Getting better at mining the minerals needed for clean energy

To create a clean-energy economy, the US badly needs an advanced
mining industry that can provide huge amounts of key minerals for
batteries and other technologies — and it’s nowhere close to
where it needs to be. In this episode, KoBold Metals CEO Kurt
House describes the current state of mineral exploration, the
significant changes it needs to make, and how machine learning
and artificial intelligence can help it get there.


(PDF
transcript)


(Active
transcript)


Text transcript:


David Roberts


Building the machines and batteries needed to decarbonize the
economy will require enormous amounts of a few key minerals. The
proven reserves of those minerals, sitting in mines now
operating, are nowhere close to enough to satisfy what is
expected to be skyrocketing demand.


Without the minerals, we can’t make the clean-energy economy. And
we don't know where the minerals are going to come from.


What's worse, exploring for new mineral deposits has been getting
less and less efficient over the last several decades, as the
amount of investment needed per successful discovery has risen.
We seem to be getting worse at finding this stuff right when we
badly need to be getting better.


That state of affairs has drawn in several new startups that
endeavor to use machine learning and artificial intelligence to
improve mining’s hit rate. The most talked-about is KoBold
Metals. With financial backing from Bill Gates, Jeff Bezos, and
other big-name investors, KoBold is now exploring for minerals on
four continents.


To get a better handle on mining and how we can improve at it, I
contacted KoBold CEO Kurt House. We talked about the projected
gap between supply and demand, the somewhat primitive way current
exploration works, the massive data-gathering and coordination
project the company has undertaken, and the role of justice and
equity in this AI-accelerated future of mining.


Kurt House, CEO of KoBold Metals, welcome to Volts. Thank you so
much for coming.


Kurt House


I'm so pleased to be here. I'm a huge fan. I listen to the
podcast all the time, so it's fun to talk to you live.


David Roberts


We're going to talk about something that is of great interest
these days, which is finding the stuff that we need to build the
clean energy economy. This is something I did a series of
articles on a couple of years ago, and it's come up repeatedly
over the years. People talk about possible shortages of materials
as one of the bottlenecks that might slow the clean energy
transition. So maybe let's just start there with setting some
context, talk a little bit about the big four minerals that you
focus on and sort of what we know about how much we have access
to and how much we project we're going to need.


Kurt House


Perfect setup question. So, the energy transition is
fundamentally about getting off fossil fuels. It's fundamentally
about electrifying the economy to the greatest extent possible.
So we electrify transport, all electric generation becomes
renewable, et cetera, et cetera. That requires a lot of very
specific materials and very specific materials because different
elements have different physical properties, obviously, and they
do different things better and worse than others. And some of
those elements are really difficult to substitute for, for very,
very deep physical reasons. So KoBold is focused on what we call
"the materials of the future," and those are lithium, cobalt,
copper and nickel.


That's not at all to say that there aren't other important
materials for the energy transition.


David Roberts


Are those four the most important? By just mass, just, we need
most of those —


Kurt House


No, by total mass, it'd probably be aluminum and steel, iron for
steel. The reason these are so important, there's two orthogonal
reasons that we focus on these. One is how difficult they are to
substitute for in specific applications. And I'll talk about
that. And then the orthogonal element to it is that they are
exploration problems. So aluminum is really useful in a whole
bunch of reasons, but it's not an exploration problem. There's
just gobs of bauxite, aluminum silicon oxide on the planet, and
we know where it is. It's just a matter of processing it in more
efficient and less carbon intensive ways.


So, it's a metallurgical challenge. It's not an exploration
challenge. In the case of lithium, cobalt, copper and nickel, you
could take any forecast you want, but basically the end state is
something like 2 billion electric vehicles on the planet, plus a
whole bunch of renewable energy build out. And any way you slice
it, those are just gigantic numbers, and they require gigantic
amounts of lithium, cobalt, copper and nickel. And then you can
say, "Okay, that's how much we need at, say, 2050 to be mostly
off fossil fuels by 2050, how much exists in the reserves of
current mines?"


So if we take all the mines that are producing today, and they're
going to produce out for the next several decades, that's another
number. That's another quantity. And that you also have to add in
all of the other uses for these minerals. Right. If the economy
just goes on, and there's lots and lots of uses for nickel and
copper, in particular in stainless steel and all manner of
electrical applications for copper that just happen anyway. So
you have to add up those, plus the energy transition metals and
compare them to existing mine supply and existing mine reserves,
and you get a gap.


And then, if you multiply by current commodity prices, that gap
is about $15 trillion.


David Roberts


Good Lord.


Kurt House


We call that — exactly — the "missing metals gap". And so it's
not the total amount of metal we need. We actually need a lot
more. But that's the value of the metal that we need to find,
right? We need to find and then develop into mines.


David Roberts


That represents metal we need, but we don't yet know where it is
or where it's going to come from?


Kurt House


Exactly. Because I've subtracted out existing mine supply. Right.
And not just existing mine supply, but existing mine supply, plus
mines that are in late stages of development. We know they're
going to be mines. We've just included that in existing mine
supply. So it's really the things that we need to find new
deposits that no one in the world knows where they are right now.
And then we need to develop them into operating mines — to build
new mines. And then those mines need to go into production, and
they need to operate for many, many years to produce the
necessary amount of metal.


And the value of that metal, roughly speaking, is $15 trillion.


David Roberts


And that all needs to happen — if you look, like, 2050 used to be
a lot farther away than it is these days. And now if you look at
those lines, they're going up and to the right pretty steeply. So
all of that stuff needs to happen much more quickly than it has
in the past.


Kurt House


Exactly right, David. And that is what makes this so difficult.
So, in round numbers, in very round numbers, it's about 1000 new
deposits need to be found, and then 1000 new mines need to be
developed.


David Roberts


Wow.


Kurt House


And that, obviously, that's a function of I'm using sort of
median mine production. It could be 700 if they're bigger or
whatever, but it's order of magnitude 1000. It's a huge number.
And then you can say, "okay, well, how fast are we building new
mines today? Finding new discoveries and building new mines
today?" And the answer is, "not nearly fast enough."


David Roberts


Well, this is something you told me about last time we talked,
which has stuck in my head ever since. You called it "Eroom's law
of mining," which is Moore's law backward. Explain what you mean
by that.


Kurt House


Yeah, precisely. So, Moore's law. The audience will be very
familiar with Moore's law. Right. One of the most remarkable
demonstrations of human ingenuity of all time, which is that the
density of transistors on chips has doubled every 18 to 24 months
for 55 years now. And the result is a ten to the 10th order of
magnitude increase in computational speed, computational power.
So we get better and better and better computation, and that's
why you and I can talk remotely in real time from far —
everything else that people know. Okay, that's Moore's law. So go
back to 1990 and look at how much money the industry was spending
in aggregate on exploration, and then divide that number by the
number of good new discoveries they were making per year.


David Roberts


Right? Dollars per discovery.


Kurt House


Dollars per discovery. And by good discovery, I just mean a
discovery that definitely becomes a mine. It's a good tier one,
tier two discoveries, we'd say, in the industry, but it becomes a
mine. That number was about $300 million in today's dollars. In
2023 dollars, that was about $300 million per good discovery.
Today, that number is about $3 billion. It's gotten an order of
magnitude more expensive to find the next deposit. We're getting
worse. So we call this Eroom's law, because over the last 40
years, we've gotten ten x worse at exploration, we have to put in
ten times the amount of resources to find the same amount of
stuff.


Or put it another way, we'll find one 10th the stuff if we invest
the same amount in exploration. And exploration expenditure is
basically flat, roughly speaking. So we are way, way, way behind
and we're spending roughly half a percent a year of what would be
needed based on the current exploration effectiveness, dollars
per discovery. So on the current rates and current expenditure
investment, it will take about 200 years for humanity to find
enough deposits.


David Roberts


$3 billion. If you think about 1000 new mines needed —


Kurt House


There you go.


David Roberts


1000 times $3 billion adds up to some large —


Kurt House


$3 trillion. And that's just exploration expenditure. That's not
including the cost to actually build the mines, which is a lot
more.


David Roberts


So let's break this down a little bit or unpack this a little
bit, because with Moore's law, I think people get, on the one
hand, getting more computing power out of tinier and tinier
spaces gets harder and harder. Like the job gets harder and
harder because you're working with just less space and tighter
materials and et cetera. But our improvement at doing it is
growing faster than the difficulty, basically. Like, we're
getting better faster than it's getting harder, I guess, is the
way you would put it. If we just remained the same good at doing
that, productivity would be declining because it would be getting
harder and harder and we wouldn't be getting better.


That seems to be what's happening in mining. It's not that we're
getting dumber or worse at mining, it's just that finding the
stuff is harder because we've already found the easiest stuff. So
finding stuff gets harder and harder, but we're just not getting
better at it.


Kurt House


You explained it perfectly. That's exactly right. Another example
that I've used, that's even a better example because you just
took Moore's law a step further. But another example I use is
like fastballs in Major League Baseball were like 85 miles an
hour in 1970, and then today they're like close to 100 mph. It's
objectively harder to hit that fastball, but batting averages are
about the same. Right? So pitchers got better, batters got
better. Right. And your point is exactly right is that — are we
actually getting dumber? I don't think so. We're not actually
getting dumber. It's that the search space is getting way harder
because, as you say correctly, the easy things have been found.


And what is an easy thing? It's actually really easy to
understand. An easy thing is a deposit, an ore body that's going
to be mined, that's sticking out of the ground that a skilled
field geologist walks up to the outcrop, looks at the outcrop,
identifies ore minerals in the outcrop, and says, "These are ore
minerals right here. We should explore this because there might
be enough of them to constitute a mine here."


David Roberts


Yeah, you told me that when we talked before, and it blew my mind
a little bit because one of the things I'm finding out about this
as I do this job more and more is just like normal american
consumers are so used to everything going digital and everything
being sort of like fancy and computerized now that when I go ask
about other areas, I'm often struck by how analog they remain,
sort of how kind of primitive they remain. And something you told
me is that almost all, like literally almost all of the
discoveries we've had and the mines we now have come from someone
just seeing something on the surface, like literally the same way
they found stuff to mine in 1800.


Kurt House


Yeah, it's absolutely right. If you were to build a time machine
and bring the best exploration geologists from 1960 to today,
they would be very comfortable working in the industry. Very
comfortable. You have to teach them email. Right. You have to
teach them a few things —


David Roberts


Zoom meetings.


Kurt House


Exactly. Zoom meetings. But in terms of the field work and the
techniques, they would be very similar.


David Roberts


And so should we envision groups of people out walking around
looking, or how do we search the surface today?


Kurt House


Yeah, I mean, field geology is a skill, and it's a hard skill to
learn. It takes a lot of practice. And there are very skilled
field geologists that KoBold employs, and they're absolutely
essential to our business. They're fantastic. And just because a
technology or technique is old doesn't mean it's bad. There's a
lot tried and true methods just sort of continue for some good
reason. Right. And so field mapping, for instance, which is
basically, if you walk along the ground, think about, you're
either walking along bedrock that's an exposed outcrop, or you're
walking along soils or something else that's kind of covering the
bedrock.


And so field mapping exercises are, there's actually a lot of
kind of tricky geometry to it, and it's about identifying the
outcrops. It's about making measurements about where the outcrops
go underground and then extrapolating in a kind of heuristic way
what those rock bodies would look like underneath the cover.
Those are sort of useful techniques, and that's what historic
field geology is all about.


David Roberts


And so, historically, when someone finds something sticking out
of the surface and they say, "hey, this looks like an ore
concentration. This looks like a concentration of some ore that
we would like to mine" at that point, then what, the mining
company just goes in and just starts poking holes down, digging
down and looking?


Kurt House


Yeah. Once you find an occurrence, you might call that an
occurrence, mineral occurrence. Then you go and explore to see if
it's large enough and sufficiently high concentration to be an
economic deposit. It's a good lead. Not every occurrence turns
into a mine, but every mine at one point was an occurrence.
Right.


David Roberts


Right. That's what I'm trying to get my head around is, what does
that look like? What do those holes look like? Are they narrow,
little pokey holes, or is this, like, a big operation when you're
digging down, exploring?


Kurt House


So, for the exploration component, they're very narrow holes.
Think a few inches in diameter that you drill. And what you're
trying, you're extracting core samples. Right. You're extracting
to characterize the full geologic body and to characterize the
deposit. Right. And understand what the composition is, what the
grade is, how extensive it is. Does it keep going at 100 meters
deep, at 500 meters deep, or does it stop at 20 meters deep?
Those are all the questions that you try to answer.


David Roberts


Of the occurrences that geologists find and say, "Hey, mining
company, this looks promising." What percentage of those pan out
into a mine?


Kurt House


Very low. Yeah, very low.


David Roberts


Oh, really?


Kurt House


Yeah. Well, less than 1%.


David Roberts


No shoot. No kidding.


Kurt House


No shoot. No kidding.


David Roberts


1%! I guess that explains the $3 billion.


Kurt House


Yeah, that's right. I'm glad you came full circle back to that,
because the reason we've gotten on an underlying cause for
Eroom's law, but a more directly observable cause, is that the
success rate has dropped. And so you're spending — the individual
success cases are sort of just as economic as ever, and they're
wildly economic. The difference is your false positive rate has
increased or your success rate has decreased. And so you spend
more small investments on things that don't pan out. To make the
numbers simple, imagine each little small investment
characterization effort is $10 million.


David Roberts


So that's to go poke all the little holes and look, that's $10
million.


Kurt House


Exactly. That's to go see if that occurrence is actually an
economic deposit. Right. And we're using round numbers here. 40
years ago, you might have gotten 1 out of 30, and today you get 1
out of 300.


David Roberts


Yeah. So $10 million. If you're having to do it 100 times to find
metals —


Kurt House


That's the problem.


David Roberts


This isn't in danger of making mining overall uneconomic. Right.
If you find the minerals, you're going to —


Kurt House


Right. So the individual success cases are still as good as ever.
Actually better than ever, really, because you still spend, say,
$10 million, roughly, and then you fail, fail, fail. And then the
person that succeeds — and these are often different groups,
right — the one that succeeds spends $10 million and then has
something that's worth billions.


David Roberts


Right.


Kurt House


And all of the successes, sort of, by definition, have to pay for
the failures or the industry just won't attract any capital at
all in the aggregate. So you can kind of assume that the
successes are worth something like $3 billion, and that's about
right. And so the success cases do incredibly well.


David Roberts


A bit of a lottery vibe to it.


Kurt House


For sure. I mean, this is where the word eureka comes from.
Literally. The unit economics of exploration are just superb.
They're fantastic. They've always been fantastic.


David Roberts


Right.


Kurt House


And so if you can imagine, okay, if you could build a technology
or a set of technologies to increase your success rate just
marginally, just a little bit better, maybe two or three times
better than the industry, then you'd have an incredibly valuable
set of technology and company because you could turn exploration
into a science. Yes, you're going to fail a lot, but if you fail
nine out of ten times, as opposed to 99 out of 100 times, then
you make superb money. Because every success —


David Roberts


If you think of it as a lottery, I mean, winning the lottery
twice out of 100 times is a lot more than winning it 1 out of 100
times, right?


Kurt House


Exactly.


David Roberts


Because the winnings are very large. And I just want to get this
on the record before we move past. As we're talking about
exploration, just by way of background-background. None of the
things we're discussing in terms of the difficulty of finding and
extracting minerals are about absolute scarcity.


Kurt House


Correct.


David Roberts


All four of the minerals you cite —


Kurt House


yes.


David Roberts


are, on an absolute basis, far more common than we could ever
use, right?


Kurt House


Absolutely. There's roughly in the top kilometer of the earth's
crust, there's enough nickel to give every man, woman, and child
on the planet about a million electric vehicles. So it has
nothing to do with the number of atoms in the earth's crust.
That's not a problem at all. In fact, right below your feet right
now, below your house, or wherever you are, I would bet long odds
that the concentration of nickel, say, in the ground below you is
about 100 parts per million. That's about what it is in the
background concentration of the earth. And so that's there.


And we could mine it. We could do it. We could go and mine 100
ppm. In fact, that would be a spectacularly good platinum mine if
you had it in that concentration. But it would cost us like
$100,000 a kilogram to extract it. The current nickel price is
$16 a kilogram.


David Roberts


Right. So what you need to find is concentrations of these
things. And the reason that the vast, vast, vast majority of the
concentrations we found of these minerals are close to the
surface is just that the surface is where we're looking. So what
we find tends to be adjacent to the surface.


Kurt House


Exactly right. And it is the mother of all selection biases.
Right. Understandably so. You could pose an alternative
hypothesis: You could say, "Okay, yes, we found all these things
at zero meter elevation, but how do we know? Maybe that's just
where they form. Maybe they form when the right minerals contact
the atmosphere or something." You can make up a scientific
hypothesis.


David Roberts


Maybe they drift up toward the surface for some reason.


Kurt House


Yeah, exactly. That would be a reasonable scientific hypothesis.
We know for certain it's not true for these four minerals. We
know for certain because we know the pressures, temperatures, and
oxidation conditions in which these minerals form, and they form
deep in the subsurface. So actually, that's really good news,
because that tells us that as we descend into the subsurface,
take 100 meters depth slice as opposed to a zero meter depth
slice. Your strong expectation should be that the aerial density
of deposits increases because since they form deep in the
subsurface, a kilometer deeper, it's only the rare few that have
been moved to the surface through tectonics and erosion and then
been exposed.


Right. And so we really have high confidence they're there in
high concentration form. They're just way, way harder to find.


David Roberts


Right. And talk a little bit before we get to how your company is
making that easier. Talk a little bit about what we're looking
for. We talked before; you mentioned composition, depth and
grade; just quickly sort of go over those. What makes for a good
deposit? A promising deposit.


Kurt House


Yeah, those are sort of the big three. There's lots of small
details, but grade is king. That's a phrase you hear in the
industry all the time. And the reason that grade is king is
really obvious. Like concentration really matters. So imagine
this. Imagine you're looking at two different deposits, let's
call them copper deposits. Okay. One copper deposit — so the
average grade coming out of copper mines around the world today
is 0.6%.


David Roberts


Yeah, I've heard about it. And that's been declining, pretty
sharply declining. Right. I've been reading about that. It's very
alarming.


Kurt House


Yeah, it has —


David Roberts


Because we need a lot of copper.


Kurt House


We need a lot of copper. And we've been creaming the curve. We've
been getting the lowest cost stuff first.


David Roberts


Right.


Kurt House


So 0.6%. So that means if you mine a ton of rock, you do all the
work to get a ton of rock out of the ground. You get 1000
kilograms rock. Right. And you get 6 kilograms copper. 0.6%. So
you get 6 kilograms of copper for every one ton of rock that you
mine.


David Roberts


God, that just seems crazy.


Kurt House


It is crazy, seems crazy, but stay with me on this. So the ton of
rock, all your costs go into mining the ore. The rock. Right. The
costs scale with ore. Revenue scales with metal, because you
don't sell the ore, you sell the metal. Right.


David Roberts


Right.


Kurt House


So now imagine a different deposit that is 6% copper instead of
0.6% that deposit. Now you spend the same amount of money to get
that one ton of rock out of the ground. So their costs are the
same. But now you get to sell 60 kilograms copper instead of 6.
So you sort of, by definition, have a 90% plus profit margin
because the costs — the other operator was there and maybe
breaking even at 0.6%. And now you're selling 54 more kilograms
of copper for no incremental cost.


David Roberts


So find distinctions in the concentration of the mineral in the
ore make a huge difference.


Kurt House


An enormous difference.


David Roberts


That's great.


Kurt House


Yeah. So grade is king. The second most important variable is
composition, which really gets down to the ore minerals
themselves. Right. So a good example here is nickel. And so you
actually can find relatively high grade nickel in silicate form.
So olivine is a nickel silicate mineral that can get relatively
high concentrations. It's not uncommon to get 1% nickel
silicates, but it's very hard to process, very expensive to
process that. Alternatively, you can get nickel sulfides at 1% or
2% that are very easy to process, relatively speaking, very easy.
So a 1% nickel sulfide deposit is way better than 1% nickel and
silicate deposit.


And then depth obviously matters a lot. It matters kind of less
than you might think. Believe it or not, there's a big
distinction between whether it's an open pit mine, i.e. just a
big hole that you're digging in the ground, versus an underground
mine where you're digging tunnels. There's a relatively large
distinction there in terms of cost of operations. But once you go
underground, depth doesn't actually matter that much. I mean, up
to a point, you can't go 10 km below the earth, but the
difference between 300 meters and 700 meters doesn't matter that
much, actually.


David Roberts


So digging that additional 100 meters down is not a huge cost.


Kurt House


It doesn't matter a lot. Yeah. I'd much rather have higher grade
deeper than lower grade, shallower.


David Roberts


So what would be like a really high grade, say, for copper? You
say 0.6 is the average these days. That seems — I mean, what do I
know about. I have nothing to compare it to, but it sounds low.


Kurt House


It is low.


David Roberts


What's a really good grade?


Kurt House


Over 3% is superb. Like superb, superb.


David Roberts


Got it.


Kurt House


Over 5% is absolutely world class. And there's almost nothing
like it out there.


David Roberts


Interesting. Okay. What we've established is currently we're
finding minerals based on some exploration geologist on the
surface, squinting at rocks, finding good rocks. And then you
poke a bunch of holes. If you find a decent looking rock, you
poke a bunch of holes around it, find out if it's down there in
the 1 out of 100 times that you find suitable deposits down
there. You dig down there and open a mine. And this is why 98%,
99% of mines and deposits have been near the surface or close to
the surface.


Kurt House


Correct.


David Roberts


So this is all background for current mining. And the
productivity of that is declining, presumably just because the
big obvious surface concentrations that we could find, we've
mostly found. And so now we're like squinting, we're going after
harder to find stuff, et cetera. And the whole productivity of
the sector is declining, even as we desperately need it to 10x
its output in the next 20 years. All in all, an alarming
situation.


Kurt House


That was a perfect summary.


David Roberts


So then along comes KoBold. So just tell us, how is KoBold going
to help that situation? What is it you do to help miners?


Kurt House


Yes. So KoBold's main objective is to improve the success rate of
exploration, right? Exactly. This problem. The declining success
rate of exploration is Eroom's law that's resulting in the higher
cost, the more cumulative expense to make a discovery. And so our
objective is to improve the overall success rate or really the
exploration efficiency or effectiveness, which is dollars per
discovery. That's our real goal. We want to have our own
exploration success rate of closer to $100 million per discovery,
as opposed to $3 billion per discovery.


David Roberts


And that's the money side. What about the percentage side? Like
you said, 1% success rate. Now, we're skipping ahead a little bit
here, but if KoBold continued to advance and miners really took
it up and took it seriously, what would a really good success
rate look like?


Kurt House


Yeah, well, let me actually correct you on one thing. We're not a
service company or a SaaS company at all. We don't provide, we
don't sell anything to mining companies. Yeah, we are a full
stack exploration, development and mining company ourselves,
started in Silicon Valley. We are Silicon Valley's mining
company, so to speak. Right. Your confusion is totally
understandable, and it's common.


David Roberts


I thought you were striking deals with big mining companies.


Kurt House


We do, but only on co investment into particular projects. We own
— we don't get paid anything for services or anything for
software. We make money by making discoveries, and we partner
with mining companies on their properties as well as we have our
own. So we have something like 60 projects worldwide, and about
half of those we own 100% by ourselves, and about half of those
we own in partnership with other companies. Partnerships are very
important to us because we want to extend the reach of our
technology. And lots of companies own a lot of ground that
they're not exploring.


And so we can sort of leverage that opportunity and say, "Okay,
this is interesting ground. It's actually worth more in our hands
than it is in your hands, because we can deploy our technology,
let's work it together, and we'll both benefit from any
discovery."


David Roberts


So you're co-mining those, you're both involved in the mining?


Kurt House


Very common misconception and very understandable, because if you
look at our team, it's about two thirds of the technical staff
have never worked in the mining business before. They come from
Google and Apple and Meta and Silicon Valley and all the tech
monopolies that you know and love or hate, and they probably
never will again. Right. They don't think of themselves
necessarily as working for a mining company. I mean, of course
they do. They understand the business, but they think of
themselves as working for a tech company. And they are deploying
their skills as data scientists, software engineering, software
engineers for this purpose.


David Roberts


Right. So you own big machines that dig up the earth.


Kurt House


Principal asset are licenses to deposits. Right. Our principal
assets are the deposit itself or the deposit itself. And then, of
course, we use all kinds of capital equipment to explore and
ultimately develop the deposits.


David Roberts


Okay, so if you own these yourself, then you don't have to
speculate what better discovery rate would look like. Presumably,
you have established one. 1 out of 100 is the baseline here. What
does your discovery rate look like?


Kurt House


Yeah. So I prefer dollars per discovery. And the reason I prefer
— I'm going to answer your question, but it's important to think
about the different metrics. The reason I prefer dollars prefer
discovery is we encourage lots of little projects that falsify
their hypotheses fast and for low money. Right. So, there was a
project we had recently where one of our scientists had a
hypothesis about a particular deposit. They came up with a clear
falsification criteria. We staked the property for probably
$1,000. We went out to the field. They made several measurements,
geochemical measurements. It falsified the hypothesis.


We moved on from the project at something like $5,000. Right.
It's an extremely efficient condemnation. And that's really
important because there is an enormous amount of uncertainty in
this business. That is the nature of the data science problem is
it's a sparse data problem, and we're making inference on very
select data about the physics and chemistry of the Earth crust,
trying to make predictions with quantified uncertainty and then
seeing how accurate those predictions are and then updating our
models accordingly. So we want lots and lots and lots and lots
and lots of shots on goal. So I don't actually track total number
of attempts because actually the numerator is high.


But dollars per discovery, I very much do track. And we are on
track for we have made one major discovery, which is public. It's
in northern Zambia. Cumulative exploration expenditure up to the
point of that discovery was actually less than 50 million.


David Roberts


That's quite a bit better than 3 billion.


Kurt House


Yeah, exactly. So we're very much on track in that sense, except
that we only have one that's totally unambiguous, and then we
have a lot more that we're working toward. So we'll see. It'll
take time, we'll see over the next five or ten years. But I'm
very encouraged that we are on track at this point.


David Roberts


Okay, well, so let's back up. What the company does is gather
data, use AI, machine learning to analyze the data in order to
predict where you're going to find concentrations, basically.


Kurt House


Correct.


David Roberts


That's the nutshell. So let's talk about the data. I think when
people hear this, their first thought is like, "Well, if the data
was there, why weren't other people using it to find these
things?" Do you know what I mean? If the data was publicly
available, what's your magic sauce? So, first of all, let's just
talk about, and this struck me, too, the last time we talked, is
just the wild range of data that you guys are gathering. Talk a
little bit about your data gathering.


Kurt House


For sure. So that is the right question for any kind of AI/ML
technology ever, any application is, what is the data? Right.


David Roberts


What are you learning from?


Kurt House


Right. The algorithms, with all due respect, the algorithms are
pretty straightforward, actually. They're brilliant, but they're
easy to replicate and they're kind of a commodity at this point.
What the real secret sauce of any AI company is, do they have
superior data source?


David Roberts


Yeah, so I should mention, I was going to mention later, but I'll
just mention now, obviously, KoBold is not alone in this. There
are several companies now trying to use basically data gathering
and AI to improve success rates in mining exploration. There's a
bunch in Australia, I think there's one in Europe. So insofar as
you're competing, I mean, it's probably a giant market, and
there's room for plenty more entrants, I would imagine. But
insofar as you're competing, that's going to be the arena in
which you're competing is who can find the cleverest new sources
of data to improve their results.


Kurt House


Yeah. There's a lot of truth to what you just said. I will
mention on competition — this is useful — I am really rooting for
those other companies. And the reason is I literally never think
about competitors. And that sounds cocky, but it's not really,
because let's say we need 1000 new mines for the energy
transition. If KoBold is responsible for 50 of those new mines,
we are going to be wildly successful beyond my possible
imagination. And that's 5% of the problem.


David Roberts


Right.


Kurt House


And so we still need other people to find and develop the other
900-plus mines. And so what is it that's going to stop KoBold
from getting from 1 to 50? It is not that someone finds that one
first because there's another 950 we need to find. Right. What it
is, is that we just fail to find it. Right. So we are
fundamentally just kind of competing with ourselves, in a sense.


David Roberts


Right. More of a pass-fail than a grade relative to competitors.


Kurt House


Yeah, totally. It's just like, if you're like what I tell my son
in track, you're just focusing on your own pr. Just get your own
time down and the rest will work out. Right.


David Roberts


All right, so let's talk about data.


Kurt House


Yeah, so, data. So this is really interesting. Right? So humans
have been collecting information about the physics and the
chemistry of the earth's crust for a very long time. Yeah.


David Roberts


Mining is real old.


Kurt House


Really, really old. Right. In some ways, they've been doing this
for. I mean, we've been doing this for millennia. In some ways,
certainly in the last century, been collecting information in
more and more sophisticated ways. And virtually all of that
information is in the public domain, actually. And the reason
it's in the public domain is either it was actually collected at
the public's expense in the form of various state geologic
surveys around the world and then made public. That's thing one.
Thing two: It was collected by academics and then made public.


David Roberts


Right.


Kurt House


That's thing two. Thing three is created by private companies,
but almost always, with some exceptions, the rules. The laws in
every jurisdiction out there are that when you have an
exploration license or a mining license, you are required to
submit the data that you collect.


David Roberts


Oh, interesting. That's by law. Everywhere by law.


Kurt House


That's right. And these are really good rules, actually, because
what you don't want, if you want efficient exploration of
resources, you don't want everyone collecting the same data,
proving the false positive again and again and again. Right.
That's bad. So usually the way the rules work is the company gets
a couple of years to husband the data by themselves, and then
they have to make it public. Right. And that's true in basically
every jurisdiction we operate in. So there's a huge amount of
information. We'll get to what the information is in a moment.
But the challenge is that this is the messy data of all messy
data problems.


Right? So it has been collected. You're talking about everything
from modern worldview three, high resolution, high spatial, high
band resolution spectral imagery all the way to 1920s handwritten
drilling reports. Right? That's all the information. And there's
everything in between. And so it's every media, every storage
media, from paper to cloud storage, every storage media you can
imagine.


David Roberts


Microfiche?


Kurt House


Microfiche, you know, magnetic tape, you name it — it's all out
there. And then it's every jurisdiction; every sub-jurisiction
has different formatting requirements over time.


David Roberts


To what extent has there been a kind of shared format for this?
Or is it all completely disparate?


Kurt House


Basically none. I mean, people could quibble with me and they
could point out some standardization efforts in the past, which
is not wrong. But basically, to first order, if I show you 100
data sets that we found in the public domain, they're going to be
in 100 different formats.


David Roberts


Oh, good grief.


Kurt House


Yeah. So the first thing that we do, and this is a major effort,
we've been doing this for five years, and we're going to continue
to do it for many more years, is we identify these data stores,
and then we ingest the data, bring it into our system. Which
could mean, it could mean, digitizing paper records, right? And
we have those operations around the world, including a very
extensive one in Zambia, which is really cool. We digitize the
paper records, we do various optical character recognition
techniques on those records, and then we use various extraction,
NLP and other extraction technologies to extract the data,
transform it into what we call our universal data schemas.


So we are the major effort to create a standardized format for
all of this data, which is our own proprietary format.


David Roberts


When I'm thinking about this process, how much of that is done by
AI and machine learning versus some poor schmuck squinting at two
columns of numbers and typing things in?


Kurt House


Great question. So I like to say that KoBold runs on AI, HI and
HS. So that's artificial intelligence, human intelligence, and
human sweat. And the last one may be the most important. It's
hugely important. And we're automating things all the time.
There's all kinds of methods of automation, but these are really
hard problems, because not just is the data in all these myriad
formats, but what the data is requires real scientific judgment.
Right? Different. A magnetic survey collected in 1965 is just
hugely different than a magnetic survey collected in 1990.


David Roberts


So this is not something like, you can't hire like a teenager to
do data entry kind of thing. You need an expert to be looking at.


Kurt House


Yeah. The things that you could easily outsource to a low skilled
person that's automated, we just automate those things. But then
there's lots and lots and lots of areas where human judgment is
really needed. And the technology basically makes the humans much
more productive. Right. Because it enables them to see, like,
kinds of data fast, to see what the units were, to see what the
data collection method was. It highlights the relevant
information for humans, but it is definitely a human in the loop
process, and it's, for all intent and purposes, will stay that
way for a very long time. It requires a lot of investment.


David Roberts


Right. So this sounds like the bulk of your value add then,
mainly like this precisely. Gathering and standardizing
information.


Kurt House


I would say it slightly differently. But I think it's a
reasonable assertion. I would say it's the least ambiguous value
add. Right. Like, it's clearly really valuable to get all the
information and make it all usable. That is like, nobody will
disagree with that. Right. And so that's definitely the case,
then, once we have the information, I should talk about what the
data types are in a second. Once we have the information in
accessible form, then there's just a huge number of scientific
computing and AI algorithms that interrogate the data and do all
manner of things to predict where we're likely to find
compositional anomalies and concentration anomalies.


David Roberts


So what are some of the kinds of data like? Presumably people
have dug down and pulled up cores.


Kurt House


Yeah. So we'll start with geophysics. So the earth's
gravitational field changes from place to place on the planet
because the density of the rocks beneath you change from place to
place. Yeah, it's really cool. And you can actually measure these
changes with gravitometers that existed in terms of sufficient
precision for 50 years. People have been doing gravitational
surveys for many, many decades in different places and different
evolving technology platforms. But that's one type of geophysical
data. The earth's magnetic field changes from place to place as
you go around the earth because the background field gets
distorted by the magnetic properties of the rocks in the near
surface.


There's various types of electromagnetic data that tell you
something about the distribution of conductivity of the rocks in
the subsurface. Right? And there's electromagnetic data that's
based on active surveys as well as it's based on passive surveys.
There is all manner of imagery. Right? So aircraft and satellite
imagery in the visual and in the wider non visual bands. And that
can tell you lots about both outcropping rocks as well as the
materials overlying outlying rocks. There's all manner of
chemistry. Right? So you referenced this just now. There's
groundwater chemistry, there's soil chemistry, there's sediment
chemistry and streams.


And then there's rock chemistry, both from outcrops and from deep
drill holes. Then there's mineralogic data. So chemistry tends.
We use that as a shorthand for the elemental concentrations in a
rock sample. So the percentage of copper, the percentage of
nickel, the percentage of cobalt, et cetera, and the percentage
of weird stuff, too. And those trace elements are really
important. The percentage of caesium, the percentage of tantalum.
Right. This stuff tells you a lot. So you end up getting, if you
have 50 or 60 element concentrations, you can do some very
sophisticated high dimensionality sort of machine learning to
predict how the rocks are changing in the subsurface.


And then there's mineralogic data, which tells you about not just
the elemental concentrations, but what minerals they're in, the
forms of the molecules themselves. Right. Is it nickel in
silicate, which would be less interesting than nickel in sulfur?
Right. And you need to actually understand the molecular form,
not just the elemental concentration.


David Roberts


So how close — and maybe this is like too sprawling of a question
to answer, but how close are you? Well, a) are you still finding
new sources of information? And b) how close are you to ingesting
and systematizing and standardizing all the data that you do
have?


Kurt House


The last question, I would say we're way ahead of anyone else in
terms of aggregating all of the information about the earth's
crust. And we got a long way to go.


David Roberts


Oh, really? It's still early days with the information that's
available.


Kurt House


That's available. Yeah. I would be shocked if we've already fully
aggregated a few percentage points.


David Roberts


Oh, interesting.


Kurt House


There's a huge amount, yeah.


David Roberts


So that's good reason then to think that there's lots of Runway
for improvement here as more and more information comes into the
system.


Kurt House


For sure. Maybe the neatest innovation we have, data science
innovation, kind of foundational innovation, is something we call
efficacy of information. And what this is is we ingest all of
this legacy information and we make these predictions. We have,
in most cases, very sparse data, we have little hints of data
here and there. So we have a huge amount of uncertainty. And the
game now is to make a set of decisions that will decrease our
uncertainty. That will maximally decrease our uncertainty. Right.
And so that's actually the way we think of exploration. We think
of it as an information problem.


That's about the maximum reduction of uncertainty. No other
mining company talks that way. Right? Talks about sort of
information theory and maximally reducing uncertainty. But it's
fundamentally the way we think of exploration, of the
exploitation process. So efficacy of information technology, what
it does is it actually indicates what information we should
collect that will reduce our uncertainty the most. Right. Which
is different than what information you should collect to find a
deposit. Like, it's a different question. It's no, how do we
decrease our uncertainty the most? I'll give you a fun example, a
very tangible example of this.


So we have a lithium prospect in Quebec. And the way we got on to
this prospect was by initially searching old records for the
lithium mineral that we're looking for is called spodamine. So
that's an aluminum silicate lithium mineral. And so you can
imagine that you'd go look for spodamine, and there's the word in
old geologic records, but it turns out that that's not very
fruitful. And the reason that's not very fruitful is because
unlike copper and nickel, people have not been looking for
lithium for all of human history. In fact, they weren't even
really looking for lithium ten years ago.


It is an extremely new thing, and that makes it really
interesting. So most geologists weren't even thinking about it.
They weren't thinking about spodamine 40 or 50 years ago. So if
they weren't thinking about it, they were less likely to see it
also. And this is non obvious at all, but it turns out that the
spodamine mineral is really, really hard to detect in rocks,
which some minerals are easy to see for a skilled geologist, and
some are really hard, and spodamine just turned out to be really
hard.


David Roberts


So you're looking for proxies, then it's what you end up doing.


Kurt House


Exactly. So we're looking for proxies, things that we have
figured out through basic science and through data science that
correlate with the presence of spodamine. So we found some old
reports that were just littered with those proxies. So we send
people out, but we didn't send them. We actually staked a big
claim, 300 square kilometers of area, and this is an area that
has snow on the ground most of the year. So you have a narrow
window to send people out and collect new information. And it's
expensive. Right. This is another cool element of the data
science challenge of exploration, is that the marginal cost of
data is very high.


Right. And that's really different than like a SaaS company or a
social media company that's getting swamped with data, and
they're trying to find subtle signals in the noise. We have this
high marginal cost of data, so the whole game is to collect the
most useful next piece of information. So we have this big area,
we have maybe two weeks in the field, helicopter supported. So
this is not cheap. Ten highly skilled people supported by a
helicopter for ten days. And so we use machine learning
algorithms to tell them where they're likely to find the right
rock sticking out of the ground.


Okay? And so the algorithms were trained on data that we had a
priori, and the team goes out there and they find a couple of
good examples, and they say, "Yeah, this is good. This is a
pegmatite. This is the sort of rock type we're looking for." And
it had this white moss growing on it, it's a white like rock and
has this white moss growing on it. Most of the places that the ML
sent them, most of the places were not the right rocks with that
white moss growing on them. They were just fields of white moss.


It was bad, it was false positives all over the place. So we sent
them to all. So it was really bad, right? So they're in the field
and they're on satellite, and they're like, "You guys, data
scientists, this is not right, this is moss, these aren't
pegmatites." But that's okay, that's the way this iterative
process works. So the team goes, "Okay, we have a few true
positives in the pegmatites you did find, and we have lots of
true negatives now, right? Those false positives are the same
thing as true negatives, right? We know those are not the right
things."


So that's really, really good training data.


David Roberts


Somebody's going to go write a moss algorithm now.


Kurt House


Exactly right. So we include that into the model as not the thing
we're looking for, as well as some positive cases of the thing
we're looking for. The model gets way better, way more predictive
in just a couple of days, inside the window of the field
campaign. So then the new model, while the people are still out
there, the new model says, "Okay, here's a much more precise
model. Many fewer predictions, some novel new predictions, many
fewer predictions. Go check out these that are like 50 km away."
And they were spot on. They were exactly right.


And now, the model was really predictive. Just a little bit of
high quality training data made the model way, way better and got
people to not just the right pegmatype deposits, but pegmatite
deposits that were rich in spodamine, which is the lithium
bearing mineral. So that could be a new lithium discovery, and
we're able to make it way faster and for way less expense because
we directed the team so efficiently in the field.


David Roberts


Right. This kind of gets up. My next question, which is most of
what you're doing, is assessing and analyzing existing data
sources that you've gathered. And the next thing I was going to
ask is, are you yourself producing another data set? Are you
doing any sort of large scale scanning or, I don't know, whatever
it would be to produce another data set. But it sounds like
you're doing something much more targeted than that.


Kurt House


Yes, both. So, yes and yes. So the targeted exploration is
critical for efficient exploration, and then we are collecting
new data, highly selective, high quality data, like I just
described, like those moss covered fields good, strong, true
negatives, okay? But here's what we also did, and it was really
cool. We actually have a whole hardware program where we're
developing new measurement techniques and new data types, and we
have something we've invented called the KoBold hyperpod, which
is a camera that we designed that is the highest spatial
resolution and highest spectral resolution of any camera used in
mineral exploration.


We mount it to the side of a Cessna aircraft or a helicopter. So
after we found this remote, good outcrop, we then flew over it
with the hyperpod. And the hyperpod, to make this really clear,
so the satellite imagery has a pixel resolution of about four
meters. So it's about a four meter spatial resolution, okay. And
then it had a band resolution, spectral resolution of eight
different bands. So eight different wavelengths of light. The
intensity was being measured. So you can imagine for a four x
four pixel, you would get eight numbers, okay? The hyperpod. The
new hyperpod has, in a four x four area, 20 pixels, so it has 20
times the spatial resolution.


So you get 20 different pixels for every one pixel, and then
every pixel has 80 times the number of bands. So it's 80 times
the band resolution going all the way up to three microns, like
really long wavelength. So we collect much more rich information,
1600 times as much information about that outcrop than you had
from the satellite imagery. And then we fly a wide area, we fly
that camera around a wide area, looking for more of the same. So
in this way, the whole exploration machine becomes much more
effective at every stage, because the software enabled the more
effective exploration, which found the better training data.


And the novel hardware then collected really good quality data,
which then gets used to find the next prospect after that.


David Roberts


So you're laddering up then, and ought to be getting better and
better at this, basically, because.


Kurt House


Correct.


David Roberts


The more information you get, the better data you have. So we're
getting close to the end. One or two other things I wanted to get
before we're done. One is just about what you found so far. You
said you're relatively new at this. You found one big
demonstrated deposit of what is the —?


Kurt House


Copper and cobalt, mostly copper, a little bit of cobalt in
Zambia.


David Roberts


And it's new that you're looking for lithium. That was a
relatively recent announcement, I thought.


Kurt House


Yeah, we've been — so we started looking for nickel, copper, and
cobalt. And for the first two years of the company, actually, for
the first three years of the company, we exclusively looked for
those three. We started our lithium program about a year and a
half ago, and it's a very different search space in really
interesting ways, but we've spun up a really exciting lithium
program. Now we're exploring on four continents. We're exploring
in Asia, Australia, North America, and Africa for lithium, and we
have projects on all four.


David Roberts


Oh, wow. So you have one big demonstrated find on your record
now, and you're exploring in other places. You're exploring in a
bunch of other places.


Kurt House


Yeah. We have nickel, copper, and lithium projects in twelve
jurisdictions, I think Greenland, Quebec, Ontario, Saskatchewan,
Nevada, Alaska, Western Australia, Namibia, Botswana, Zambia,
South Korea. So, yeah, we're exploring all over the world for
those key metals.


David Roberts


Okay, and final question, and this is, I'll get yelled at by my
audience if I don't ask you this, which is just, we've been
discussing all this as a sort of purely mechanical kind of a
technical challenge, but obviously, anytime you talk about
mining, I'm sure you're very familiar, having been in the space
for a while, first thing that leaps to people's mind is
environmental degradation and social problems. The mining there
are, quite famously, a lot of bad mines, a lot of bad conditions
at a lot of mines. How do you think about the equity justice
angle in your work?


Kurt House


Yeah, I'm so glad you asked. It's so, so important. Right. So the
mining industry has a spotted history, for sure. For sure.
There's some good stories and there's some very bad stories, for
sure. These are things we take enormously seriously for a whole
host of reasons. Sort of with the giant picture, the big picture,
which, of course, we started with in the beginning, which is just
humanity has a choice, right? We either get off fossil fuels or
we fry the planet. And I don't think that's an acceptable choice.
And so what does it mean to get off fossil fuels?


It means massive electrification, and that, just because of
physics, requires a lot more of these key materials. If we're
going to solve climate change, we need to find and develop these
materials one way or another. My view is very strong, that if the
mining industry doesn't act exceptionally well in development
over the next ten years, then reasonable local stakeholder
opposition to any projects will thwart that effort. Right. It'll
thwart the effort to get the materials. So we have to be
absolutely best in class, and we strive to lead in that way. So
most companies will start a major community relations program
once they're there to actually develop a deposit.


We start it earlier. We start when we're exploring. Right. Even
though we know most of our projects won't actually come to
fruition, we want to make sure the places we are exploring are
places that we are welcomed and we want to be, we want to be
welcomed by the locals and we want to work with the locals so
that we remain welcomed by the locals. And that means a huge
number of things. But it means fundamentally, it means a really
serious investment, right. With an entire group inside the
company that's dedicated exactly to that. And people that spend
all day, every day talking to the local community about their
concerns.


At this point, KoBold, the majority of KoBold employees are
actually in Zambia. And so one of the things we are trying to
lead on heavily is to mostly hire Zambians for our major project
in Zambia. It is kind of standard operations that when there's a
big project like this, western companies move in and bring in a
lot of expats to do the high priced jobs. We have a strong
commitment to not do that. Right. We have some expats, of course,
for very specific technical expertise, but mostly we're hiring
local and we're training local very aggressively. And that's in
our long term interest.


We want to have the best zambian workforce that we can possibly
have because we don't want to just develop one project in Zambia.
We want to develop ten projects in Zambia. And we're investing in
all the neighboring countries. And so the more capabilities we
have in that country, the more effective we'll be in the
neighboring countries.


David Roberts


Let me ask you this, since I discovered that you're an actual
mining company. It's nice that you are making efforts to do this,
right in terms of dealing well with the locals and labor
standards and stuff like that. Is there demand side pull for
better standards? Is there now a market force pushing for better
standards, as one would hope?


Kurt House


Great question, and this is a great coda here for the
conversation, because I think your audience can help here. I
think demanding high environmental standards and high labor
standards in your key critical materials in end use products is
something that every listener can do. You guys should actually
tell when you buy your next iPhone, you should ask, right? Like
you should ask about these things.


David Roberts


Are there programs or standards or certificates or something?
Like what can a consumer use to know?


Kurt House


Yes, there are programs they're starting. They're nascent. Right.
And so they need just grassroots consumer support. Right. So
right now, no products that you buy — you don't have two
different prices. Like a price for a —


David Roberts


"Dirty phone."


Kurt House


Yeah, dirty phone or a clean phone. Maybe that's impossible
because maybe no one would want to advertise their phone as a
dirty phone. But you definitely want companies advertising their
phones as clean phones, right? You definitely want that. And
honestly, I would say the diamond industry, even though I think
diamond exploration is kind of stupid and wasteful, they've
actually done a pretty good job here with the so called Kimberley
process and things like that, because blood diamonds were a
horrendous issue.


Still are. But they did a pretty darn good job with that. Such
that the provenance can be relatively tracked. So there are
techniques, but it really requires sustained consumer engagement
so that all the companies involved, so the entire industry takes
it up. The providence of our metals will be exceptional. You'll
be able to track every — once we're producing, our mines aren't
producing yet, but once they're producing — you will be able to
track every gram all the way back in the entire supply chain in
an extremely transparent way. And we hope we can set an example
that way.


David Roberts


Awesome. Well, that's something listeners can do, especially if
listeners are associated with institutions. Try to get
institution procurement or government procurement aimed in the
right direction is a big piece of the puzzle, too. Well, Kurt,
this has been absolutely fascinating. Mining is not something I
thought I would ever have to get into or care about. But as you
say, you pull this string and here's where you end up. So I'm
glad somebody's out there working on it. Thanks for coming on and
walking us through it.


Kurt House


It was a great conversation. Thanks so much for having me, David.


David Roberts


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