Speaker 1: Pushkin. Digging up metal from out of the ground is
Speaker 1: a business that is literally thousands of years old, but
Speaker 1: mining suddenly has new importance. The energy transition, going from
Speaker 1: fossil fuels to renewable energy is going to take a
Speaker 1: ridiculous amount of metal, metal like copper and lithium. The
Speaker 1: need is so great and so urgent that we're gonna
Speaker 1: have to come up with new ways to find metal
Speaker 1: buried in the earth. And as it happens, a new
Speaker 1: kind of mining company, a mining company you might call
Speaker 1: an AI driven startup, just made the biggest copper discovery
Speaker 1: in over a decade. It's worth tens of billions of dollars.
Speaker 1: I'm Jacob Goldstein, and this is What's Your Problem, the
Speaker 1: show where I talk to people who are trying to
Speaker 1: make technological progress. My guest today is Kurt House, the
Speaker 1: founder and CEO of Cobald Metals. Kurt's problem is this,
Speaker 1: how do you use AI machine learning data science to
Speaker 1: find the metals we need for the energy transition. As
Speaker 1: you'll hear, my conversation with Kurt goes beyond mining and
Speaker 1: AI to cover Kurt's really compelling way of just thinking
Speaker 1: about making decisions in an uncertain world. We started though,
Speaker 1: by talking about how he came up with the idea.
Speaker 2: For his company.
Speaker 3: So if you go back about eight years ago, my
Speaker 3: co founders and I were looking at the trends in
Speaker 3: the energy transition, seeing the electric vehicle and renewable energy
Speaker 3: sort of revolutions coming, and it's quite easy to convince
Speaker 3: yourself that the material requirements for the energy transition will
Speaker 3: be tremendous. The amount of very specific materials that the
Speaker 3: world needs copper, lithium, cobalt, nickel, graphite, others.
Speaker 1: Uh, this is basically stuff to build, like batteries and wires.
Speaker 2: Tony right.
Speaker 1: This is motors electrification.
Speaker 3: Just an electric motor is a bundle of copper wire
Speaker 3: surrounded by surrounding a permanent magnet. Every battery require, every
Speaker 3: mobile battery requires lithium and nickel and cobalt.
Speaker 2: These are all that These are.
Speaker 3: These are the key materials for which which in some cases,
Speaker 3: like the humanity has been using lots of copper for
Speaker 3: a long time, there's a big copper market, but it
Speaker 3: needs to at least double from a very large base lithium.
Speaker 3: Humanity has not been using much lithium for very long
Speaker 3: and now the lithium market needs to grow by well
Speaker 3: more than a factor of ten uh to to UH
Speaker 3: fully electrify just just the transportation sector. So the the
Speaker 3: sort of macro needs were very obvious. So that's observation one.
Speaker 3: Observation two say, okay, well, maybe the incumbents are really
Speaker 3: good at finding new materials, and as prices rise a
Speaker 3: little bit, they'll find new materials and the market will
Speaker 3: just be well supplied. And that turns out to be
Speaker 3: definitely wrong. And it's actually really easy to verify that
Speaker 3: it's wrong because the large, well resourced mining companies basically
Speaker 3: don't even do exploration. Actually, the big mining companies out
Speaker 3: of they spend sixty sixty five billion dollars a year
Speaker 3: on dividends and share buybacks and less than half a
Speaker 3: billion on exploration activities.
Speaker 2: But that half a billion, that's the.
Speaker 3: Deployment of conventional exploration technologies, right, things that would be
Speaker 3: natural to most geologists from the nineteen sixties or seventies, right, almost,
Speaker 3: so you can round to zero how much money they're
Speaker 3: spending on research and development for new techniques and new
Speaker 3: technologies to improve.
Speaker 2: The exploration process.
Speaker 3: So it was basically those sets of observations, those two
Speaker 3: sets of observations.
Speaker 1: We need metals, and nobody's really looking.
Speaker 3: They're not looking for them, but they're certainly not getting
Speaker 3: better at it, they're getting worse at We call this,
Speaker 3: we call that that that that trend and the increasing
Speaker 3: cost of discovery, we call that e Rooms law of mining.
Speaker 1: Moore's law back.
Speaker 2: Good, Yeah, I'm impressed.
Speaker 1: Yeah, they talk about that in biotech as meaning, I'm
Speaker 1: where whereas microchips get cheaper and better every year, mining
Speaker 1: gets worse and slower and more more expensive.
Speaker 3: And specifically exploration, exploit discovery specifically. Okay, yeah, so those
Speaker 3: were the those were the major needs. So then you say, okay,
Speaker 3: what what can we do, how can we do? What
Speaker 3: can we do differently? How can we help? And And
Speaker 3: the answer is that exploration is fundamentally an information problem,
Speaker 3: fundamentally right, if you we know, for deep physical reasons
Speaker 3: which I can explain in a minute, we know there's
Speaker 3: there's gobs and gob of undiscovered uh Rich deposits out there.
Speaker 3: We don't know where they are. So so the gap
Speaker 3: is the knowledge of where they are. Right If if
Speaker 3: God gave you a perfect model of the Earth's crust,
Speaker 3: right the location and form of every atom.
Speaker 2: You'd be a perfect explorer.
Speaker 3: You'd know where all the where all the high grade
Speaker 3: high concentration anomalies were.
Speaker 2: You'd also be a perfect miner.
Speaker 1: The miner's religious vision, right, is the gift from God
Speaker 1: of perfect information.
Speaker 3: Yes, perfect exactly, But it's not that it's a so
Speaker 3: we don't have that. So we have we have a
Speaker 3: huge amount of uncertainty. But the sort of managing the
Speaker 3: data that you have and then making probabilistic inferences on
Speaker 3: that data, uh is fundamentally an information problem. We look
Speaker 3: at it as this is this is kind of a
Speaker 3: perfect tailored application for data science and modern scientific computing.
Speaker 3: It has it's it's it's a little different, it has
Speaker 3: some some sort of unique, really cool attributes to it.
Speaker 3: But it is fundamentally an information problem and fundamentally a
Speaker 3: search problem. And so the thing that could be massively
Speaker 3: different would be a company built from the ground up,
Speaker 3: a sort of a Silicon Valley company built from the
Speaker 3: ground up that combines the best existing knowledge of geoscientists
Speaker 3: with world class data scientists and software engineers coming out
Speaker 3: of the major tech monopolies Google, Apple, Facebook, you name it,
Speaker 3: who have never worked in the metals and mining business before, right.
Speaker 1: So it's fundamentally sort of bringing the tools of data science,
Speaker 1: machine learning AI to bear on geoscience.
Speaker 2: Absolutely, if I'm going to reduce totally totally.
Speaker 1: Yeah, Yeah, it's amazing that nobody got to it before
Speaker 1: you did. There are these giant billion dollar mining companies
Speaker 1: and it was right there for them, but they didn't
Speaker 1: do it. I mean, why didn't somebody do it before you?
Speaker 3: What you will definitely hear is, oh, we use data
Speaker 3: science like we we we use scientist, right, And it's
Speaker 3: like and it's like not totally wrong. But what is
Speaker 3: what is definitely unambiguously different, if not unique to Cobold,
Speaker 3: is that we're we're a full stack explorer.
Speaker 2: We were started, we were started and built.
Speaker 3: On the concept that that applying uh vanguard scientific competing
Speaker 3: techniques to these problems would improve the efficacy and efficiency
Speaker 3: of exploration, right, that that is the goal. So we
Speaker 3: have our technical staff is about sixty percent data scientists
Speaker 3: or software engineers, about forty percent geo scientists, right, so
Speaker 3: we're roughly equal equal numbers across the three disciplines.
Speaker 2: And that's that's completely Uh, that's that's unique.
Speaker 1: Let's let's talk about data, right. I feel like, uh,
Speaker 1: discussions about AI, for me tend to get more interesting
Speaker 1: when we get into data and and it seems like
Speaker 1: that's where a lot of the action is and and
Speaker 1: from what I understand about the story of your company,
Speaker 1: kind of building the data set is a big part
Speaker 1: of the story and a big part of what has
Speaker 1: differentiated you. So you have all these data scientists, what
Speaker 1: they need is data. How do you go about building
Speaker 1: this data set to find these metals?
Speaker 2: Yeah, it's incredibly good question.
Speaker 3: So most of the data we use was collected by
Speaker 3: other people at other times. Humans have been collecting information
Speaker 3: about the physics and the chemistry of the Ear's crust
Speaker 3: for a very very long time. Right, They've been doing
Speaker 3: it for well, I mean, in some sense for millennia,
Speaker 3: but certainly over the last century they've been doing it
Speaker 3: in ever more sophisticated ways, and for reasons I can explain.
Speaker 3: Most almost all of that data is actually in the
Speaker 3: public domain. The problem is it is a utter mess
Speaker 3: It is like an end member hard messy data problem.
Speaker 3: Think of different humans in different decades, speaking different languages
Speaker 3: and different places of the world.
Speaker 2: Collecting different types of.
Speaker 3: Data, and I'll get into the types of data in
Speaker 3: a moment, with different measurement techniques based on the vintage
Speaker 3: of their of the era, and then storing it in
Speaker 3: all manner of storage media, everything from literally hand written
Speaker 3: geologic notes or handwritten drilling notes all the way to
Speaker 3: cloud native data data structures right and everything in between.
Speaker 2: And so it is this incredible mess of data.
Speaker 1: Give me some specific examples. What are specific like did
Speaker 1: you find stuff in a drawer or something sort like,
Speaker 1: give me some specific examples.
Speaker 3: So I'll give you examples of there's geologic libraries archives
Speaker 3: right with carefully carefully constructed geologic maps that were that
Speaker 3: might be one hundred years old, and there they were
Speaker 3: a smart skilled geologist just make it a doing field mapping,
Speaker 3: which basically means deserving and recording the observations of outcrops
Speaker 3: and describing the rock, the rock types and those outcrops
Speaker 3: right and locating them in space. And the Earth's crust
Speaker 3: changes very slowly. So provided that was provided that was
Speaker 3: like a well done one hundred years ago, it's still valid.
Speaker 3: It's just that it's it's it's you know, literally in
Speaker 3: drawers piled on top of each other in you know,
Speaker 3: and uh and.
Speaker 2: And basically not used.
Speaker 3: It would only be used by a very industrious human
Speaker 3: being who spent who spent countless hours sort.
Speaker 2: Of searching through the old archives.
Speaker 3: Right, so we go, so we go to various archives,
Speaker 3: and we make an arrangement to digitize the information at
Speaker 3: our expense, and we give the.
Speaker 2: Owners all full digital copy.
Speaker 3: It's almost always public domain data, and so we we
Speaker 3: have a right to use it, or we negotiate a
Speaker 3: specific use right. So digitizing a geologic map is like
Speaker 3: is the very very beginning.
Speaker 2: Then then you need to extract the information from from the.
Speaker 3: Digital copy of the map, uh and, and you have
Speaker 3: many different types of information there.
Speaker 2: You can have in the paper records.
Speaker 3: You might have you might have chemical assays, so measurements
Speaker 3: of the concentrations of the elemental concentrations of samples taken
Speaker 3: from different locations on the map, and that could be a.
Speaker 2: Part of a part of the record.
Speaker 3: And so that's tabular data because it'll say, well, this
Speaker 3: sample sample whatever had had x percent calcium and y
Speaker 3: percent magnesium and z percent silica, and et cetera, et cetera,
Speaker 3: et cetera. That's all valuable information. So that's tabular information
Speaker 3: that then gets extracted by by our systems and populated
Speaker 3: into into what we call our universal schema, which just
Speaker 3: means that every data type is stored in a in
Speaker 3: a consistent format.
Speaker 1: You're standardizing this wildly messy heterogeneous test.
Speaker 3: That's exactly right, And we should talk about more about
Speaker 3: what the data is because it's really fascinating, right, So
Speaker 3: I gave you, I give you two examples. I give
Speaker 3: you the sort of qualitative almost like drawn geologic map,
Speaker 3: which is incredibly useful information, but qualitative and continuous in nature.
Speaker 3: Then there's the sort of tabular data that would be
Speaker 3: any kind of any kind of assay data, measurements of composition.
Speaker 3: But then you have a whole different classes of data,
Speaker 3: like geophysical data, which tells you something.
Speaker 2: About the physics of the Earth's crust.
Speaker 3: So, for example, you probably know that the Earth's gravitational
Speaker 3: field changes from place to place as you move around.
Speaker 3: It changes because you can go up or down in elevation. Well,
Speaker 3: that's easy to adjust for because you know the elevation.
Speaker 3: It also changes because the density of the rocks below
Speaker 3: you change. And so if you're standing over over a
Speaker 3: or body that has twice the density of whose rocks
Speaker 3: are twice as dense as the surrounding rocks that'll pull
Speaker 3: on you slightly more.
Speaker 2: Okay, and you can measure that that I did not know.
Speaker 3: And this is let's go down this rabbit hole actually
Speaker 3: because it's super interesting. Okay, because imagine, so imagine you
Speaker 3: make this measurement. What are you actually measuring. You're measuring
Speaker 3: the force of gravity in a particular location, and you
Speaker 3: can measure Okay, I've adjusted for elevation and the force
Speaker 3: of gravity is a little bit higher here. Okay, that's
Speaker 3: all you actually know at this moment. So what is
Speaker 3: that telling you? Is it telling you you have a
Speaker 3: modestly more dense object like just below the surface, or
Speaker 3: is it telling you you have a massively more dense
Speaker 3: object deeper. Well, it turns out that this is a
Speaker 3: fundamentally degenerate problem or non unique problem would be the
Speaker 3: way to describe it.
Speaker 2: Mathematically.
Speaker 3: There are many ways to solve that problem. There are
Speaker 3: many different configurations.
Speaker 1: You don't know the answer, you know the ant, but.
Speaker 3: You know, what you do know is that there's a
Speaker 3: there's a very large class of in of invalid solutions,
Speaker 3: and then there's a smaller, uh but still very large
Speaker 3: class of valid solutions.
Speaker 1: Ok there's a lot of things that it is not,
Speaker 1: and there's some things that it could be.
Speaker 3: Could exactly incredibly well said, that's that's that's exactly right.
Speaker 3: So here's a there's a really cool application of our
Speaker 3: of our our technology and our approach. So the industry
Speaker 3: standard approach to this is basically, what what does a
Speaker 3: does a normal conventional company do with this gravitational noma. Well,
Speaker 3: they do one of two things. Most of the time,
Speaker 3: maybe ninety percent of the time. They'll just look at
Speaker 3: it and say, okay, here, here is a gravitational anomaly.
Speaker 3: This is hot, it's higher here than here. That's interesting,
Speaker 3: and they just see it on a two D map.
Speaker 2: Okay. So that doesn't do anything for the non uniqueness.
Speaker 2: It just tells you. It just tells you what the
Speaker 2: measurement is, okay. So that now they would just use it.
Speaker 3: They would just say that, well, it's interesting because it's higher, Okay.
Speaker 3: So what Cobold does is very very different and sort
Speaker 3: of impossible to have done even ten years ago, maybe
Speaker 3: even five years ago. What we do is we solve
Speaker 3: a conditioned on a set of geologic hypotheses that we
Speaker 3: find interesting. We solve for the full set of possible subsurfaces.
Speaker 3: So we might actually test like a billion subsurfaces and
Speaker 3: say nine hundred and nine d nine nine nine million,
Speaker 3: nine hundred thousand of those don't match the data we tried.
Speaker 2: They don't match it.
Speaker 3: So there they've been rejected, But we still have one
Speaker 3: hundred thousand now that do. So now we have all
Speaker 3: of the good we've rejected the bad possibilities and we've
Speaker 3: narrowed on the good possibilities. But it's still an incredibly
Speaker 3: large search space.
Speaker 1: Yes, impractical, impractically, you've got to get a lot smaller
Speaker 1: for you to do anything right.
Speaker 3: But there's there's a lot of information in the uncertainty
Speaker 3: that we've now quantified. We've now quantified the uncertainty, and
Speaker 3: so we apply something that we call efficacy of information,
Speaker 3: which is is a phrase that we coined and you
Speaker 3: can read scientific papers about it. It's it's a very
Speaker 3: general and really cool concept, and it's also kind of
Speaker 3: obvious actually, although it's hard to formalize.
Speaker 1: But you look very happy right now. This is an
Speaker 1: audio medium, Like your face is full of delight right now.
Speaker 1: I don't want to interrupt to you. Keep going good.
Speaker 3: Yeah, I'm excited. I love talking about this stuff because
Speaker 3: it's super cool. So we say, okay, the basic idea
Speaker 3: behind EOI is you're going to collect some information next, okay,
Speaker 3: And that's really what every exploration process always is.
Speaker 1: You're going to go try and get more information to
Speaker 1: figure out what is going on in the rocks of
Speaker 1: the surface.
Speaker 3: So the question you obviously want to want to ask
Speaker 3: is what next piece of information will I will tell
Speaker 3: me the most given sort of uh per unit, per
Speaker 3: unit of dollar that I expend, what am I going
Speaker 3: to learn the most from?
Speaker 1: What has what has the highest return on investments?
Speaker 2: Yeah?
Speaker 3: And from a from a knowledge an information perspective, what
Speaker 3: am I going to learn the most from? And so
Speaker 3: here's a way to think about that. It is the
Speaker 3: piece of information that decreases your uncertainty the most. And
Speaker 3: because we solved, we solved the we have the one
Speaker 3: hundred thousand possible answers, right. Yeah, The piece of information
Speaker 3: that will decrease our uncertainty the most is in fact,
Speaker 3: the piece of information that tests the most number that
Speaker 3: falsifies the highest number of those of those one hundred thousand. So,
Speaker 3: for instance, say we're gonna we're gonna drill in different
Speaker 3: in different directions. Okay, yeah, So we're gonna drill and
Speaker 3: we're gonna intersect the sort of various predictions of concentration.
Speaker 3: If we can do one drill hole and it would
Speaker 3: test one hundred of the one hundred thousands, yeah yeah.
Speaker 2: Okay, that's only.
Speaker 3: That's only, we're only testing zero point one percent of
Speaker 3: the possible answers.
Speaker 1: Don't don't drill there, Yeah, don't do that.
Speaker 2: We're gonna learn very little.
Speaker 3: We're gonna end up with basically the same amount of uncertainty.
Speaker 3: But if we could drill a different hole, a different
Speaker 3: core that tests fifty thousand, say half.
Speaker 2: Of them, we massively reduce our search space.
Speaker 3: We we we falsify fifty fifty percent of the possible answers, right,
Speaker 3: and sometimes we can actually falsify like eighty and ninety
Speaker 3: percent of the possible answers. So we massively reduce our
Speaker 3: search space. And we fundamentally it's the most most possible
Speaker 3: information you can get per unit dollar. So every time
Speaker 3: we go to collect any information, we try we try
Speaker 3: to form a formally quantify the uncertainty, and then calculate
Speaker 3: this EOI cont of ter, which is which is the
Speaker 3: piece of information that we have the greatest expectation will
Speaker 3: reduce our uncertainty the most compelling.
Speaker 2: I'm glad you think so.
Speaker 1: It would be nice to be able to do that
Speaker 1: in life. More generally, it's.
Speaker 3: Super super hard, and it comes. The really hard part
Speaker 3: is quantifying the uncertainty correctly. Once you have that quantified correctly,
Speaker 3: then calculating the EOI, like if you know for sure
Speaker 3: you correctly quantified the uncertainty, optimize the calculating the EI
Speaker 3: is kind of an engineering optimization.
Speaker 2: It's kind of it's kind of straightforward.
Speaker 1: In this instance, quantifying the uncertainty is basically how many
Speaker 1: how many ways could the rock under the surface.
Speaker 2: Be exactly Yeah, that's that's exactly right.
Speaker 1: So you've been talking about sort of gathering this very
Speaker 1: old school data and making it useful to you and
Speaker 1: then what you do with it. There is another piece
Speaker 1: of your data gathering operation, or another set of pieces
Speaker 1: that are more high tech and that involve going out
Speaker 1: into the world and getting new data that the then
Speaker 1: already exist. And some of those are kind of fun
Speaker 1: and so I want to talk about that a little bit.
Speaker 1: Tell me about detecting muons.
Speaker 3: Yeah, so this is this is this is kind of
Speaker 3: one of our frontier of R and D projects within
Speaker 3: the company.
Speaker 1: Something we would the opposite of a hundred year old map.
Speaker 3: Yeah, exactly exactly, And we have a lot of a
Speaker 3: lot of physicists at the company, so they love the stuff.
Speaker 3: So what is a muan. Let's start with that and
Speaker 3: then I'll get to the why why they can be useful.
Speaker 3: So in the so cosmic grays are hitting are hitting
Speaker 3: air molecules in the in the upper atmosphere all the time,
Speaker 3: and when when they collide, sometimes they produce they produce
Speaker 3: muons in the in the in the reaction. So it's
Speaker 3: a it's a it's a sub atomic uh particle, a muon,
Speaker 3: and it's it travels very very very fast. It's a
Speaker 3: sort of relativistic particle, travels near the near the speed
Speaker 3: of light.
Speaker 2: So right now muons.
Speaker 3: Are showering through us, you and you in your studio
Speaker 3: and me and my home. Newons are coming through us,
Speaker 3: and they I think about like if you put your
Speaker 3: hand flat, you can expect about one muon per second
Speaker 3: to be going through you through your hand. You don't notice.
Speaker 3: They mostly go right through you. Uh, it doesn't cause
Speaker 3: you any harm. They do interact with electrons, and it
Speaker 3: turns out that when they every time they interact with electron,
Speaker 3: they they.
Speaker 2: Slow down a little bit.
Speaker 1: Uh.
Speaker 3: And when they eventually when when they slow down, eventually
Speaker 3: they slow down enough that they decay into other things.
Speaker 2: Okay, so they they disappear.
Speaker 3: So if you if you are measured, Let's say you
Speaker 3: have a muon detector and it's sitting at the surface
Speaker 3: and you're listening. You know, you like listen to its detection.
Speaker 3: So it's like click click, click click. That's just telling you, okay,
Speaker 3: mwan's going through means going through me's going through right.
Speaker 2: Okay.
Speaker 3: Now I drill a borehole and I start lowering the
Speaker 3: muon detector into the borehole, and you know the rate
Speaker 3: it was at the surface.
Speaker 2: Then as I get to say one hundred meters, now
Speaker 2: it'll be like.
Speaker 3: Click, click click, And then as I go to like
Speaker 3: five hundred meters, it'll be like click.
Speaker 2: Okay.
Speaker 3: What's happening is you're getting fewer and fewer muons are
Speaker 3: hitting that location. And the reason is because you've got
Speaker 3: so many more, so many more atoms between you and
Speaker 3: the at and the top of the atmosphere that the
Speaker 3: muon that the muons aren't surviving, they're they're hit.
Speaker 1: They're hitting the.
Speaker 2: R're hitting the rock.
Speaker 3: And so now you think of think of the journey
Speaker 3: of a specific muon as it's going through the rock. Okay,
Speaker 3: the the muon that interacts with the fewest electrons is
Speaker 3: the most likely to hit you, and the interro and
Speaker 3: the muon that interacts with the most atoms will say
Speaker 3: is is less likely to hit you because it's likely
Speaker 3: to to decay, because it's likely to lose its energy.
Speaker 3: You know, think of a think of a of a
Speaker 3: of a ball bouncing hitting a bunch of other bals.
Speaker 2: Right.
Speaker 3: Yeah, So, now if you're sitting in the you're sitting
Speaker 3: at the muon detector that's been lowered into into a
Speaker 3: location underground, and you're looking up in kind of a
Speaker 3: cone and you can look in three dimensions. Yeah, you're
Speaker 3: seeing muons from stay say, they're coming from the right
Speaker 3: a lot, but they're not coming from the left very much.
Speaker 1: Yes, Yeah, that's.
Speaker 3: Telling you something about the number of atoms, the relative
Speaker 3: number of atoms to the left, which tells you about
Speaker 3: the density.
Speaker 2: That tells you the rocks to the.
Speaker 3: Up and above you, into the left of you right
Speaker 3: are denser than the rocks up into the right of you.
Speaker 3: And denser might mean an ore body, it might mean
Speaker 3: a high concentration set of metal in the rock.
Speaker 2: Uh.
Speaker 3: And so that is it allows us to probe in
Speaker 3: really sophisticated ways the density of the Earth in the
Speaker 3: same way we were talking about the gravitational force changes
Speaker 3: around the Earth.
Speaker 2: It's the same thing, but it's a much higher precision measurement.
Speaker 1: UH.
Speaker 2: So we've designed our our own novel muon detector.
Speaker 3: We did it in collaboration with Occidental College and it's working,
Speaker 3: and it's it's in a pilot hole, uh, collecting muons.
Speaker 3: And then we have a bunch of new ideas about
Speaker 3: how to use this this you this uh you kind
Speaker 3: of new data data type. There's there's a few other
Speaker 3: companies doing this, but it's very new, very new concept.
Speaker 1: After the break from Fury to Practice, Kurt talks about
Speaker 1: Cobold's huge copper discovery in Zambia. Let's talk about Zambia.
Speaker 1: I want to talk about Zambia because it suggests that
Speaker 1: your hypothesis for the company is worked right, Yeah, to.
Speaker 2: A large degree.
Speaker 1: Yes.
Speaker 2: Let me first sEH, why are we in Zambia in
Speaker 2: the first place.
Speaker 3: So we look around the world and we evaluate jurisdictions
Speaker 3: on four dimensions. Okay, our physical prospectivity or how we
Speaker 3: perceive the physical prospect the likelihood that there's going to
Speaker 3: be something new to discover a particular location.
Speaker 2: Yeah, that's thing one. Thing two is the rule of law.
Speaker 3: Right if we if we make it discovery, we have
Speaker 3: a property right.
Speaker 2: How robust is that property? Right? That's thing two. Thing
Speaker 2: three is access to markets infrastructure.
Speaker 3: Right. If you find something in the middle of Antarctica,
Speaker 3: you're not going to be able to get it into
Speaker 3: the market, no matter how great it is. And then
Speaker 3: thing thing four is how much resistance or slash support
Speaker 3: will there be to building a new industrial project in
Speaker 3: that location?
Speaker 2: Right?
Speaker 3: If you find something in Palo Alto, no one's going
Speaker 3: to allow you to build it, right, So it just doesn't.
Speaker 1: You can't even build an apartment building there, much less
Speaker 1: a lithium exactly exactly.
Speaker 3: So those are the four dimensions we look at, and
Speaker 3: we looked around the world early on Zambia rose to
Speaker 3: the very top on all four of those. It's a
Speaker 3: fantastic jurisdiction. It's the most consistent and stable democracy in
Speaker 3: Southern Africa. The physical prospectivity is tremendous because there's been
Speaker 3: mining there for one hundred years and so well you
Speaker 3: might look at that and say, well, sure it was
Speaker 3: a good place to look a hundred years ago, but
Speaker 3: isn't it all picked over?
Speaker 2: And the answer is definitively not.
Speaker 3: This is easy to verify because the uh, basically all
Speaker 3: the all the deposits that were mined over the last
Speaker 3: one hundred years were actually sticking out of the surface.
Speaker 3: They were they were known about a century ago, and
Speaker 3: there's been almost no exploration into the deep parts of
Speaker 3: the basins. Uh, the what we'd call blind exploration. Right,
Speaker 3: this is not directly directly evident, like you can see
Speaker 3: it at the surface. There's there's been almost none. It
Speaker 3: was this kind of perfect perfect location in that sense.
Speaker 3: It's right on the Central African copper belt, which provides
Speaker 3: a significant majority of the world's copper, so it's easy
Speaker 3: to get it to market, right, and it's a legacy
Speaker 3: mining country. That's that's very supportive of development, right, and
Speaker 3: so it's like perfect it was. It rose to the
Speaker 3: top across the board and we'd love it, and we
Speaker 3: love our Zambian colleagues, uh, and we think it's just
Speaker 3: it's one of the best jurisdictions in the world for us,
Speaker 3: for us to operate.
Speaker 2: So that that's why we were there in the first instance.
Speaker 3: And we we we we started we started exploring in
Speaker 3: twenty twenty there in very modest fashion, loosely, and we
Speaker 3: but we were exploring in areas right in the heart
Speaker 3: of in the heart of active mining basins because again
Speaker 3: there were sort of active minds, but if you went
Speaker 3: out into the deeper parts of the basin, deeper than
Speaker 3: five hundred six hundred meters, there were areas that just
Speaker 3: had never been probed or explored.
Speaker 1: Yeah, So like briefly, what did you find and how
Speaker 1: did you find it?
Speaker 3: Looking at all the data we had, some of our
Speaker 3: geoscientists had really really clever ideas about how the mineralogy
Speaker 3: was changing and how we actually might have very very
Speaker 3: distinct mineralogy, so we might have areas where it looks
Speaker 3: like it's all this kind of one distribution, one sort
Speaker 3: of set of statistics, but actually, as you cross this boundary,
Speaker 3: it's a totally different geochemical system. Different sets of geochemical
Speaker 3: reactions occurred. So if you're able to draw that boundary
Speaker 3: and then only go and explore within that comple CAD
Speaker 3: three dimensional boundary, then you'd consistently have high grade and
Speaker 3: thick right.
Speaker 2: That was that was the sort of assertion.
Speaker 3: What you have in a in the in the reservoir,
Speaker 3: we have nine in the legacy data. Ninety five percent
Speaker 3: of the data is this low, you know, kind of
Speaker 3: modest grade thin stuff. And then there's five percent of
Speaker 3: the data is this higher grade stuff. And if we
Speaker 3: and if we drill there and within that boundary and
Speaker 3: only drill there, it's gonna be.
Speaker 2: Good whole, good hohole, good hold, good hoole, good hole. Right,
Speaker 2: And that's and now.
Speaker 1: That's the hypothesis. Yes, you test the hypothesis and does
Speaker 1: it happen, right.
Speaker 3: Yeah, exactly, So you found it, Yes, and we proved it,
Speaker 3: and it's and it's there, and it's it's it's very
Speaker 3: it's it's gone from marginally economic to very economic. For
Speaker 3: the same unit of rock that we move, we we
Speaker 3: we sell ten times as much copper as the average
Speaker 3: copper mind today.
Speaker 1: Yes, okay, so you found it correct. The hypothesis was true,
Speaker 1: the system worked. You found it correct. Now you are
Speaker 1: like gonna become a why different company. It seems to
Speaker 1: me right, Like you are in Silicon Valley, you are
Speaker 1: working with data scientists, you have a technical background. You
Speaker 1: have been running essentially a high tech startup. You are
Speaker 1: about to be running a mining company. Where like your
Speaker 1: problems are not just being very clever and hiring the
Speaker 1: right AI people. They're like, you know, getting the US
Speaker 1: government to finance a railroad in Zambia and making sure
Speaker 1: that the Zambians like you. Like that seems entirely different
Speaker 1: than what you have been doing so far.
Speaker 3: Partially true, there's it's it's a little more continuous than
Speaker 3: you might have implied.
Speaker 1: Yeah, I'm just trying to sight but but it's not.
Speaker 1: It's not a regular startup anymore, correct, Like maybe it
Speaker 1: never was. But like it's a super different job. It's
Speaker 1: a super different skill set. I mean, I'm sure to
Speaker 1: some extent you've been dealing with this, but it's really
Speaker 1: different than what we have been talking about. It's a
Speaker 1: whole different universe of problems and hard things to deal
Speaker 1: with and a very different domain.
Speaker 3: Sure, right, No, you're you're, you're these are these are
Speaker 3: excellent questions. So you can think about think about the
Speaker 3: company now as there is the discovery machine. The discovery
Speaker 3: machine is everything we've been talking about, right, the and
Speaker 3: the discovery the unit economics of exploration are great, right
Speaker 3: because the you can make a hundred times your money
Speaker 3: or even more on proving the deposit because it's worth
Speaker 3: so much once it's clearly clearly economic.
Speaker 1: Right, so you could just sell the rights in some fashion. Right,
Speaker 1: you don't have to be a mining company. You could
Speaker 1: be a discovery company.
Speaker 3: Correct, And it's correct, so so so the most important
Speaker 3: part of them, the heart and soul of the company
Speaker 3: is the discovery machine. Now we have we have at
Speaker 3: least one deposit that is sort of unambiguously going to
Speaker 3: be a mine.
Speaker 1: Uh.
Speaker 3: And and the question is what happens from here with
Speaker 3: that mind and the odds are very high that we're
Speaker 3: going to bring in a partner uh with complimentary capabilities
Speaker 3: to sort of help bring it to production and bring
Speaker 3: in a partner.
Speaker 1: You know.
Speaker 2: It s a simple way to think of this is
Speaker 2: we own eighty percent of it.
Speaker 3: Now the Zambian Parastatal Mining company owns twenty percent of it.
Speaker 3: It's worth a certain amount. You could imagine what it's
Speaker 3: someone would pay to own it entirely, and.
Speaker 1: That order of magnitude is billions of dollars.
Speaker 2: It's like, right, yeah, yeah, yep.
Speaker 3: And so someone could come in and pay and contribute
Speaker 3: to capital and capabilities to bring it into a mine
Speaker 3: and we would still have and we can principle spend
Speaker 3: no more money and we still have a large share
Speaker 3: of all the future cash flows.
Speaker 1: It's worth a lot to have the steak you have
Speaker 1: in this thing. Yeah, you have a set of choices
Speaker 1: about what to do with it and how much sort
Speaker 1: of money to take and how much of your interest
Speaker 1: to sell.
Speaker 2: Correct, That's that's exactly right.
Speaker 1: So is the answer you're trying to figure out how
Speaker 1: much you're gonna be a mining company versus a discovery company.
Speaker 3: I mean, it's that yeah, And we know, we know
Speaker 3: for sure that the most important thing is to not
Speaker 3: not weaken the discovery engine, right, that's the most most
Speaker 3: of them. And there's a lot of culture around the
Speaker 3: discovery engine. There's a lot of attention. That's the most
Speaker 3: important thing. We also know that it's it's very very
Speaker 3: important that this that the value potential gets realized in
Speaker 3: the Zambian deposit.
Speaker 1: Is there about to be some deal if this show
Speaker 1: comes out in two weeks?
Speaker 3: Actually no, and I can I can confirm that we're
Speaker 3: not actually looking for a partner for a couple of years.
Speaker 3: Actually we're not. We won't formally partner with anyone for
Speaker 3: a couple of years. And the reason they're actually really obvious.
Speaker 3: It's just that the the what what we have discovered
Speaker 3: sits on about four square kilometers and there's another one
Speaker 3: hundred and fifty square kilometers on the license that we
Speaker 3: own that are totally untouched, totally unexplored, right, completely unexplored.
Speaker 3: So we are going to fully explore that whole area
Speaker 3: and totally know what we have before we formally do
Speaker 3: any partnership. But we're also building the capabilities to take
Speaker 3: to take the project as far as we we want to.
Speaker 2: Right, so we're hired. We recently hired.
Speaker 3: We stood up an amazing Zambian leadership team of people
Speaker 3: of project developers and engineers, metallurgists, et cetera, hydrologists to
Speaker 3: continue to do the engineering optimization of what a mind
Speaker 3: will look like in this location, right, And so we're
Speaker 3: doing all of that because that that has to be
Speaker 3: done anyway. It's it adds a ton of value to
Speaker 3: figure out exactly how you'll optimize the operations and and
Speaker 3: it just moves the project forward.
Speaker 2: And our our goal, our our stated intention.
Speaker 3: Is to start construction on the mine within two years,
Speaker 3: uh and and to have.
Speaker 2: It in production in the early part of the next decade.
Speaker 1: So I know that we need a lot of copper,
Speaker 1: and it's great that you just found more. It is
Speaker 1: also the case that minds have often been bad for
Speaker 1: the places where the minds were and for the people
Speaker 1: who worked in the minds, Like how do you deal
Speaker 1: with that, you know, harming people and the world.
Speaker 3: What's super ex about this particular deposit, and in general
Speaker 3: the deposits we're looking for is super high grade, right,
Speaker 3: ten times higher grade than the average.
Speaker 2: Copper mine around the world. That means ten times less
Speaker 2: waste for the same amount of copper. Huh Okay. It's
Speaker 2: also an underground mine as opposed to an open pit mine.
Speaker 2: So when you consider the overburden that open pit minds
Speaker 2: big holes in the ground would otherwise have it actually
Speaker 2: ends up being about thirty times less waste, and we're
Speaker 2: going to take almost all of that waste. And as
Speaker 2: we mind, as we excavate the locations underground, we take
Speaker 2: we take the waste and we put it back in
Speaker 2: and we backfill is what it's called. We we we
Speaker 2: stuff it back into the area.
Speaker 3: So at any given time, there's only a modest volume,
Speaker 3: you know, modest cavity that's open. And then in terms
Speaker 3: of the we are passionate and obsessed with skills transfer
Speaker 3: and so it's the reason that we are really building
Speaker 3: a Zambian mining company to develop this project. Our CEO,
Speaker 3: vi Kay Mackay is uh the CEO of Cobold Africa.
Speaker 3: We have ninety percent of the employees in country, our
Speaker 3: Zambian chief metallurgist, chief mind engineer, chief project director, right,
Speaker 3: a psych geologist. All of them are our Zambian and
Speaker 3: they're extraordinary and we're investing tremendous amounts in helping them
Speaker 3: get be the best professionals they can be because we're
Speaker 3: going to be there for fifty years, right, and we
Speaker 3: want to be there for fifty years.
Speaker 1: So what's the next big discovery?
Speaker 2: So I'm predicted I will predict nickel and Canada, lithium
Speaker 2: in Australia, and another and another copper discovery in Zambia.
Speaker 1: I appreciate the specificity that I love falsifiable prediction.
Speaker 2: God bless you. So something I make.
Speaker 3: I make wagers all the time, weird, dis weird wagers.
Speaker 3: But it's not it's not because I'm a gambling man.
Speaker 3: I've never played a spin of roulette in my life, right,
Speaker 3: But what I love to do is people make vague
Speaker 3: predictions about the future, and I try to I try
Speaker 3: to pin them down on something that's clearly testable, and
Speaker 3: then we and then we usually bet, like a nice
Speaker 3: bottle of wine or a dinner that we enjoy together.
Speaker 3: So it's you know, it's it's fun. But the loser
Speaker 3: pay is obviously uh and yeah, So this is so
Speaker 3: we have the in Fact company. A big part of
Speaker 3: our company culture is what we call the culture of falsification. Right,
Speaker 3: So when you go out to test hypothesis, your job
Speaker 3: is not to collect information to confirm that hypothesis, because
Speaker 3: you can always do that. You could always paint a
Speaker 3: new story. Right, that's inductive reasoning, it's invalid, yeah, right,
Speaker 3: What your job is to go out is to tell
Speaker 3: me how you're gonna test it, how you're gonna prove
Speaker 3: it wrong, and go and go falsify it.
Speaker 2: And you either one of two things happens.
Speaker 3: You either successfully falsify it, in which we move on
Speaker 3: and we celebrate that, we celebrate falsification, or you fail
Speaker 3: to falsify it, which means, okay, how do okay, so
Speaker 3: it's not dead yet?
Speaker 2: How do we how do we now? How do we
Speaker 2: test it?
Speaker 1: Now?
Speaker 2: How do we test it again? Right? Death?
Speaker 1: Uh? And what's the most efficient way? What's the highest
Speaker 1: return we can get?
Speaker 2: God? Question, what's the EOI? We got it.
Speaker 1: We'll be back in a minute with the lightning round. Okay,
Speaker 1: we're gonna finish with the lightning round. And so I
Speaker 1: got to ask, what is one weird bet you made.
Speaker 2: Since we just had the Olympics.
Speaker 3: I bet that I bet that Usain Bolt's nine point
Speaker 3: five eight second world record in the one hundred meter
Speaker 3: will still be the world record in the year twenty
Speaker 3: thirty six, by the end of the year twenty thirty six.
Speaker 1: That's a long bet. You're playing the long day.
Speaker 3: Yeah, that's a long standing record. He said it in
Speaker 3: two thousand and nine.
Speaker 1: It's crazy for a record to last that long.
Speaker 2: Yeah, would you like to would you like to make
Speaker 2: that wager with me?
Speaker 1: I don't have enough information. I know enough to know
Speaker 1: that I'm ignorant. But what made you make the bet? Uh?
Speaker 2: It's a dramatic outlier time. It's a total outlier. Yeah.
Speaker 3: And he has the four fastest time, so nobody you
Speaker 3: know that the next fastest human is is the fifth
Speaker 3: fifth fastest time.
Speaker 1: It's like he is an outlier and that time is
Speaker 1: an outlier for him.
Speaker 2: Correct? U huh correct?
Speaker 1: What's a cobalt?
Speaker 2: Oh? Good question. So it's a it's a creature from
Speaker 2: German mythology that lives underground, kind of like a goblin
Speaker 2: like creature. Uh, lives underground and controls the mineral wealth
Speaker 2: of the earth.
Speaker 1: Huh.
Speaker 3: And it's also the namesake for the word cobalt, uh.
Speaker 2: For the for the for the metal cobalt.
Speaker 1: Right. I mean, as I understand it, people used to
Speaker 1: think cobalt was bad, right, and then now we're like, oh,
Speaker 1: actually cobalt is good. Good. Uh.
Speaker 3: It looks a lot like nickel sulfides when it's a
Speaker 3: cobalt arsenide and arsenic is toxic, so it would it
Speaker 3: would poison you know, poison miners, and they called it
Speaker 3: the goblin metal.
Speaker 1: What's one thing I should do if I go to Zambia?
Speaker 3: Oh that's I love that question. Well, you can't miss
Speaker 3: most tuna. I call it, we call it most tuna.
Speaker 3: That's the traditional name. It means the smoke that thunders.
Speaker 3: You will know it as Victoria falls. It is completely spectacular.
Speaker 3: Niagara Falls is amazing. It blows it away, totally blows
Speaker 3: it away.
Speaker 2: You just can't. You just can't go to Zambia and
Speaker 2: miss and miss most Tuna.
Speaker 1: We've talked a lot about, you know, trying to predict
Speaker 1: things and trying to quantify uncertainty in the context of
Speaker 1: your company. Do you think that way outside of work? Uh?
Speaker 3: Well, yeah, I guess the way I make, you know,
Speaker 3: wager on things, right, is a form of.
Speaker 2: It's certainly the way I think of.
Speaker 3: Like, I try to be scientific in every aspect of
Speaker 3: my life, and I say that what science is not
Speaker 3: is empiricism, right. It is not looking at the data
Speaker 3: and drawing the inevitable conclusion, because there's no such thing
Speaker 3: you can look at with any set of data. You
Speaker 3: can tell you can fit many, many, many hypotheses that
Speaker 3: explain the data. Right, that's always this is this is
Speaker 3: the non unique thing where it's it's always true. Basically
Speaker 3: it's always true. And so science really is about myth making.
Speaker 3: It's about it's about making a making up a myth
Speaker 3: that explains the data. That's your hypothesis. The difference between
Speaker 3: science and religion is that we test our myths. That's
Speaker 3: the difference, right, That's that's of good science, right.
Speaker 1: And like in your heart, you should want to disprove it, right,
Speaker 1: Like if you're really the best scientist, you should want
Speaker 1: to prove yourself wrong.
Speaker 3: Yes, that's this the thing, as scientists can say that
Speaker 3: means you learn something you really only learn when you
Speaker 3: realize you were wrong.
Speaker 1: Kurthouse is the co founder and CEO of Cobold Medals.
Speaker 1: Today's show was produced by Gabriel Hunter Cheng. It was
Speaker 1: edited by Lyddy jeene Kott and engineered by Sarah Bruguier.
Speaker 1: You can email us at problem at Pushkin dot FM.
Speaker 1: I'm Jacob Goldstein and we'll be back next week with
Speaker 1: another episode of What's Your Problem