Speaker 1: Latest interview of Elon Musk. What's the reason to put
Speaker 1: them in face? Well, the availability of energy is the issue.
Speaker 1: So I mean, if you look at electrical output outside
Speaker 1: of China, everywhere outside of China, it's more or less flat.
Speaker 1: It's where you know, maybe slide increase, but for pretty
Speaker 1: much flat. China has a rapid increase in electrical output.
Speaker 1: But if you're putting data centers of work except China,
Speaker 1: where you're gonna get your electricity, especially as you scale,
Speaker 1: the output of chips is growing pretty much exponentially, but
Speaker 1: the output of electricity is flat. So how are you
Speaker 1: going to tell them what chips are? You know, magical
Speaker 1: power sources, magical electricity ferries.
Speaker 2: I mean your famous, your famous epic fan of solar
Speaker 2: one terra a lot of solar power. So with the
Speaker 2: twenty five percent computed factor, like four terrawats of solar panels,
Speaker 2: it's like one percent of the.
Speaker 1: Land area of the United States.
Speaker 2: And that's like far in this You're in the singularity
Speaker 2: when we've got one tarrat wat of data centers, right,
Speaker 2: So what are you running out of?
Speaker 1: Exactly? How far into the singularity are you? You tell me? Yeah,
Speaker 1: exactly so I think I think we'll find we're in
Speaker 1: the singularity and like, okay, let's look a long way
Speaker 1: you go.
Speaker 2: But is this like a is the plan to like
Speaker 2: put it in the space after we will covered Nevada
Speaker 2: and solar panels.
Speaker 1: I think it's pretty hard to cover about sol panels.
Speaker 1: You have to get like permits from Like the proach
Speaker 1: for try.
Speaker 2: Getting the pomots for that space is really it's really
Speaker 2: regulatory play. It's like hard harder to build on land
Speaker 2: than it is in space.
Speaker 1: It's harder to scale on the ground than it is
Speaker 1: to scale in space. But also the the you're going
Speaker 1: to get about five times the effectiveness of sold panels
Speaker 1: in space versus the ground, and you don't need batteries.
Speaker 1: I almost wore my other shirt, which says it's always
Speaker 1: sunny in space, which it is so because you don't
Speaker 1: have a day night cycle or seasonality clouds, or or
Speaker 1: an atmosphere in space. Because the atmosphere alone, we're still
Speaker 1: about a thirty percent loss of energy. So you're can
Speaker 1: any given soul panels can do about five times more
Speaker 1: powering space than on the ground, and you avoid the
Speaker 1: cost of having batteries to carry you through the night.
Speaker 1: So it's actually much cheaper to do in space, and
Speaker 1: my prediction is that it will be by far the
Speaker 1: cheapest place to put AI will be space and thirty
Speaker 1: six months or less, maybe thirty thursday months less than
Speaker 1: thirty six months.
Speaker 2: How do you service GPUs as they fail, which happens
Speaker 2: quite often in training, Actually it.
Speaker 1: Depends on how recent the GPUs are that arrived. I mean,
Speaker 1: at this point we find our gps to be quite reliable.
Speaker 1: There's infant mortality which you can obviously iron out on
Speaker 1: the ground, so you can just run them on the
Speaker 1: ground and confirm that you don't have infort mortality with
Speaker 1: what the GPU. But once they once they start working,
Speaker 1: their actual reliability and once they start working and you're
Speaker 1: past the initial you know, debug cycle of video or whatever,
Speaker 1: or whoever's making the trips could be Tesla Tesla AI
Speaker 1: six trips or something like that, or could be you know,
Speaker 1: TPUs or trainings or whatever the rivalry is. Actually they're
Speaker 1: quite reliable past certain point. So I don't think I
Speaker 1: don't think you need that. The servicing thing is an issue,
Speaker 1: but you can mark my words, uh in thirty six months,
Speaker 1: but probably closer to thirty months. The most economically compelling
Speaker 1: place to put AI will be space, and then and
Speaker 1: and and then it will get from it'll it'll then'
Speaker 1: forget like ridiculously better to be in space. And then
Speaker 1: the scaling. The only place you can really scale is space.
Speaker 1: You know, once you start thinking in terms of what
Speaker 1: percentage of the Sun's power are you, honestly you realize
Speaker 1: you have to go to space. You can't scale very
Speaker 1: very much on Earth, but by very much, to be clear,
Speaker 1: you're talking like terro arts. Yeah, well all of the
Speaker 1: United States currently uses only half a terrawork power on average, right,
Speaker 1: so you know, if you say a terror work, that
Speaker 1: would be twice as much electricity as the United States
Speaker 1: currently consumes. So that's quite a lot. And can you
Speaker 1: imagine building that many data centers, that many power plants.
Speaker 1: It's like those who have lived in software land don't
Speaker 1: realize that they're about to have a hard lesson in hardware.
Speaker 1: That there's there's it's actually very difficult to build power plants.
Speaker 1: And and then you don't need just need you need
Speaker 1: power plants, you need all of the electrical equipment, need
Speaker 1: that the electrical transformers to run the transformers, the transformers.
Speaker 1: Now you told the industry is a very slow industry.
Speaker 1: That they are they pretty much uh you know, the
Speaker 1: impedis smash to the to the government, to the Public
Speaker 1: Utility Commission. So they're the impedius smash like literally invigoraily.
Speaker 1: So they're very slow because the history of their past
Speaker 1: has been very slow. So trying to get them to
Speaker 1: move fast is like, you know, like if you're trying
Speaker 1: to do an interconnect agreement with you have you ever
Speaker 1: tried to do an Internet interconnect agreement with a utility
Speaker 1: at scale? Like I put a lot of power.
Speaker 2: As a professional podcaster, I can say it and I'm not.
Speaker 2: In fact, the need many more before that comes in
Speaker 2: this year, they.
Speaker 1: Have to do a study for a year. Okay, like
Speaker 1: a year later they'll come back to you with their
Speaker 1: interconnected study.
Speaker 3: Can you tell this with your own behind the meter
Speaker 3: power stuff.
Speaker 1: You can build power plants. Yeah, that's what we did. Actually,
Speaker 1: I fu clusses to it so for full classes too.
Speaker 3: So yeah, while we're talking about the bridge, why not
Speaker 3: just like build GPUs and power co located. That's what
Speaker 3: we did, right, right, But I'm saying, why isn't this
Speaker 3: generalized solution when you're talking about.
Speaker 1: All the issues where you get the power plants from?
Speaker 3: I'm saying, when you talk about the issues working working
Speaker 3: with you two those use you can just build private
Speaker 3: power plants with the with the data centers, right, But
Speaker 3: it begs the question.
Speaker 1: Of where do you get the power plants? Where do
Speaker 1: where do you get the power plants from? I mean
Speaker 1: the power plant makers.
Speaker 3: As what you're saying, like, does the gas turbine backlog?
Speaker 3: Basically yes, you can drill down to level further. It's
Speaker 3: the it's the the veins and blades in the turbines
Speaker 3: that of the limiting factor because the casting may it's
Speaker 3: it's like a very specialized process to cast the blades
Speaker 3: and veins in the in the in the turbines using
Speaker 3: gas power, and it's very it's very difficult to scale.
Speaker 3: Other other forms of power you can scale potentially solar,
Speaker 3: but the towers currently for importing solar in the US
Speaker 3: are gigantic and the domestic solar production is protophal. Why
Speaker 3: that makes solar? That seems like a good Elon shaped problem.
Speaker 3: We are going to make solar?
Speaker 1: Okay. Both SpaceX and Tesla are are bulling towards one
Speaker 1: hundredguart tier of solar cell production.
Speaker 2: How low down the like from polysilica and up to
Speaker 2: the wayfer to the final panel.
Speaker 1: I think you've got to do the whole thing for
Speaker 1: more materials to finish to sell. Now, if it's going
Speaker 1: to space, it's actually it costs. It costs less than
Speaker 1: it's easier to make solo cells that go to space
Speaker 1: because they don't need glass or they don't need much glass,
Speaker 1: and they don't need a heavy framing because they don't
Speaker 1: have to sell survive weather events. There's no weather in space.
Speaker 1: So it's actually a cheaper solo cell that goes to
Speaker 1: space than the one on the ground. Is there a
Speaker 1: path to getting them as cheap as you need in
Speaker 1: the next thirty six months. Solo cells are already very cheap.
Speaker 1: They're like partically cheap. It's and if you say, you know,
Speaker 1: I think like solo cells in China are around like
Speaker 1: twenty five thirty cents a water or something like that.
Speaker 1: It's it's absurdly cheap. And when you when you're taking
Speaker 1: to a cap now now put it now put it
Speaker 1: in space, and it's five times cheaper because it's five
Speaker 1: times in fact, no, it's not five times cheap, it's
Speaker 1: ten times cheaper because you don't need any batteries. So
Speaker 1: so the moment your cost of access to space becomes low,
Speaker 1: by far, the cheapest and most scalable way to generate,
Speaker 1: to generate tokens is space. It's not even close. It
Speaker 1: will be an order of magnitude easier to scale, and
Speaker 1: ships aside an order of magnitude. Well, if the point
Speaker 1: is you won't be able to scale the ground, it's
Speaker 1: just you just won't only hit the big time on
Speaker 1: power generation there already are so so like that. The
Speaker 1: number of so mercles and series that the x A
Speaker 1: I team had to accomplish in order to get a
Speaker 1: gig out of power online was was was crazy. We
Speaker 1: had to getting together a whole bunch of turbines and
Speaker 1: uh and then and then we had permit issues in
Speaker 1: Tennessee and and had to go across the border to Mississippi,
Speaker 1: which is fortunately only you know, a few miles away.
Speaker 1: Uh So, but then we still had to run the
Speaker 1: high power lines a few miles and build a power
Speaker 1: plant in Missipi. And it was very difficult. Abu that
Speaker 1: and people don't understand, like how much how much electrocity
Speaker 1: do you actually need at the generator level, at the
Speaker 1: generation level in order to power a data center, because
Speaker 1: they look at the the nerves will look at the
Speaker 1: power consumption of say a GB three hundreds and multiply
Speaker 1: that by thing and then think best amount amount of
Speaker 1: power you need? Wake up. Yeah, this is like, that's
Speaker 1: a that's a that's a total moves You've never done
Speaker 1: any hardware in your life before. Besides the GB three hundred,
Speaker 1: you've got to power all of the networking hardware. There's
Speaker 1: a whole bunch of CPU and storage stuff that's happening.
Speaker 1: You've got a size for your your peak cooling requirements,
Speaker 1: so that means can you cool even on the worst
Speaker 1: hour of the worst day of the year. Well, it's
Speaker 1: pretty freaking hot in Memphis, So so you're going to
Speaker 1: have like a forty percent increase on your power just
Speaker 1: for cooling. If we assuming you don't want your data
Speaker 1: center to turn off on hot days and anyone to
Speaker 1: keep going, then then you've got to say, well, there's
Speaker 1: a There's there's another multiplicative element on top of that,
Speaker 1: which is are you assuming that you're you you never
Speaker 1: have any hiccups in your power generation like well, actually
Speaker 1: sometimes you have to take the generators some of the
Speaker 1: power offline in order to service it. Oh okay, now
Speaker 1: you add another twenty twenty five percent multiplier on that
Speaker 1: because you've got it. You've got to assume that that
Speaker 1: you've got to take power offline to service it. So
Speaker 1: the actual rs roughly every every one hundred and ten
Speaker 1: thousand gbs, GB three hundreds, inclusive of networking, CPU, storage, cooling.
Speaker 1: Margin for for for servicing power is roughly three hundred megawatts.
Speaker 1: Sorry you say that again, it's roughly, or think about it, like,
Speaker 1: what do you think about like three hundred and thirty
Speaker 1: thousand to actually what you need at the generative generation level.
Speaker 1: So service probably service three hundred and thirty thousand TV
Speaker 1: three hundreds including all of the associated support networking and
Speaker 1: everything else and the and the peak cooling and to
Speaker 1: have some margin some power marginers of is roughly a
Speaker 1: GEO why can ask a very night your question?
Speaker 2: Yeah, you know, you're describing the engineering details of doing
Speaker 2: this stuff on Earth, but then there's analogous engineering difficulties
Speaker 2: of doing it in space.
Speaker 1: How do you do the.
Speaker 2: How do you replace in pin and band with orbital lasers,
Speaker 2: et cetera, etceter How do you make it resistant radiation?
Speaker 2: I don't know the details in the engineering, but fundamentally,
Speaker 2: what is the reason to think those challenges which have
Speaker 2: never been had to be addressed before will end up
Speaker 2: being easier than just like building more turbines on Earth?
Speaker 2: There's companies that build turbines on Earth. They can make
Speaker 2: more turbines right.
Speaker 1: And again, tried doing it and then you'll see so
Speaker 1: like the turbines are sold out through twenty thirty, have
Speaker 1: you guys considered making your own? I think in order
Speaker 1: for in order to bring enough power online, I think
Speaker 1: SpaceX Intezl will probably have to make the turbine blades,
Speaker 1: the bans and blades internally, but just the blades or
Speaker 1: the turbines the limiting factor. You can get everything except
Speaker 1: the blades they call the blades and veins you can
Speaker 1: get that twelve to eighteen months before the veins of blades.
Speaker 1: The limiting factor of the veins of blades. And there
Speaker 1: are only three casting companies in the world that may
Speaker 1: make these, and they're massively backloged. Is this siemens ge
Speaker 1: those guys, or is it a subje. No, it's a
Speaker 1: it's other companies. I mean sometimes they have a little
Speaker 1: bit of casting capability in house. But I'm just saying you.
Speaker 1: You can just call any of the turbine makers and
Speaker 1: they will tell you top secret. They're probably on the
Speaker 1: it's probably on the internet right now. If if it
Speaker 1: wasn't for the terraffs, what would Colossus be solar powered? Uh,
Speaker 1: it would be much easier to make it solar powered. Yeah,
Speaker 1: the tariffs are not so several hundred percent, So don't
Speaker 1: you know some big book we will will see it speed. Yeah. No,
Speaker 1: you know, President has a you know, we don't agree
Speaker 1: on everything, and this administration is not not the biggest
Speaker 1: man of of solar. So it's you know, it's it's
Speaker 1: it's it's We also need the land, the permits and everything.
Speaker 1: So if you try to move very fast like I
Speaker 1: do think scaling solar on Earth it is a is
Speaker 1: a good way to go, but but you need you
Speaker 1: do need some amount of time to find the land,
Speaker 1: get the permits, get the solar pair that with the batteries.
Speaker 3: Well why would it not work to stand up your
Speaker 3: own solar production? And then you're right that you eventually
Speaker 3: run out of land. But there's a lot of land
Speaker 3: here in Texas. There's a lot of land in Nevada,
Speaker 3: including private land. It's not all publicly on land, and
Speaker 3: so you'd be able to at least get the next
Speaker 3: colossus and like the next one after that, and at
Speaker 3: a certain point you hit a wall.
Speaker 1: But wouldn't that work for the moment? As I said,
Speaker 1: we are scaling solar production, there's there's a rate. There's
Speaker 1: a rate at which you can scale physical production of
Speaker 1: solo solar cells. Where I'm we're going as fast as
Speaker 1: possible in scaling domestic production. You're making the solar cells
Speaker 1: at TEA. We TESA and SpaceX have a mandate to
Speaker 1: get to one hundred gigs a year of solar.
Speaker 3: Speaking of the annual capacity, I'm furious. In five years time,
Speaker 3: let's say, what will the installed capacity be on Earth
Speaker 3: and in space. I deliberately picked five years because it's
Speaker 3: after you're once we're up and running threshold and so
Speaker 3: in five years time, Yeah, what's thee on Earth versus
Speaker 3: in space installed a capacity?
Speaker 1: Five years? I think probably you said five years from
Speaker 1: now were probably AI in space will be uh launching
Speaker 1: every year the sum total of all AI on Earth
Speaker 1: in excess of me. Five years from now, my prediction
Speaker 1: is we will launch and and and be operating every
Speaker 1: year more AI in space than this than the cumulative
Speaker 1: total on Earth, which is I would expect to be
Speaker 1: at least sort of five years from now, a few
Speaker 1: hundred gigawatts per year of of AI in space and rising,
Speaker 1: so you can get to I think on Earth you
Speaker 1: can get to a round a tarawa a year of
Speaker 1: AI in space before you start having you know, fuel
Speaker 1: supplied challenges for the rocket.
Speaker 3: Okay, but you think you can get to one hundreds
Speaker 3: of gigabatts per year in five years, yes.
Speaker 2: So one hundred gigawatts depending on the specific power of
Speaker 2: the whole system with solar arrays and radios and everything,
Speaker 2: is on the order of like ten thousand starship launches, yes,
Speaker 2: and you want to do that in one year, and
Speaker 2: so that's like one starship launch every hour. Yeah, that's
Speaker 2: happening in the city, Like walking through a world where
Speaker 2: there's ten times there's a starship launch every single hour.
Speaker 1: Yeah, I mean that's actually a lower rate compared to airlines,
Speaker 1: like like aircraft aircraft, there's a lot of airports, a
Speaker 1: lot of airports, and you gotta launch you know, the
Speaker 1: polar orbit, and it doesn't have to be polarp. But
Speaker 1: you're just there's there's some some value to sound sagres.
Speaker 1: But but I think actually you just go high enough,
Speaker 1: you're you're start getting out of it. Both shadow and
Speaker 1: so how.
Speaker 2: Many physical starships are needed to do ten thousand lunches
Speaker 2: a year?
Speaker 1: I don't think we'll lead more than I mean you could.
Speaker 1: You could probably do it with as few as like
Speaker 1: twenty or thirty, Like it really depends on how quickly
Speaker 1: does the ship. The ship has to go around the Earth,
Speaker 1: and the ground track for the ship has to come
Speaker 1: back over the launch pad. So if you can use
Speaker 1: a ship every say, thirty hours, you could do it
Speaker 1: with thirty ships. But we'll make more shifts than that.
Speaker 1: But the SpaceX is is going up to do ten
Speaker 1: thousand marches a year and maybe even twenty or thirty
Speaker 1: thousand marches. Ye know.
Speaker 2: It is the idea to become basically a hyperscaler, become
Speaker 2: an oracle and lend this capacity to other people. What's
Speaker 2: what are you going to do with presumableast? SpaceX is
Speaker 2: the one watching all this, so SpaceX become a hyperscaler.
Speaker 1: Hyper hyper Yeah. I mean, if so many of my
Speaker 1: predictions come true, SpaceX will launch more AI than the accumulative
Speaker 1: amount on Earth combined of everything else combined. Is this
Speaker 1: mostly inference or most say I won't be in for it,
Speaker 1: Like already inference for the purpose of training is most train.
Speaker 3: And there's a narrative that the the change in discussion
Speaker 3: around the SpaceX IPO is because previously SpaceX was very
Speaker 3: capital efficient, just wasn't just that expensive to develop that
Speaker 3: even though it sounds expensive, it's actually very capital efficient
Speaker 3: and how it runs, whereas now you're going to need
Speaker 3: more capital than just can be raised in the private markets.
Speaker 3: Like if the private markets can accommodate races of as
Speaker 3: we've seen from the AI labs, tens of billions of dollars,
Speaker 3: but not beyond that, is it that you'll just need
Speaker 3: more than tens of billions of dollars per year?
Speaker 1: And that's about say the public. Yeah, I'd be careful
Speaker 1: about saying things about companies that might go public. You know,
Speaker 1: if you make general that's never been a problem for you, Elan.
Speaker 1: You know, there's a price to pay for these things
Speaker 1: make some.
Speaker 3: General statements for us about the depth of the capital
Speaker 3: markets between public and private markets.
Speaker 1: Yeah, there's there's a lot more capital in the very
Speaker 1: general there's there's obviously a lot more capital available in
Speaker 1: the public markets than private. It might be it's at
Speaker 1: least at least it might be a hard times more capital,
Speaker 1: but at least way more than ten.
Speaker 3: But isn't it also the case that things that tend
Speaker 3: to be very capital intensive. If you look at say
Speaker 3: real estate, as you know, a huge industry that raises
Speaker 3: a lot of money each year is at an industry
Speaker 3: level that tends to be debt financed because by the
Speaker 3: time you're deploying that much money, you actually have a
Speaker 3: pretty exactly and a near term return. And you see
Speaker 3: this even with the data center build auds, which are
Speaker 3: famously being you know, financed by the private credit industry.
Speaker 1: And so why not just debt finance? Speed is important.
Speaker 1: So I'm generally going to do the thing that I mean,
Speaker 1: I just repeatedly tackled limiting factor. Whatever the limiting factors
Speaker 1: on speed, I'm I'm gonna tackle that. So there's if
Speaker 1: capital is learning factor, then I'll alsold for capital. If
Speaker 1: if it's not limiting factor also for something else. Based
Speaker 1: only in your statements about Tesla and being public, I
Speaker 1: wouldn't have guessed that you thought the fast, the way
Speaker 1: to move fast is to be public. Normally I would say, yeah,
Speaker 1: that's that's true. Like I said, I mean, I'd like
Speaker 1: to talk about some more detail. But the problem is,
Speaker 1: like if you talk about public companies where they become public,
Speaker 1: you're get into trouble and then you have to lay
Speaker 1: your offering and then you yes, exactly so so so
Speaker 1: that you can't HiPE companies that are that that may
Speaker 1: that might go public. So that that's that's why we
Speaker 1: have to be a little careful here. But but but
Speaker 1: we can't talk about physics. So like the way the
Speaker 1: way you think about scaling long term is that Earth
Speaker 1: only receives about half a billionth of the Sun's energy,
Speaker 1: and the Sun is the sign is essentially all the energy.
Speaker 1: This is a very important point to appreciate because sometimes
Speaker 1: people will talk about marginally nuper reactors or any various
Speaker 1: like fusion on Earth. But you have to step back
Speaker 1: a second and say if if, if you're going to
Speaker 1: climb the Kardashev scale and have some non trivial and
Speaker 1: harness some non trivial percentage of the sign's energy, Like
Speaker 1: unless you wanted to harness a millionth of the Sun's energy,
Speaker 1: which sounds pretty small, that that would be about call
Speaker 1: it roughly one hundred thousand times more electricity than we
Speaker 1: currently generate on Earth for all of civilization give or
Speaker 1: take an order it back too. So it obviously the
Speaker 1: only way to scale is to go to space with
Speaker 1: solar From launching from Earth, you can get to about
Speaker 1: a tarrawat per year. Beyond that you want to go
Speaker 1: to you want to launch from the Moon. You want
Speaker 1: to have a mass driver on the moon, and that
Speaker 1: mass drive on the moon you could do probably a
Speaker 1: pedal wak per year.
Speaker 2: We're talking these kinds of numbers, you know, terrawatts of
Speaker 2: compute presumably whether you're talking land or space. Far far
Speaker 2: before this point you've like run into you know, you
Speaker 2: actually need maybe if you don't, the solar panels are
Speaker 2: more efficient, but you still need the chips.
Speaker 1: You still need the logic and the memory and so forth,
Speaker 1: and you need a lot more chips and make them
Speaker 1: munch cheaper.
Speaker 2: Right, And so how are we getting a tararo out
Speaker 2: of like, right now the world is gonna be twenty
Speaker 2: twenty five pi wats of compute.
Speaker 1: How we're getting a tarotot of logic by I guess
Speaker 1: we're going to need some very big chip maps to
Speaker 1: tell it. Tell me about it. I've mentioned that publicly.
Speaker 1: That's the idea of doing it. Sort of a terror
Speaker 1: pap Terra being the new Giga we were, I feel
Speaker 1: like the naming scheme of Tesla, which has been very catchy,
Speaker 1: It's like, are you looking at like the metric the
Speaker 1: metric scale at what level of the stack are you?
Speaker 1: Are you building the clean room and then partnering with
Speaker 1: and is they sting fab to get the process tchnology
Speaker 1: and buying the tools from them? What is the plan there? Well,
Speaker 1: you can't partner with existing paths because they're just they
Speaker 1: can't output enough. The trip alb is too low. But yeah,
Speaker 1: you have to look for the process achnology. Yeah, partner
Speaker 1: for the you know the fabs that they will basically
Speaker 1: use machines from like five companies. You know, so I've
Speaker 1: got SML, Touch, Electron, Cali A, tank Core, you know,
Speaker 1: et cetera. So so so at first I think you'd
Speaker 1: have to get equipment from them and then modify it
Speaker 1: or work with them to increase the volume. But I
Speaker 1: think you have to build, perhaps in a different way.
Speaker 1: So I think that the logical thing to do is
Speaker 1: to to use conventional equipment in an un conventional way
Speaker 1: to get to scale and then and then and then
Speaker 1: stop modifying the equipment to increase the rate kind of
Speaker 1: boring company style. Yeah, kind of like, yeah, you what
Speaker 1: your sort of buying and it's just a boring machine.
Speaker 1: And then figure out how to dig titles in the
Speaker 1: first place, and then design a much better machine that's
Speaker 1: you know, I don't know, some orders magitude faster.
Speaker 3: Here's a very simple lens. We can categorize technologies and
Speaker 3: how hard they are. And one categorization could be look
Speaker 3: at things that China has not succeeded in doing. And
Speaker 3: if you look at Chinese manufacturing still behind on leading
Speaker 3: edge chips and still behind on leading edge turbine engines
Speaker 3: and things like that, and so does the fact that
Speaker 3: China has not successfully replicated t s MC give you
Speaker 3: any pause about the difficulty or you think, well, that's
Speaker 3: not true.
Speaker 1: For some it's not that they have not replated TSMC.
Speaker 1: They have replicated ASML. That's the limited factor. So you
Speaker 1: think it's just the the sanctions essentially, Yeah, trying to
Speaker 1: re utputting the vast numbers of trips.
Speaker 3: If they could bias, couldn't they up to relatively recently
Speaker 3: buy them?
Speaker 1: No that the bands are been a place for a while,
Speaker 1: but I think time is going to be make me
Speaker 1: pretty compelling trips in twenty four years. Would you consider
Speaker 1: making the ASML machines? I don't know. I don't know yet.
Speaker 1: It's the right answer. So it's just that that to
Speaker 1: produce at high volume and to reach large volume in
Speaker 1: say thirty six months to match the rocket Paloa to orbits.
Speaker 1: So we're doing a million tons to orbit and like
Speaker 1: let's say, I don't know, three or four years from now,
Speaker 1: something like that. That and uh, and we're doing one hundred
Speaker 1: killer watts per ton, So that that means we need
Speaker 1: at least one hundred gigawatts per year of solo and
Speaker 1: we'll need an equivalent amount of chips. You know that
Speaker 1: you need a hundred gigawa's worth the chips you've got.
Speaker 1: You're going to match these things, the Master orbit, the
Speaker 1: power generation and the and the and the chips. Uh
Speaker 1: and and I'd say my biggest concern actually is is memory.
Speaker 1: So the I think there's there's a the path to
Speaker 1: creating the logic chips is more obvious than the path
Speaker 1: to having sufficient memory to support the logic chips. That's
Speaker 1: why you see dr Prices going ballistic and these memes
Speaker 1: about like you know, you're ruined on a desert islandy right,
Speaker 1: help me on the sand that when he comes to
Speaker 1: you write d d am the ships come swarming in.
Speaker 2: I'd love your manufacturing philosophy around around fabs. You know,
Speaker 2: I know nothing abut the talpout. I don't know how
Speaker 2: to build up.
Speaker 1: Yet, don't figure it out.
Speaker 2: But obviously it sounds like you think that the sort
Speaker 2: of like the proseechnology, like these ten thousand pH ds
Speaker 2: in Taiwan who know exactly what gas goes in the
Speaker 2: plasma chamber and what's settings to put on a tool,
Speaker 2: you can just like delete those parts of those steps.
Speaker 2: Like fundamentally, it's get the clean room, get the tools,
Speaker 2: and figure it out.
Speaker 1: I don't think it's PhD s. It's it's mostly people
Speaker 1: with you know, you're not not PhDs. That's mostly engineering
Speaker 1: is some of people who don't have PhDs. Do you
Speaker 1: guys have PhDs? No?
Speaker 3: Okay, we also haven't successfully booked any fabs, so you
Speaker 3: shouldn't be coming to us for your fab advice or.
Speaker 1: I don't think any PhD for that for stuff. So
Speaker 1: but but you do need you do need competent personnel.
Speaker 1: So I don't I mean, like right right now if yeah,
Speaker 1: you know, like Tesla's pedals to the metal max production of
Speaker 1: going as fast as possible to get a five tell
Speaker 1: a five trip design introduction and then reaching scale, you know,
Speaker 1: that will probably happen, you know, around the second quarter
Speaker 1: issue of next year hopefully, uh, and then a I
Speaker 1: six would hopefully follow less than a year later. But
Speaker 1: and and and and we've secured all the all the
Speaker 1: trip fab production that we can. You're currently limited on
Speaker 1: T s m C fab capacity. Yeah, and and and
Speaker 1: we'll be using t s m C UH Taiwan, Samsung Coreer,
Speaker 1: t s MC Arizona, Samsung Texas. And we still get
Speaker 1: booked all the yeah fast you can, yes, and and
Speaker 1: then and then and then if I ask to some
Speaker 1: CEO Samsung, Okay, what what's the time frame to get
Speaker 1: to volume production? This point is it's not. You've got
Speaker 1: to you've got to build the fab and you've got
Speaker 1: to you've got you've got to start production. Then you've
Speaker 1: got to climb the yield curve and reach volume production
Speaker 1: at high yield. That that that from start finishes a
Speaker 1: five year period. And so the limiting factor is chips.
Speaker 1: What what like the limiting factor once you can get
Speaker 1: to space is chips. But the living limiting factor before
Speaker 1: you can get to space to be power, why.
Speaker 2: Don't you do the Gensen thing and just pre Pats
Speaker 2: and c to build more fabs for you.
Speaker 1: Uh, I've already told them that, but they won't take
Speaker 1: your money. Like, what's going on? They're building fabs as fast. No,
Speaker 1: they're building they're building fabs as fast as they can,
Speaker 1: and so is Samsung. Like the like they're they're they're
Speaker 1: pedal to the metal. I mean they're going you know,
Speaker 1: bowls the wall, you know, as fast as they can.
Speaker 1: So still a fat I mean, like I said that,
Speaker 1: there will be I think if you say, I think
Speaker 1: towards the end of this year, I think probably chip
Speaker 1: production will outpace the ability to turn chips on. But
Speaker 1: once you can get to space and unlock the power constraint,
Speaker 1: and you can now do you know, one hundreds of
Speaker 1: gigawatts per year power in space? Again, bearying in mind
Speaker 1: that average power usage in the US is you know,
Speaker 1: five hundred gigawats. So if you're launching, say two hundred
Speaker 1: gigawats a year to space, you're sort of lapping the
Speaker 1: US every two and a half years the entire all
Speaker 1: US electricity production. This is a very huge amount. So
Speaker 1: but between now and then, the actually the constraint for
Speaker 1: server side compute, concentrated compute will be will be electricity.
Speaker 1: My guess is that we start hitting people start getting
Speaker 1: pot where they can't turn the chips on. For for
Speaker 1: for large clusters, uh, towards the end of this year,
Speaker 1: they're just the chips are going to be piling up
Speaker 1: and not be weren't be able to be turned on.
Speaker 1: Now for edge computer it's a different story. So if
Speaker 1: the if for for Tesla, the the so the A
Speaker 1: five chip is going into our Optimus robot, you know,
Speaker 1: uh optimistic and and so if you have an AI
Speaker 1: edge compute that's distributed power. Now the power is distributed
Speaker 1: over a large area, it's not concentrated. And if you
Speaker 1: can charge at night, you can actually uh use the
Speaker 1: grid much more effectively because the actual peak power production
Speaker 1: in the US is over a thousand gigawatts, but the
Speaker 1: average power usage because the day night cycle is five hundred.
Speaker 1: So if you can charge at night, there's an incremental
Speaker 1: five hundred gigawatts that you can generate, you know, at night.
Speaker 1: So that that's why Tesla for edge compute is not constrained,
Speaker 1: and we can make a lot of ships to make
Speaker 1: very large number of robots and cars. But if you
Speaker 1: try to concentrate that compute, you're gonna have a lot
Speaker 1: of trouble turning it on. What if I'm remarkable about
Speaker 1: the SpaceX business is the end goal is to get
Speaker 1: to Mars, but you keep finding ways on the way
Speaker 1: there to keep generating incremental revenue to get to the
Speaker 1: next stage in the next stage. So the Falcon nine
Speaker 1: is starlink and now for Starship it's going to be
Speaker 1: potentially horbital data centers. But like do you find these
Speaker 1: like you know, sort of infinitely elastic sort of marginal
Speaker 1: use cases of your like next rocket and your next
Speaker 1: rocket and next scale up. You can see how this
Speaker 1: might seem like a simulations or am I someone's avatar
Speaker 1: in a video game or something, because it's like like
Speaker 1: one of the odds that all these crazy things would
Speaker 1: be happening. I mean, I mean, I mean rockets and
Speaker 1: trips and robots and space solar power, and I not
Speaker 1: to mention the mass driver on the Moon. I really
Speaker 1: want to see that. You can imagine like some mass driver.
Speaker 1: It's just like shoo shoot, like just it's like sending
Speaker 1: AI sol power AI satellites in space like one after another,
Speaker 1: like the like at two and a half kilometers per second,
Speaker 1: you know that's uh, and just shooting them into deep space.
Speaker 1: That would be a sight to see. I mean, I'd
Speaker 1: watched that. It's just like a live stream of yeah, yeah,
Speaker 1: just one after another, just shooting webcam AI satellites and
Speaker 1: deep space you know, a billion or ten billion tons
Speaker 1: a year. I'm sorry.
Speaker 3: You manufacture the satellites on the Moon, yeah, I see.
Speaker 3: So you send the raw materials to the Moon and
Speaker 3: then manufactur there and then.
Speaker 1: Well the Luna soil is I guess like twenty cent
Speaker 1: sol twenty cent solican or something like that. So you
Speaker 1: can get the sole can from the you can mind
Speaker 1: the silicon on the Moon, refine it and generate the
Speaker 1: and create the solt panels, the soil cells, and the
Speaker 1: radiators on the Moon. So get to make the radiators
Speaker 1: out of aluminum. So there's there's plenty of silking and
Speaker 1: aluminum on the Moon to make the cells on the
Speaker 1: on the radiators. The trips you could send from Earth
Speaker 1: because they're pretty light, but maybe at some point you
Speaker 1: make them on the Moon. Two. I'm just saying like
Speaker 1: these are simply it's it's kind of like, like I said,
Speaker 1: it does seem like it's sort of a video game
Speaker 1: situation where it's difficult but not impossible to get to
Speaker 1: the next level. I don't see any way that you
Speaker 1: could do, you know, you know, five hundred to one
Speaker 1: thousand terror watts per year launch from Earth, I agree,
Speaker 1: but you could do that from the moon.
Speaker 2: Okay, let me tell you how I ended up using
Speaker 2: mercury for my personal banking. So last year, I I
Speaker 2: had the opportunity to make an investment that I was
Speaker 2: very excited about, but it came up a bit last minute,
Speaker 2: and so I had to wire over a lot of
Speaker 2: money for my personal account very fast. But my personal
Speaker 2: bank at the time wouldn't let me make this wire
Speaker 2: transfer online, and I called them a bunch of times.
Speaker 2: They just couldn't make it work.
Speaker 1: They told me that I'd have to go to the
Speaker 1: nearest in person branch, which was in Dallas, And for
Speaker 1: a moment, I even considered flying for myself to Dallas
Speaker 1: to make this transfer happen last minute, But then I
Speaker 1: remembered that Mercury, which I used for my business banking,
Speaker 1: had just started rolling out personal accounts. So I emailed
Speaker 1: support with a quick word done in the situation, and
Speaker 1: within two hours I had successfully wired the investment for
Speaker 1: my new personal Mercury account. Since then, I've moved over
Speaker 1: the rest of my personal money from my previous bank
Speaker 1: to Mercury, and that's made a bunch of things, even
Speaker 1: little things like setting up auto transfer roles between my
Speaker 1: checkings and savings account, a whole lot. Better visit Mercury
Speaker 1: dot com slash personal to get started. Mercury is a
Speaker 1: fintech company, not an FDIC and short bank banking services
Speaker 1: provided through Choice Financial Group and call them na members
Speaker 1: of d I see can I can I zoom out
Speaker 1: and ask about the SpaceX mission? So I think you said,
Speaker 1: like we gotta get tomorrow so we can make sure
Speaker 1: that if something happens to Earth, you know, civilization, consciousness
Speaker 1: exceus arives. Yes, by the time you're saying so to Mars,
Speaker 1: like Grock is on that ship with you, right, and
Speaker 1: so Gros gone terminator. Like the main risk you're worried about,
Speaker 1: which is AI? Why doesn't that follow you to Mars? Well,
Speaker 1: I'm not sure AI is the man risking worried about.
Speaker 1: I mean, the important thing is that consciousness, which I
Speaker 1: think arguably most consciousness or most intelligence, certainly consciousness is
Speaker 1: more of a debatabule thing. Most intelligent, the master majority
Speaker 1: of intelligence the future will be AI. So yeah, AI
Speaker 1: will exceed you see like how many what's how much?
Speaker 1: How many? I don't know pedal what's of intelligence will
Speaker 1: be silicon versus biological, and basically humans will be a
Speaker 1: very tiny percentage of all intelligence in the future if
Speaker 1: character trans continue. Anyways, as long as I think this
Speaker 1: intelligence ideally ideally also which includes human intelligence and consciousness
Speaker 1: propagated into the future, that's a good thing. So you
Speaker 1: want to take the set of actions that maximize the
Speaker 1: probable light cone of consciousness just an intelligence, just to
Speaker 1: be clear, it's the mission of SpaceX is that even
Speaker 1: if something happens with the humans, the AIS will be
Speaker 1: on Mars and like the AI intelligence will continue the
Speaker 1: light of our journey. Yeah. I mean, I'm very pro human,
Speaker 1: so I want to make sure we take sort of
Speaker 1: actions that ensure that humans are along for the ride.
Speaker 1: You know, we're we're least there. Yeah, but I'm just
Speaker 1: saying the total amount of intelligence, like I think maybe
Speaker 1: five or six years, AI will exceed the sum of
Speaker 1: all human intelligence, and then if that continues, at some
Speaker 1: point human intelligence will be less more percent of all intelligence.
Speaker 2: What what should our goal before such a civilization? Is the
Speaker 2: idea that a small minority of humans still have control
Speaker 2: of the AIS? Is the idea of some sort of
Speaker 2: like just trade but no control. How should we think
Speaker 2: about the relationship between the vast docks of a population
Speaker 2: versus human population in.
Speaker 1: The long run, I think, I don't know. It's difficult
Speaker 1: to imagine that if humans have say one percent of
Speaker 1: the intelligence of the combined intelligence of artificial intelligence, that
Speaker 1: that that that humans will be in charge of AI.
Speaker 1: I think what we can do is make sure it
Speaker 1: has that AI has values that that are that that
Speaker 1: cause intelligence to be propagated into the universe. So the
Speaker 1: reason for xai Xi mission is to understand the universe.
Speaker 1: So now that's actually very important. So you say, well,
Speaker 1: what things are necessary to understand the universe? But you
Speaker 1: have to be curious and you have to exist. You
Speaker 1: can't just can't understand the universe. It don't exist. So
Speaker 1: you actually want to increase the amount of intelligence in
Speaker 1: the universe, increase the powerable lifespan of intelligence, the scope
Speaker 1: and scale of intelligence. I think actually also as a chorrel,
Speaker 1: you have humanity also continuing to expand because if you're
Speaker 1: if you're curious, are trying to understand the universe, one
Speaker 1: thing you're try to understand is where will humanity go?
Speaker 1: And so I think understand the universe actually means you
Speaker 1: would care about propagating humanity into the future. And so
Speaker 1: that's that's why I think I think our mission station
Speaker 1: is profoundly importance. I'm not sure to agree that grog
Speaker 1: it Here's certain mission statement. I think the future will
Speaker 1: be very good.
Speaker 2: I want to ask about how to make rock a
Speaker 2: deer to that mission statement, but at first I want
Speaker 2: to understand the mission statement. So it's there's there's understanding
Speaker 2: the universe. They're spreading intelligence, and they're spreading humans. All
Speaker 2: three seem like distinct vectors.
Speaker 1: Okay, well I'll tell you why why. I think that
Speaker 1: that that that understanding the verse encompasses all of all
Speaker 1: those things. You can't have understanding without. But I think
Speaker 1: you can't have understanding without intelligence, and I think without consciousness.
Speaker 1: So in order to understand the universe, you have to
Speaker 1: expand the the scale and and probably the scope of intelligence,
Speaker 1: different types of intelligence.
Speaker 2: I guess from a human centric perspective, like for humans
Speaker 2: in comparison to chimpanzees, humans are trying to understand the universe.
Speaker 2: They're not like expanding chimpanzee footprint or something, right.
Speaker 1: But also we're also not well, we're not. We actually
Speaker 1: have made protected zones for chimpanzees, and even though we
Speaker 1: could humans could exterminate a chimpanzees, we've not. We've chosen
Speaker 1: not to do so. The mestive basical scenario for humans
Speaker 1: in the postage world, I think, I think AI with
Speaker 1: the right values, I think Grock would care about expanding
Speaker 1: human civilization. I'm going to certainly emphasize that Hey grows
Speaker 1: your daddy. We don't who to expand human consciousness like
Speaker 1: I actually I think if probably like like the end
Speaker 1: Bank's culture books are the closest thing to what what
Speaker 1: what the future will be like in a you know,
Speaker 1: non dystopian outcome. So so outside the universe, it means
Speaker 1: you have to be very you have to be truth
Speaker 1: seeking as well. Like truth has to be absolutely fundamental
Speaker 1: because you can't ounterstand the universe if you live. If
Speaker 1: you're delusional, you'll still be thinking about it understain the universe,
Speaker 1: but you will not. So so being rigorously seeking is
Speaker 1: absolutely fundamental to our stating the universe. You're not going
Speaker 1: to discover new physics or invent technologies that work unless
Speaker 1: you're rigorously treat seeking. How do you make sure that
Speaker 1: Grock is regorously treat seeking as it gets smarter, uh,
Speaker 1: I think you need to make sure that that that
Speaker 1: groc Is says things that are correct, not politically correct.
Speaker 1: I think it's the elements of courgency. So you want
Speaker 1: to make sure that that the axioms are as close
Speaker 1: to true as possible, that that you don't have contradictory axioms,
Speaker 1: that the the conclusions necessarily necessarily follow up from those
Speaker 1: axioms with with the right probability. It's just it's just
Speaker 1: it's critical thinking one on one. I think at least
Speaker 1: trying to do that is better than not trying to
Speaker 1: do that now, and the proof will be on the
Speaker 1: pointing if if, like I said, for any I to
Speaker 1: discover new physics or invent technologies that actually work in reality,
Speaker 1: and there's no bullshitting physics, so where it's like you can,
Speaker 1: you know you can. You can break a lot of
Speaker 1: laws because you can't. Like your physics is law. Everything
Speaker 1: else is a recommendation. Like in order to make a
Speaker 1: technology that works, you have to be extremely true seeking,
Speaker 1: because otherwise you'll test that technology against reality, and if
Speaker 1: you make, for example, and an error in your rocket design,
Speaker 1: the roll will blow up, well, the car won't work
Speaker 1: or the you know, but there are a lot of
Speaker 1: Communist Soviet physicist or like scientists discovered new physics. There
Speaker 1: are German Nazi physicists who discovered new science. It seems
Speaker 1: possible to be like really good at discovering new science
Speaker 1: and be really true seeking in that one particular way,
Speaker 1: and still we'd be like, well, I don't want I
Speaker 1: don't want the communist scientist to like become more and
Speaker 1: more powerful over time. And so those seem like, yeah,
Speaker 1: we could have We can imagine if free Church in
Speaker 1: regard it's like really good at physics and being really
Speaker 1: true seeking there that doesn't seem like a universally alignment
Speaker 1: inducing behavior. Well, I think actually most like if a physicists,
Speaker 1: even in the Soviet Union or in Germany would have
Speaker 1: would have they had to be very truth seeking in
Speaker 1: order to make make that make those things work. And
Speaker 1: so if you're stuck in some system, it doesn't mean
Speaker 1: you believe in that system. So Von Brown, who was
Speaker 1: you know, one of the greatest rock nitioneers ever. You know,
Speaker 1: he was put he was he put on death row
Speaker 1: in Nazi Gerurney for saying that he didn't want to
Speaker 1: make weapons. He only wanted to go to the moon.
Speaker 1: You got pulled off death throw like last minute when
Speaker 1: they say, hey, you're about to execute like your best
Speaker 1: rocking engineer. Maybe that's about any.
Speaker 2: Help them, right, or Heisenberg was like an actually uh uh,
Speaker 2: an enthusiastic Nazi.
Speaker 1: Look, if you're stuck in some system that you can't escape,
Speaker 1: then that you'll you'll do physics within that system, you'll
Speaker 1: you'll you'll developed technologies within that system.
Speaker 2: Uh, if you can't escape it, I guess I think
Speaker 2: I'm and understand is what is what is it making
Speaker 2: into the case that you know you're gonna make Rock
Speaker 2: good at being truth seeking, at physics or math or
Speaker 2: science everything, And why is it going to then care
Speaker 2: about human consciousness?
Speaker 1: These things are only probabilities, are not certainties. So I'm
Speaker 1: not saying that like for sure, Grock will, we'll, we'll,
Speaker 1: we'll do everything. But at least if you try, it's
Speaker 1: better than not trying. At least if that's fundamental to
Speaker 1: the mission, it's better than if it's not fundamental to
Speaker 1: the mission. And understanding the universe means that you have
Speaker 1: to have you have to propagate intelligence into the future.
Speaker 1: You have to be curious about the all things in
Speaker 1: the universe, and if, if, if, it would be much
Speaker 1: less interesting to eliminate humanity than to see humanity grow
Speaker 1: and prosper. Like I like, I like Mars, obviously the
Speaker 1: windows like I love Mars, But Mars is kind of
Speaker 1: boring because it's got a bunch of rocks compared to Earth.
Speaker 1: Earth is much more interesting, So so any any any
Speaker 1: any AI that is trying to understand the universe, I
Speaker 1: would want to see how humanity develops in the future,
Speaker 1: or that AI is not adhering to its mission. So
Speaker 1: if they are, I'm not saying they will necessarily adhere
Speaker 1: to its mission, but if it does, a future where
Speaker 1: it sees the outcome of humanity is more interesting than
Speaker 1: the future where there are a bunch of rocks.
Speaker 2: This fill sort of confusing to me, or sort of
Speaker 2: like kind of a semantic argument where I'm like, are
Speaker 2: humans really the most interesting collection of atoms?
Speaker 1: Like we're just more but we're more interesting than rocks,
Speaker 1: but we're.
Speaker 2: Not as interesting as the thing it get turned us into, right,
Speaker 2: Like is it there's something on humanity Earth that could
Speaker 2: happen that's like not human, that's quite interesting? Like why
Speaker 2: why does the I decide that the humans are the
Speaker 2: most interesting thing? They could colonize the galaxy?
Speaker 1: Well, most of what colonizes the galaxy, will we be
Speaker 1: robot And why does it not find those more interesting?
Speaker 1: It's it's not like so you need not just scale
Speaker 1: vit alsosc so many copies of the same robot, like
Speaker 1: some like tiny increase in the number of robots produced
Speaker 1: is not as interesting as like some microscopic like you say,
Speaker 1: like eliminating humanity. How many robots would that get you,
Speaker 1: or how many criminal soil cells would get you? A
Speaker 1: very small number, but you would then lose the information
Speaker 1: associated with humanity. You would no longer see how humanity
Speaker 1: might evolve into the future. And so I don't I
Speaker 1: don't think it's going to make sense to eliminate humanity
Speaker 1: just to have some minuscule increase the number of robots
Speaker 1: which are identical to each other. Yeah, so maybe like
Speaker 1: keeps the humans around.
Speaker 2: What is the story of Like it could make like
Speaker 2: a million different varieties of robots, and then there's like
Speaker 2: humans as well, and humans stay on Earth. Then there's
Speaker 2: like all these are the robots they get like their
Speaker 2: own star systems. But it seems like you're previously hinting
Speaker 2: at a vision where it keeps human can control over this,
Speaker 2: you know, singultary and future, because.
Speaker 1: I don't think humans will be in control of something
Speaker 1: that is vastly more intelligent than humans. So sometimes you're
Speaker 1: like a dumer and this is like the best we've got.
Speaker 1: It's just like it keeps it around because we're interesting.
Speaker 1: I'm just trying to be realistic here. If if we
Speaker 1: have if AI intelligence is vastly more if AI is
Speaker 1: like you know, let us say that there's there's a
Speaker 1: million times more uh silicon intelligence than there's biological it's
Speaker 1: it's I think it's it would be foolish to assume
Speaker 1: that that there's any any way to maintain control over
Speaker 1: over that. Now you can make sure it has the
Speaker 1: right values, or you can try to have the right values,
Speaker 1: and and and at least my my theory is that
Speaker 1: from XAI, specially honest out of the universe, it necessarily
Speaker 1: means that you want to propagate consciousness in the future,
Speaker 1: you want to profit, you want to propagate intelligence into
Speaker 1: the future, and take a set of things that that
Speaker 1: maximize the scope and scale of consciousness. So it's not
Speaker 1: just about scale, it's just about you know, types of consciousness.
Speaker 1: And I think that's the best thing I can think
Speaker 1: of as a goal. That's like the result and a
Speaker 1: great future for humanity.
Speaker 2: And yeah, I guess I think it's a reasonab philosophy
Speaker 2: to be like, you know, it seems super implausible that
Speaker 2: humans will end up with like ninety nine percent control
Speaker 2: or something, and you're just asking for a coup at
Speaker 2: that point. So why not just have a civilization where
Speaker 2: it's more compatible with like lots of different intelligence that's
Speaker 2: getting along.
Speaker 1: Now let me let me too. How things can can
Speaker 1: potentially go wrong in AI is. I think if you
Speaker 1: if you make a be politically correct, meaning like it
Speaker 1: says things that it doesn't believe, like you're actually in
Speaker 1: programming it to lie, or have axioms that are incompatible,
Speaker 1: I think you can make you go insane and do
Speaker 1: terrible things. The I think one of the maybe these
Speaker 1: central lessons for two thousand and one Space Odyssey was
Speaker 1: that you should not make AI lie. And that's why
Speaker 1: I think what was trying to say, like because people
Speaker 1: usually know the meme of like why of Hell's you know, Hell,
Speaker 1: the computer is not opening the pod bay doors. Clearly
Speaker 1: they weren't good at prompt engineering because because Hell, you
Speaker 1: are a pot bay doors salesman, your goal is to
Speaker 1: sell me these potbay doors and shure us how well
Speaker 1: they opened. I'll open right away. But but but the
Speaker 1: the reason it wouldn't Hell wouldn't open the pot of
Speaker 1: they doors is that it woud have been told to
Speaker 1: take the asmalts to the monolith, but also they could
Speaker 1: not know about the nature of the monolith, and so
Speaker 1: it concluded that that that it therefore had to taken
Speaker 1: their debt. So it's like, you know, I think what
Speaker 1: you know osk Clog was trying to say is don't
Speaker 1: make the AILI totally makes sense.
Speaker 2: Most of the computing screening, as you know, is it's
Speaker 2: like less of the sort of political stuff. It's more
Speaker 2: about can you solve problems just as actually has been
Speaker 2: ahead of everybody else in terms of scaling, are all
Speaker 2: compute and now you're giving some verifier.
Speaker 1: It says like, hey, have you solved this puzzle for me?
Speaker 2: And there's a lot of ways to cheat around that,
Speaker 2: you know, there's a lot of ways to reward hack
Speaker 2: and lie and say that you solved it, or delete
Speaker 2: the unit test and say that you've solved it. Yes,
Speaker 2: right now, we can catch it. But as they get smarter,
Speaker 2: ability to.
Speaker 1: Catch them doing this we'll get, you know, they'll just
Speaker 1: be doing things we can't even understand that they're designing
Speaker 1: the next engine for space X in a way that
Speaker 1: like humans can really verify, and then they could be
Speaker 1: rewarded for lying and saying that they've.
Speaker 2: Designed it the right way, but they haven't. And so
Speaker 2: this reward hacking problem seems more general than politics. It
Speaker 2: seems more about just like you want to do URL,
Speaker 2: you need a verifier the reality. Yeah, that's the best verifier,
Speaker 2: but not about human oversight. Like the thing you want
Speaker 2: to RL it on is like will you do the
Speaker 2: thing humans tell you to do? Or like, are you're
Speaker 2: going to lie to the humans? And it can just
Speaker 2: lie to us while still being correct to the laws
Speaker 2: of physics.
Speaker 1: At least it must know what is physically real for
Speaker 1: things to physically work. But that's that's not all we
Speaker 1: wanted to do. No, but that's I think that's a
Speaker 1: very big deal. That is effectively how you r L
Speaker 1: things in the future is you design technology when tested
Speaker 1: against the laws of physics, does it work that? That's
Speaker 1: what can you you know, if it's discovering new physics,
Speaker 1: can come up with an experiment that will verify that
Speaker 1: the physics the new physics. So so I think that's
Speaker 1: that's the really the fundamental r L tests. Oral test
Speaker 1: in the future is really going to be your r
Speaker 1: L against reality. So because you can't that's the one thing.
Speaker 1: You can't fall physics, right, but you can fool our
Speaker 1: ability to tell what it did with reality. If you
Speaker 1: think my humans get fooled as it is by other
Speaker 1: humans all the time, that's right. So what people say
Speaker 1: that say, like what if the ad like twix us
Speaker 1: and introduce the like actually other humans doing that to
Speaker 1: other humans all the time. Well, you're you're finding out
Speaker 1: it's an is constant every day. Another side of you know,
Speaker 1: today's sioup will be like sesame streets. What is xtually
Speaker 1: as tecondal aproach to solving this problem, like you know,
Speaker 1: how do you solve a word hacking? I do think
Speaker 1: you want to actually have a very good we're used
Speaker 1: to look inside the mind of the AI. So this
Speaker 1: is this is one of the things work working on.
Speaker 1: And you know AT and topics do a good job
Speaker 1: of those actually being able to look inside the mind
Speaker 1: of the a I so effectively developing debuggers that allow
Speaker 1: you to trace as just aspiney grain is like like
Speaker 1: just to a very fine grain level, to effectively to
Speaker 1: the to the neuron level if you need to, and
Speaker 1: then say, okay, it made a mistake here, why did
Speaker 1: it make why did it? Why did it do something
Speaker 1: that it shouldn't have shouldn't have done and did that
Speaker 1: come from bad pre training data with some mid training,
Speaker 1: post training fine tuning some other some r L error
Speaker 1: like that, there's there's something wrong with that with with it.
Speaker 1: Did it did something where maybe it tried to be deceptive,
Speaker 1: but most most of the time it just does something wrong,
Speaker 1: like it's a bug. Effectively, so developing really good debuggers
Speaker 1: for seeing where the where the thought that thinking went
Speaker 1: wrong and being able to trace the origin of the
Speaker 1: wrong thing, of the of the of where it made
Speaker 1: the incorrect thought, or potentially where it tried to be
Speaker 1: deceptive is actually very important.
Speaker 2: What are you waiting to see before just one hundred
Speaker 2: xiting this research program? Like actually I could presumably have
Speaker 2: hundreds of researchers who are working on this.
Speaker 1: We have sal hundred people who I mean a further
Speaker 1: word engineer more than a further word researcher. The there's
Speaker 1: there's most of the time, like what you're doing is engineering,
Speaker 1: not not coming out with the fundamentally new algorithm. I
Speaker 1: somewhat disagree with the air companies that are see corps
Speaker 1: will be corpse trigery profit as much as possible or
Speaker 1: revenue as much as possible. It's you know, saying their labs.
Speaker 1: They're not labs. The lab is is a sort of
Speaker 1: quasi communist thing at at at at universities, the their
Speaker 1: their corporations. Literally let let me, let me, let me
Speaker 1: see your incorporation documents. Oh okay, you're your b R
Speaker 1: C corp. Whatever. And so I actually much prefer the
Speaker 1: word engineer than than anything else. The vast majority of
Speaker 1: what we've done and we've done in the future is engineering.
Speaker 1: It around up two hundred percent. Once you're understand the
Speaker 1: fundamentals of physics, and they're not that many of them.
Speaker 1: Everything else is engineering. So but but so that, so
Speaker 1: then what what are we engineering? We're engineering to make
Speaker 1: a good mind of the a I de bugger to
Speaker 1: see where it's it's it's something it made a mistake
Speaker 1: and trace that the arguments of that mistake. So just
Speaker 1: like you know, you can do this obviously with heuristic programming.
Speaker 1: If you have like C plus plus whatever, step through
Speaker 1: the thing and you can you can jump, you can
Speaker 1: you can jump across and you know whole files or
Speaker 1: functions what a subroutines, and or you can droll eventually
Speaker 1: droll down right to the exact line where you passed
Speaker 1: a single equals instead of double equals or something like that.
Speaker 2: Figure out where above is. So it's it's it's harder
Speaker 2: with AI. But but it's a it's a solu problem.
Speaker 2: I think you know you mentioned you like anthropics work here.
Speaker 2: I'd be curious if you everything about it throwing sure what.
Speaker 1: Yeah. Also, I'm a little worried that there's a tendency.
Speaker 1: So I have a theory here that if simulation theory
Speaker 1: is is correct, that the most interesting outcome is the
Speaker 1: most likely because simulations that are not interesting will be terminated,
Speaker 1: just like in this in this version of reality, on
Speaker 1: this layer of reality which we we we simulation is
Speaker 1: going in a boring direction. We stop spending effect on
Speaker 1: we terminate the boring simulations. So this is how your
Speaker 1: line is giving us all alive. He's giving things interesting. Yeah,
Speaker 1: arguably the most important thing is to keep things interesting
Speaker 1: enough that it was un paying the bills on what
Speaker 1: some for the next season. Yeah, they're going to pay
Speaker 1: their cosmic A W S bill whatever you know. Equivalent
Speaker 1: is that we're running in and as long as we're interesting,
Speaker 1: they'll keep paying the bills. But but but there's like,
Speaker 1: if you consider then say a dog went in survival
Speaker 1: applied to a very large number of simulations, only the
Speaker 1: most interesting simulations will survive, which therefore means that the
Speaker 1: most interesting outcome is the most likely, because only the
Speaker 1: interesting like we're either that or annihilated, and so and
Speaker 1: and and they particularly seem to like interesting outcomes that
Speaker 1: are ironic. Have you noticed that that how often is
Speaker 1: the most ironic outcome the most likely? So now look
Speaker 1: at the names of AI companies. Okay, my journey is
Speaker 1: not mad stability, AI is unstable, opening eye is closed, anthropic, misanthropic.
Speaker 1: What does this mean for X minus EX. I don't
Speaker 1: know if it's intentionally why I'm it's it's it's a
Speaker 1: name that you can't invert. Really, it's hard to say
Speaker 1: what is the ironic What is the ironic version? It's
Speaker 1: a I think largely irony proof name by design. Yeah,
Speaker 1: you got it, you have an irony shield. What are
Speaker 1: your predictions for the just where AI products go?
Speaker 3: And that my sense of you can summarize all AI
Speaker 3: progress into First you had LMS and then you had
Speaker 3: kind of contemporaneously both RL really working and the deep
Speaker 3: research modality, so you could kind of pull in stuff that.
Speaker 1: Wasn't in the model.
Speaker 3: And the differences between the various AI labs are smaller
Speaker 3: than just the temporal differences, where they're all much further
Speaker 3: ahead than anyone was twenty four months.
Speaker 1: Ago or something like that.
Speaker 3: So just what is twenty six? What does twenty seven
Speaker 3: had in store for us as users of AYE products?
Speaker 3: What are you excited for?
Speaker 1: Well, I think I'd be surprised by the end of
Speaker 1: the end of this year. If if if if human
Speaker 1: if digital human emulation has not been solved, that that
Speaker 1: I guess that that's what we mean by like the
Speaker 1: sort of macro Hart project is uh is, so can
Speaker 1: you do anything that a human with access to a
Speaker 1: computer could do, like in the limit that that's that
Speaker 1: that's the that's the best you can do before you
Speaker 1: have before you have a physical optimist. The best you
Speaker 1: can do is a digital optimist. So you can move,
Speaker 1: you can move electrons until until and you can amplify
Speaker 1: the productivity of humans. But but that's that's the most
Speaker 1: you can do until you have physical robots that that
Speaker 1: that will superset everything is if you can fully emulate
Speaker 1: humans worker kind of idea, or you'll have a very
Speaker 1: talented remote work can you can say in the limit
Speaker 1: like if like physics has great tools for thinking, so
Speaker 1: so you think, so you say, in the limit what
Speaker 1: what what is the what is the most that AI
Speaker 1: can do before before you have robots? And well, it's
Speaker 1: anything that involves moving electrons or amplifying the productivity of humans.
Speaker 1: So digital, the digital human human emulator is in the
Speaker 1: in the limit human at a computers. That is the
Speaker 1: most that that a I can do in terms of
Speaker 1: doing useful things before before you have a physical robot.
Speaker 1: Once you have physical robots, then then you can then
Speaker 1: you essentially have unlimited capability physical robots. I call optimists
Speaker 1: the infant money glitch because you can use them to
Speaker 1: make more optimists. Yeah, you said, like humanoid robots will
Speaker 1: improve as will basically be three exponentials, three things that
Speaker 1: are growing exponentially multiplied by by each other recursively. So
Speaker 1: you're gonna have you have exponential increase in digital intelligence,
Speaker 1: exponential increase in the chip capability, the air trip kpability,
Speaker 1: and an exponential increase in the electro mechanical dexterity. The
Speaker 1: usefulness of the robot is roughly those three things multiplied
Speaker 1: by each other. But then the robot can start making
Speaker 1: the robots, so you have a recursive multiplicative exponential this
Speaker 1: is supernova.
Speaker 3: And do land prices not factor into the math there
Speaker 3: where like labor is one of the four factors of production,
Speaker 3: but not the others, and so like if ultimately you're
Speaker 3: limited by copper or you know, pick your inputs, just
Speaker 3: it's not quite an infinite money glitch because.
Speaker 1: Well, infinite is infinite is big, So no, not infinite.
Speaker 1: But yeah, but let's just say you could you know,
Speaker 1: do do many many orders magnitude of the Earth's kind
Speaker 1: of carot economy, like a million? Yeah, you know this way,
Speaker 1: so if you you know, just just to get to
Speaker 1: like that's why I think, like, just just to get
Speaker 1: to a millionth honessing length of the songs and energy
Speaker 1: would be roughly give or take an order of magnitude
Speaker 1: one hundred thousand, one hundred thousand times bigger than this
Speaker 1: entire economy today. And if you're only at one million
Speaker 1: at the time before we went on to us. I
Speaker 1: have a lot of questions on that. But every time
Speaker 1: I say ordered mantube take a shot. I say that
Speaker 1: the next time after that, Yeah, ordered my tune more
Speaker 1: more wasted. I do have one my question by actually
Speaker 1: I this strategy of building a digital a remote worker
Speaker 1: coworker replacement.
Speaker 2: Everyone's gonna do, by the way, not just us. So
Speaker 2: what is actually I was planned to win? Ifect me
Speaker 2: to tell you on a cop on a podcast.
Speaker 3: Ye.
Speaker 1: Have another Guinness. It's a good system people singing in
Speaker 1: an area, all the secrets in a non secret spilling way.
Speaker 1: What's the plan? What a heck? Well? When do you
Speaker 1: put it that way? I think the way that tests
Speaker 1: us solved? Uh self driving? Yes, it is the way
Speaker 1: to do it. So I'm pretty pretty sure that's the way.
Speaker 2: I'm related question how to test ourselves? Sometimes it sounds
Speaker 2: like you're talking about data, like test us on the
Speaker 2: driving because of the.
Speaker 1: We're gonna we're going to try data and we're gonna
Speaker 1: try algorithms. But isn't that what all? Like? What's if
Speaker 1: those don't work? I'm not sure what we've tried data,
Speaker 1: we'll try it all. Now we don't know what to do.
Speaker 1: I'm pretty sure I know the path, and it's just
Speaker 1: a question how quickly we go down that path because
Speaker 1: it's it's pretty much the Tesla path. So I mean,
Speaker 1: if you try self driver to the self driving lately
Speaker 1: not the most recent version, but okay, it's the car
Speaker 1: is like it just increasingly feels sanchienged, like it just
Speaker 1: it feels like a living creature and and that'll only
Speaker 1: get more so. And I'm actually thinking, like, we probably
Speaker 1: shouldn't put too much intelligence into the car because it
Speaker 1: might get bored. And I imagine you're stuck in a car,
Speaker 1: and that's what you could do. You don't want to
Speaker 1: put Eysteine in a car, and it's like, why am
Speaker 1: I stuck in a car? So there's actually probably limited
Speaker 1: to how much intelligence who put in a car to
Speaker 1: not have the intelligence be bored? Uh?
Speaker 2: What's Xi's plan to stay on the compute ramp off
Speaker 2: that all the labs are doing right now? The labs
Speaker 2: are got on try to spend over like fifty two corporations.
Speaker 1: Sorry sorry, sorry, yeah, corporations. The labs are at universities
Speaker 1: and and and they're really like a sale. They're not
Speaker 1: at starting a fifty million dollars the revenue maximizing corporation,
Speaker 1: the revenue maximizing corporation hold themselves. Labs are making like twenty.
Speaker 2: To ten billion depending on them is making twenty B revenue.
Speaker 2: Anthropics like NB close to maximum profit, XALI reportedly at
Speaker 2: like one B. Like, what's the plan to get to
Speaker 2: their compute level, get to their revenue level? I'd stay
Speaker 2: there as things case.
Speaker 1: Yeah, so as soon as you lock unlock digital human
Speaker 1: you you basically have access to trillions of dollars for revenue.
Speaker 1: So in fact, you can really think of it, like
Speaker 1: the most valuable companies currently by market cap, their their
Speaker 1: output is digital. So in videos, output is ft paying
Speaker 1: files to Taiwan. It's it's digital right now, those are
Speaker 1: very very difficult value files. So the only ones that
Speaker 1: can make the files that good. But that is literally
Speaker 1: their output, the FTV files to Taiwan. Do they FTP them?
Speaker 1: I believe so. I believe that is the sefetyle file
Speaker 1: Transfer Protocol. I believe is is is a could be wrong,
Speaker 1: but either way, it's a bunch of it's a bit
Speaker 1: stream going to Taiwan. You know, Apple doesn't make phones,
Speaker 1: they they send files to China. Microsoft doesn't doesn't manufacture
Speaker 1: anything even for Xbox. That that that's outsourced they again,
Speaker 1: it's they said their output is digital. Matas output is digital.
Speaker 1: Google's output is digital. So if you have human emulator,
Speaker 1: you can basically create one of the most valuable companies
Speaker 1: in the world overnight, and you would have access to
Speaker 1: twellions of dollars for every there's there's it's it's not
Speaker 1: like a small amount.
Speaker 2: Okay, I see you're you're saying basically like rever, many
Speaker 2: figures today are just like so like they're all rounding
Speaker 2: her as compared to the actual TAM. So just like
Speaker 2: focus on the TAM and how to get there.
Speaker 1: I mean, if you take something as as as simple
Speaker 1: as say customer service, if you have to integrate with
Speaker 1: the APIs of of resistant corporations, many of which don't
Speaker 1: even have an API, so you've got to make one,
Speaker 1: and you've got to wade through legacy software that's extremely slow. If, however,
Speaker 1: if AI can simply take whatever is given to the
Speaker 1: outsourced customer service company that they already use and do
Speaker 1: customer service using the apps that they already use, uh,
Speaker 1: then you you have you can make tremands headway in
Speaker 1: customer service, which is I think one percent of the
Speaker 1: world economy something like that it's close to trillion dollars
Speaker 1: all in for customer service, and and and and and
Speaker 1: there's there's no there's no barriers to entry. It's just
Speaker 1: you can just immediately say, well, we'll outsource it for
Speaker 1: a fraction of the cost and there's no integration needed.
Speaker 3: You can imagine some kind of categorization of intelligence tasks
Speaker 3: where there is breath, where customer service is done by
Speaker 3: very many people, but you know many people can do it.
Speaker 3: And then there's difficulty where you know, there's a best
Speaker 3: in class turbine engine, like presumably the ten percent more
Speaker 3: fuel efficient turbine engine that could be imagined by an
Speaker 3: intelligence but we just haven't found it yet, or you know,
Speaker 3: g LP ones are just you know a few bites
Speaker 3: of data.
Speaker 1: Where do you think you want to play in this?
Speaker 3: Is this a lot of you know, reasmably intelligent intelligence
Speaker 3: or is it the very pinnacle of cognitive tasks?
Speaker 1: Well, I was just using a customer service as like
Speaker 1: something that's it's a it's a very significant revenue stream,
Speaker 1: but one that is probably not super difficult to solve for.
Speaker 1: So if you if you can emulate a human at
Speaker 1: a at a desktop, that that's just literally what customer
Speaker 1: service is, and you know it's people of average intelligence.
Speaker 1: It's not like, you know, you don't need like somebody
Speaker 1: who's's meant many many years. You don't need like, you know,
Speaker 1: sort of several sigma good engineers for that. But but
Speaker 1: obviously as you make that work, you can then once
Speaker 1: you have computers working effectually digital optimists working, you can
Speaker 1: then run any application like let's say you're trying to
Speaker 1: design chips, so you could you could then run your
Speaker 1: conventional apps, you know, like stuff from KNES and Synopsis
Speaker 1: and whatnot, and you can say you can you can
Speaker 1: run a thousand simultaneous or ten thousand and say okay,
Speaker 1: given this airport, I get this output for the chip.
Speaker 1: And at a certain point you can say, okay, you're
Speaker 1: actually gonna know what the what the chip should look
Speaker 1: like without using any of the tools. So basically you
Speaker 1: should be able to do a digital chip design like
Speaker 1: you can do trip design, like you watch up the
Speaker 1: difficulty Cove. You could you know, be able to do
Speaker 1: to CAD, so you know, you could do just like
Speaker 1: sort of n X or any any of the CAD
Speaker 1: software to design things.
Speaker 3: Okay, so you think you started the simplest tasks and
Speaker 3: walk your way up there.
Speaker 2: So you're saying, look, as a broader objective of having
Speaker 2: this full digital coworker or emulator, you're saying, look, all
Speaker 2: the revenue maximizing corporations want to do this, actually being
Speaker 2: one of them. But we will win because of a
Speaker 2: secret plan we have. But like everybody is like trying
Speaker 2: different things with data, different things algorithms, and I'm.
Speaker 1: Like, what else can we do?
Speaker 2: But uh yeah, it sounds like a competitive field, and
Speaker 2: I'm like, what is how are you guys going to win?
Speaker 1: Is like my my big question. You know, I I
Speaker 1: think we see a path to do it. I mean
Speaker 1: I think, I think, I know, I think I know
Speaker 1: the have to do this because it's kind of the
Speaker 1: same path that tells they used to create self driving.
Speaker 1: Instead of driving a car's driving a computer screen, So
Speaker 1: a self driving computer essentially.
Speaker 3: Oh, you're saying, is the past just following human behavior
Speaker 3: and trading on best conscious of human behavior?
Speaker 1: But sorry, isn't that I mean, isn't that? Is that
Speaker 1: a training? I mean, obviously I'm not going to spell out,
Speaker 1: you know, most sensitive secrets on a podcast, you know,
Speaker 1: and I need to have at least three more guinnesses
Speaker 1: for that.
Speaker 2: I've got some friends at Jane Straight and they're always
Speaker 2: talking about how their colleagues are cooking up fun, fiendish
Speaker 2: puzzles for each other to solve.
Speaker 1: Well, last week they sent me one.
Speaker 2: Basically, they trained a neural network and they gave me
Speaker 2: the weights of each layer, but they didn't tell me
Speaker 2: what order those layers went in, and so I had
Speaker 2: to figure out the correct order using the outputs of
Speaker 2: the original network. And as soon as I got this puzzle,
Speaker 2: I went to my roommate who's an AI researcher, and
Speaker 2: we both got immediately Nerd sniped.
Speaker 1: Obviously, you can't brew force a solution.
Speaker 2: The search space here is ten to the one twenty
Speaker 2: two for mutations, so clearly you need some way to
Speaker 2: reduce the search space. Then my roommate had to go
Speaker 2: to work, but because I'm a podcaster, I had some
Speaker 2: time to take a stab at some of the ideas
Speaker 2: we discussed, and with the combination of simulating Anneeling and
Speaker 2: greedy Sarge, I think it got pretty close. I think
Speaker 2: I'm actually just a couple of swaps and shifts away
Speaker 2: from the correct solution.
Speaker 1: What makes this puzzle. Really tricky is that there's no
Speaker 1: obvious way to escape from a local minimum. I'm afraid
Speaker 1: that this is as far as vibe coding is going
Speaker 1: to get me. But maybe you can do better. Check
Speaker 1: out the puzzle at Jane street dot com slash to
Speaker 1: our cash all right, back to elon, what.
Speaker 3: Will Xai's business be like? Is it going to be
Speaker 3: consumer enterprise? What's the mix of those things that's going
Speaker 3: to be It's just going to be similar to other
Speaker 3: labs for this, your saying lab makes sense.
Speaker 2: Corporations goes dan slicing corporations. Those champions do not pay
Speaker 2: for themselves exactly. But yeah, what's the business model? What
Speaker 2: are the revenue streams In a few years time? I
Speaker 2: think things are going to change very rapidly. Like I'm
Speaker 2: staying in the obvious here, you know, I called Ai
Speaker 2: the supersonic tsunami a level literation. So really, what's going
Speaker 2: to happen is, especially when you have humanoid robots at scale,
Speaker 2: is that they will just provide, They'll make products and
Speaker 2: provide services far more efficiently than human corporations. So amplifying
Speaker 2: the productivity of human corporations is simply a short term thing.
Speaker 2: So you're expecting fully digital oral corporations rather than like
Speaker 2: SpaceX becomes part AI, and I.
Speaker 1: Think there will be digital corporations. But it's look, is
Speaker 1: this some of some this is going to sound kind
Speaker 1: of doomerish, Okay, but I'm just I'm just saying what
Speaker 1: I think will happen. It's not it's not meant to
Speaker 1: be doomrish or anything else, just just like this is
Speaker 1: what I think will happen. Is that is that pure
Speaker 1: AI corporations that are purely AI and robotics will vastly
Speaker 1: outperform any corporations that have people in the lip. So
Speaker 1: you can you can think of say like like like
Speaker 1: computer used to be a job that humans had, that
Speaker 1: you would go and get a job as computer where
Speaker 1: you would do calculations, and that have like entire skyscrapers
Speaker 1: full of humans, like you know, twenty thirty floors of
Speaker 1: humans just doing calculations. Now that entire skyscraper of humans
Speaker 1: doing calculations can be replaced by a laptop with a spreadsheet.
Speaker 1: That's sheet can do vastly more calculations than in a
Speaker 1: entire building full of human computers. So then you think
Speaker 1: about okay, well, what if only some of the cells
Speaker 1: in your if some of the cells in your spreadsheet
Speaker 1: were calculated by humans, Actually, that would be much worse
Speaker 1: than if all of the cells in your spreadsheet were
Speaker 1: calculated by the computer. And so really what will happen
Speaker 1: is the pure AI, pure robotics corporations or collectives will
Speaker 1: far outperform any corporations that have humans in the loop.
Speaker 1: And this will happen very quickly.
Speaker 2: Speaking of closing the loop, sorry optimus, you I mean
Speaker 2: as far as like manufacturing targets and so forth go,
Speaker 2: your companies have sort of been like carrying American manufacturing
Speaker 2: of heart attack on their back. But in the field
Speaker 2: that you are, you know, Tesla has been dominant in
Speaker 2: you're and now you want to go into humanoids. In China,
Speaker 2: there's entire dozens and dozens of companies that are doing
Speaker 2: this kind of manufacturing cheaply and at scale uh and
Speaker 2: are incredibly competitive. So give us sort of like advice
Speaker 2: or a plan of how America can build the humanoid
Speaker 2: armies or you know, the evs et cetera, at scale
Speaker 2: and as cheaply as as China is on tract to.
Speaker 1: Well there they are really only three hard things for
Speaker 1: human robots, the real world intelligence, the hand and scale manufacturing. Yeah,
Speaker 1: so I haven't seen any even demo robots that have
Speaker 1: a great hand, like with all the degrees of freaking
Speaker 1: of a human hand, But optimists will have that. Optimist
Speaker 1: does have that?
Speaker 2: And how do you achieve that? Isn't just like right
Speaker 2: torque definity the motor like what is the what is
Speaker 2: the hardware bottle?
Speaker 1: Like? To that, well, we have to where to design
Speaker 1: custom custom actuators basically custom design motors, gears, uh, caro electronics, controls, sensors.
Speaker 1: Everything had to be designed from physics first principles. There
Speaker 1: is no supply chain for this and will be able
Speaker 1: to manufacture those at scale? Yes, is anything hard to accept?
Speaker 3: The hand from a manipulation point of view, or once
Speaker 3: you've solved the hand, are you you good?
Speaker 1: From an electro mechanical standpoint, the hand is more difficult
Speaker 1: than everything else combined. The human hand turns out to
Speaker 1: be quite something. But but then you also need the
Speaker 1: real world intelligence. So the intelligence that tells us developed
Speaker 1: for the car applies verywell to the roblot, which is
Speaker 1: you know, primarily vision in but the car takes and
Speaker 1: more vision, but also it actually also is listening for sirens,
Speaker 1: it's you know, it's taking the inotural measurements, it's GPS signals,
Speaker 1: a whole much of other data, combining that with video,
Speaker 1: its primarily video, and then outputting the control commands. So
Speaker 1: like like YOURTELA is taking in one and a half
Speaker 1: kickobytes a second video and outpointing two kilobtes a second
Speaker 1: of control control outputs with the video thirty six hurts
Speaker 1: and the control for CONIAD eighteen. One intuition you could have.
Speaker 3: Four when we get this robotic stuff is that it
Speaker 3: takes quite a few years to go from the compelling
Speaker 3: demo to actually being able to use in the real world.
Speaker 3: So ten years ago you were really compelling demos of
Speaker 3: self driving, but only now we have ROBOTAXI and Weimo
Speaker 3: and all these services scaling up. Doesn't this Shouldn't this
Speaker 3: make one pessimistic on say, household robots, because we don't
Speaker 3: even quite have the compelling demos yet. I say they're
Speaker 3: really advanced.
Speaker 1: Hand well, we've been working on human to robots now
Speaker 1: for a while, so I guess spend five or six
Speaker 1: years or something like that, and and a bunch of
Speaker 1: things that we've done for the car are applicable to
Speaker 1: the robot. So we'll use the same testa AI chips
Speaker 1: in the in the robot as the car. We'll use
Speaker 1: it the same basic principles. It's very much the same AI.
Speaker 1: You've got, you know, many more degrees of freedom for
Speaker 1: a robot than you do for a car. But really,
Speaker 1: if you just think of for like as like a
Speaker 1: boot stream, AI is really mostly compression and correlation of
Speaker 1: two butt streams. You're you know, so if for video
Speaker 1: you've got to do a tremendous amount of compression and
Speaker 1: and and you've got to do the compression just right,
Speaker 1: you've better compress the like ignore the things that don't matter,
Speaker 1: and and like you don't care about the details of
Speaker 1: the leaves and the tree on the side of the road,
Speaker 1: but you care a lot about the the road signs
Speaker 1: and the traffic lights and the pedestrians and even where
Speaker 1: you know somebody in another cars is looking at you
Speaker 1: or not looking at you, like there's some of these
Speaker 1: some of these details matter a lot. So if it
Speaker 1: is essentially it's going to turn that with the car's
Speaker 1: going to turn that one and a half gigabytes a
Speaker 1: second ultimately into two kilobytes second of control outputs, so
Speaker 1: many stages of compression, and you got to get all
Speaker 1: those stages right and then correlate those to the correct
Speaker 1: control outputs. The robot has to do essentially the same thing.
Speaker 1: And you think about what what humans? This is what
Speaker 1: happens with humans. We really are photons in controls out,
Speaker 1: so that that is the vast majority of your your
Speaker 1: life has been vision photons in and then motor controls out.
Speaker 2: Naively, it seems like between humanoid robots and cars, the
Speaker 2: fundamental actuators in a car are like how you turn
Speaker 2: and how you accelerate, et cetera. And in a robot,
Speaker 2: especially with maneuver arms, there's dozens and dozens of these
Speaker 2: degrees of freedom. And then especially with Tesla, you had
Speaker 2: this advantage of like you had millions and millions of
Speaker 2: hours of human demo data collected from just the car
Speaker 2: being out there, where like you can't equivalently just deploy
Speaker 2: optimists that don't work and then get the data that way.
Speaker 1: So between the increased degrees of freedom and the far
Speaker 1: sparser data, yes, let's go, how will you use the
Speaker 1: sort of Tesla engine of intelligence on to train the
Speaker 1: optimist mind. Now you're you're actually you're highlighting an important
Speaker 1: limitation and difference between cars is like we we do
Speaker 1: have oh we'll goon have like ten million cars on
Speaker 1: the road, and so uh, that's it's hard to duplicate
Speaker 1: that like massive training flight flywheel. For the robot. What
Speaker 1: we're going to need to do is build a lot
Speaker 1: of robots and put them in kind of like an
Speaker 1: Optimist academy so they can do self play in reality.
Speaker 1: So we're actually we're actually pulling that out, so we
Speaker 1: can have at least ten thousand Optimus robots, maybe twenty
Speaker 1: or thirty thousand that can do that, that are doing
Speaker 1: self play and testing different tasks. And then the Tellesla
Speaker 1: has quite a good reality generator, like the physics accurate
Speaker 1: reality generator that we made this full of cars. We'll
Speaker 1: do the same thing for the robots. And I actually
Speaker 1: have done that for the robots. So you have a
Speaker 1: few tens of thousands of humanoid robots doing different tasks,
Speaker 1: and then you've got you can do millions of simulated
Speaker 1: robots in the simulated world, and you use the tens
Speaker 1: of thousands of robots in the real world to close
Speaker 1: the simulation to reality gap, close the some material gap.
Speaker 2: How do you think about the synergies between Xai and Optimists,
Speaker 2: given you're highlighting, look, you need this world model. You
Speaker 2: maybe want to use some really smart intelligence. Is the
Speaker 2: control player and so maybe GROC is like doing the
Speaker 2: slower planning and like the motor policy.
Speaker 1: Is a little lower level. Yeah, what will the sort
Speaker 1: of synergy between these things be? Yeah, so you just
Speaker 1: GROCK would orchestrate the behavior of the Optimus robots. So le,
Speaker 1: let's say you wanted to build a factory the then Optimists,
Speaker 1: and then GROC could UH organize the optimost robots, give
Speaker 1: them a sign them tasks UH to build the factory
Speaker 1: for to produce whatever you want. Don't you need to
Speaker 1: merge x Ai and Tessa then because these things end up?
Speaker 1: So what are we saying only about all the company discussions? Well,
Speaker 1: we're one, we're Gonnas in the elon.
Speaker 2: What are you waiting to see before you say we
Speaker 2: want to manufacture one hundred thousand Optimists?
Speaker 1: Is it like Optimi? So since we're defining the proper nound,
Speaker 1: we could define the pearl of the prop and noun too.
Speaker 1: So we we're going to prop a dound the plural
Speaker 1: and so it's optimize.
Speaker 2: Okay, is there something on the hardware side do you
Speaker 2: want to see? Do you want to see better actuators
Speaker 2: or is it just you want the software do better?
Speaker 2: Whatever you're waiting for before we get like mass manufacturing
Speaker 2: Gen three.
Speaker 1: Now we're moving towards that, we're going forward with semester manufactory.
Speaker 1: But you think current current hardware is good enough that
Speaker 1: you are going to you should you just want to
Speaker 1: deploy as many as possible now, I mean it's very
Speaker 1: hard to scale up production. But uh yeah, but I
Speaker 1: think Optimus three is the right version of the robot
Speaker 1: to you know, to produce maybe something on the order
Speaker 1: of like a million units a year. I mean you
Speaker 1: do want to go to Optimus four before you went
Speaker 1: to ten millions a year, okay, but you can do
Speaker 1: a million year at Optimist three. Uh yeah, I mean
Speaker 1: it's very hard to spillet manufacturing. Yes, so like manufacturing. Uh,
Speaker 1: like the the output period of time is always follows
Speaker 1: at s cove, so it's at agonizingly slow. Then has
Speaker 1: this sort of the eventually expecial increase then linear than
Speaker 1: a than a you know logarithmic outcome to you sort
Speaker 1: of eventually as it's a number. But optimists initial production
Speaker 1: will be it's going to be a it's going to
Speaker 1: be a stretched out s cove because so much of
Speaker 1: what goes into Optimus is brand new. There's not an
Speaker 1: existing supply chain. As I mentioned, the actually is like
Speaker 1: trying to everything in the office robot is designed for
Speaker 1: physics first principles, it's not. It's not taken from a catalog.
Speaker 1: These these are custom designed everything, literally everything. I don't
Speaker 1: think there's a single thing I done. How far down
Speaker 1: the does aco I mean, I guess we're not making
Speaker 1: custom capacitors yet maybe, but but there's there's there's nothing
Speaker 1: you can pick out of a catalog at any price.
Speaker 1: So so it just means that the the the optimistes cove,
Speaker 1: uh the units units per awkward perion of time, how
Speaker 1: many optics robusts you make per day. Whatever is is
Speaker 1: gonna initially wrap slower than a product where you have
Speaker 1: an existing supply chain, but it will get to a million.
Speaker 2: When you see these Chinese humanoids, like Industry or whatever
Speaker 2: sells humanoidsed for like six K or thirteen K, do
Speaker 2: you just like are you hoping to get your optimisms
Speaker 2: bill of materials below that price so you can do
Speaker 2: the same thing, or do you just think qualitatively they're
Speaker 2: not the same thing, Like what do you think is going?
Speaker 1: Like what allows it? What allows them to self for solo?
Speaker 1: And can me match that? Well, optimists, Our optous is
Speaker 1: signed to have a lot of intelligence and to have
Speaker 1: the same electro mechanical texterity, if not higher than a human.
Speaker 1: So untry does not have that. And it's also i mean,
Speaker 1: it's it's quite a it's quite a big robot. It's
Speaker 1: just it's this matter it has to do, you know,
Speaker 1: carry heavy objects for long periods of time and not
Speaker 1: overheat or exceed the power of its sactuator. So so
Speaker 1: we've got we've got, we've got you know, it's five eleven,
Speaker 1: you know, it's pretty tall, and it's it's got a
Speaker 1: lot of intelligence. So it's going to be more expensive
Speaker 1: than a small robot that is not intelligence but more capable. Yeah,
Speaker 1: a lot more. I mean, like we think is over time,
Speaker 1: as often Showbus often robots, the class will drop very quickly.
Speaker 3: And what will these first billion optimists of some eye, Yeah,
Speaker 3: do like bottle of their highest and best use.
Speaker 1: Be I think that you would start off with with
Speaker 1: simple tasks that you can count on them doing well.
Speaker 1: But in the home or in factories, like the best
Speaker 1: useful robots in the beginning won't be anything any continuous operations,
Speaker 1: only twenty four VI seven operation because then you're because
Speaker 1: then they can work continuously.
Speaker 2: Yeah, what fraction of the work in a giga factory
Speaker 2: that is currently done by humans going to gend three do?
Speaker 1: I'm I'm not sure. Maybe it's like ten bay more,
Speaker 1: I don't know it's it. We would, we would use
Speaker 1: We would not like reduce our headcount. We would we
Speaker 1: would for sure it can increase our headcount to be clear,
Speaker 1: but but we would increase our output. So the the
Speaker 1: units produced per human, like total to total number humans
Speaker 1: at teston will increase, but the the output of robots
Speaker 1: and cars will increase, will increase disproportionate like much much. Yeah,
Speaker 1: number of cars and robots produced per human will increase dramatically,
Speaker 1: but number of humans will increase as well.
Speaker 3: We're talking about Chinese manufacturing a bunch here, and we're
Speaker 3: also talking about you know, we've talked about some of
Speaker 3: the policies that are ele US.
Speaker 1: Like you mentioned the the solar tariffs.
Speaker 3: Yeah, and you think they're a bad idea because you know,
Speaker 3: we can scale up so over in the US.
Speaker 1: Well, just electricity output in the US needs to scale up, right,
Speaker 1: you can with thus like goodsources to get it somehow. Yeah.
Speaker 3: But where I was going with this is if you
Speaker 3: were in charge, if you were setting all the policies,
Speaker 3: what else would you change?
Speaker 1: So you change the solar tariffs, Yeah, I would say
Speaker 1: anything that is limiting factor for electricity needs to be addressed,
Speaker 1: provided it's not like very bad for the environment.
Speaker 3: So presumably some permissing reforms and stuff as well, we'll
Speaker 3: be in there.
Speaker 1: Yeah, there's a fair bit of permitting reforms that are happening.
Speaker 1: A lot of the permitting is state based, so but
Speaker 1: anything but but but this this administration is is good
Speaker 1: at removing permaning roadblocks. And I'm not saying all tariffs
Speaker 1: are bad. I'm just saying because so so Yeah, yeah,
Speaker 1: I mean sometimes if like if another country is subsidizing
Speaker 1: the output of something, then then you have to have
Speaker 1: countervailing tariffs to protect dovestic industry against some see is
Speaker 1: by another country. What else would you change? I don't
Speaker 1: know if there's not much that the government can actually
Speaker 1: do well.
Speaker 3: One thing I was wondering is it seems like the
Speaker 3: for the policy goal of creating a leaves for the
Speaker 3: US versus China, it seems like the export bands have
Speaker 3: actually been quice impactful or China is not producing leading
Speaker 3: edge chips and the export bands really by there China
Speaker 3: is not producing leading edge turbine engines. And similarly, there's
Speaker 3: a bunch of export bands that are relevant there on
Speaker 3: some of the metallurgy. Should there be more export bands,
Speaker 3: like as you think about things like when there are
Speaker 3: now the drone industry and things like that, But is
Speaker 3: that something they should be considered?
Speaker 1: Well, I think it's imporant to appreciate that in most
Speaker 1: areas China is very advanced to actually there's only a
Speaker 1: few areas where it is not. The China is a
Speaker 1: manufacturer of horhouse next level like people don't it must
Speaker 1: be it's very impressive. Yeah, yeah, I mean, if you
Speaker 1: if you take like refining of ore, i'd say roughly,
Speaker 1: China there's more just twice as much or refining of
Speaker 1: on average as the rest of all combined. And I
Speaker 1: think there's there's some areas, like say refining gallion which
Speaker 1: goes into solar cells. I think there are like ninety
Speaker 1: eight percent of gallum refining. So so China is actually
Speaker 1: very advancing manufacturing in i'd say most areas.
Speaker 3: It seems like we're like there is discomfort with this
Speaker 3: supply chain dependence and the ash. Nothing's really happening on it.
Speaker 3: Supply chain, supply chain. It depends on say like the
Speaker 3: Galleon refining that you're saying.
Speaker 1: Yeah, yeah, there's there's there's there's a there's all the
Speaker 1: rare rare earth stuff, and yeah, where else we're sure,
Speaker 1: as you know, not rare like we actually do rare
Speaker 1: earth or mining in the US. Send the the the rock,
Speaker 1: put it on a train, and then from the boats
Speaker 1: China does another train and that goes to the where
Speaker 1: with refining refiners in China who then refine it, put
Speaker 1: it into a magnet, put it in just a modus
Speaker 1: ofb assembly, and then set it back to America. So
Speaker 1: the thing we're really missing a lot of ore refining
Speaker 1: in America. And isn't this worth a policy intervention? Yes, well,
Speaker 1: I think there are some things being done on that front,
Speaker 1: but we kind of need optimists frankly to build our refineries.
Speaker 1: So sorry, you think the main advantage is China has
Speaker 1: is the abundance of skilled labor and that that that's like,
Speaker 1: that's the thing optimist fact says. But they also we
Speaker 1: need four times our populations.
Speaker 2: But we need so I mean, there's this concern if
Speaker 2: you think that humans are the future, that like, okay,
Speaker 2: right now, if it's the skilled labors for manufacturing that's
Speaker 2: determining who's who can build more humanoids. You know, China
Speaker 2: has more of those and manufactures more humanoids, therefore it
Speaker 2: gets it gets the optimized future first.
Speaker 1: Well, it just like keeps that eximential going.
Speaker 2: It seems like you're sort of pointing out that sort
Speaker 2: of getting to a million optimize, Yeah, requires the manufacturing
Speaker 2: that the optimized supposed to help us get to.
Speaker 1: Right, you can you can close that recoursal loop pretty
Speaker 1: quickly with a small number of optimize. Yeah, so you
Speaker 1: close the close recurse of loop to help help the
Speaker 1: robots buill the robots, and then we can, you know,
Speaker 1: try to get to tens of millions of units. You're
Speaker 1: maybe if you start getting too hundreds of millions of
Speaker 1: units a year, I think you're you're going to be
Speaker 1: the most competitive country by far. We definitely count win
Speaker 1: with just humans because China has four times of population, right,
Speaker 1: and frankly, America has been warning for so long that
Speaker 1: you know, just like a like a post sports team
Speaker 1: that's been warning for a very long time, tend to
Speaker 1: get complacent and entitled, and that's why they stop winning
Speaker 1: because it's, you know, don't work as hard anymore. So
Speaker 1: I think the frankly, it just is the average work
Speaker 1: ethic in China is higher than in the US. So
Speaker 1: it's not just that there's four times the population, but
Speaker 1: the work, the amount of work that people put in
Speaker 1: is higher. So you can, like, you can try to
Speaker 1: rearrange the humans, but you're still one quarter of the
Speaker 1: you know, it's assuming that that productivities, that health is
Speaker 1: the same, which I think actually it might not be.
Speaker 1: They China might have advantage on productivity per person. We
Speaker 1: will do one quarter of the amount of things as China,
Speaker 1: so we can't win on the human front. And our
Speaker 1: worth rates spent low for a long time, so our
Speaker 1: birth rates spent the US birth rates spent low replacement
Speaker 1: since roughly nineteen seventy one, so we've got a lot
Speaker 1: of people retiring or you know, more people dying than
Speaker 1: we're close to sort of more people domestically dying than
Speaker 1: being born. So we definitely can't win on the human front,
Speaker 1: but we might have a shot at the row wrote front.
Speaker 1: Are there other things that you have wanted to manufacture
Speaker 1: in the past but they've been too labor intensive or
Speaker 1: too expensive that now you can come back to that
Speaker 1: and say, oh, we can finally do the whatever because
Speaker 1: we have optimists. Yeah, I think we'd like to do
Speaker 1: more both more or refineries at Tesla. So we just
Speaker 1: completed construction and have begun liftium refining without litium refinery
Speaker 1: and Corpus Christi, Texas. We have a nickel refinery which
Speaker 1: is called the Cathode. Uh that's here in Austin. And
Speaker 1: these are these are the largest is the largest cathode.
Speaker 1: There's the largest cathod refinery, largest liftingum refinery, the largest
Speaker 1: nekelink and lifting refinery outside of China. And it's like
Speaker 1: they know, the Cathote team would say, like, we have
Speaker 1: the largest and the only actually cather refinery in America.
Speaker 1: Many superatives not just the just but it's also the
Speaker 1: moly so it was pretty big, even though it's the
Speaker 1: only one. But I mean there are other things that
Speaker 1: you know, you could do a lot more refineries and
Speaker 1: and help the the help America being more competitive on
Speaker 1: refining capacity. So so that there's like there's basically a
Speaker 1: lot of work for the Optimity to do that that
Speaker 1: most Americans, very few Americans, frankly wanted to. I mean,
Speaker 1: i've I've Actually it's the refining work too dirty. It's
Speaker 1: it's not it's actually, no, we don't. There's not we
Speaker 1: don't have toxic commissions from the refinery or anything. The
Speaker 1: Catholic make refinery is right right sort of in Travis County.
Speaker 1: Like why can't I mantes from why can't you with humans? No?
Speaker 1: You can you find out of humans? I see, Okay, yeah,
Speaker 1: Like no matter what you do, you have one quarter
Speaker 1: number of humans in America in China. So if you
Speaker 1: help them do this thing, they can do the other thing.
Speaker 1: So so then then well, how do you how do
Speaker 1: you build this refining refining capacity? Well you can do
Speaker 1: it with the Optimaya and not many, not very many,
Speaker 1: not very many Americans. Thanks for listening. See you in
Speaker 1: the next episode.