it, which means, okay, how do okay, so it's not dead yet?
How do we how do we now? How do we test it?
Now?
How do we test it again? Right? Death?
Uh? And what's the most efficient way? What's the highest return we can get?
God? Question, what's the EOI? We got it.
We'll be back in a minute with the lightning round. Okay, we're gonna finish with the lightning round. And so I got to ask, what is one weird bet you made.
Since we just had the Olympics.
I bet that I bet that Usain Bolt's nine point five eight second world record in the one hundred meter will still be the world record in the year twenty thirty six, by the end of the year twenty thirty six.
That's a long bet. You're playing the long day.
Yeah, that's a long standing record. He said it in two thousand and nine.
It's crazy for a record to last that long.
Yeah, would you like to would you like to make that wager with me?
I don't have enough information. I know enough to know that I'm ignorant. But what made you make the bet? Uh?
It's a dramatic outlier time. It's a total outlier. Yeah.
And he has the four fastest time, so nobody you know that the next fastest human is is the fifth fifth fastest time.
It's like he is an outlier and that time is an outlier for him.
Correct? U huh correct?
What's a cobalt?
Oh? Good question. So it's a it's a creature from German mythology that lives underground, kind of like a goblin like creature. Uh, lives underground and controls the mineral wealth of the earth.
Huh.
And it's also the namesake for the word cobalt, uh.
For the for the for the metal cobalt.
Right. I mean, as I understand it, people used to think cobalt was bad, right, and then now we're like, oh, actually cobalt is good. Good. Uh.
It looks a lot like nickel sulfides when it's a cobalt arsenide and arsenic is toxic, so it would it would poison you know, poison miners, and they called it the goblin metal.
What's one thing I should do if I go to Zambia?
Oh that's I love that question. Well, you can't miss most tuna. I call it, we call it most tuna. That's the traditional name. It means the smoke that thunders. You will know it as Victoria falls. It is completely spectacular. Niagara Falls is amazing. It blows it away, totally blows it away.
You just can't. You just can't go to Zambia and miss and miss most Tuna.
We've talked a lot about, you know, trying to predict things and trying to quantify uncertainty in the context of your company. Do you think that way outside of work? Uh?
Well, yeah, I guess the way I make, you know, wager on things, right, is a form of.
It's certainly the way I think of.
Like, I try to be scientific in every aspect of my life, and I say that what science is not is empiricism, right. It is not looking at the data and drawing the inevitable conclusion, because there's no such thing you can look at with any set of data. You can tell you can fit many, many, many hypotheses that explain the data. Right, that's always this is this is the non unique thing where it's it's always true. Basically it's always true. And so science really is about myth making. It's about it's about making a making up a myth that explains the data. That's your hypothesis. The difference between science and religion is that we test our myths. That's the difference, right, That's that's of good science, right.
And like in your heart, you should want to disprove it, right, Like if you're really the best scientist, you should want to prove yourself wrong.
Yes, that's this the thing, as scientists can say that means you learn something you really only learn when you realize you were wrong.
Kurthouse is the co founder and CEO of Cobold Medals. Today's show was produced by Gabriel Hunter Cheng. It was edited by Lyddy jeene Kott and engineered by Sarah Bruguier. You can email us at problem at Pushkin dot FM. I'm Jacob Goldstein and we'll be back next week with another episode of What's Your Problem
