The following is a conversation with Elon Musk.
His third time on this, the Lex Friedman podcast.
Yeah, make yourself comfortable.
Boo.
No, wow, okay.
You don't do the headphones thing?
No.
Okay.
I mean, how close do I get?
I need to get to this thing.
The closer you are, the sexier you sound.
Hey, babe.
Yep.
Can't get enough of the all that, baby.
I'm gonna clip that out anytime somebody messes with me.
You messaged me about it, I don't know if it's just my body.
And you think I'm sexy.
Come right out and tell me so.
Do you, do you, do you, do you, do you, do you.
So good.
So good.
Okay.
Serious mode activate.
All right.
Serious mode.
Come on, you're Russian, you can be serious.
Yeah, I know.
Everyone's serious all the time in Russia.
Yeah.
Yeah, we'll get there, we'll get there.
Even in America too.
It's gotten soft.
Allowed me to say that the SpaceX launch of human beings
to orbit on May 30th, 2020 was seen by many as the first step
in a new era of human space exploration.
These human spaceflight missions were a beacon of hope
to me and to millions over the past two years
as our world has been going through one of the most
difficult periods in recent human history.
We saw, we see the rise of division, fear, cynicism
and the loss of common humanity right when it is needed most.
So first, Elon, let me say thank you
for giving the world hope and reason
to be excited about the future.
Oh, it's kind of you to say.
I do want to do that.
Humanity has obviously a lot of issues
and you know, people at times do do bad things,
but you know, despite all that, you know,
I love humanity and I think we should
make sure we do everything we can to have a good future
and an exciting future and one where that maximizes
the happiness of the people.
Let me ask about Crew Dragon demo two.
So that first flight with humans on board,
how did you feel leading up to that launch?
Were you scared?
Were you excited?
Was it going through your mind?
So much was at stake.
Yeah, no, that was extremely stressful, no question.
We obviously could not let them down in any way.
So extremely stressful, I'd say, to say the least.
I was confident that at the time that we launched
that no one could think of anything at all to do
that would improve the probability of success
and we racked our brains to think of any possible way
to improve the probability of success.
We could not think of anything more
and nor could NASA and so that's just the best
that we could do.
So then we went ahead and launched.
Now, I'm not a religious person,
but I nonetheless got on my knees
and prayed for that mission.
Were you able to sleep?
No.
How did it feel when it was a success?
First, when the launch was a success
and when they returned back home or back to Earth?
It was a great relief.
Yeah, for high stress situations,
I find it's not so much elation as relief.
And I think once, as we got more comfortable
and proved out the systems,
because we really, you gotta make sure everything works.
It was definitely a lot more enjoyable
with the subsequent astronaut missions
and I thought the inspiration mission
was actually very inspiring, inspiration for mission.
I'd encourage people to watch the inspiration
documentary on Netflix, it's actually really good.
And it really isn't, I was actually inspired by that.
And so that one I felt I was kind of able
to enjoy the actual mission and not just be super stressed
all the time.
For people that somehow don't know,
it's the all civilian first time,
all civilian out to space, out to orbit.
Yeah, and it was, I think the highest orbit
that in like 30 or 40 years or something,
the only one that was higher was the one shuttle,
sorry, Hubble servicing mission.
And then before that, it would have been Apollo in 72.
It's pretty wild.
So it's cool, I think as a species,
like we want to be continuing to do better
and reach higher ground and like,
I think it would be tragic, extremely tragic
if Apollo was the high watermark for humanity
and that's as far as we ever got.
And it's concerning that here we are 49 years
after the last mission to the moon.
And so almost half a century and we've not been back
and that's worrying.
It's like, does that mean we've peaked as a civilization
or what?
So like, I think we got to get back to the moon
and build a base there, you know, a science base.
I think we could learn a lot about the nature of the universe
if we have a proper science base on the moon,
you know, like we have a science base in Antarctica
and you know, many other parts of the world.
And so that's like, I think the next big thing
we've got to have like a serious like moon base
and then get people to Mars and, you know,
get out there and be a space bearing civilization.
I'll ask you about some of those details,
but since you're so busy with the hard engineering challenges
of everything that's involved,
are you still able to marvel at the magic of it all,
of space travel, of every time the rocket goes up,
especially when it's a crewed mission?
Are you just so overwhelmed
with all the challenges that you have to solve?
And actually sort of to add to that,
the reason I wanted to ask this question of May 30th,
it's been some time so you can look back
and think about the impact already.
It's already, at the time,
it was an engineering problem, maybe.
Now it's becoming a historic moment.
Like it's a moment that,
how many moments would be remembered
about the 21st century?
To me, that or something like that,
maybe inspiration for one of those would be remembered
as the early steps of a new age of space exploration.
Yeah.
I mean, during the launches itself,
so I mean, I think maybe some people will know,
but a lot of people don't know,
it's like I'm actually the chief engineer of SpaceX.
So I've signed off on pretty much
all the design decisions.
And so if there's something that goes wrong
with that vehicle, it's fundamentally my fault.
So I'm really just thinking about all the things that,
like so when I see the rocket,
I see all the things that could go wrong
and the things that could be better.
And the same with the Dragon spacecraft,
it's like other people will say,
oh, this is a spacecraft or a rocket.
And that's, this looks really cool.
I'm like, I've like a readout of like,
this is the, these are the risks.
These are the problems.
That's what I see.
Like, so it's not what other people see
when they see the product, you know?
So let me ask you then to analyze Starship
in that same way.
I know you have, you'll talk about
and more detail about Starship in the near future.
Perhaps you had that.
I'll talk about it now if you want.
But just in that same way, like you said,
you see when you see a, when you see a rocket,
you see a sort of a list of risks.
In that same way, you said that Starship
is a really hard problem.
So there's many ways I can ask this,
but if you magically could solve one problem perfectly,
one engineering problem perfectly,
which one would it be?
On Starship?
Sorry, on Starship.
So is it maybe related to the efficiency,
the engine, the weight of the different components,
the complexity of various things,
maybe the controls of the crazy thing
as they do to land?
No, it's actually, by far the biggest thing absorbing
my time is engine production.
Not the design of the engine,
but I've often said prototypes are easy, production is hard.
So we have the most advanced rocket engine
that's ever been designed.
Because I say currently the best rocket engine ever
is probably the RD-180 or RD-170,
that's the door washing engine, basically.
And still, I think an engine should only count
if it's gotten something to orbit.
So our engine has not gotten anything to orbit
yet, but it is, it's the first engine
that's actually better than the Russian RD engines,
which were amazing design.
So you're talking about Raptor engine,
what makes it amazing?
What are the different aspects of it that make it,
like what do you get the most excited about
if the whole thing works
in terms of efficiency, all those kinds of things?
Well, it's,
but Raptor is a full-flow staged combustion engine
and it's operating at a very high chamber pressure.
So one of the key figures of merit,
perhaps the key figure of merit is
what is the chamber pressure
at which the rocket engine can operate?
That's the combustion chamber pressure.
So Raptor is designed to operate
at 300 bar, possibly maybe higher,
that's 300 atmospheres.
So the record right now for operational engine
is the RD engine that I mentioned, the Russian RD,
which is, I believe around 267 bar.
And the difficulty of the chamber pressure
increases on a non-linear basis.
So 10% more chamber pressure is more like
50% more difficult, but that that chamber pressure
is that that is what allows you to get
a very high power density for the engine.
So enabling a very high thrust to weight ratio
and a very high specific impulse.
So specific impulse is like a measure
of the efficiency of a rocket engine.
It's really the effect of exhaust velocity
of the gas coming out of the engine.
So with a very high chamber pressure,
you can have a compact engine
that nonetheless has a high expansion ratio,
which is the ratio between the exit nozzle and the throat.
So you see a rocket engine's got sort of
like a hourglass shape, it's like a chamber,
and then it necks down and there's a nozzle.
And the ratio of the exit diameter
to the throat expansion ratio.
So why is it such a hard engine to manufacture at scale?
It's very complex.
So what is complexity mean here
is a lot of components involved.
There's a lot of components
and a lot of unique materials.
So we had to invent several alloys
that don't exist in order to make this engine work.
So materials problem too.
So materials problem.
And in a stage combustion, a full flow stage combustion,
there are many feedback loops in the system.
So basically you've got propellant and hot gas flowing
simultaneously to so many different places on the engine.
And they all have a recursive effect on each other.
So you change one thing here,
it has a recursive effect here,
it changes something over there.
And it's quite, it's hard to control.
Like there's a reason no one's made this before.
But, and the reason we're doing a stage combustion full flow
is because it has the highest,
the highest theoretical possible efficiency.
So in order to make a fully reusable rocket,
which that's the really the holy grail of orbital rocketry.
You have to have, everything's got to be the best.
It's got to be the best engine,
the best airframe, the best heat shield,
extremely light avionics,
very clever control mechanisms.
You've got to shed mass in any possible way that you can.
For example, we are,
instead of putting landing legs on the booster and chip,
we are going to catch them with a tower
to save the weight of the landing legs.
So that's like, I mean,
we're talking about catching the largest flying object
ever made on a giant tower with, with chopstick arms.
It's like a cruddy kid with the fly, but much bigger.
I mean, pulling something like that won't work the first time.
Anyway, so this is bananas, this is banana stuff.
So you mentioned that you doubt, well, not you doubt,
but there's days or moments when you doubt
that this is even possible.
It's so difficult.
The possible part is,
well, at this point we'll,
I think we'll get Starship to work.
There's a question of timing.
How long will it take us to do this?
How long will it take us to actually achieve
full and rapid reusability?
Because it will take probably many launches
before we are able to have full and rapid reusability.
But I can say that the physics pencils out,
like we're not,
at this point, I'd say we're confident that that,
like let's say, I'm very confident that success
is in the set of all possible outcomes.
All right, it's not an all set.
For a while there, I was not convinced
that success was in the set of possible outcomes,
which is very important actually.
But so we were saying there's a chance.
I'm saying there's a chance, exactly.
Just not sure how long it will take.
We're a very talented team.
They're working night and day to make it happen.
And like I said, the critical thing to achieve
for the revolution in spaceflight
and for humanity to be a space-faring civilization
is to have a fully and rapidly reusable rocket,
orbital rocket.
There's not even been any orbital rocket
that's been fully reusable ever.
And this has always been the holy grail of rocketry.
And many smart people, very smart people
have tried to do this before and they've not succeeded.
So, because it's such a hard problem.
What's your source of belief in situations like this?
When the engineering problem is so difficult,
there's a lot of experts, many of whom you admire
who have failed in the past.
Yes.
And a lot of people, a lot of experts,
maybe journalists, all the kind of,
the public in general have a lot of doubt
about whether it's possible.
And you yourself know that even if it's a non-null set,
non-empty set of success, it's still unlikely
or very difficult.
Like, where do you go to?
Both personally, intellectually as an engineer, as a team,
like for source of strength needed to sort of persevere
through this and to keep going with the project,
take it to completion.
It's also strength.
It doesn't really know how I think about things.
I mean, for me, it's simply this,
this is something that is important to get done.
And we should just keep doing it or die trying.
And I don't need a source of strength.
So quitting is not even like...
That's not, it's not in my nature.
Okay.
And I don't care about optimism or pessimism.
Fuck that, we're gonna get it done.
We're just gonna get it done.
Can you then zoom back in to specific problems with Starship
or any engineering problems you work on?
Can you try to introspect your particular biological
neural network, your thinking process
and describe how you think through problems,
the different engineering and design problems?
Is there like a systematic process
you've spoken about first principles, thinking,
but is there a kind of process to it?
Well, I like saying like physics is low
and everything else is a recommendation.
Like I've met a lot of people who can break the law,
but I haven't met anyone who could break physics.
So first, for any kind of technology problem,
you have to sort of just make sure
you're not violating physics.
And you know, first principles analysis,
I think is something that could be applied
to really any walk of life, anything really.
It's really just saying, let's boil something down
to the most fundamental principles,
the things that we are most confident
are true at a foundational level.
And that sets your axiomatic base
and then you reason up from there
and then you cross check your conclusion
against the axiomatic truths.
So, you know, some basics in physics
would be like are you violating conservation of energy
or momentum or something like that,
you know, then you're just not gonna work.
So that's just to establish, is it possible?
And another good physics tool
is thinking about things in the limit.
If you take a particular thing
and you scale it to a very large number
or to a very small number, how do things change?
Both like in number of things you manufacture
or something like that and then in time?
Yeah, let's say you take an example of like manufacturing,
which I think is just a very underrated problem.
And like I said, it's much harder
to take an advanced technology product
and bring it into volume manufacturing
than it is to design it in the first place.
My orders of magnitude.
So, let's say you're trying to figure out
is like, why is this part or product expensive?
Is it because of something fundamentally foolish
that we're doing or is it because our volume is too low?
And so then you say, okay,
well, what if our volume was a million units a year?
Is it still expensive?
That's what I mean by thinking about things in the limit.
If it's still expensive at a million units a year,
then volume is not the reason
why your thing is expensive.
There's something fundamental about design.
And then you then can focus on reducing complexity
or something like that in the design?
You can change the design to change the part
to be something that is not fundamentally expensive.
But that's a common thing in rocketry
because the unit volume is relatively low.
And so a common excuse would be, well, it's expensive
because our unit volume is low.
And if we were in like automotive
or something like that or consumer electronics,
then our costs would be lower.
I'm like, okay, so let's say we scale,
now you're making a million units a year.
Is it still expensive?
If the answer is yes, then economies of scale
are not the issue.
Do you throw into manufacturing,
do you throw like supply chain,
talk about resources and materials and stuff like that?
Do you throw that into the calculation
of trying to reason from first principles
like how we're gonna make the supply chain work here?
Yeah, yeah.
And then the cost of materials, things like that.
Or is that too much?
Exactly, so another good example
I think of thinking about things in the limit
is if you take any product, any machine or whatever,
like take a rocket or whatever and say,
if you look at the raw materials in the rocket,
so you're gonna have like an aluminum, steel, titanium,
incanal, specialty alloys, copper,
and you say, what's the weight of the constituent elements
of each of these elements
and what is their raw material value?
And that sets the asymptotic limit
for how low the cost of the vehicle can be
unless you change the materials.
So, and then when you do that,
I call it like maybe the magic one number
or something like that.
So that would be like if you had the,
like just a pile of these raw materials here
and you could wave the magic one
and rearrange the atoms into the final shape.
That would be the lowest possible cost
that you could make this thing for
unless you change the materials.
So then, and that is always, almost always a very low number.
So then what's actually causing things to be expensive
is how you put the atoms into the desired shape.
Yeah, actually, if you don't mind me taking a tiny tangent,
I often talk to Jim Keller,
who's somebody who worked with you as a professor.
Oh yeah, yeah, Jim was, yeah, did great work at Tesla.
So, I suppose he carries the flame
of the same kind of thinking that you're talking about now.
And I guess I see that same thing at Tesla
and SpaceX folks who worked there,
they kind of learned this way of thinking
and it kind of becomes obvious almost.
But anyway, I had argument, not argument.
He educated me about how cheap it might be
to manufacture a Tesla bot.
We just, we had an argument.
How can you reduce the cost, the scale of producing a robot?
Because I got the chance to interact quite a bit,
obviously in the academic circles with human robots
and then my Boston Dynamics and stuff like that.
And then they're very expensive to build.
And then Jim kind of schooled me on saying like,
okay, like this kind of first principles thinking
of how can we get the cost of manufacturing down?
I suppose you do that, you have done
that kind of thinking for Tesla bot
and for all kinds of, all kinds of complex systems
that are traditionally seen as complex.
And you say, okay, how can we simplify everything down?
Yeah, I mean, I think if you are really good
at manufacturing, you can basically make at high volume,
you can basically make anything for a cost
that asymptotically approaches the raw material value
of the constituents, plus any intellectual property
that you need to do license, anything.
Right.
But it's hard.
It's not like that's a very hard thing to do,
but it is possible for anything.
Anything in volume can be made of, like I said,
for a cost that asymptotically approaches
its raw material constituents,
plus intellectual property license rights.
So what'll often happen in trying to design a product
is people will start with the tools
and parts and methods that they're familiar with
and then try to create a product
using their existing tools and methods.
The other way to think about it is actually imagine the,
try to imagine the platonic ideal of the perfect product,
or technology, whatever it might be.
And so what is the perfect arrangement of atoms
that would be the best possible product?
And now let us try to figure out
how to get the atoms in that shape.
I mean, it sounds,
it's almost like Rick and Morty absurd
until you start to really think about it
and you really should think about it in this way.
Because everything else is kind of,
if you think, you might fall victim to the momentum
of the way things were done in the past,
unless you think in this way.
Well, just as a function of inertia,
people will want to use the same tools and methods
that they are familiar with.
They just, that's what they'll do by default.
And then that will lead to an outcome
of things that can be made with those tools and methods,
but it is unlikely to be the platonic ideal
of the perfect product.
So then, so that's why it's just good to think of things
in both directions.
They're like, what can we build with the tools that we have?
But then, but also what is the,
what is the perfect,
the theoretical perfect product look like?
And that theoretical perfect product
is going to be a moving target.
Because as you learn more,
the definition for that perfect product will change.
Because you don't actually know what the perfect product is,
but you can successfully approximate a more perfect product.
So the thing about it like that,
and then saying, okay, now what tools, methods, materials,
whatever do we need to create
in order to get the atoms in that shape?
But for people very rarely think about it that way.
But it's a powerful tool.
I should mention that the brilliant Siobhan Zillis
is hanging out with us,
in case you hear a voice of wisdom from outside,
from up above.
Okay, so let me ask you about Mars.
You mentioned it would be great for science
to put a base on the moon to do some research,
but the truly big leap,
again, in this category of seemingly impossible,
is to put a human being on Mars.
When do you think SpaceX will land a human being on Mars?
Hmm, best case is about five years, worst case, 10 years.
Best case is about five years, worst case, 10 years.
What are the determining factors, would you say,
from an engineering perspective?
Or is that not the bottlenecks?
What are the determining factors, would you say, from an engineering perspective, or is
that not the bottlenecks?
You know, it's fundamentally engineering the vehicle, I mean, Starship is the most complex
and advanced rocket that's ever been made by, I don't know, whatever magnitude or something
like that.
It's really next level.
The fundamental optimization of Starship is minimizing cost per tonne to orbit, and ultimately
cost per tonne to the surface of Mars.
This may seem like a mercantile objective, but it is actually the thing that needs to
be optimized.
There is a certain cost per tonne to the surface of Mars where we can afford to establish
a self-sustaining city, and then above that we cannot afford to do it.
So right now, you couldn't fly to Mars for a trillion dollars, no amount of money could
get you a ticket to Mars.
So we need to get that above, you know, to get that like something that is actually possible
at all.
But we don't just want to have, you know, with Mars flags and footprints and then not
come back for a half century like we did with the moon.
In order to pass a very important to great filter, I think we need to be a multi-planet
species.
This may sound somewhat esoteric to a lot of people, but like eventually given enough
time, the Earth is likely to experience some calamity that could be something that humans
do to themselves or an external event like happen to the dinosaurs.
And eventually, if none of that happens and somehow magically we keep going, then the
sun will, the sun is gradually expanding and will engulf the Earth.
And probably Earth gets too hot for life in about 500 million years.
It's a long time, but that's only 10% longer than Earth has been around.
And so if you think about like the current situation, it's really remarkable and kind
of hard to believe, but Earth's been around four and a half billion years.
And this is the first time in four and a half billion years that it's been possible to extend
life beyond Earth.
And that window of charity may be open for a long time, and I hope it is, but it also
may be open for a short time.
And we should, I think it was wise for us to act quickly while the window is open, just
in case it closes.
Yeah, the existence of nuclear weapons, pandemics, all kinds of threats should kind of give us
some motivation.
I mean, civilization could die with a bang or a whimper.
If it dies, the demographic collapse, then it's more of a whimper, obviously, but if
it's World War III, it's more of a bang.
But these are all risks.
I mean, it's important to think of these things and just think of things as like probabilities,
not certainties.
There's a probability that something bad will happen on Earth.
I think most likely the future will be good.
But there's like, let's say for argument's sake, a 1% chance per century of a civilization
ending event.
Like that was Stephen Hawking's estimate.
I think he might be right about that.
So then we should basically think of this like being a multi-planet species is like
taking out insurance for life itself, like life insurance for life.
It's turned into an infomercial real quick.
Life insurance for life, yes.
And we can bring the creatures from plants and animals from Earth to Mars and breathe
life into the planet and have a second planet with life.
That would be great.
They can't bring themselves there.
So if we don't bring them to Mars, then they will just for sure all die when the sun expands
anyway.
And then that'll be it.
What do you think is the most difficult aspect of building a civilization on Mars, terraforming
Mars?
From an engineering perspective, from a financial perspective, human perspective, to get a large
number of folks there who will never return back to Earth.
No, they could certainly return.
Some will return back to Earth.
They will choose to stay there for the rest of their lives.
Many will.
But we need the space ships back, like the ones that go to Mars.
We need them back.
So you can hop on if you want.
But we can't just not have the space ships come back.
Those things are expensive.
We need them back.
I'd like to come back after the trip.
I mean, do you think about the terraforming aspect, like actually building, are you so
focused right now on the space ships part that's so critical to get to Mars?
We absolutely, if you can't get there, nothing else matters.
So, and like I said, we can't get there with at some extraordinarily high cost.
I mean, the current cost of, let's say, one ton to the surface of Mars is on the order
of a billion dollars.
So because you don't just need the rocket and the launch and everything, you need like
heat shield, you need guidance system, you need deep space communications, you need some
kind of landing system.
So like rough approximation would be a billion dollars per ton to the surface of Mars right
now.
Mars is obviously way too expensive to create a self-sustaining civilization.
So we need to improve that by at least a factor of a thousand.
A million per ton?
Yes, ideally less than, much less than a million ton.
But if it's not, like it's got to be, you have to say like, well, how much can society
afford to spend or just want to spend on a self-sustaining city on Mars?
The self-sustaining part is important.
Like it's just the key threshold, the great filter will have been passed when the city
on Mars can survive even if the spaceships from Earth stop coming for any reason, doesn't
matter what the reason is, but if they stop coming for any reason, will it die out or
will it not?
So if there's even one critical ingredient missing, then it still doesn't count.
It's like, you know, if you're in a long sea voyage and you've got everything except
vitamin C, it's only a matter of time, you know, you're going to die.
So we're going to get Mars, a Mars city to the point where it's self-sustaining.
I'm not sure this will really happen in my lifetime, but I hope to see it at least have
a lot of momentum.
And then you can say, okay, what is the minimum tonnage necessary to have a self-sustaining
city?
And there's a lot of uncertainty about this, you could say like, I don't know, it's probably
at least a million tons because you have to set up a lot of infrastructure on Mars.
Like I said, you can't be missing anything that in order to be self-sustaining, you can't
be missing, like you need, you know, a sandbake conductor, fabs, you need iron ore refineries,
like you need lots of things, you know.
So, and Mars is not super-hospitable, it's the least inhospitable planet, but it's definitely
a fixer of a planet.
Outside of Earth.
Yes.
Earth is pretty good.
Earth is like easy.
Yeah.
And also we should clarify in the solar system.
Yes.
In the solar system.
There might be nice, like, vacation spots.
There might be some great planets out there, but it's hopeless.
Too hard to get there?
Yeah, way, way, way, way, way too hard, to say the least.
Let me push back on that, not really a pushback, but a quick curveball of a question.
So, you did mention physics as the first starting point, so general relativity allows
for warm holes.
They technically can exist.
Do you think those can ever be leveraged by humus to travel fast in the speed of light?
Well, the one whole thing is debatable.
We currently do not know of any means of going faster than the speed of light.
There is, like, there are some ideas about having space, like, so, you can only move
at the speed of light through space, but if you can make space itself move, that's what
means space.
Space is capable of moving faster than the speed of light.
Like the universe, in the Big Bang, the universe expanded at much more than the speed of light
by a lot.
But if this is possible, the amount of energy required toward space is so gigantic, it boggles
the mind.
So, all the work you've done with propulsion, how much innovation is possible with rocket
propulsion?
Is this, I mean, you've seen it all, and you're constantly innovating on every aspect.
How much is possible?
Like, how much can you get 10x somehow?
Is there something in there in physics that you can get significant improvement in terms
of efficiency of engines and all those kinds of things?
Well, as I was saying, like, really, the Holy Grail is a fully and rapidly reusable orbital
system.
So right now, the Falcon 9 is the only reusable rocket out there, but the booster comes back
and lands, you've seen the videos, and we get the nose cone fairing back, but we do not
get the upper stage back.
So that means that we have a minimum cost of building an upper stage.
You can think of like a two-stage rocket of sort of like two airplanes, like a big airplane
and a small airplane, and we get big airplane back, but not the small airplane.
And so it still costs a lot, you know, so that upper stage is, you know, at least $10 million.
And then the degree of the booster is not as rapidly and completely reusable as we'd
like in order of the fairings, so, you know, our kind of minimum marginal cost, not counting
overhead for per flight is on the order of $15 to $20 million, maybe.
So that's extremely good for, it's by far better than any rocket ever in history.
But with full and rapid reusability, we can reduce the cost per ton to orbit by a factor
of 100.
But just think of it like, like imagine if you had an aircraft or something or a car.
And if you had to buy in your car every time you went for a drive, it would be very expensive,
every silly, frankly.
But in fact, you just refuel the car or recharge the car, and that makes your trip, like, I
don't know, a thousand times cheaper.
So it's the same for rockets.
If you, it's very difficult to make this complex machine that can go to orbit.
And so if you cannot reuse it and have to throw even any part of, any significant part
of it away, that massively increases the cost.
So, you know, Starship in theory could do a cost per launch of like a million, maybe
two million dollars or something like that and put over 100 tons in orbit, which is
crazy.
Yeah.
So.
That's incredible.
So you're saying like it's by far the biggest bang for the buck is to make it fully reusable
versus like some kind of brilliant breakthrough in theoretical physics.
Yeah, no.
There's no, there's no brilliant break.
No, there's no, just make, you're going to make the rocket reusable.
This is an extremely difficult engineering problem.
Got it.
But no, no new physics is required.
Just brilliant engineering.
Let me ask a slightly philosophical fun question.
Got to ask, I know you're focused on getting to Mars, but once we're there on Mars, what
do you, what form of government, economic system, political system do you think would
work best for an early civilization of humans is, I mean, the interesting reason to talk
about this stuff, it also helps people dream about the future.
I know you're really focused about the short term engineering dream, but it's like, I
don't know, there's something about imagining an actual civilization on Mars that gives
people, it really gives people hope.
Well it would be a new frontier and an opportunity to rethink the whole nature of government
just as was done in the creation of the United States.
So I mean, I would suggest having direct democracy, like people vote directly on things as opposed
to representative democracy.
So representative democracy I think is too subject to a special interest and a coercion
of the politicians and that kind of thing.
So I'd recommend that there's just direct democracy, people vote on laws, the population
votes on laws themselves, and then the laws must be short enough that people can understand
them.
Yeah.
And then like keeping a well-informed populace, like really being transparent about all the
information about what they're voting for.
Yeah.
Absolutely transparency.
Yeah.
And not make it as annoying as those cookies, we have to accept the cookies.
Accept cookies.
You know, there's always like a slight amount of trepidation when you click accept cookies.
Like I feel as though there's like perhaps like a very tiny chance that it'll open a
portal to hell or something like that.
That's exactly how I feel.
Why do they, why do they keep accepting, why do they want with this cookie?
Like somebody got upset with accepting cookies or something somewhere, who cares?
Like it's so annoying to keep accepting all these cookies.
To me, it's just a great experience.
Yes, you can have my damn cookie, I don't care, whatever.
He heard it from me on first.
He accepts all your damn cookies.
Yeah.
And it's not asking me, it's annoying.
Yeah.
It's one example of implementation of a good idea done really horribly.
Yeah.
It's somebody who was like, there's some good intentions of like privacy or whatever, but
now everyone just has to accept cookies and it's not, you know, you have billions of people
who have to keep clicking except cookie.
It's super annoying.
Then we just accept the damn cookie, it's fine.
There is like, I think a fundamental problem that we're, because we've not really had a
major like a world war or something like that in a while, and obviously we would like to
not have world wars, there's not been a cleansing function for rules and regulations.
So wars did have, you know, some sort of lining in that there would be a reset on rules and
regulations after a war.
So world wars one and two, there were huge resets on rules and regulations.
Now as if society does not have a war, and there's no cleansing function or garbage
collection for rules and regulations, then rules and regulations will accumulate every
year because they're immortal.
There's no actual humans die, but the laws don't.
So we need a garbage collection function for rules and regulations.
They should not just be immortal, because some of the rules and regulations that are put
in place will be counterproductive, done with good intentions, but counterproductive, sometimes
not done with good intentions.
So if rules and regulations just accumulate every year, and you get more and more of them,
then eventually you won't be able to do anything.
You're just like Gulliver with, you know, tied down by thousands of little strings.
And we see that in, you know, U.S. and like basically all economies that have been around
for a while, and regulators and legislators create new rules and regulations every year,
but they don't put effort into removing them.
And I think that's very important that we put effort into removing rules and regulations.
But it gets tough because you get special interests that then are dependent on, like
they have a vested interest in that whatever rule and regulation, and they fight to not
get it removed.
Yeah.
So I mean, I guess the problem with the Constitution is it's kind of like C versus Java, because
it doesn't have any garbage collection built in.
I think there should be, when you first said the metaphor of garbage collection, I love
that.
Yeah, it's from a coding standpoint.
From a coding standpoint.
Yeah, yeah.
It's interesting, it's the laws themselves kind of had a built in thing where they kind
of die after a while unless somebody explicitly publicly defends them.
So that's sort of, it's not like somebody has to kill them, they kind of die themselves.
They disappear.
Yeah.
Not to defend Java or anything, but you know, C++, you know, you could also have a great
garbage collection in Python and so on.
Yeah.
Yeah, something needs to happen, or just the civilization's arteries, arteries just harden
over time.
And you can just get less and less done because there's just a rule against everything.
So I think like, I don't know, for Mars or whatever I say, I would say for Earth as well,
like I think there should be an active process for removing rules and regulations and questioning
their existence.
Just like if we've got a function for creating rules and regulations, because rules and regulations
can also think of as like, they're like software or lines of code for operating civilization.
That's rules and regulations.
So it's not like we shouldn't have rules and regulations, but you have code accumulation,
but no code removal.
And so it just gets to become basically archaic bloatware after a while.
And it's just, it makes it hard for things to progress.
So I don't know, maybe Mars, you'd have like, you know, any given law must have a sunset,
you know, and require active voting to keep, to keep it up there, you know, and I actually
also say like, and these are just, I don't know, recommendations or thoughts and ultimately
we'll be up to the people on Mars to decide, but I think it should be easier to remove
a law than to add one because of the, just to overcome the inertia of laws.
So maybe it's like, for argument's sake, you need like say 60% vote to have a law take
effect, but only a 40% vote to remove it.
So let me be the guy, you posted a meme on Twitter recently where there's like a row
of urinals, a guy just walks all the way across and he tells you about crypto.
I mean, that's how to be so many times.
I think maybe even literally, do you think, technologically speaking, there's any room
for ideas of smart contracts or so on, because you mentioned laws.
That's an interesting use of things like smart contracts to implement the laws by which
governments function, like something built on Ethereum or maybe a dog coin that enables
smart contracts somehow.
I don't quite understand this whole smart contract thing, you know, I mean, I'm too
down to understand smart contracts.
That's a good line.
I mean, my general approach to any kind of like deal or whatever is just make sure there's
clarity of understanding.
That's the most important thing.
And just keep any kind of deal very, very short and simple, plain language.
And just make sure everyone understands this is the deal, is it clear?
And what are the consequences if various things don't happen?
But usually deals are business deals or whatever are way too long and complex and overly lawyered
and pointlessly.
You mentioned that Doge is the people's coin and you said that you were literally going
SpaceX may consider literally putting a Doge coin on the moon.
Is this something you're still considering, Mars perhaps?
Do you think there's some chance we've talked about political systems on Mars that Doge
coin is the official currency of Mars that's happening in the future?
Well, I think Mars itself will need to have a different currency because you can't synchronize
due to speed of light or not easily.
So it must be completely standalone from Earth?
Well, yeah, because Mars is at closest approach, it's four light minutes away roughly, and
then at furthest approach, it's roughly 20 light minutes away, maybe a little more.
So you can't really have something synchronizing if you've got a 20 minutes to be a light issue,
if it's got a one minute blockchain, it's not going to synchronize probably.
So Mars, I don't know if Mars will have a cryptocurrency as a thing, but probably seems
likely, but it would be some kind of localized thing on Mars.
And you let the people decide?
Yeah, absolutely.
The future of Mars should be up to the Martians.
So I think the cryptocurrency thing is an interesting approach to reducing the error
in the database that is cold money.
I think I have a pretty deep understanding of what money actually is on a practical day-to-day
basis because of PayPal, we really got in deep there.
And right now, the money system, actually for practical purposes, is really a bunch of heterogeneous
mainframes running old cobalt.
Okay, you mean literally, that's literally what's happening.
In batch mode.
Okay.
In batch mode.
Yeah, pretty the poor, fast as you have to maintain that code.
Okay, that's pain.
Not even Fortran, it's cobalt, yep.
It's cobalt.
And the banks are still buying mainframes in 2021 and running ancient cobalt code.
And the Federal Reserve is probably even older than what the banks have, and they have
an old cobalt mainframe.
And so the government effectively has editing privileges on the money database.
And they use those editing privileges to make more money whenever they want.
And this increases the error in the database that is money.
So I think money should really be viewed through the lens of information theory.
And so it's kind of like an internet connection, like what's the bandwidth, you know, total
bit rate, what is the latency, jitter, packet drop, you know, errors in network communication.
Just think of money like that, basically.
I think that's probably why I really think of it.
And then say what system from an information theory standpoint allows an economy to function
the best, and, you know, crypto is an attempt to reduce the error in money that is contributed
by governments diluting the money supply as basically a pernicious form of taxation.
So both policy in terms of with inflation and actual technological cobalt, like cryptocurrency
takes us into the 21st century in terms of the actual systems that allow you to do the
transaction to store wealth, all those kinds of things.
Like I said, just think of money as information.
People often will think of money as having power in and of itself.
It does not.
Money is information and it does not have power in and of itself, like applying the physics
tools of thinking about things in the limit is helpful.
If you are stranded on a tropical island and you have a trillion dollars, it's useless
because there's no resource allocation.
Money is a database resource allocation, but there's no resource to allocate except yourself,
so money is useless.
If you're stranded on a desert island with no food, all the Bitcoin in the world will
not stop you from starving.
Like just think of money as a database for resource allocation across time and space.
And then what system, in what form should that database or data system, what would be
most effective?
There is a fundamental issue with, say, Bitcoin in its current form in that the transaction
volume is very limited and the latency for a properly confirmed transaction is too long,
much longer than you'd like.
So it's actually not great from a transaction volume standpoint or a latency standpoint.
So it is perhaps useful to solve an aspect of the money database problem, which is the
sort of store of wealth or an accounting of relative obligations, I suppose, but it is
not useful as a currency, as a day-to-day currency.
The people have proposed different technological solutions, lighting network and the layer
of two technologies on top of that, it seems to be all kind of a trade-off, but the point
is, it's kind of brilliant to say that just think about information, think about what
kind of database, what kind of infrastructure enables the exchange of information.
Like you're operating an economy and you need to have some thing that allows for the
efficient to have efficient value ratios between products and services.
So you've got this massive number of products and services and you need to, you can't just
barter, just like that would be extremely unwieldy.
So you need something that gives you a ratio of exchange between goods and services and
then something that allows you to shift obligations across time, like debt and equity, shift obligations
across time.
Then what does the best job of that?
Part of the reason why I think there's some merits to Dogecoin, even though it was obviously
created as a joke, is that it actually does have a much higher transaction volume capability
than Bitcoin and the costs of doing a transaction, the Dogecoin fee is very low.
Like right now, if you want to do a Bitcoin transaction, the price of doing that transaction
is very high.
So you could not use it effectively for most things.
And nor could it even scale to a high volume.
And when Bitcoin started, I guess around 2008 or something like that, the internet connections
were much worse than they are today, like order of magnitude, there's the way worse in 2008.
So like having a small block size or whatever and a long synchronization time is made sense
in 2008.
But 2021 or fast forward 10 years, it's like comically low.
So and I think there's some value to having a linear increase in the amount of currency
that is generated.
So because some amount of the currency, like if a currency is too deflationary or should
say if a currency is expected to increase in value over time, there's reluctance to
spend it because you're like, oh, if I, I'll just hold it and not spend it because it's
scarcity is increasing with time.
So if I spend it now, then I will regret spending it.
So I will just, you know, total it.
But if there's some dilution of the currency occurring over time, that's more of an incentive
to use it as a currency.
So those coins somewhat randomly has just a fixed number of sort of coins or hash strings
that are generated every year.
So there's some inflation, but it's not a percentage base.
So it's a fixed number.
So the percentage of inflation will necessarily decline over time.
So it just, I'm not saying that it's like the ideal system for a currency, but I think
it actually is just fundamentally better than anything else I've seen just by accident.
So like how you said around 2008, so you're not, you know, some people suggested you might
be set to Oshinakamoto, you've previously said you're not, you're not for sure.
Would you tell us if you were?
Yes.
Do you think it's a feature or a bug that he's anonymous or she or they?
It's an interesting kind of quirk of human history that there is a particular technology
that is a completely anonymous inventor.
Well, I mean, you can look at the evolution of ideas before the launch of Bitcoin and
see who wrote, you know, about those ideas.
And then I don't know, obviously I don't know who created Bitcoin for practical purposes,
but the evolution of ideas is pretty clear for that.
And like it seems as though like Nick Szabo is probably more than anyone else responsible
for the evolution of those ideas.
So he claims not to be Oshinakamoto, but I'm not sure that's neither here nor there.
But he seems to be the one more responsible for the ideas behind Bitcoin than anyone else.
So it's not perhaps like singular figures aren't even as important as the figures involved
in the evolution of ideas that led to a thing.
So yeah, you know, most perhaps it's sad to think about history, but maybe most names
will be forgotten anyway.
What is the name anyway?
It's a name attached to an idea.
What does it even mean really?
I think Shakespeare had a thing about roses and stuff, whatever you said.
Rose by any other name, it smells sweet.
I gotta yield on to quote Shakespeare.
I feel like I accomplished something today.
Shall I compare you to a summer's day?
Oh, I clipped that out instead of doing it.
Not more temperate, not more fair.
Autopilot.
Tesla Autopilot has been through an incredible journey over the past six years, or perhaps
even longer in the minds of in your mind, in the minds of many involved.
I think that's where we first like connected really was the autopilot stuff, autonomy.
The whole journey was incredible to me to watch.
I was, because I knew, well, part of it was I was at MIT and I knew the difficulty of
computer vision and I knew the whole, I had a lot of colleagues and friends about the
DARPA challenge and knew how difficult it is.
So there was a natural skepticism when I first drove a Tesla with the initial system based
on Mobileye.
I thought there's no way, so the first one I got in, I thought there's no way this car
could maintain, like staying in the lane and create a comfortable experience.
So my intuition initially was that the lane keeping problem is way too difficult to solve.
Oh, lane keeping, yeah.
That's relatively easy.
Yeah.
Well, like, but not the, but solve in the way that we just, we talked about previous
is prototype versus a thing that actually creates a pleasant experience over hundreds
of thousands of miles and millions.
Yeah.
I mean, we had to wrap a lot of code around the Mobileye thing.
It doesn't just work by itself.
Yes.
I mean, there's part, that's part of the story of how you approach things sometimes.
Sometimes you do things from scratch.
Sometimes at first you kind of see what's out there and then you decide to do from scratch.
That was one of the boldest decisions I've seen is both on the hardware and the software
to decide to eventually go from scratch.
I thought, again, I was skeptical of whether that's going to be able to work out because
it's such a difficult problem.
And so it was an incredible journey.
What I see now with everything, the hardware, the compute, the sensors, the things I maybe
care and love about most is the stuff that Andre Carpathi is leading with the data set
selection, the whole data engine process, the neural network architectures, the way
that's in the real world, that network is tested, validated, all the different test
sets, you know, versus the ImageNet model of computer vision, like what's in academia
is real world artificial intelligence.
And Andre is awesome and obviously plays an important role, but we have a lot of really
talented people driving things.
And Ashok is actually the head of autopilot engineering, Andre is the director of AI.
AI stuff, yeah, yeah.
So yeah, I'm aware that there's an incredible team of just a lot going on.
Yeah, obviously, people will give me too much credit and they'll give Andre too much credit.
And people should realize how much is going on under the whole...
Yeah, it's just a lot of really talented people.
The Tesla autopilot AI team is extremely talented.
It's like some of the smartest people in the world.
So yeah, we're getting it done.
What are some insights you've gained over those five, six years of autopilot about the
problem of autonomous driving?
So you leaped in having some sort of first principles, kinds of intuitions, but nobody
knows how difficult the problem.
I thought the self-driving problem would be hard, but it was harder than I thought.
It's not like I thought it'd be easy.
I thought it'd be very hard, but it was actually way harder than even that.
So what it comes down to at the end of the day is to solve self-driving, you basically
need to recreate what humans do to drive, which is humans drive with optical sensors,
eyes, and biological neural nets.
And so in order to...
That's how the entire road system is designed to work with basically passive optical and
neural nets biologically, and now that we need to...
So for actually for full self-driving to work, we have to recreate that in digital form.
So we have to...
That means cameras with advanced neural nets in silicon form, and then it will obviously
solve for full self-driving.
That's the only way.
I don't think there's any other way.
But the question is, what aspects of human nature do you have to encode into the machine?
So you have to solve the perception problem, like detect, and then you first realize what
is the perception problem for driving, like all the kinds of things you have to be able
to see.
Like what do we even look at when we drive?
I just recently heard Andre talked about at MIT about car doors.
I think it was the world's greatest talk of all time about car doors.
The fine details of car doors.
What is even an open car door, man?
So the ontology of that, that's the perception problem.
We humans solve that perception problem, and Tesla has to solve that problem.
And then there's the control and the planning coupled with the perception.
You have to figure out what's involved in driving, especially in all the different
edge cases.
And then maybe you can comment on this, how much game theoretic kind of stuff needs to
be involved at a four-way stop sign.
As humans, when we drive, our actions affect the world.
Sure.
It changes how others behave.
Most of the time, if you're usually just responding to the scene, as opposed to really
asserting yourself in the scene.
What do you think?
I think these sort of control logic conundrums are not the hard part.
The, you know, let's see.
What do you think is the hard part in this whole beautiful, complex problem?
So it's a lot of frigging software, man.
A lot of smart lines of code.
For sure, in order to have, create an accurate vector space.
So like, you're coming from image space, which is like this flow of photons, you're going
to the cameras, and then you have this massive boot stream in image space, and then you have
to effectively compress the, a massive boot stream corresponding to photons that knocked
off an electron in a camera sensor, and turn that boot stream into vector space.
By vector space, I mean like, you know, you've got cars and humans and lane lines and curves
and traffic lights and that kind of thing.
Once you have an accurate vector space, the control problem is so much that of a video
game, like a Grand Theft Auto of Cyberpunk, if you have accurate, accurate vector space.
The control problem is, I wouldn't say it's, it's trivial, it's not trivial, but it's not
like some insurmountable thing, but having an accurate vector space is very difficult.
Yeah, I think we humans don't give enough respect to how incredibly human perception
system is, mapping the raw photons to the vector space representation in our heads.
Your brain is doing an incredible amount of processing, and giving you an image that is
a very cleaned up image, like when we look around here, we see, like you see color in
the corners of your eyes, but actually your eyes have very few cones, like cone receptors
in the peripheral vision.
Your eyes are painting color in the peripheral vision, you don't realize it, but their eyes
are actually painting color, and your eyes also have like this blood vessels and also
to gnarly things, and there's a blind spot, but do you see your blind spot?
No.
Your brain is painting in the missing, the blind spot, and you're going to do these
like, see these things online where you look here, and look at this point, and then look
at this point, and it's, if it's in your blind spot, your brain will just fill in the missing
bits.
The peripheral vision is so cool.
Yeah.
It makes you realize all the illusions for vision science, and so it makes you realize
just how incredible the brain is.
The brain is doing crazy amount of post-processing on the vision signals from your eyes.
It's insane, and then even once you get all those vision signals, your brain is constantly
trying to forget as much as possible, so human memory is perhaps the weakest thing about the
brain is memory, so because memory is so expensive to a brain and so limited, your brain is trying
to forget as much as possible, and there's still the things that you see into the smallest
amounts of information possible, so your brain is trying to not just get to a vector space,
but get to a vector space that is the smallest possible vector space of only relevant objects.
I think you can sort of look inside your brain, or at least I can, when you drive down the
road and try to think about what your brain is actually doing consciously, and it's like
you'll see a car, because you don't have cameras, I don't have eyes in the back of
your head or the side, so you say like, you basically have like two cameras on a slow
gimbal, and I say it's not that great, you and I are like, people are constantly distracted
and thinking about things and texting and doing all sorts of things they shouldn't do
in a car, changing the radio station, so having arguments is like, when's the last time you
look right and left, and you know, and rearward, or even diagonally forward to actually refresh
your vector space.
So you're glancing around, and what your mind is doing is trying to still the relevant
vectors, basically objects with a position and motion, and then editing that down to
the least amount that's necessary for you to drive.
It does seem to be able to edit it down or compress it even further into things like
concepts.
So it's not, it's like it goes beyond, the human mind seems to go sometimes beyond vector
space to sort of space of concepts, to where you'll see a thing, it's no longer represented
spatially somehow, it's almost like a concept that you should be aware of, like if this
is a school zone, you'll remember that as a concept, which is a weird thing to represent,
but perhaps for driving, you don't need to fully represent those things, or maybe you
get those kind of indirectly.
You need to establish vector space, and then actually have predictions for those vector
spaces.
Like if you drive fast, say a bus, and you see that there's people, before you drove
fast the bus, you saw people crossing, or some just imagine there's like a large truck
or something blocking site.
But before you came out of the truck, you saw that there were some kids about to cross
the road in front of the truck.
Now you can no longer see the kids, but you would now know, okay, those kids are probably
going to pass by the truck and cross the road, even though you cannot see them.
So you have to have memory, you have to need to remember that there were kids there, and
you need to have some forward prediction of what their position will be at the time of
relevance.
So with occlusions and computer vision, when you can't see an object anymore, even when
it just walks behind a tree and reappears, that's a really, really, I mean, at least
in academic literature, it's tracking through occlusions.
It's very difficult.
Yeah, we're doing it.
I understand this.
Yeah.
So some of it.
It's object permanence.
Like same thing happens with humans, with neural nets, like when like a toddler grows
up, like there's a point in time where they develop, they have a sense of object permanence.
So before a certain age, if you have a ball or a toy or whatever, and you put it behind
your back and you pop it out, if they don't, before they have object permanence, it's like
a new thing every time.
It's like, whoa, this toy went poof, just spared, and now it's back again, and they
can't believe it, and that they can play peek-a-boo all day long because the peek-a-boo is fresh
every time.
But then we figure out object permanence, then they realize, oh no, the object is not gone,
it's just behind your back.
Sometimes I wish we never did figure out object permanence.
Yeah, so that's an important problem to solve.
Yes, so like an important evolution of the neural nets in the car is memory across both
time and space.
So now you can't remember, like you have to say like, how long do you want to remember
things for?
And there's a cost to remembering things for a long time.
You can run out of memory to try to remember too much for too long, and then you also have
things that are stale if you remember them for too long.
And then you also need things that are remembered over time.
So even if you like say, have like for a good sake five seconds of memory on a time basis,
but like let's say you're parked at a light, and you saw, use a pedestrian example that
people were waiting to cross the road, and you can't quite see them because of an occlusion,
but they might wait for a minute before the light changes for them to cross the road.
You still need to remember that that's where they were, and that they're probably going
to cross the road type of thing.
So even if that exceeds your time-based memory, it should not exceed your space of memory.
And I just think the data engine side of that, so getting the data to learn all of the concepts
that you're saying now is an incredible process.
It's this iterative process of just, it's this hydranet of many-
Hydranet.
Yeah.
We're changing the name to something else.
Okay.
I'm sure it'll be equally as Rick and Morty like-
There's a lot of, yeah.
We've re-architected the neural net, the neural nets in the cars so many times it's crazy.
Also, every time there's a new major version, you'll rename it to something more ridiculous
or-
I-
Or memorable and beautiful.
Sorry.
Not ridiculous, of course.
If you see the full array of neural nets that are operating in the cars, it kind of
boggles the mind.
There's so many layers, it's crazy.
So yeah, but, and we started off with simple neural nets that were basically image recognition
on a single frame from a single camera and then trying to knit those together with, you
know, with the C, I should say, we're really primarily running C here because C++ is too
much overhead.
And we have our own C compiler.
So to get maximum performance, we actually wrote our own C compiler and are continuing
to optimize our C compiler for maximum efficiency.
In fact, we've just recently done a new river on our C compiler that will compile directly
to our autopilot hardware.
So you want to compile the whole thing down with your own compiler?
Yeah.
Like, so efficiency here-
Absolutely.
Because there's all kinds of compute.
There's CPU, GPU, there's like the ASIC type of thing and you have to somehow figure out
the scheduling across all of those things.
And so you're compiling the code down.
Yeah.
It does all, okay.
So that's why there's a lot of people involved.
There's a lot of hardcore software engineering at a very sort of bare metal level because
you, we're trying to do a lot of compute that's constrained to the, you know, our full self-driving
computer.
And we want to try to have the highest frames per second possible in a sort of very finite
amount of compute and power.
So we really put a lot of effort into the efficiency of our compute.
And so there's actually a lot of work done by some very talented software engineers at
Tesla that at a very foundational level to improve the efficiency of compute and how
we use the trip accelerators, which are basically, you know, doing matrix math dot products,
like a bazillion dot products.
And it's like, what are neural nets?
It's like, compute-wise, like 99% dot products.
So, you know.
And you want to achieve as many high frame rates like a video game.
You want full resolution, high frame rate?
High frame rate, low latency, low jitter.
So I think one of the things we're moving towards now is no post-processing of the image
through the image signal processor.
So, like, what happens for cameras is that there's a lot of post-processing done in order
to make pictures look pretty.
And so we don't care about pictures looking pretty.
We just want the data.
So we're moving to just raw photon counts.
So the image that the computer sees is actually much more than what you'd see if you're represented
on a camera.
It's got much more data.
And even in very low light conditions, you can see that there's a small photon count
difference between, you know, this spot here and that spot there, which means that...
So it can see in the dock incredibly well because it can detect these tiny differences
in photon counts.
Much better than you'd possibly imagine.
And then we also save 13 milliseconds on latency.
So...
From removing the post-processing on the image?
Yes.
Yeah.
It's like...
Because we've got eight cameras and then there's roughly, I don't know, one and a half milliseconds
also, maybe 1.6 milliseconds of latency for each camera.
And so, like, going to just basically bypassing the image processor gets us back 13 milliseconds
of latency, which is important.
And we track latency all the way from, you know, photon hits the camera to, you know,
all the steps that it's got to go through to get, you know, go through the various neural
nets and the C code and there's a little bit of C++ there as well.
Well, maybe a lot, but the core stuff is heavy-duty computers all in C. And so we track that
latency all the way to an output command to the drive, you know, to accelerate the brakes
just to slow down, steering, you know, turn left or right.
So because you got to output a command that's going to go to a controller and like some
of these controllers have an update frequency, that's maybe 10 hertz or something like that,
which is slow.
That's like, now you lose 100 milliseconds potentially.
So then we want to update the drivers on the, like, say, steering and braking control to
have more like 100 hertz instead of 10 hertz and you got a 10 millisecond latency instead
of 100 milliseconds, worst-case latency.
And actually, Jeter is more of a challenge than latency.
Because latency is like, you can anticipate and predict, but if you've got a stack up
of things going from the camera to the computer through a series of other computers and finally
to an actuator on the car, if you have a stack up of tolerances, of timing tolerances, then
you can have quite a variable latency, which is called Jeter.
And that makes it hard to anticipate exactly what, how you should turn the car or accelerate
because, you know, if you've got maybe 150 to 200 milliseconds of Jeter, then you could
be off by, you know, up to 0.2 seconds and this could make a big difference.
So you have to interpolate somehow to deal with the effects of Jeter so that you can
make like robust control decisions.
So the Jeter is in the sensor information or the Jeter can occur at any stage in the
pipeline.
If you have just, if you have a fixed latency, you can anticipate and like say, okay, we
know that our information is, for argument's sake, 150 milliseconds stale.
Like 150 milliseconds from photon taken camera to where you can measure a change in the acceleration
of the vehicle.
So then you're going to say, okay, well, we're going to, we know it's 150 milliseconds,
so we're going to take that into account and compensate for that latency.
However, if you've got then 150 milliseconds of latency plus 100 milliseconds of Jeter,
which could be anywhere from 0 to 100 milliseconds on top.
So then your latency could be from 150 to 250 milliseconds.
Now you've got 100 milliseconds that you don't know what to do with and that's basically
random.
So getting rid of Jeter is extremely important.
And that affects your control decisions and all those kinds of things.
Okay.
Yeah, the car is just going to fundamentally maneuver better with lower Jeter.
Got it.
Yeah.
The cars will maneuver with superhuman ability and reaction time much faster than a human.
I mean, I think over time, the autopilot, full stop driving will be capable of maneuvers
that are far more than what James Bond could do in the best movie type of thing.
That's exactly where I was imagining my mind as you said it.
It's like impossible maneuvers that a human couldn't do.
Well, let me ask, sort of looking back the six years, looking out into the future, based
on your current understanding, how hard do you think this full self-driving problem,
when do you think Tesla will solve level four FSD?
I mean, it's looking quite likely that it will be next year.
And what does the solution look like?
Is it the current pool of FSD beta candidates?
They start getting greater and greater as they have been degrees of autonomy.
And then there's a certain level beyond which they can do their own, they can read a book.
Yeah.
So, you can see that anybody who's been following the full self-driving beta closely will see
that the rate of disengagement has been dropping rapidly.
So, disengagement be where the driver intervenes to prevent the car from doing something dangerous
potentially.
So, the interventions per million miles has been dropping dramatically at some point.
And that trend looks like it happens next year is that the probability of an accident
on FSD is less than that of the average human and then significantly less than that of the
average human.
So, it certainly appears like we will get there next year.
Then, of course, then there's going to be a case of, okay, well, we now have to prove
this to regulators and prove it to, you know, and we want a standard that is not just equivalent
to a human, but much better than the average human.
I think it's got to be at least two or three times higher safety than a human.
So, two or three times lower probability of injury than a human before we would actually
say like, okay, it's okay to go.
It's not going to be equivalent.
It's got to be much better.
So, if you look at FSD 10.6 just came out recently, 10.7 is on the way.
Maybe 11 is on the way, so we're in the future.
Yeah.
We were hoping to get 11 out this year, but it's, 11 actually has a whole bunch of fundamental
rewrites on the neural net architecture and some fundamental improvements in creating
vector space.
So, there is some fundamental leap that really deserves the 11.
I mean, that's a pretty cool number.
Yeah.
11 would be a single stack for all, you know, one stack to rule them all.
And but there are just some really fundamental neural net architecture changes that will
allow for much more capability, but, you know, at first they're going to have issues.
So, like, we have this working on like sort of alpha software and it's good, but it's
basically taking a whole bunch of C++ code and leading a massive amount of C++ code and
replacing it with the neural net and, you know, Andre makes this point a lot, which
is like neural nets that kind of eating software, you know, over time, there's like less and
less conventional software, more and more neural net, which is still software, but it's,
you know, still comes out the lines of software, but it's more neural net stuff and less, you
know, heuristics, basically, if you're more matrix-based stuff and less heuristics-based
stuff, and, you know, like one of the big changes will be, like right now the neural
nets will deliver a giant bag of points to the C++ or CNC++ code.
We call it the giant bag of points.
And it's like, so you got a pixel and something associated with that pixel, like this pixel
is probably car, this pixel is probably lane-line, then you've got to assemble this giant bag
of points in the C code and turn it into vectors.
And it does a pretty good job of it, but it's, we want to just, we need another layer of
neural nets on top of that to take the giant bag of points and distill that down to vector
space in the neural net part of the software as opposed to the heuristics part of the software.
This is a big improvement.
Neural nets all the way down is what you want.
It's not even all neural nets, but it's, this will be just a game changer to not have
the bag of points, the giant bag of points that has to be assembled with many lines of
C++ and have the neural net just assemble those into vectors.
So the neural net is outputting much, much less data.
It's outputting, this is a lane line, this is a curve, this is a drivable space, this
is a car, this is a pedestrian or a cyclist or something like that.
It's outputting, it's really outputting proper vectors to the C C++ control code as opposed
to the sort of constructing the vectors in C, which we've done, I think, quite a good
job of, but it's kind of hitting a local maximum on how well this you can do this.
So this is really a big deal.
And just all of the networks in the car need to move to surround video.
There's still some legacy networks that are not surround video.
And all of the training needs to move to surround video.
And the efficiency of the training, it needs to get better than it is.
And then we need to move everything to raw photon counts as opposed to processed images.
It's just quite a big reset on the training because the system is trained on post-processed
images.
So we need to redo all the training to train against the raw photon counts instead of the
post-processed image.
So ultimately, it's kind of reducing the complexity of the whole thing.
So reducing the lines of code will actually go lower.
Yeah, that's fascinating.
So you're doing fusion of all the sensors or reducing the complexity of having to deal
with each other.
Usually the cameras.
There's a lot of cameras, really.
Right.
Yes.
Same with humans.
Yeah.
Well, I guess we've got years, too.
Okay.
Yeah, well, actually, you need to incorporate sound as well because you need to listen for
ambulance sirens or firetrucks, if somebody is yelling at you or something, I don't know.
There's a little bit of audio that needs to be incorporated as well.
Do you need to go back to break?
Yeah, let's do it.
Sure, let's take a break.
Okay.
Honestly, frankly, the ideas are the easy thing and the implementation is the hard thing.
The idea of going to the moon is the easy part, but going to the moon is the hard part.
But there's a lot of hardcore engineering that's got to get done at the hardware and
software level, optimizing the C compiler and just cutting out latency everywhere.
If we don't do this, the system will not work properly.
So the work of the engineers doing this, they are unsung heroes, but they are critical
to the success of the situation.
I think you made it clear.
I mean, at least to me, it's super exciting, everything that's going on outside of what
Andre is doing, just the whole infrastructure, the software, I mean, everything is going
on with data engine, whatever it's called, the whole process is just a work of art to
me.
Yeah, I think the sheer scale of it is boggles my mind.
The training, the amount of work done with, like we've written all this custom software
for training and labeling and to do auto labeling.
Auto labeling is essential, because especially when you've got surround video, it's very
difficult to label surround video from scratch is extremely difficult.
Take a human such a long time to even label one video clip, like several hours.
The auto label, basically, we just apply heavy duty, a lot of compute to the video clips
to pre-assign and guess what all the things are that are going on in this round video.
And then there's correcting it.
Yeah, and then all the human has to do is tweet, like say, adjust what is incorrect.
This is like increase this productivity by a hundred or more.
Yeah, so you've presented Tesla Bot as primarily useful in the factory.
First of all, I think human robots are incredible.
From a fan of robotics, I think the elegance of movement that human robots, the bipedal
robots show are just so cool.
So it's really interesting that you're working on this and also talking about applying the
same kind of all the ideas of some of which we've talked about with data engine, all the
things that we're talking about with Tesla autopilot, just transferring that over to
the just yet another robotics problem.
I have to ask, since I care about human robot interaction, so the human side of that, so
you've talked about mostly in the factory, do you see it, do you see part of this problem
that Tesla Bot has to solve is interacting with humans and potentially having a place
like in the home?
So interacting, not just not replacing labor, but also like, I don't know, being a friend
or an assistant or something like that.
I think the possibilities are endless.
It's obviously, it's not quite in Tesla's primary mission direction of accelerating
sustainable energy, but it is an extremely useful thing that we can do for the world,
which is to make a useful humanoid robot that is capable of interacting with the world and
developing in many different ways.
So in fact reason, I mean, I think if you say extrapolate to many years in the future,
it's like, I think work will become optional.
So like there's a lot of jobs that if people weren't paid to do it, they wouldn't do it.
Like it's not fun necessarily.
Like if you're washing dishes all day, it's like, you know, even if you really like washing
dishes, you really want to do it for eight hours a day every day, probably not.
So and then there's like dangerous work.
And basically if it's dangerous, boring, has like potential for repetitive stress injury,
that kind of thing, then that's really where humanoid robots would add the most value initially.
So that's what we're aiming for is to, for the humanoid robots to do jobs that people
don't voluntarily want to do.
And then we'll have to pair that obviously with some kind of universal basic income in
the future.
So I think.
So do you see a world when there's like hundreds of millions of Tesla bots doing different performing
different tasks throughout the world?
Yeah, I haven't really thought about it that far into the future, but I guess that there
may be something like that.
So guess a wild question.
So the number of Tesla cars has been accelerating has been close to 2 million produced.
Many of them have autopilot.
I think we're over 2 million now.
Yeah.
Do you think there will ever be a time when there'll be more Tesla bots than Tesla cars?
Yeah.
Actually, it's funny you asked this question because normally I do try to think pretty far
into the future, but I haven't really thought that far into the future with the Tesla bot
or it's co-named Optimus.
I call it Optimus subprime because it's not like a giant transformer robot.
So it's meant to be a general purpose helpful bot.
And basically like the things that we're basically like Tesla, I think is the has the most advanced
real world AI for interacting with the real world, which should develop as a function
to make self-driving work.
And so along with custom hardware and like a lot of hardcore low-level software to have
it run efficiently and be power efficient because it's one thing to do in neural nets
if you've got a gigantic solar room with 10,000 computers, but now let's say you just, you
have to now distill that down into one computer that's running at low power in a humanoid robot
or a car.
That's actually very difficult and a lot of hardcore software work is required for that.
So since we're kind of like solving the, navigate the real world with neural nets problem for
cars, which are kind of like robots with four wheels, then it's like kind of a natural extension
of that is to put it in a robot with arms and legs and actuators.
So like the two hard things are like you basically need to have the robot be intelligent enough
to interact in a sensible way with the environment.
So you see real world AI and you need to be very good at manufacturing, which is a very
hard problem.
You need to be very good at manufacturing and also has the real world AI, so making the
humanoid robot work is basically means developing custom motors and sensors that are different
for what a car would use, but we also, I think we have the best expertise in developing advanced
electric motors and power electronics.
So it just has to be for a humanoid robot application or a car.
Still, you do talk about love sometimes.
So let me ask, this isn't like for like sex robots or something like that.
Love is the answer.
Yes.
There is something compelling to us, not compelling, but we connect with humanoid robots or even
legged robots like with a dog and shapes of dogs.
It seems like there's a huge amount of loneliness in this world.
All of us seek companionship with other humans, friendship and all those kinds of things.
We have a lot of here in Austin, a lot of people have dogs.
There seems to be a huge opportunity to also have robots that decrease the amount of loneliness
in the world or help us humans connect with each other so in the way that dogs can.
Do you think about that?
We'll test about it all or is it really focused on the problem of performing specific tasks,
not connecting with humans?
I mean, to be honest, I have not actually thought about it from the companionship standpoint,
but I think it actually would end up being, it could be actually a very good companion.
And it could develop like a personality over time that is like unique, it's not like they're
just all the robots are the same.
And that personality could evolve to be match the owner or the, yes, the owner, whatever
you want to call it.
The companion.
The half, right?
In the same way that friends do.
See, I think that's a huge opportunity.
I think.
Yeah, no, that's interesting, because there's a Japanese phrase like the, you know, the
subtle imperfections are what makes something special.
And the subtle imperfections of the personality of the robot mapped to the subtle imperfections
of the robot's human friend, I don't know, owner sounds like maybe the wrong word, but
could actually make an incredible buddy, basically.
In that way, the imperfections.
Like R2D2 or like a C3PO sort of thing, you know.
So from a machine learning perspective, I think the flaws being a feature is really
nice.
You could be quite terrible at being a robot for quite a while in the general home environment
or all in general world.
And that's kind of adorable.
And that's like those are your flaws and you fall in love with those flaws.
So it's a very different than autonomous driving where it's a very high stakes environment
you cannot mess up.
And so it's, yeah, it's more fun to be a robot in the home.
Yeah.
In fact, if you think of like C3PO and R2D2, like they actually had a lot of like flaws
and imperfections and silly things and they would argue with each other.
Were they actually good at doing anything?
I'm not exactly sure.
They definitely added a lot to the story, but there's sort of quirky elements and you
know, that they would like make mistakes and do things.
It was like, it made them relatable, I don't know, enduring.
So yeah, I think that that could be something that probably would happen.
But our initial focus is just to make it useful.
So I'm confident we'll get it done.
I'm not sure what the exact timeframe is, but like we'll probably have, I don't know,
a decent prototype towards the end of next year or something like that.
And it's cool that it's connected to Tesla, the car.
So yeah, it's using a lot of, you know, it would use the autopilot inference computer
and a lot of the training that we've done for the four cars in terms of recognizing real
world things could be applied directly to the robot.
So but there's a lot of custom actuators and sensors that need to be developed.
And an extra module on top of the vector space for love.
Yeah.
That's amazing.
Okay.
We can add that to the car too.
That's true.
Yeah, that could be useful in all environments.
Like you said, a lot of people argue in the car, so maybe we can help them out.
You're a student of history, fan of Dan Carlin's Hardcore History podcast.
Yeah, that's great.
Greatest podcast ever.
Yeah, I think it is actually.
It almost doesn't really count as a podcast.
Yeah, it's more like an audio book.
So you were on the podcast with Dan, just had a chat with him about it.
He said you guys want military and all that kind of stuff.
Yeah, it's literally, it was basically, it should be titled Engineer Wars.
Essentially like when there's a rapid change in the rate of technology, then engineering
plays a pivotal role in victory and battle.
How far back in history did you go?
Did you go World War II?
Well, it was supposed to be a deep dive on fighters and bomber technology in World War
II, but that ended up being more wide-ranging than that because I just went down the total
rathole of studying all of the fighters and bombers of World War II and the constant
rock-paper-scissors game that one country would make this plane, then it'd make a plane
to beat that, and that's what I'm trying to make a plane to beat that.
And really what matters is the pace of innovation and also access to high-quality fuel and raw
materials.
So Germany had some amazing designs, but they couldn't make them because they couldn't get
the raw materials, and they had a real problem with the oil and fuel, basically.
The fuel quality was extremely variable.
So the design wasn't the bottleneck, was it?
Yeah.
So the US had kick-ass fuel that was very consistent.
The problem is if you make a very high-performance aircraft engine, in order to make high-performance,
you have to, the fuel, the aviation gas, has to be a consistent mixture, and it has to
have a high octane.
High octane is the most important thing, but it also can't have impurities and stuff because
you'll foul up the engine, and Germany just never had good access oil.
They tried to get it by invading the Caucasus, but that didn't work too well.
Never works well.
Never worked out for them.
See you, Jeff.
See you, Jeff.
Nice to meet you.
So Germany was always struggling with basically shitty oil, and then they couldn't count on
high-quality fuel for their aircraft, so then they had to have all these additives and stuff.
So whereas the US had awesome fuel, and that provided that to Britain as well.
So that allowed the British and the Americans to design aircraft engines that were super
high-performance, better than anything else in the world.
Germany could design the engines, they just didn't have the fuel, and then also the quality
of the aluminum alloys that they were getting was also not that great.
Is this like, you talked about all this with Dan?
Yeah.
Awesome.
Broadly looking at history, when you look at Genghis Khan, when you look at Stalin, Hitler,
the darkest moments of human history, what do you take away from those moments?
Does it help you gain insight about human nature, about human behavior today, whether
it's the wars or the individuals or just the behavior of people, any aspects of history?
Yeah, I find history fascinating.
There's a lot of incredible things that have been done, good and bad, that they just help
you understand the nature of civilization and individuals, and...
Does it make you sad that humans do these kinds of things to each other?
You look at the 20th century, World War II, the cruelty, the abuse of power, talk about
communism, Marxism, and Stalin.
I mean, some of these things do, I mean, there's a lot of human history.
Most of it is actually people just getting on with their lives, and it's not like human
history is just a nonstop war and disaster.
Those are actually just those are intermittent and rare.
If they weren't, then humans would soon cease to exist, but it's just that wars tend to
be written about a lot, whereas something being like, well, a normal year where nothing
major happened, doesn't get written about much, but that's, you know, most people just
like farming and kind of like living their life, you know, being a villager somewhere.
Every now and again, there's a war, and I'd say there aren't very many books where I just
had to start reading because it was just too dark, but the book about Stalin, the Court
of the Red Czar, I had to start reading.
It was just too dark, rough.
Yeah.
The 30s, there's a lot of lessons there to me, in particular that it feels like humans,
like all of us have that as the old Solzhenitsyn line, that the line between good and evil
runs to the heart of every man, that all of us are capable of evil, all of us are capable
of good.
It's almost like this kind of responsibility that all of us have to tend towards the good.
To me, looking at history is almost like an example of, look, you have some charismatic
leader that convinces you of things, it's too easy based on that story to do evil onto
each other, onto your family, onto others, and so it's like our responsibility to do
good.
It's not like now is somehow different from history.
That can happen again, all of it can happen again, and yes, most of the time, you're right.
I mean, the optimistic view here is mostly people are just living life, and as you've
often memed about, the quality of life was way worse back in the day and keeps improving
over time through innovation through technology, but still, it's somehow notable that these
blimps of atrocities happen.
Sure.
Yeah.
I mean, life was really tough for most of history.
I mean, for most of human history, a good year would be one where not that many people
in your village died of the plague, starvation, freezing to death, or being killed by a neighboring
village.
It's like, well, it wasn't that bad.
It was only like, we lost 5% this year, that was a good year.
That would be par for the course.
Just not starving to death would have been the primary goal of most people throughout
history, is making sure we'll have enough food to last for the winter and not freeze
or whatever.
Now food is plentiful, I have an obesity problem.
Well, yeah, the lesson there is to be grateful for the way things are now for some of us.
We've spoken about this offline.
I'd love to get your thought about it here.
If I sat down for a long-form in-person conversation with the president of Russia, Vladimir Putin,
would you potentially want to call in for a few minutes to join in on a conversation
with him, moderated and translated by me?
Sure.
Yeah.
Sure, I'd be happy to do that.
You've shown interest in the Russian language.
Is this grounded in your interest in history of linguistics, culture, general curiosity?
I think it sounds cool.
It looks cool.
Well, it takes a moment to read Cyrillic.
Once you know what the Cyrillic characters stand for, actually, then reading Russian
becomes a lot easier because there are a lot of words that are actually the same, like
bank is bank.
So find the words that are exactly the same and now you start to understand Cyrillic.
Yeah.
If you can sound it out, there's at least some commonality of words.
What about the culture?
You love great engineering, physics, there's a tradition of the sciences there.
Sure.
You look at the 20th century from rocketry, so some of the greatest rockets of the space
exploration has been done in the Soviet and the former Soviet Union.
So do you draw inspiration from that history, just how this culture that in many ways, one
of the sad things is because of the language, a lot of it is lost to history because it's
not translated, all those kinds of, because it is in some ways an isolated culture.
It flourishes within its borders.
Do you draw inspiration from those folks from the history of science engineering there?
I mean, the Soviet Union, Russia, and Ukraine as well, and have a really strong history
in spaceflight, like some of the most advanced and impressive things in history were done
by the Soviet Union.
So one cannot help but admire the impressive rocket technology that was developed.
After the Soviet Union, there's much less that happened.
But still things are happening, but it's not quite at the frenetic pace that was happening
before the Soviet Union kind of dissolved into separate republics.
Yeah.
I mean, there's Roscoe's most of the Russian agency.
I look forward to a time when those countries with China are working together, the United
States are all working together, maybe a little bit of friendly competition, but...
I think friendly competition is good.
Governments are slower, and the only thing slower than one government is a collection
of governments.
So the Olympics would be boring if everyone just crossed the finishing line at the same
time.
Yeah.
Nobody would watch, and people wouldn't try hard to run fast and stuff.
So I think friendly competition is a good thing.
This is also a good place to give a shout out to a video titled the entire Soviet rocket
engine family tree by Tim Dodd, a.k.a. everyday astronaut in a second hour and a half.
That gives a full history of Soviet rockets, and people should definitely go check on support
Tim in general.
Yeah.
That guy's super excited about the future, super excited about a space fight.
Every time I see anything by him, I just have a stupid smile on my face because he's so
excited about stuff.
Yeah.
I love people like that.
Yeah.
Tim Dodd is really great.
If you're interested in anything to do with space, he's, in terms of explaining rocket
technology to your average person, he's awesome, the best, I'd say.
And I should say like the part of the reason like I switched us from, like Raptor at one
point was going to be a hydrogen engine, but hydrogen has a lot of challenges.
It's very low density.
It's a deep cryogen, so it's only liquid at a very, very close to absolute zero, requires
a lot of insulation.
So there's a lot of challenges there.
And I was actually reading a bit about Russian rocket engine development, and at least the
impression I had was that Soviet Union, Russia, and Ukraine primarily were actually in the
process of switching to methalox.
And there was some interesting test and data for ISP, like they were able to get like up
to like a 380-second ISP with a methalox engine.
And I was like, well, okay, that's actually really impressive.
So I think you could actually get a much lower cost, like optimizing cost per ton to orbit,
cost per ton to Mars, it's, I think, methane oxygen is the way to go.
And I was partly inspired by the Russian work on the test stands with methalox engines.
And now for something completely different.
Do you mind doing a bit of a meme review in the spirit of the great, the powerful PewDiePie?
Let's say one to 11, just go over a few documents, print it out.
We can try.
Let's try this.
I present to you document number Uno.
Vlad the Impaler discovers marshmallows.
Yeah, that's not bad.
So you get it because he likes impaling things?
I don't know, three, whatever.
That's not very good.
This is grounded in some engineering, some history.
Yeah, give us an eight out of 10.
What do you think about nuclear power?
I'm in favor of nuclear power.
I think it's in a place that is not subject to extreme natural disasters.
I think it's a nuclear power is a great way to generate electricity.
I don't think we should be shutting down nuclear power stations.
Yeah, but what about Chernobyl?
Exactly.
So I think people, there's like a lot of fear of radiation and stuff.
And it's, I guess, probably like a lot of people just don't, they didn't study engineering
or physics.
It's just the word radiation just sounds scary.
So they don't, they can't calibrate what radiation means.
But radiation is much less dangerous than you think.
So like, for example, Fukushima, you know, when the Fukushima problem happened due to
the tsunami, I got people in California asking me if they should worry about radiation from
Fukushima.
And I'm like, definitely not, not even slightly, not at all.
That is crazy.
And just to show like, look, this is how like the dangers is so much overplayed compared
to what, what it really is that I actually flew to Fukushima and I actually, I donated
a solar power system for water treatment plant and, and I made a point of eating locally grown
vegetables on TV in Fukushima.
Like I'm still alive.
Okay.
So it's not even at the risk of these events is low, but the impact of them is.
Impact is greatly exaggerated.
It's just human nature.
So people don't know what radiation is.
Like I've had people ask me, like, what about radiation from cell phones, quoting, causing
brain cancer?
I'm like, when you say radiation, do you mean photons or particles than like that?
I don't know what, what do you mean photons, particles?
So do you mean, let's say photons, what, what, what frequency or wavelength?
And they're like, no, I have no idea.
Like, do you know that everything's radiating all the time?
Like what do you mean?
Like, yeah, everything's radiating all the time.
Photons are being emitted.
By, by all objects all the time, basically.
So, um, and if you want to know what it's, it's what, what it means to stand in front
of nuclear fire, go outside.
The sun is a gigantic, you know, thermonuclear reactor that you're staring right at it.
Yeah.
Are you still alive?
Yes.
Okay.
Amazing.
Yeah.
I guess radiation is one of the words that could be used as a tool to, to, to fear
monger by certain people.
That's it.
And I think people just don't, don't understand.
So, I mean, that's the way to fight that, that fear, I suppose, is to understand, is
to learn.
Yeah.
Just say like, okay, how many people have actually died from nuclear accidents?
It's like practically nothing.
And say how many people have, have died from, you know, coal plants and it's a very big
number.
So like, obviously we should not be starting up coal plants and shutting down nuclear plants.
It just doesn't make any sense at all.
Coal plants like, I don't know, a hundred to a thousand times worse for, for health than
nuclear power plants.
Do you want to go to the next one?
This is really bad.
So that 90, 180 and 360 degrees, everybody loves the math.
Nobody gives a shit about 270.
It's not super funny.
I don't like two or three.
Yeah.
This is not, uh, you know, LOL situation.
Yeah.
That's pretty good.
The United States oscillating between establishing and destroying dictatorships.
It's like, uh, is that a metronome?
Yeah.
What is that?
Metronome.
Yeah.
It's, uh, kind of seven out of 10.
It's kind of true.
Oh yeah.
This is, uh, this is kind of personal for me.
Next one.
Oh man.
This is Leica?
Yeah.
Yeah.
Or it's like referring to Leica or something?
As Leica's, uh, like, uh, uh, husband.
Husband.
Yeah.
Yeah.
Hello.
Yes.
This is dog.
Your wife was launched to space.
And then the last one is him with his eyes closed and a bottle of vodka.
Yeah.
Leica didn't come back.
No.
Um.
They don't tell you the full story of, you know, what, what the love, the impact
they had on the loved ones.
True.
That one gets an 11 for me.
Sure.
The Soviet shadow.
This keeps going on the Russian theme.
First man in space, nobody cares.
First man on the moon.
Well, I think people do care.
No, I know.
But, um, there's, you've got Garand's names will, will, will be forever in history, I
think.
There is something special about placing like stepping foot onto another totally foreign
land.
It's, it's not the journey like, uh, people that explore the oceans, it's not as important
to explore the oceans as to land on a whole new continent.
Yeah.
Oh, this is about you.
Oh yeah.
I'd love to get your comment on this.
Elon Musk, after sending $6.6 billion to the UN to end world hunger, you have three hours.
Um.
Yeah.
Well, I mean, obviously $6 billion is not going to end world hunger.
So, um, so, I mean, the reality is at this point, the world is producing.
Uh, far more food than it can really consume it.
Like we don't have a caloric, uh, constraint to this point.
So where there is hunger, it is almost always due to, um, it's like, like civil war or strife
or some like, um, it's not, it's, it's not a thing that is extremely rare for it to
be just a matter of like lack, lack of money.
It's like, you know, it's like some, there's civil war in some, some country and then like
one part of the country is literally trying to starve the other part of the country.
Um, so it's much more complex than something that money could solve as Paul is geopolitics.
It's, it's a lot of things.
It's human nature.
It's governments.
It's monies, monetary systems, all that kind of stuff.
Yeah.
Food is extremely cheap these days.
It's like, um, I mean, the US at this point, um, you know, it's, it's, it's, it's, it's
like, you know, among low income families, obesity is the actual, the other problem.
It's not like obesity is, it's not hunger.
It's like too much, it's, you know, too many calories.
So it's not that nobody's hungry, hungry anywhere.
It's just, it's just, this is, uh, not, not a simple matter of adding money and solving
it.
Hmm.
What do you think that one gets?
It's getting.
Uh, two.
It's going after empire's world.
Uh, where did you get those artifacts?
The British museum.
Shout out to Monty Python.
We found them.
Yeah.
The British museum is, it's pretty great.
I mean, it, it, it, it admittedly Britain did take, uh, these historical artifacts from
all around the world and put them in London, but, uh, you know, it, it, it's not like people
can't go see them.
Uh, so it is like a convenient place to see these, uh, ancient artifacts is, is London
for, you know, for, for a large segment of the world.
So I think, you know, unbalanced, the British museum is a net good.
Well, I'm sure that a lot of countries argue about that.
Yeah.
It's like, you want to make these historical artifacts accessible to as many people as
possible.
And the British museum, uh, I think does a good job of that.
Even if there's a darker aspect to like the history of empire in general, whatever the
empire is, however things were done, it's, it is the history that happened.
You can't sort of erase that history.
Unfortunately, you could just become better in the future.
That's the point.
Yeah.
I mean, it's like, well, how are we going to pass from all judgment on these, these
things?
Like it's like, if, uh, you know, uh, if, if one is going to judge say the grocery empire,
you got to judge, you know, what everyone was doing at the time and how were the British
relative to everyone?
Um, and I think they were, first would actually get like a relatively good grade, relatively
good grade, not an absolute terms, but compared to what everyone else was doing, um, they
were not the worst.
Like I said, you got to look at these things in the context of the history at the time,
and say, what, what were the alternatives and what are you comparing it against?
And I, I, I, I do not think it'll be the case that, um, Britain would get a, uh, a bad
grade in, in when looking at history at the time.
You know, if you judge history from, you know, from what is morally acceptable today, you,
you basically are going to give everyone a failing grade.
I'm not clear.
It's not, I don't think anyone would get a passing grade, um, in their morality of like
you go back 300 years ago, like who is getting a passing grade?
Well, basically no one.
And we might not get a passing grade from generations that, uh, that come after us.
Uh, what, what does that one get?
Uh, sure, uh, it's six, uh, seven.
For the Monty Python, maybe.
Uh, there's a Monty Python, they're great.
Uh, the life of Brian and the Quist of the Holy Grail are incredible.
Yeah.
Yeah.
Damn, those serious eyebrows.
This is, brash enough.
Like, damn.
You know, how important do you think is facial hair to, to great leadership?
Well, you got a new haircut.
Is that, is that, is this, how does that affect your leadership?
I don't know.
Hopefully not.
It doesn't.
Um, yeah.
The second is no one.
There is no one competing with Brezhnev.
No one too.
Those are like epic eyebrows.
So sure.
That's ridiculous.
Give us six or seven.
I don't know.
Uh, I like this, like Shakespeare analysis of memes.
Brezhnev, he had a, he had a flair for drama as well, like, you know, showmanship.
Yeah.
Yeah.
It must come from the eyebrows.
All right.
Um, invention, great engineering.
Look what I invented.
Yeah.
That's the best thing since ripped up bread.
Yeah.
Cause they invented sliced bread.
Am I just explaining memes at this point?
This is where my life has become.
Um, I'm a meme, what it, like a, you know, like a scribe that like runs around with
the kings and just like writes down memes.
I mean, when was the cheeseburger invention?
That's like an epic invention.
Yeah.
Like, like, wow.
You know, that was versus just like a burger or burger.
I guess the burger in general is like, you know, um, then there's like, what is the
burger?
What's, what's the sandwich?
And then you start getting the pizza sandwich and what is the original it's, it's, it gets
into an ontology argument.
Yeah.
But everybody knows like if you order like a burger or cheeseburger or whatever and you
like, you got like, you know, tomato and some lettuce and onions and whatever and you
know, mayo and ketchup and mustard.
It's like epic.
Yeah.
But I'm sure they've had bread and meat separately for a long time and it was kind of a burger
on the same plate, but somebody who actually combined them into the same thing and the bite
and hold it make, makes it convenient.
It's a materials problem.
Yeah.
Like your hands don't get dirty and whatever.
Yeah.
It's brilliant.
Well, that is not what I would have guessed.
But everyone knows like you, you, you, you, if you order a cheeseburger, you know, what
are you getting?
You know, it's not like some obtuse, like I wonder what I'll get.
You know, um, you know, uh, Fries are, I mean, great, I mean, they're the devil, but Fries
are awesome.
Um, and, uh, yeah, pizza is incredible.
Food innovation doesn't get enough love.
Yeah.
I guess is what we're getting at.
Great.
Um, uh, what, what about the, uh, Matthew McGonaghey, Austinite here, uh, President Kennedy, do
you know how to put men on the moon yet?
NASA?
No.
President Kennedy, be a lot cooler if you did.
Pretty much.
Sure.
That's the last one.
That's funny.
Someone drew a bunch of dicks all over the walls of Sistine Chapel boys' bathroom.
Sure.
I'll give it nine.
It's super, it's really true.
All right.
This is our highest ranking meme for today.
I mean, it's true.
Like, how do they get away with it?
Lots of nakedness.
I mean, dick pics are, I mean, just something throughout history, uh, as long as people
can draw things, there's been a dick pic.
It's a staple of human history.
It's a staple.
Consistence throughout human history.
You tweeted that you aspire to comedy.
Your friends with Joe Rogan, might you, uh, do a short standup comedy set at some point
in the future?
Maybe, um, open for Joe, something like that.
Is that, is that-
Really standup?
Actual, just full on standup?
Full on standup.
Is that in there or is that-
I've never thought about that.
Um, it's extremely difficult.
And, uh, at least that's what, uh, like Joe says in the comedians say.
Huh.
I wonder if I could.
Um, I mean-
There's only one way to find out.
You know, I, I have done standup for friends, just, uh, impromptu, you know, I'll get, get
on like a roof, uh, and they, they do laugh, but they're our friends too.
So I don't know if, if you gotta call, you know, like a room of strangers, are they
gonna actually also find it funny, but I could try, see what happens.
I think you'd learn something either way.
Um, yeah.
I kind of love, um, both the, when you bomb and when, when you do great, just watching
people, how they deal with it.
It's, it's so difficult.
It's so, you're so fragile up there.
It's just you.
And you, you think you're gonna be funny and when it completely falls flat, it's just,
it's beautiful to see people deal with like that, uh, I think I might have enough material
to do standup.
I've never thought about it, but I might have enough material, um, I dunno, like 15 minutes
or something.
Oh yeah.
Yeah.
Do a, do a Netflix special.
A Netflix special.
Sure.
Um, what's your favorite Rick and Morty concept, uh, just to spring that on you.
Is there, there's a lot of sort of scientific engineering ideas explored there.
There's the-
Yeah.
There's the butter robot.
That's great.
Uh, that's a great show.
You like it?
Um, yeah.
Rick and Morty is awesome.
Somebody that's exactly like you from an alternate dimension showed up there.
Elon Tusk.
Yeah.
That's right.
That you voiced.
Yeah.
Rick and Morty certainly explores a lot of interesting concepts.
Uh, I'm sure like what's the favorite one I know.
The, the, the butter robot certainly is, uh, you know, it's like, it's certainly possible
to have too much sentence in a device, um, like you don't want to have your toast to
be like a super genius toaster.
It's going to hate, hate life because well, it could just make his toast, but if, you know,
it's like, you don't want to have like super intelligent stuck in a, a very limited device.
Um,
Do you think it's too easy from a, if we're talking about from the engineer perspective
of super intelligence, like with Marvin, the robot, like is it just, it seems like it might
be very easy to engineer just a depressed robot.
Like it, it's not obvious to engineer a robot that's going to find a friend.
It's not a fulfilling existence, same as humans, I suppose.
But um, I wonder if that's like the default.
If you don't do a good job on building a robot, it's going to be sad a lot.
Well, we can reprogram robots easier than we can reprogram humans.
So I guess if you let it evolve without tinkering, then it might get sad.
Uh, but you can change the optimization function and have it be a cheery robot.
You uh, like I mentioned with, with SpaceX, you give a lot of people hope and a lot of
people look up to you, millions of people look up to you.
Uh, if we think about young people in high school, maybe in college, um, what advice
would you give to them about if they want to try to do something big in this world,
they want to really have a big positive impact, what advice would you give them about their
career, maybe about life in general?
Try to be useful.
Um, you know, do things that are useful to your fellow human beings to the world.
It's very hard to be useful, um, very hard, um, you know, are you contributing more than
you consume, you know, like, uh, like, can you try to have a positive net contribution
to society?
Um, I think that's the thing to aim for, you know, not, not to try to be sort of a leader
for, for the sake of being a leader or whatever.
Um, a lot of the time people who, the, the, the, the, the people you want as leaders are
other people who don't want to be leaders.
So, um, if you can live a useful life, that is a good life, a life worth having lived.
Um, you know, and like I said, I would, I would encourage people to use the mental tools
of physics and apply them broadly in life.
They are the best tools.
When you think about education and self-education, what do you recommend that, so there's the
university, there's, uh, self-study, there is, uh, hands-on sort of finding a company
or a place or a set of people that do the thing you're passionate about and joining
them as early as possible.
Um, there's, uh, taking a road trip across Europe for a few years and writing some poetry,
which, uh, which, which trajectory do you suggest?
In terms of learning about how you can become useful, as you mentioned, how you can have
the most positive impact.
I encourage people to read a lot of books, just read, basically try to ingest as much
information as you can, uh, and try to also just develop a good general knowledge.
Um, so, so you at least have like a rough lay of the land of the, the knowledge landscape.
Um, like try to learn a little bit about a lot of things, um, cause you might not know
what you're really interested in, how would you know what you're really interested in
if you at least aren't like doing it peripheral exploration or broadly of, of the knowledge
landscape, um, and talk to people from different walks of life and different, uh, industries
and professions and skills and occupations, like just try to, you know, learn as much
as possible, man search for a meeting, isn't the whole thing a search for a meeting is
Yeah, what's the meaning of life and all, you know, but just generally, like I said,
I would encourage people to read broadly, um, in many different subject areas, um, and,
and, and then try to find something where there's an overlap of your talents and, and
what you're interested in.
So if people may, may, may be good at something, but they may have skill at a particular thing,
but they don't like doing it.
Um, so you want to try to find this thing where you have your, that's a good, a good
uh, combination of, of your, of the things that you're inherently good at, but you also
like doing, um, and, um,
And reading is a super fast shortcut to, to figure out which, where are you, you're both
good at it, you like doing it, and it will actually have positive impact.
Well, you got to learn about things somehow.
So re reading a broad range, it's just really read, read it.
You know, one important one is that kid, I, I read through the encyclopedia, uh, so that's
pretty helpful.
Um, and, uh, there's also things that I didn't even know existed or lots of, obviously, and
it's like as broad as it gets.
Encyclopedias were digestible, I think, uh, you know, whatever, 40 years ago.
Um, so, um, you know, maybe read through the condensed version of the encyclopedia Britannica.
I'd recommend that.
Um, you can always like skip subjects or you read a few paragraphs and you know, you're
not interested.
Just jump to the next one.
So read the encyclopedia or scan, scan through it, um, and, um, you know, put a lot of stock
and certainly have a lot of respect for someone who puts in an honest day's work, uh, to do
useful things.
And, and just generally to have like, uh, not a zero sum mindset, um, or, or, uh, like
have, have more of a grow the pie mindset, like the, if you, if you sort of say like,
when, when we see people like perhaps, um, including some very smart people kind of taking
an attitude of, uh, like, like, like doing things that seem like morally questionable.
It's often because they have at, at a base sort of axiomatic level, a zero sum mindset.
Um, and, and they, without realizing it, they don't realize they have a zero zero sum mindset
or at least they don't realize it consciously.
Um, and so if you have a zero sum mindset, then the only way to get ahead is by taking
things from others.
If, if it's like, if, if, if the, if the pie is fixed, then the only way to have more
pie is to take someone else's pie, but, but this is false.
Like obviously the pie has grown dramatically over time, the economic pie.
Um, so the reality, in reality, you can have the over-users analogy, you can have a lot
of, you can have this, a lot of pie pie is not fixed.
Um, uh, so you really want to make sure you don't, you're not operating, um, without
realizing it from a zero sum mindset where, where the only way to get ahead is to take
things from others.
You're going to result in you take, try to take things from others, which is not, not
good.
It's much better to work on, uh, adding to the economic pie, maybe, you know, so creating,
like I said, creating more than you consume, uh, doing more than you.
Yeah.
Um, so that, that's a big deal.
Um, I think there's like, you know, a fair number of people in, in finance that, uh,
do have a bit of a zero sum mindset.
I mean, it's all walks of life.
I've seen that.
And one of the, one of the reasons, uh, Rogan inspires me is he celebrates others a lot.
This is not, not creating a constant competition.
Like there's a scarcity of resources.
What happens when you celebrate others and you promote others, the ideas of others, it,
it, uh, it actually grows that pie.
I mean, it, every, like the, uh, the resource, the resources become less scarce.
And that, that applies in a lot of kinds of domains.
It applies in academia where a lot of people are very, uh, see some funding for academic
research is zero sum and it is not, if you celebrate each other, if you make, if you
get everybody to be excited about AI, about physics, about mathematics, I think it, there
would be more and more funding and I think everybody wins.
Yeah.
That applies.
I think broadly.
Uh, yeah.
Yeah.
Exactly.
So last, last question about love and meaning, uh, what is the role of love in the human
condition broadly and more specific to you?
How has love, romantic love, or otherwise made you a better person, a better human being?
Better engineer?
Now you're asking really perplexing questions.
Um, it's hard to give a, I mean, there are many books, poems and songs written about
what is love and what is, what exactly, you know, um, you know, what is love, maybe don't
hurt me.
Um, that's one of the great ones.
Yes.
Yeah.
You've, you've earlier quoted Shakespeare, but that, that's really up there.
Yeah.
Love is, love is a many splendid thing.
Uh, I mean, there's, um, it's, cause we've talked about so many inspiring things like
be useful in the world, sort of like solve problems, alleviate suffering, but it seems
like connection between humans is a source, you know, it's, uh, it's a source of joy is
a source of meaning and that, that's what love is, friendship, love.
I just wonder if you think about that kind of thing when you talk about preserving the
light of human consciousness and us becoming a multi-planetary, multi-planetary species.
I mean, to me, at least, um, that, that means like, if we're just alone and conscious and
intelligent, it doesn't mean nearly as much as if we're with others, right?
And there's some magic created when we're together, the, uh, the, the friendship of it.
And I think the highest form of it is love, which I think broadly is, is much bigger than
just sort of romantic, but also yes, romantic love and, um, family and those kinds of things.
Well, I mean, the reason I guess I care about us becoming a multi-planet species in a space
frank civilization is foundationally, I love humanity, um, and, and so I wish to see it
prosper and do great things and be happy and, um, and if I did not love humanity, I would
not care about these things.
So when you look at the whole of it, the human history, all the people who's ever lived, all
the people alive now, it's pretty, we're, we're, we're okay on, on the whole, we're
a pretty interesting, uh, bunch.
Yeah.
All things considered, and I've read a lot of history, including the darkest, worst parts
of it, and, uh, despite all that, I think on balance, I still love humanity.
You joked about it with the 42, uh, what do you, what do you think is the meaning of
this whole thing?
Is it like, is there a non-numerical, uh, representation?
Yeah.
Well, really, I think what Dr. Saddam was saying in the Hitchhiker's Guide to the Galaxy
is that, um, the universe is the answer, and, uh, what we really need to figure out are
what questions to ask about the answer that is the universe, um, and that the question
is the, really the hard part.
And if you can properly frame the question, then the answer relatively speaking is easy.
Uh, so, so, so therefore, if you want to understand what questions to ask about the universe,
if you want to understand the meaning of life, we need to expand the scope and scale of consciousness
so that we're better able to understand the nature of the universe and, and understand
the meaning of life.
And ultimately, the most important part would be to ask the right question.
Yes.
Uh, thereby elevating the role of the interviewer, which is the most important human in the
room.
Yeah, interview, interview, good questions are, you know, it's a hard, it's hard to come
up with good questions.
Uh, absolutely.
Um, but yeah, like, it's like that, that is the foundation of my philosophy is that,
um, I, I am curious about the nature of the universe and, uh, you know, and obviously
I will die, I don't know when I'll die, but I won't live forever, um, but I would like
to know that we're on a path to understanding the nature of the universe and the meaning
of life and what questions to ask about the answer that is the universe.
And, um, and so if we expand the scope and scale of humanity and consciousness in general,
um, which includes silicon consciousness, then, you know, that, that, that seems like
a fundamentally good thing.
Elon, like I said, um, I'm deeply grateful that you will spend your extremely valuable
time with me today and also that you have given millions of people hope in this difficult
time, this divisive time in this, uh, cynical time.
So I hope you do continue doing what you're doing.
Thank you so much for talking today.
Oh, you're welcome.
Uh, thanks for excellent questions.
Thanks for listening to this conversation with Elon Musk to support this podcast.
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And now let me leave you with some words from Elon Musk himself.
When something is important enough, you do it, even if the odds are not in your favor.
Thank you for listening and hope to see you next time.