Hey, everybody. Welcome back to First Principles. We are here today with Deep Prasad,
who is the CEO and founder of GenMat, who is a
really uniquely fascinating company, and I think maybe the perfect company
for First Principles, because there are so many places that we could take this
conversation. I'll let Deep describe what the company is
actually building, but just to give you a glimpse, it's going to be everything from material
science to AI to quantum computing. There is literally
anywhere that we could take this conversation, even literally space, which you'll
find out how that's possible in just a second. Deep, thank
you so much for joining. I'm really happy to have you. Why don't you tell us
Yeah, thank you so much, Christian. Pleasure to be here. And
it's always a fun time chatting with you. And I'm looking forward to talking about
what we're doing at GenMat. So a
quick little breakdown on GenMat. Our goal is very simply stated,
but very hard to do. The goal is to build an
AI system that is smarter than humans at physics.
That's it. So how do we basically create
something that can, let's say, discover the next generation of
physics theories that advance what we did with quantum mechanics
and general relativity in the early 1900s? How do we
build really advanced physics simulation systems that
can essentially simulate these very hard regimes, like the hypersonic regimes
and atomic oxidative environments in
space, for example, which require a lot of multiple different physics modeling
techniques. And so at GenMAT, we work on
the sort of intersection of physics and machine learning
That's a perfect intro. So basically you're building a physics understanding
AI, like an AI that knows the laws of the universe
and basically can tell you what to expect if you give it inputs and
then something comes out the other end, basically, right? That's exactly it.
I think it's fascinating to think that AI wouldn't just naturally know
that, right? Like wouldn't, okay, so AI is super smart. AI is now over
100 IQ. So how, like, why is it difficult to just program in
just F equals MA and equals MC squared? Like shouldn't, like,
I don't know. I'm obviously just like, Don't need a little softball, but like, why
It used to be a very philosophical question before we
made so many advancements in these large world models, before we could actually figure
out how far can we push, you know, silicon transistor based,
let's say, large language models and their understanding of the
physical world. And so the reason why it's so hard is,
A, because we have embodiment. But let
me take a step back before I go deeper into that. Embodiment kind
of covers three parts of human intelligence,
if you will, the general intelligence that actually affects
our ability to understand the physical world. And those three sort
of components that I think separate us from, let's say, my
dog, right? Who's like, obviously brilliant in her own way, but
certainly not making rockets or, you know, working with satellites anytime,
seem like you are. So our, you know, our team is. So
the difference is that humans have These
three components, which is the sensory apparatus, so
up to 50% of our brain can be dedicated towards visual processing of
information. So it's not that our eyes are as good as a hawk, right?
signal processing that we do, the computational processing, that
makes it powerful. And then there's the second component, which is our
ability to imagine, or creativity, the ability, more
precisely, to simulate physical systems that have yet to
be tampered with. So take, for example, Einstein's thought
experiments when he was discovering relativity, right? This guy's
literally thinking about trains and lightning and then using that to
derive like special, right, general relativity principles.
And so we're like, you know, dreaming how one does as
they traverse through the universe as a photon. All
of these things require the ability to simulate physical systems. and
that to manage and store in memory abstract concepts. And
these representations of the world that are physically accurate and
useful is what's so hard to do even now. Today's large
language models don't come close, actually, when
you come to think about it. And then the third component between
sensing simulation is the engineering and
manipulation of matter. So when you have a system that's
capable of sensing, of simulating the physical
world and manipulating the physical world, you should have something
as smart as a human or smarter. But because today's large
language models don't have any of those things in a really cohesive framework,
yeah, we haven't really been able to crack, you know, just F equals MA. A
four-year-old child, for example, will know more about physics, to
Jan LeCun's point, right, than like chachi
bt4 does or five or six why because when
that child let's say jumps from Four
or five foot like tall ledge like something really freaking high
here right for the kid Like imagine when we did that when we
were young right and we were troublemakers What would we
do? Well, you would jump from a high place and And you bend your knees, right?
And why do you bend your knees? Because the equation for
power is energy dissipated over time, divided by time.
So if you can increase the denominator, the amount of time, right,
you're reducing the amount of power or shock that your body is experiencing.
So we don't have these explicit equations that
our brain is solving when we jump. our muscle memories
and cells and neurons that compute have learned
the right sort of representations of physics. And so all that is
So it's the sensing, the simulation, and the manipulation of
matter. Those are the three things. I mean, intuitively to me, computers
in general are pretty good at those things, right? Like sensing, we
can definitely make cameras that are better than my eyes. And for
whatever, for simulation, we have Monte Carlo sims
that can run millions of possible futures and my puny brain could never handle that. So
I don't know, it seems to me that, why aren't computers better at those
They're not better at those things today because they're not integrated like
we are. So we're an integrated sensing simulation and
manipulation of matter system. However, if
you take a, let's say, sensor in a Tesla FSD, right,
why can't, what's stopping that FSD from
visualizing the warping of space-time under
different metro tensors? It doesn't have
the physics simulation-based models, right, to do those
things. But those are the things that we work on. So hopefully one
day in theory, right? Maybe your Tesla can think
about relativity because of us. And
so where the price of manipulation matters. So, you know, the Tesla in
this case is moving around, you know, you're trusting it,
but it's not going to go and build like an arc reactor
in somewhere in a cave, right? To go and test its physics theories that
I can't think of right now. So yeah, integration. And
so our brain has had, right, like billions of years, to
really integrate all these systems and evolve the integration of the systems.
So that's the edge we have currently, but we'll see how long that
Presumably the fundamental constraint here is sort of twofold. There's
like, one is finding ways to represent the laws of physics to a computer
in a way that a computer can understand it. I mean, I'm sure at some point you do literally
plug in like f equals ma or like equals mc squared or something. That's
probably part one. And then part two, I would assume, is just you have to
get a ton of data. Like, you have to feed in, like, I
don't know, what happens when you drop a ball? Or what happens when you drop,
like, jump off something? Like, you probably have to feed in examples of physics. And
so I wonder, like, what did that even look like? Or what did physics data look like? And
how do you capture it and feed it in? So I don't know. We could take you those two halves in
I love to, like, yeah, look at what the physics data looks like and
the difficulties there. And really, it's a combination
of both. Our equivalent of f equals
ma, of dropping a ball, and seeing how each has a little bounce given
the friction coefficients and so on, is
we started with the atomic world first. There's a reason for
that, and I can get into it if you like at any time, but
we wanted to build our world models, our physical world
models in our inter-AI systems truly from the
ground up, from base reality up. because
that's the best way to predict everything else, right, is
if you can model the underlying crazy atomic worlds, since
that dictates everything. And so for us, that data
of like, what does a different basketball under different scenarios look like? A
real life example of that is our titanium dioxide photocatalyst system.
So we recently put out a preprint, for example, where We
created the following setup. We have this titanium dioxide nanoparticle.
There are 10 to 25 nanometers across. And
on top of those nanoparticles, we deposit copper and
platinum nanoparticles or nanoclusters. And
these sort of like nanoparticles are just globs, right?
If you want to think about them more like amorphous globs. of copper and
platinum. And the way that their configurations end
up completely dictate where the photocatalytic activity
will be. And the photocatalytic activity is
what we care about because this system absorbs carbon
dioxide absorbs water and sunlight and
turns it directly into natural gas in a very clean reaction.
There's no waste products, all you get is literally methane out at
the other end of it. So what we've done over the past few
years is taken this system and we've looked at
optimizing the nanoparticles and determining what
configuration of the copper and platinum atoms would be
most conducive for sucking in more CO2, basically. How
do you accelerate that? And so, traversing that
search space, combinatorial search space configurations, goes up
very quickly, exponentially, essentially, and factorially, when
you take into account that you have three dimensions. So, one, sort
of, just take the nanoparticle system that's
sitting on top of the TiO2, right? So there's a combinatorial system
where The copper and platinum atoms have different permutations
that they can be in. And the more of those atoms you introduce, the
more different configurations they can all fall under. And on top of
that, you can introduce different oxygen vacancies on
the surface of the TiO2. When you pluck out
the oxygen atoms on the surface of the TiO2, you
also change the catalytic activity and what's happening at the interface. And
the interface between the TiO2 and the nanoparticle now
is this really rich area of super complex chemical activity
that you can optimize to do something useful. And
so it's being able to model those things, creating data sets
that represents all these different systems, form first principles, and
then validating our predictions in the labs is where we
started. And then over the past year and a
half, we expanded into geophysics, which is where our satellite
Yeah, wow. Okay, now I'm definitely convinced that computers are way better
than me at this. I got a
little bit of that, a little bit, but I mean, I, what I, what I
really take, so what I take away, what I take away from that is basically you
are trying to super, super detailed and in, you
know, a lot of granularity model, literal atomic level
activity. You're taking, you're saying like, what happens when you put titanium,
titanium oxides, like titanium and oxygen together with copper
globs, like what, what ways can the globs be oriented? And
Trying to change something, you change the material properties by the way. So
good luck doing that for millions and billions just for a tiny, small material.
You see, you're simulating to try to figure out which things are worthy of
test. And then once you have like a candidate list of
stuff, then you go and like test it and actually like measure the material properties. Is
So how does it work today? Like if I'm a, if I'm a material scientist today and I'm trying
to like come up with the next, you know, room temperature superconductor, like
what do I, what do I actually do if I don't have a physics
Yeah, absolutely. So right now, it's very much trial and error. Literally,
you're mixing shit. That's it. It sounds super complicated, this
kind of synthesis and characterization, but it's just, that's what dudes in
the lab, right, and girls and so on, just mixing different
chemicals and solvents to synthesize different compounds. And
they're doing it based off of decades of experience and intuition. And
very, very, very slow archaic computational chemistry tools
that are written in Fortran. So that's the art
of material science today. And that's what powers the semiconductor industry,
the aerospace industry, the battery industry. All these industries are
I know about the discovery of some materials. I know that, I
don't know, back when we discovered bronze, it was literally just a mistake. Someone
accidentally dropped some iron or something in copper, and they're like, oh shit, this
is actually harder. This is actually better. And this happened with
penicillin. I don't know. There's rubber. I think that's literally how
vulcanized rubber was made. It was an accident. So it
makes sense. It makes sense that people are just... making
mistakes in labs, and then every once in a while it turns out this super material. So
Yeah, absolutely. In fact, even when you have, let's
say, experts with some intuition of what they're doing, like
some idea of what they're doing, right, it's not just a straight up lab accident
that they're banking on, like perhaps the potential LK99,
right, submit Submitters were even
then it takes a decade or deck many decades to drive
the material. So if you take the first high temperature superconductor that
was discovered, what's awesome is that was discovered by
a private company, IBM, right, that was discovered by IBM
scientists. And when they discovered it, There's
a lot of history to that. So it was the world's first cupids, right?
These copper-based, essentially ceramic-based superconducting materials,
which in itself was a profound sort of discovery, right? That, oh,
wow, you no longer need just a metal or perfect conductor
or a normal conductor to become a perfect conductor. It can literally
just be this weird material, right? And so that
wasn't by accident, however. So Werners and
Muller, had spent the past sort of decade,
particularly Mueller, really just focusing on
the crystal structure of what would be expected, right, of
these cuprates and of high temperature superconductivity. So
they were looking at all these other crystal structures that
were precursors to the final cuprate crystal structure, the
arrangement of atoms that's like essentially fingerprint for
that particular material. So that family of fingerprints they
had been looking at for so long. And so finally they
like stumbled upon this, but that's what they were looking for, right? And
it was because of some, one of the key ingredients, funnily
enough, was access to some of the first like really
large-scale density functional theory large-scale for their time in
the 80s and late 70s so that's pretty awesome too
right it's a combination of computational chemistry and
real experiment and you know maverick science coming together in
a private industry setup so we should be trying to do that again
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first principles. Let's get back to the show. So maybe let's take
a step back and just talk about materials. Like, what is material science?
And what are the properties of materials? Like, I imagine what you're
searching for are specific properties. Like, you want
to find materials that are really conductive, or like super conductive. You
want to find materials that are extremely hard. You want to find materials that are whatever,
like a variety of different things. So do you mind just telling us at a super high level,
like, what are materials? And why do they matter? And what makes them good or bad?
Happily. Happily. I love that question. So materials, which
is arrangements of atoms that repeat at
a scale that's relevant to you. That's how I would define it. So
you wouldn't care about like three, four or five groups of atoms, right?
Or like five atoms. But you care if you have trillions
of atoms that intricately dance with each other and represent
the next generation of semiconductors, right? That are more power
efficient. You care when it's carrying you to the
moon and to Mars. That's when it matters. So that's what
I would define a material. And so you can have many, many different
kinds of material properties that you're interested in, right, all the way from
conductive properties, like you were mentioning, like electrical properties,
thermodynamic properties, structural properties, those
are really common as well, right? So within those, you
have a very wide range of trade-offs. Every
material has its own trade-offs for its properties. For
example, you might be interested in looking for a transparent solar
cell, like a truly invisible solar cell. We're
about 70% of the way there last time I checked. We
can create about 70% translucent solar cells. That
field of research is largely being enabled by perovskites, which
in itself is a fascinating material, right? So in
material science, let's go back to the like 30s
and 40s when perovskites were first discovered. There were these interesting
rocks with these interesting piezoelectric, you
know, electric qualities to them, right? People were around
rubbing them together and seeing that they create charge if you hit them against each
other. And they're like fighting other rocks like that. And
then we find out that about 70% of
the Earth's crust, or a very, very large percentage, have
these perovskite materials. It's largely composed of
perovskites all the way down, instead of turtles. And
so in the 1960s, we have one, about
60 research papers, let's just say, per
year on the subject of perovskites. And
I'll tell you what they are in a second. And now
today, we easily have 80 to 100
research papers published per day in that field. It's
the most famous, I would say, after superconductors or even
on pallets. It really does become the most famous material that people actually
put resources towards in practice. And
there's a reason for that because perovskites are these
They have an ABX3, so an
anion, anion, and cation-like configuration.
Basically, the ABs are anions and the X3 is
a cation. The
anions create this octahedral pattern. when you
repeat the atoms, right, and you look at the fingerprints and
what they look like at scale, they create these octahedra. And
when you tilt the octahedra, meaning when you change the bond lengths,
when you change the chemical composition, you tilt the octahedra,
or you displace the cation in the center of the cubic
lattice, it changes the properties completely. So
just displacing the cation, if you want to visualize it, and the cation, And
then you have an octahedra covering the cation.
So if I tilt, axial tilt, this octahedra
across X, Y, or Z, just one tilt, even a little bit, changes the
properties. A little bit more, changes the properties. How are you possibly
going to predict all those things? But anyways, right? So that's why you
have so many papers, because it's a straight up surprise. You
So these materials that are in solar cells, basically,
we've known about them forever. So what do you mean that
they're in all of the Earth's crust, that they're in everything? What
Yeah, so those magnesium-based perovskites that
are largely what the, I believe, Earth's mantle or
Earth's crust is. Oh, OK. So that's one
example, right, where it's literally a large perovskite-based mineral
It's the name of the geologist slash
And the class of things is like the shape
of the thing defines it, if it has that
Each of the add ions creating the octahedra defines
it. Wow, cool. It's just an infinite family
of materials that it represents. And so the
cool thing is that these near-invisible scellular
cells are based off of the perovskites. The
first PLC electrics were perovskite-based and
are mostly, largely perovskite-based. Currently,
for example, in our labs, we work with perovskites for lithium-ion-based
solid-state electrolytes. How do you find a much
To tie this back to what you're doing as a company, let's stick on this
example. Let's keep talking about this new material. Would
you help, would a lab come to you and say, Hey, I have this idea for
what I think we could do with the orientation of the thing. And can you
please test it for us? Or like, are you trying to discover your own
ones yourself? Or maybe both? Like, can you just make it tangible, like what you're doing
Happily, happily. So we're kind of doing both. We're dogfooding our
own products and technologies for what I call ubiquitous materials.
So we don't care about creating Let's say
a material for a better, lighter, stronger weight tennis racket,
or like a better golf club, right? That, like, don't care if somebody
else can commercialize that. We're happy to help them discover that, right? No problem.
For us, we really want to focus on these materials that will change
and affect all 8 billion lives. Like, that's our metric. So, for example,
what does that look like? A truly 100% or 99%
invisible glass panel, right, that's also a solar cell,
that's totally up for rally. We're definitely interested in that. A
lithium ion battery that's, let's say, an order of magnitude safer
and more energy dense than what's available on the market that Tesla can
deploy. We are happy to help find that and work on
that. But then, yeah, so we want to kind of do
both of these things. And I think that there's a
world, right? The world of materials is large enough that
there's never going to be enough players making their own materials, making
their own supply chains and processes to scale those materials.
And so that's why we think that, you know, we just need to keep contributing
like crazy to the acceleration of these materials, one way or another, whether
it's us or helping other people do it. Our
basis model is that we offer one product and two services around
the materials discovery side. And then I can get into
our geophysics-based models, because I mentioned we expanded recently into
another domain of physics, right? But so for materials work,
we have one product, and that product is a series
of physics-informed machine learning models that model crystal
structures and chemical compositions. And they're useful
for a variety of tasks. We have some models that are really, really good
at predicting the properties. So let's say you change these
parameters, the axial tilt, the anions, and so on. What
can we expect? We have models for that. We also have models
that look at what happens when you have a very
complex interface, like the titanium dioxide example that
I was talking about, where the interatomic potentials are
very complex and you need to discover where a representation for
it. And then we have generative models for the inverse design
task. So that's for when a customer, and
this is what we're finding most people are interested
in, is particularly like a much larger market. We
also have models for the inverse design task or capabilities where
you give us target material properties, and we will
discover, right, and we will figure out and generatively dream up
Are there certain properties that are fundamental enough for
you to model and other properties that are sort of like emergent and you
Yeah, and when I say can't model, I'll be specific, can't model with
today's physics AI technology. Like it's constantly an
active field of research for us to be able to, you know, improve this
multi-skills and multi-physics domains
Yeah, absolutely. So fundamental ones like the band gap, charge density, local
density of states, electrical properties like connectivity,
both DC and AC. We can also
model ionic conductivity if you're looking at battery materials. So
information energies as well is a really, really critical fundamental property.
So yeah, we can model these fundamental properties, which already
give you a lot of information about is this material going to
be useful for your particular engineering task at
hand. So, you know, let's say you're trying to find like a really efficient solar
cell that works really well in atomic you know, oxygen environments in
space, that would be a set of material properties
for us, right? Charging properties that we would then just like go run
our models against. It's one of the directions that we kind of going. We
are trying to elevate the aerospace, you know, in space industry. Well,
So, so those are fundamental properties that apply to a
wide variety of materials. and material families, from
transition metal oxides to, let's say ceramics, right,
you're looking at formation energy and so on and so forth, battery materials, solar
cells, you get the point. But there are also measured
properties that you asked about, and I think that's a brilliant question. Because
there are emergent material properties that require
deep quantum computing and quantum computing related technologies
and thermodynamic computing related technologies to exist.
And so that's what we can't do today. For example, modeling
high temperature superconductivity. right, is
a unsolved problem because it requires being
able to capture really strongly correlated
electron systems and many body systems. And
it's so computationally intractable, even for the most powerful silicon semiconductors
based AI models today. So that's where we're
constantly like pushing up against the physics battery, if
And so today you're just doing this all with classical computing or do you use
We use quantum computers only as a test bed to test like how far
I know that you originally, the reason I asked that though is because I know that at one
point in time, you and Guillaume, like the guy who's the
CEO of Xtropic, people might know him as Bef Jezos, we did an episode on him
a little while back. I know that you guys knew each other back in
your university days and you were interested at the time in quantum
deep learning. Was there a time when you were founding this
company and you thought it was going to be a quantum company and then it ended up just being classical?
Or is it just like, you know, someday you
Jay and I, we both like determined, right, that okay, quantum
hardware right now is not going to make it for quantum chemistry tasks. So like,
one of the things he worked on was Even
now, apparently, the world's most powerful quantum computing simulator, right, based on
benchmarks. So, you know, he built that at Google X
in the secret division that we built. And
so, that was like, really a lot of that, some of that was
boring, right, out of this acknowledgement that him and I had on
the sort of limitations of being able to do quantum chemistry tasks
today. That simulator, even today, and the concept of
using simulators and decomposing problems across simulators remains
an active field of research that we're pursuing. where we've
recently been able to perform certain simulations, certain
material properties that we can calculate thousands of times faster using
classical hardware, but using an entirely different paradigm of
quantum probabilistic algorithms. And so those quantum probabilistic
algorithms will natively work on quantum
computers once they come online. That's one of the benefits
of writing code like this is that you're already getting benefits from
this quantum computing simulator that's giving you useful chemistry calculations
and on top of that you're ready so that
when the hardware comes along, it's literally almost a back-end switch, right? Like
you're just swapping the device driver to a quantum computing
device. Now, that's one aspect of
our strategy. But the other part, this is the part that, like,
yeah, I'll probably only say a few times, I don't know, just because
it brings a lot of... Not saying it, but we're
also deeply interested in accelerating quantum computing. I'm no
longer interested in being an observer. A large part of quantum
computing is material science-based, like constraints. And so
We talked about a bunch of, we talked about kind of the problem, like how you're
approaching it, why it's important to solve, but we
haven't yet talked about why AI is the right tool. So I'm curious, like
why, when you were trying to do this, this cool material science discovery,
So the reason why I chose AI is because it's ultimately one
of the best pattern recognition, if not the best pattern recognition
device we've ever created. That's really all it comes down
to. Nature has really, really deep complex, hard
to penetrate patterns that show up between all of
these crystal structures, between all the electrons and their bond links
and the properties. There are patterns that are meaningful in
mathematical and chemical and physical nature. And so teaching
a super intelligence that I think we have a higher probability of succeeding than
Can you talk more about the specific techniques that you're using? Is it
deep learning? Is it some other thing? What of
So we use two things. 99% or 95%, if
you will, is classical deep learning. For example,
we use something called CDVAEs. So
crystal diffusion, variational autoencoders. This
is one class of models that's really interesting to us, right? There's
some open source CDB out there, for example, that you can
play with and see exactly what we mean. So that's
taking the best of diffusion-based methods and variational autoencoder
I know what neither of those things are. So it's like a 0% helpful to me. Let's,
let's talk about it though. Let's, let's get into it. Let's get into it. So what is,
um, so let, let's, let's, let's set a goal of Christian has to
understand what a CDVA is by the end of like five minutes. Uh,
so what, how would you, how would you describe what a D like a diffusion model
Alright, let's start with the Variational Autoencoder first,
the VAE. A VAE takes some
input, learns a compressed latent representation of
that learned input, and then it has a decoder. So
the autoencoder is encoding the input into some
latent compressed representation, and the decoder has
to get really good at taking that latent compression
and decompressing it back to the original input, right?
And so the autoenclover has to get really, really efficient at compressing different
inputs into smaller and more useful and
more information rich latent representations of the
And latent, latent meaning in this case, that it's not like a
literal one-to-one, I see red, therefore I
put red in a database somewhere. It's like, it's latent meaning that
it's just some number and kind of inscrutable. Like those
Right. Bingo. That's exactly it. So for example, it's
like. What you don't want to see in your occipital cortex as
like visual data gets processed right like it would look really if
you could see that would be almost psychedelic like right like you would see like, instead
of, you know, two eyes for deep right it would be like two
Yeah, totally. If you could peer
into the brain and like look at what neurons are doing or something like
that. If you looked at neuron signals, it would make no sense, but it
processes it. So it's like the middle step between the processing. Okay.
So VAE takes inputs or whatever, turns
them into crazy gibberish, but then there's another part of the other side that
can take that crazy gibberish and turn it into meaningful stuff again. Bingo, exactly.
So that way when you give the VAE a totally new input it's
never seen before, right? It should be able to compress it
in a meaningful and useful way and then the decoder can meaningfully decompress
it, right? And you can make predictions at the ends and
beginnings of both of this process. So, for example,
with the VAE, right, you're feeding it in crystal structures.
By crystal structures, I literally just mean atomic coordinates of
materials, their fingerprints, right? Where are all the electrons and
the atoms, the neutrons and so on, particularly in the bond
lengths. That's it. You feed those in along with
the properties, because each one has different lists of
properties, right? So that now you're learning a compressed version.
of those crystal structures and their associated properties so
that when I give my VAE a new crystal structure that
it's never seen before, it should be able to come
up with a useful compression that allows me
to deconstruct the entire input, including the properties, as
an example. So that's the magic of the VAE. Now,
what does a diffusion model do? A diffusion model is
infamously known for incrementally adding
noise to a model and then training a
sort of loss function that you can go backwards and start with a
very noisy image and come up with a very coherent image. I
won't go into why Gaussian distributions and so on matter.
What I will say is that if you can teach an AI
to take a very, very noisy image and find and
learn a representation of working backwards to the original images, you
basically learn a representation. of coming up
with any coherent image, because you've trained it to come up with coherent
images thrown through, given noise. So if you give
it random noise, right, some form of structured noise, even, or
whatever, sample noise, the expectation is that it's forced
to come up with a realistic reconstruction
of coherent one, and that's what we exploit. But that's also why you
never get the same images. Every time you run mid-journey, it'll
never be the same, because you're literally just injecting noise into your
system. So now you combine the two and that's how you
A crystal diffusion or a crystal structure.
Oh, we put diffusion, and then, yeah, for variational
So crystal diffusion, variational autoencoder, is
a thing that can, that you feed it, presumably, the way
that it gets better is you just feed it tons of data in the beginning. You
say, here's a billion different crystals,
a billion different properties, and then it just learns what to
do with those sorts of things. And then on the other end, the diffusion part
of it is basically like, the equivalent of mid journey, like where it
starts all blurry and then you kind of get the lumps, random lumps, but then it like
coagulates into something that actually is meaningful. Like presumably that's
what's happening on the diffusion side of the other. Okay. So you are using, um,
CDVAEs to do the, these like
material, um, material predictions basically, and material,
and also that, what you call the reverse thing where you're actually like predicting,
And so, yeah, so the interest design problem is given a set of target properties,
you know, find me a thousand, right, or 10,000 material candidates with
these properties. Which, right, you would have had to hire entire teams of
material scientists before to even, like, get close
Seems hardest to me is the data part, is
getting billions of crystals and somehow loading them into
Yeah, yeah, and I can see why you think that it was a hard
part. So largely, there's, I would say, well,
all three things that have come easy to large language models
are very, very difficult for large physics models. Right.
So, um, The difference, for example, data, right? The
training data, if you take large language models, the training data is
available all over the internet, right? You just need a coherent sentence, literally.
It just needs to be a complete, grammatically accurate sentence,
and that's now training data. And then processing
the representations, will transformers do most of
that work for you? And so there's obviously an active field research
for transformer architectures that impose these
attention mechanisms that are useful for learning, all these
extra subtasks implicitly. And then you have the
RLHF. You have thousands of people, hundreds of thousands, millions
of people who can RLHF the output and roast
your AI. That's pretty easy. But
if you have a physics-based AI system, coming up
with valid training data is very, very expensive. You
need deep domain expertise, labs, lots of
compute. So for example, the data for us is our experimental labs.
It's also the synthetic data sets that we create using
proprietary know-how methods. It's also first principles computed data.
that we create, and that's using these massive DFT softwares,
very archaic software. What's DFT stand for? Density
Functional Theory. Density Functional Theory, okay. So
Density Functional Theory is a computational modeling
tool. It's an approximation to the many-body Schrodinger equation.
Every single material has a unique fingerprint. Every fingerprint
has an associated many-body Schrodinger equation to it. If you
can solve it in its exact form, you would be able to predict pretty
much all the material properties that you're interested in, all
the properties. And so the problem is that we
can only solve that for about three atoms. Beyond that, computationally
impossible. That's where density
functional theory was introduced. It's this computational method. So
we have these old school software codes, right? Like creating data
sets as well, in part. So it's this, how do you
just create really high quality physics data? It itself is
an art and a science. So that's hard.
I totally agree. But I would say even the representations are
hard too. So how do you impose physics
symmetry constraints? For example, if
I may have a molecule, let's say, right, a titanium dioxide. And
I may rotate that titanium dioxide molecule a
few degrees across, let's say, the x and
y plane. So I just rotate it a little bit. To my neural
network, which is my CDVAE, this deep learning algorithm,
it's only seeing numbers of positions of an
electron. So if I rotate it this way, all
the numbers change completely in no known and obvious way. So it
will think if I don't include physics constraints and symmetry constraints,
that is new data that it's seeing, that it's seeing a totally new
Um, if I show, if I show my phone like this, then it can see, oh, look, it's like
a shape like this. But if I show it like that, even though we obviously
know it's the same phone, like it's seen, it looks different because they're different pixels
That's exactly it. Basically, how do you do 3D object
recognition, right? Recognition, but for atoms, so
there's difficulties there. So for the physics symmetries, in
this case, you would impose E3 covariance, right? So it's
a part it comes from like group theory So group
theory is a form pure mathematics that studies mathematically
describing quantifying symmetries so you can have symmetries in
physics and you do deeply right like you have energy symmetries
and constraints conservation momentum and and so on and so
forth. So yeah, all these things have categories and
groups particularly attached to them. So anyways, you
have to impose really, really deep, complex mathematical rules into
the neural networks that don't just come out of the box for
large language models. And then the third part, the RLHF, you
Yeah, and that RLHF stands for reinforcement learning with human feedback, right?
Or with human features or something. Is it feedback or features? I never
know. Yeah, you better write the first time. Okay, okay.
What that means is basically you show people output number
one and output number two, and they say, oh yeah, I prefer output number one or something like that. But
if you show me this crazy material, like, I
have no idea what to do with that. Like, I don't know how to rate that on a scale of one to 10. So that
makes sense. What does that actually look like? What does the reinforcement learning
part look like? Is it just doing more measurements after you create some stuff in
a lab and testing to see if it was good or bad or right or wrong?
Yeah, that's really where it comes down to is largely nature
herself becomes the arbiter of truth for us. Like
nature holds the feedback for us. It's like NLHF, right?
Or sorry, RLNF, right? Reinforcement Learning from Nature Feedback.
So that's like the hard parts. I imagine that you're
probably just very complicated. And for a lot of people
that are not detailed, as expert as you in a lot of
these areas, I imagine it's very hard to communicate, hard
to get people the necessary background information they need to understand it.
But, and yet, you probably have to hire a bunch of chemists
who don't have all the AI background. You probably have to hire a bunch of AI people
who don't have the chemistry background and vice versa. My
question is, is there any way that you actually do that like as a
company or even just as you like going out and pitching investors or something? Like how
do you try to get people the first principles understanding they need in
I try to communicate it in a way that they can relate to deeply and intimately.
Let's say I'm talking to someone who works construction all
day long. That person may not have
the same deep technical appreciation of chemistry as
someone with a PhD in chemistry. And
that person, the patient chemistry, may be a complete novice
when it comes to applied materials in the real world, like construction
workers who actually understand melting constants and
so on and so forth, melting points and real structural integrity, what
that actually looks like. So there's a, you know, knowledge gaps in both
places. And so that what keeps it, you
know, kind of threaded right in together is that
shared understanding or appreciation actually more importantly of
materials. So I always take that kind of first principles approach.
And so that's always been pretty useful in
retrospect. It's just like finding something in their day-to-day lives that
matters and then kind of just showing them the appreciation of
how hard it is to actually find materials like that. A lot of
people, I think, just kind of take material science for granted, right? That
plastics, leather, silicon, all these things just exist. They
don't. They take very, very hardcore labs and a lot
of scientists, a lot of time, to discover. So
yeah, just communicating that. And then communicating the physics as well, right?
So like the baby example or the child example of jumping from
a high top, everybody understands that. So
Like if you, if you're just looking at your company for the first time from the outside, it
does seem like you have your, your, you know, you've, you've all these different areas
you're trying to do in sort of like all at once. Seemingly like you're, you're
not only doing like materials discovery, but you're also doing materials
analysis. Um, you're not, you're not just focusing on one material. You're focusing
on like all of them. Like you're doing photoelectrics, you're doing like whatever,
uh, like conductive stuff you're doing all these other, like you're doing so many different kinds
of things. And so is there a reason you chose to do
that? Is there a reason you chose to go really broad instead of just being like the one
ceramics lab or something? Absolutely.
I laugh because that's like the first question
That is such a common question, right? Where is your focus? And
my answer is always the same. My focus is the system. We're
trying to build an AI that is better at physics than all
humans combined. That is the goal. And so that
AI can't be trained on just ceramics, or just batteries, or
just geophysics, or just computational fluid dynamics. It
really has to sample from all sorts of domains of physics and
truly be embedded across all different kinds of sensors around
the world. So I'm kind of taking that systemic approach. It might look dramatic,
I just don't have, we just don't have the us puny VC brains or
Okay, so let's let's talk about the geophysics stuff.
And let's talk about space. Let's get there. So why? Why? Why? Why
add another thing? Come on, man. You're not focusing. Yeah, like, tell
Yeah, if disrupting material science isn't hard enough, then why
meet yourself with trying to disrupt geology, right? But
basically, the benefit is twofold. In the immediate
short term, our short term, when I say that, like
10 years or less, right? Massive benefits here is
that we're going to be able to help with critical materials and
critical minerals supply chain. As well as just a general raw
material supply chain, which also benefits us and our customers. As
our customers at, let's say, the SpaceX's, Tesla's, whatever of the world, right,
are prompting our AI to look for new materials, new alloys for
their rockets, for example, or engines and so on, there's
going to be net new atomic species or elements, net
new as in versions of these raw materials that we typically
haven't cared about right so Pick pick your poison.
I'm gonna pick an extreme one like frankium obviously it wouldn't pick something like
that, but you got the point right so There's
gonna be a sudden increase that we foresee over
the next few years in absolutely new supply chains for
raw materials that don't exist and so if we can
put up satellites in space and and have geophysics-based AI
models that reduce the cost of exploration and time for
exploration of those new minerals, we reduce the cost of
the raw materials themselves. So that's a very simple
first-order effect that we want to exploit or
leverage, if you will. And then the
kind of longer term goal that this serves, the
geophysics and satellite work, is going back to the original
three components that we talked about. So the geophysics satellite
work, hyperspectral work, also has a secondary benefit
that's more longer term. And that goes back to what we talked about earlier,
about the three critical components to general intelligence from
our perspective. And so that's the sensing, simulation, and
manipulation matter. So the more sensors we have in space, on
the ground, underneath the sea, throughout the
Yeah, it makes sense. So the, basically what you're
trying to do is be able to have cameras, like hyperspectral cameras,
right? Not just like optical cameras. So a
camera that can see every sort of wavelength from the narrow
little, like whatever we can see into the infrared,
into the ultraviolet, like just even beyond that probably. So
it's like, yeah, it takes all of that visual, visual, call it
visual data, and can look for
materials. So you say, we know that copper looks like
this when it's on the ground has this particular pattern. Basically,
That's the simplest way to describe it. That's right. So we're looking for signatures
of different minerals. Copper-rich minerals versus iron-rich minerals will
have a totally different reflectance pattern underneath the hyperspectral signature.
To you and I, it's just going to look like dumb rocks. But to a
specialized camera, they're very, very pretty and colorful. And each
color is super meaningful in terms of the chemical and
crystal structure decomposition. of those minerals. And
so some deposits, what we want to be able to do is, exactly
as you said, point to deposits, right? And point
to drill targets. Where should we actually drill and
look for these precious deposits? If
you take something like a periphery, the causal relationship between
the surface scans And the periphery are
a lot stronger a periphery is a geophysical
sort of phenomenon that occurs across millions of years when you
have a volcano nearby, essentially what happens is you have
really really hot magma is kind of circulating. and heating
up all these hot like metals and precious metals like gold and
silver and it circulates these like really
precious metals that we care about across millions of years deep
underground up anywhere from 100 feet to several
hundreds and thousands of feet beneath the surface and
so you have kind of like a eye of sauron looking thing
And around it are all these, like,
it's literally gold and silver and nickel and other things
you care about. And then on the surface, it's really,
really low grade material, but it's still like a lot
of gold, a lot of silver. And so a lot of peripheries have
yet to be discovered, by the way. That's the cool part. So
there's a lot of surface mines that yet haven't
even been detected. And we will see them with the right AI and the
So you actually launched a satellite, though. You launched a hyperspectral satellite
on a recent SpaceX launch on Transporter 9, was it? Right.
Oh, man, that was so nerve-wracking and exciting. It's like easily top, like,
experiences of my life. Hats down. That
was so cool. Like, you know, like, I'm sure you experienced this too
when you were going through your first payload. It was just like, Alright, we're putting a
lot of money into this thing. There's a lot of hardcore engineering and
risks associated It will literally go into space at tens
of thousands of miles an hour and stay where, you know,
we're about that speed. So, you know, like this
can go wrong in so many ways, right? So it's very nerve-wracking all
the way up until the point that they say, you know, genmat1 deployed. And
then after that, you know, it's like, all right, we can solve it a little bit.
And then it's like straight to Leops, right? Like how
do we manage Leops? So it was such an amazing
and thrilling and like high impact experience. Dude,
it was so inspiring. I was like, This is a private individual and
his company and 10,000 plus people who believed in the mission. In
just 20 years, this is what they've done. Like I'm literally watching a
frickin' skyscraper go to space and come back right now.
That's awesome. And can you say anything about how it's going? Like,
you know, how you guys did through, and LEOP is launch and early
operations phase. So like, how did LEOP go and how's the
Absolutely. So we're currently at the very final end of LEOP
where we're working with the ADCS and GPS. So once
those guys are stabilized, so the ADCS is the
Attitude Determination Control System, which you know, but just in
case for your audience, it helps you determine the
And so we're currently just operating and fiddling around with that, if you
will. And at that point, we're going to be able to start taking our first pictures. So
That's so exciting. Oh, my gosh. I remember. So we actually, Astronauts
launched a demo satellite before we did our big one. We
took a picture of San Francisco from our little tiny
satellite. And when that came back down, it's like that
picture is iconic in Astronauts history, just because it's like that came from a
That was so cool, right? It's like, yeah, that was
Yeah, you're about to be there, unless you've secretly already
captured some demo images and downlinked them, but I won't press
you to see if that's true. So basically, you
are probably also then developing the algorithm side
too. They're like, actually, how do you take in all that hyperspectral data and
do stuff with it, identify the deposits
or whatever? So how similar is that to
the other stuff you're doing? Is it a similar sort of like physics problem
that you have expertise solving because you've been doing these material science
Yeah, there's certainly some overlap. That's
a great question, actually. There's overlap in some
of the expertise. So for example, in geological soil sampling, we
followed Pat on this recently, where basically in
sampling, You'll do something called XRD, X-ray diffraction, and
you'll look at the crystal structures, right? You're basically taking an X-ray of
the Earth, like of the surface of the Earth, like what minerals there are, and
you map out their bones, aka their crystal structures. So
that is done in material science too, in high throughput synthesis of chemistry
for just like new ion, new battery discovery. So that
process is very, it's literally the same. So the
models are the same as well. And then there's also sort
of meta overlaps. So going back to what makes physics
AI hard compared to large language models, The
training data requires domain expertise. So to your
point, you know, we have a lot of domain experts in geospatial, geophysics. We
work, our investors is, you know, Comstock, right?
They're Nevada's oldest publicly traded mining company. So we work with
real world geologists too, to like validate our models. And
that helps. And then yeah, we also innovate on the algorithm side
too. So how do you make useful predictions and Because
we worked on doing these physics imposed constraints
and symmetry constraints and learning the nuances with material
science, a lot of those learnings quickly we were able to
Very cool. Can you tell people, can you give people a sense just, I mean, across the
material science side and this side, can you give people a sense of sort
of how far along you are? Like how many people are at the company and
like customers and sort of like traction and figuring out these
things actually, and being able to make them useful. I imagine that people
are probably like, okay, cool. It sounds like a cool idea, but like that'll never happen, but it's
I love it. Absolutely. So, you know, we're $15 million in
and 40 people in three years worth of like results and
work. So, you know, we have preprints on the LK99. We
simulated it. We have preprints on our own materials that
are titanium dioxide nanoparticles. where we've
shown that you can actually use AI to dream up these
new configurations and chemical surface reactions. We put
up and built a wet lab. It's probably one of the most
advanced photoreactor combined with machine learning workflows in
the world right now. We have a photo reactor in Toronto. That's
where we're literally creating, you know, natural gas right from
thin air, essentially. So we have that going for us.
We also, of course, have the satellite, as we mentioned. And
we also have a traction both on the technology and
customer side that includes, for example, We've
now been able to generate tens of thousands of crystal structures
across a wide base of material families that
are all potentially trillion dollar markets. What we want
to do is continue to validate that with actual industry
experts in those fields. So we won't be able to say
names, but we've attracted the attention of pretty much every brand
name you can think of. Like Ask Crazy, statement as
that sounds. So we have that kind of traction
that's like really interested in adopting our stuff. And then on
the geophysics side, I'm really excited as well, because we
were able to recently pre train, it was a transformer based
architecture that we completely hacked to include constraints that we cared
about to predict hydrothermal alterations. So
an alteration is a part of the ground where the chemical composition randomly
changes, like it just changes unevenly. And again, this
is something you may not visually see, right? But it's like
very subtle, but very powerful. And so how do
you teach a pre-trained model to predict that? On low-res Landsat
data, which is like 30 meter resolution per pixel, as
opposed to five meters, which is what the GEMMAT-1 has. So we
were able to predict alterations, hydrothermal alterations, on
the COMSTAR And that was validated by their
own geologists. It blew their mind. And this was done using
a model that we pre-trained on much worse,
lower resolution data. It's not even using our existing satellite
And what does this look like at the limit? Let's
say it works perfectly. Everything you have planned comes
to pass. Where are we? Where are you? And where
I want to bring us towards a world that I call Yuk-Siberia. So
you probably see that on my Twitter a lot, like it's in my handle, right?
I still have to write like a proper blog post describing what it is,
to be fair. So this is a good like preview. So
Yuk-Siberia is
the third level of civilization that I think we're headed to. And
I'll kind of give a brief guess as to timelines, right? Don't hold
me to them. way off one way or another. don't
even know anymore about AGI. Like this is obscure, you
don't even realize it, right? Like this warps anything, any
predictions about the future. But that being said though, you
know, for us it's like, all right, where does physics AI, physics-based
AI take us in the infinite first? And the first level
of civilization is one where I think we will exist
in a world where humans are constantly creating matter that was assisted
by an AI. So every piece of matter we ever create was
partially driven by an AI under our guidance. So we'll
have these beautiful, crazy, futuristic maglev train systems,
right? Consumer electronics that you don't have to charge for centuries. All
these things will be the desires of a human, but service to
us by an AI and just optimizing the atoms, making
it more sustainable, right? So just solving all the things that we currently
suck at because we don't have like chemical, quantum mechanical
intuition, frankly. You have to build it over years and years and years. But
if you can teach an AI that from the get-go, right? Totally different story.
So at the first level is one where you have
hypersonic maglev trains, like you mentioned, megalithic structures. But
then the second level, I think where we're headed, is where the
matter itself becomes so sophisticated that
it in itself becomes low levels of intelligence. So what
does that look like? Well, that works like me running straight
at the wall, right? And the wall folds in on itself. So
that's because I'm required to keep humans in life form safe. So
I'll just safely walk through it, right? So
my wall right now is stupid. It's an inanimate piece of
object, just inanimate matter. So I think matter
itself will just become more animated and intelligent. Arguably,
some people might, you know, sort of sent like a low-level sentience, like
belief system, right? Like this should be treated as life, potentially.
Don't know. And then the third level is what's called the excited
bearer. And so that's the point at which we merge with
this intelligent matter while keeping our humanity and
singularity. So basically, this is
a world where you can go outside and telepathically hail,
right? Like a flying cat. Where if your limb falls
off, right, the ground will grow a limb for you. This is
a world where you control matter as easily as
So, fair enough. So, Yug is short for Yuga,
which is Sanskrit for age of. And these
ages are typically measured in the tens of thousands of years, right?
So I think that if we properly unlock physics-based AI, we'll
have tens of thousands of years of innovation minimum ahead of
us. And then Cybera is just short for age of
cyber, so like cybernetic matter. So in
total, it's the age of cybernetic matter. So matter
Okay, awesome. So it's like we start off and we are just
in a kind of a normal world, but things are better. The second world is this,
the, my wall can now deform around me and like knows what I'm coming. And then the
third one is like, my wall's my friend. Uh, so
the, so in that world, like which of
those categories are we like on Mars and like, you
know, living, like, you know, doing interstellar travel and, and those sorts
of things. I know this is actually something you care about is like getting beyond
Deepa, I actually think we just need to get to the first level, even
half way to the first level, and we're already on Mars. What
I'm talking about unlocks interstellar capabilities, right? Like the
ability to potentially hack the speed of light if you will, travel faster
than light, hack physics. By hack physics, I just mean find new
physics that we didn't know about before. So Mars seems
within reach, even if we go far enough along the tech tree.
Because think about it, if we can accelerate the high throughput discovery of
materials that reduce the cost and the weight and improves
the strength and all these other thermodynamic properties of
the rockets, if we can accelerate that, that
doesn't require AGPI. That does not require a
complete understanding of every domain of physics out there. I'm
willing to bet we're actually a lot closer if we look at that
scale to Mars. And so like
another, like we look at, you know, what we're kind of doing here is
building the foundational civilizational technologies that
are needed to kickstart any civilization. Because take
the satellites, for example, if we go to Mars today, one
of the first things that we struggle with is where are all the precious metals
and minerals? Where are all the economically viable, more
importantly, minerals that we can get to? That is a
huge open-ended question that has many theoretical answers, but
nothing really solid. So you want sensors
all around the planet that have physics-based AI making
sense of that geophysical data. So you're reducing time and
resources that you need to deploy to explore and extract those
resources. And then, now you have a bunch of minerals on
Mars. Woohoo, congratulations. The process that you don't have
to worry about getting sued by the government. The
Russian government, hopefully. You know, like, hey, we stake
claims there, you're not allowed. So, assuming that's not
a problem, right, and you get those deposits. The next
thing you're going to be asking is how do you use those raw materials and
convert them into advanced materials to build colonies, to build fully
functioning nuclear reactors that power a Mars base.
All those things are now material science and physics, applied physics-based
problems. And so, yeah, I think Mars will
get there, you know, probably halfway to the first level of
civilization. And somewhere between first and let's say
second, we will have enough dominance of matter and
understanding that we may be able to explore, you know, creating
and altering gravitational fields and all these other really
That's awesome, man. I can't think of a better way to just conclude it than
there. We're going to be in the stars with talking and friendly walls. And
no, thank you so much, man, for coming. This was awesome. I
think people appreciated the wide ranging conversation, but it always came back to
Thank you so much, Fisher. It's been a pleasure and honor. So much fun. Appreciate it.