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Elon Musk – "In 36 months, the cheapest place to put AI will be space” — Transcript

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0:00Are there really three hours of questions? Are you fucking serious?
0:07You don't think there's a lot to talk about, Elon? Holy fuck man.
0:11It's the most interesting point. All the storylines are converging right now.
0:16We'll see how much we can get through. It's almost like I planned it.
0:19Exactly. We'll get to that.
0:20But I would never do such a thing… As you know better than anybody else,
0:25only 10-15% of the total cost of ownership of a data center is energy.
0:30That's the part you're presumably saving by moving this into space. Most of it's
0:33the GPUs. If they're in space, it's harder to service them or you can't service them.
0:37So the depreciation cycle goes down on them. It's just way more expensive to have
0:40the GPUs in space, presumably. What's the reason to put them in space?
0:46The availability of energy is the issue. If you look at electrical output
0:55outside of China, everywhere outside of China, it's more or less flat.
0:58It’s maybe a slight increase, but pretty close flat.
1:02China has a rapid increase in electrical output. But if you're putting data centers anywhere
1:07except China, where are you going to get your electricity? Especially as you scale. The output
1:12of chips is growing pretty much exponentially, but the output of electricity is flat.
1:17So how are you going to turn the chips on? Magical power sources? Magical electricity fairies?
1:25You're famously a big fan of solar. One terawatt of solar power,
1:29with a 25% capacity factor, that’s like four terawatts of solar panels.
1:32It's 1% of the land area of the United States. We’re in the singularity when we’ve got
1:37one terawatt of data centers, right? So what are you running out of exactly?
1:42How far into the singularity are you though? You tell me.
1:45Exactly. So I think we'll find we're in the singularity and it’ll be like,
1:48"Okay, we’ve still got a long way to go." But is the plan to put it in space after
1:54we've covered Nevada in solar panels? I think it's pretty hard to cover Nevada
1:58in solar panels. You have to get permits. Try getting the permits for that. See what happens.
2:02So space is really a regulatory play. It's harder to build on land than it is in space.
2:08It's harder to scale on the ground than it is to scale in space.
2:17You're also going to get about five times the effectiveness of solar panels in space versus
2:22the ground, and you don't need batteries. I almost wore my other shirt, which says,
2:27"it's always sunny in space". Which it is because you don't
2:35have a day-night cycle, seasonality, clouds, or an atmosphere in space.
2:44The atmosphere alone results in about a 30% loss of energy.
2:50So any given solar panel can do about five times more power in space than on the ground.
2:58You also avoid the cost of having batteries to carry you through the night.
3:03It's actually much cheaper to do in space. My prediction is that it will be by far
3:11the cheapest place to put AI. It will be space in 36 months or
3:16less. Maybe 30 months. 36 months?
3:17Less than 36 months. How do you service GPUs as they fail,
3:20which happens quite often in training? Actually, it depends on how recent the
3:27GPUs are that have arrived. At this point, we find our
3:30GPUs to be quite reliable. There's infant mortality,
3:34which you can obviously iron out on the ground. So you can just run them on the ground
3:38and confirm that you don't have infant mortality with the GPUs.
3:41But once they start working and you're past the initial debug cycle of Nvidia
3:49or whoever's making the chips—could be Tesla AI6 chips or something like that, or it could
3:56be TPUs or Trainiums or whatever—they’re quite reliable past a certain point.
4:06So I don't think the servicing thing is an issue. But you can mark my words.
4:15In 36 months, but probably closer to 30 months, the most economically compelling place to put
4:23AI will be space. It will then get
4:29ridiculously better to be in space. The only place you can really scale is space.
4:37Once you start thinking in terms of what percentage of the Sun's power you are harnessing,
4:42you realize you have to go to space. You can't scale very much on Earth.
4:47But by very much, to be clear, you're talking terawatts?
4:51Yeah. All of the United States currently uses only half a terawatt on average.
4:59So if you say a terawatt, that would be twice as much electricity as the United
5:03States currently consumes. So that's quite a lot. Can you imagine building that many
5:08data centers, that many power plants? Those who have lived in software land
5:15don't realize they're about to have a hard lesson in hardware.
5:24It's actually very difficult to build power plants.
5:27You don't just need power plants, you need all of the electrical equipment.
5:30You need the electrical transformers to run the AI transformers.
5:36Now, the utility industry is a very slow industry. They pretty much impedance match to the
5:44government, to the Public Utility Commissions. They impedance match literally and figuratively.
5:52They're very slow, because their past has been very slow.
5:56So trying to get them to move fast is... Have you ever tried to do an interconnect
6:03agreement with a utility at scale, with a lot of power?
6:06As a professional podcaster, I can say that I have not, in fact.
6:10They need many more views before that becomes an issue.
6:13They have to do a study for a year. A year later, they'll come back to you
6:18with their interconnect study. Can't you solve this with your
6:21own behind the meter power stuff? You can build power plants. That's
6:26what we did at xAI, for Colossus 2. So why talk about the grid?
6:31Why not just build GPUs and power co-located? That's what we did.
6:35But I'm saying why isn't this a generalized solution?
6:37Where do you get the power plants from? When you're talking about all the issues
6:40working with utilities, you can just build private power plants with the data centers.
6:44Right. But it begs the question of where do you get the power plants from? The power plant makers.
6:51Oh, I see what you're saying. Is this the gas turbine backlog basically?
6:54Yes. You can drill down to a level further. It's the vanes and blades in the turbines
7:02that are the limiting factor because it’s a very specialized process to cast the blades and vanes
7:09in the turbines, assuming you’re using gas power. It's very difficult to scale other forms of power.
7:17You can potentially scale solar, but the tariffs currently for importing
7:22solar in the US are gigantic and the domestic solar production is pitiful.
7:27Why not make solar? That seems like a good Elon-shaped problem.
7:30We are going to make solar. Okay.
7:34Both SpaceX and Tesla are building towards 100 gigawatts a year of solar cell production.
7:40How low down the stack? From polysilicon up to the wafer to the final panel?
7:46I think you've got to do the whole thing from raw materials to finish the cell.
7:50Now, if it's going to space, it costs less and it's easier to make solar cells that
7:56go to space because they don't need much glass. They don't need heavy framing because they don't
8:01have to survive weather events. There's no weather in space. So it's actually a cheaper solar cell
8:07that goes to space than the one on the ground. Is there a path to getting them as cheap
8:12as you need in the next 36 months? Solar cells are already very cheap.
8:19They're farcically cheap. I think solar cells in China are around $0.25-30/watt or something
8:29like that. It's absurdly cheap. Now put it in space, and it's five times cheaper.
8:37In fact, it's not five times cheaper, it's 10 times cheaper
8:40because you don't need any batteries. So the moment your cost of access to space becomes
8:48low, by far the cheapest and most scalable way to generate tokens is space. It's not even close.
8:58It'll be an order of magnitude easier to scale. The point is you won't be able to scale on the
9:06ground. You just won't. People are going to hit the wall big time on power generation.
9:11They already are. The number of miracles in series that the xAI team had to accomplish in
9:19order to get a gigawatt of power online was crazy. We had to gang together a whole bunch of turbines.
9:28We then had permit issues in Tennessee and had to go across the border to Mississippi,
9:34which is fortunately only a few miles away. But we still then had to run the high
9:39power lines a few miles and build the power plant in Mississippi.
9:44It was very difficult to build that. People don't understand how much electricity
9:50you actually need at the generation level in order to power a data center.
9:54Because the noobs will look at the power consumption of,
10:00say a GB300, and multiply that by a thing and then think that's the amount of power you need.
10:04All the cooling and everything. Wake up. That's a total noob, you’ve
10:11never done any hardware in your life before. Besides the GB300, you've got to power
10:16all of the networking hardware. There's a whole bunch of CPU and
10:19storage stuff that's happening. You've got to size for
10:24your peak cooling requirements. That means, can you cool even on the
10:30worst hour of the worst day of the year? It gets pretty frigging hot in Memphis.
10:34So you're going to have a 40% increase on your power just for cooling.
10:40That’s assuming you don't want your data center to turn off on hot days and you want to keep going.
10:49There's another multiplicative element on top of that which is, are you assuming that you never
10:54have any hiccups in your power generation? Actually, sometimes we have to take the
10:59generators, some of the power, offline in order to service it.
11:02Okay, now you add another 20-25% multiplier on that, because you've got to assume that you've
11:08got to take power offline to service it. So our actual estimate: every 110,000
11:18GB300s—inclusive of networking, CPU, storage, cooling, margin for servicing
11:27power—is roughly 300 megawatts. Sorry, say that again.
11:40What you probably need at the generation level to service 330,000 GB 300s—including all of
11:48the associated support networking and everything else, and the peak cooling, and to have some power
11:55margin reserve—is roughly a gigawatt. Can I ask a very naive question?
12:03You're describing the engineering details of doing this stuff on Earth.
12:07But then there's analogous engineering difficulties of doing it in space.
12:10How do you replace infinite bandwidth with orbital lasers, et cetera, et cetera?
12:16How do you make it resistant to radiation? I don't know the details of the engineering,
12:20but fundamentally, what is the reason to think those challenges which have never had to be
12:26addressed before will end up being easier than just building more turbines on Earth?
12:30There are companies that build turbines on Earth. They can make more turbines, right?
12:35Again, try doing it and then you'll see. The turbines are sold out through 2030.
12:44Have you guys considered making your own? In order to bring enough power online, I think
12:53SpaceX and Tesla will probably have to make the turbine blades, the vanes and blades, internally.
13:02But just the blades or the turbines? The limiting factor... you can get
13:07everything except the blades. They call them blades and vanes.
13:13You can get that 12 to 18 months before the vanes and blades.
13:17The limiting factor is the vanes and blades. There are only three casting companies in
13:24the world that make these, and they're massively backlogged.
13:27Is this Siemens, GE, those guys, or is it a sub company?
13:30No, it's other companies. Sometimes they have a little bit of casting capability in-house.
13:35But I'm just saying you can just call any of the turbine makers and they will
13:40tell you. It's not top secret. It’s probably on the internet right now.
13:44If it wasn't for the tariffs, would Colossus be solar-powered?
13:48It would be much easier to make it solar powered, yeah.
13:51The tariffs are nuts, several hundred percent. Don't you know some people?
13:57The president has... we don't agree on everything and this administration is not
14:07the biggest fan of solar. We also need the land,
14:16the permits, and everything. So if you try to move very fast,
14:21I do think scaling solar on Earth is a good way to go, but you do need some amount of
14:28time to find the land, get the permits, get the solar, pair that with the batteries.
14:33Why would it not work to stand up your own solar production?
14:37You're right that you eventually run out of land, but there's a lot of land here in Texas.
14:41There's a lot of land in Nevada, including private land. It's not all publicly-owned
14:44land. So you'd be able to at least get the next Colossus and the next one after that.
14:49At a certain point, you hit a wall. But wouldn't that work for the moment?
14:52As I said, we are scaling solar production. There's a rate at which you can scale physical
15:00production of solar cells. We're going as fast as
15:04possible in scaling domestic production. You're making the solar cells at Tesla?
15:09Both Tesla and SpaceX have a mandate to get to 100 gigawatts a year of solar.
15:14Speaking of the annual capacity, I'm curious, in five years time let's say, what will the
15:20installed capacity be on Earth…? Five years is a long time.
15:24And in space? I deliberately pick five years because it's after your "once
15:28we're up and running" threshold. So in five years time what's the
15:31on-Earth versus in-space installed AI capacity? If you say five years from now, I think probably
15:43AI in space will be launching every year the sum total of all AI on Earth.
15:53Meaning, five years from now, my prediction is we will launch and be operating every year more AI in
16:02space than the cumulative total on Earth. Which is...
16:07I would expect it to be at least, five years from now, a few hundred gigawatts per year
16:14of AI in space and rising. I think you can get to around a
16:24terawatt a year of AI in space before you start having fuel supply challenges for the rocket.
16:33Okay, but you think you can get hundreds of gigawatts per year in five years time?
16:37Yes. So 100 gigawatts, depending
16:39on the specific power of the whole system with solar arrays and radiators and everything, is
16:48on the order of 10,000 Starship launches. Yes.
16:52You want to do that in one year. So that's like one Starship launch
16:56every hour. That's happening in this city? Walk me through a world where there's a
17:03Starship launch every single hour. I mean, that's actually a lower rate
17:07compared to airlines, aircraft. There's a lot of airports.
17:11A lot of airports. And you’ve got to launch into the polar orbit.
17:14No, it doesn't have to be polar. There's some value to sun-synchronous, but
17:24I think actually, if you just go high enough, you start getting out of Earth's shadow.
17:31How many physical Starships are needed to do 10,000 launches a year?
17:35I don't think we'll need more than... You could probably do it with as few as 20 or 30.
17:46It really depends on how quickly… The ship has to go around the Earth and the ground track for
17:53the ship has to come back over the launch pad. So if you can use a ship every, say 30 hours,
17:59you could do it with 30 ships. But we'll make more ships than that.
18:06SpaceX is gearing up to do 10,000 launches a year, and maybe even 20 or 30,000 launches a year.
18:14Is the idea to become basically a hyperscaler, become an Oracle,
18:18and lend this capacity to other people? Presumably, SpaceX is the one launching all this.
18:25So, SpaceX is going to become a hyperscaler? Hyper-hyper. If some of my predictions come true,
18:33SpaceX will launch more AI than the cumulative amount on Earth of everything else combined.
18:39Is this mostly inference or? Most AI will be inference. Already, inference
18:43for the purpose of training is most training. There's a narrative that the change in
18:50discussion around a SpaceX IPO is because previously SpaceX was very capital efficient.
18:57It wasn't that expensive to develop. Even though it sounds expensive, it's
19:01actually very capital efficient in how it runs. Whereas now you're going to need more capital than
19:08just can be raised in the private markets. The private markets can accommodate raises
19:11of—as we've seen from the AI labs—tens of billions of dollars, but not beyond that.
19:16Is it that you'll just need more than tens of billions of dollars per year?
19:20That's why you'd take it public? I have to be careful about saying
19:25things about companies that might go public. That’s never been a problem for you, Elon.
19:33There's a price to pay for these things. Make some general statements for us about
19:37the depth of the capital markets between public and private markets.
19:42There's a lot more capital available... Very general.
19:46There's obviously a lot more capital available in the public markets than private.
19:50It might be 100x more capital, but it's way more than 10x.
19:57Isn't it also the case that with things that tend to be very capital intensive—if you look at, say,
20:03real estate as a huge industry, that raises a lot of money each year at an industry
20:09level—they tend to be debt financed because by the time you're deploying that much money,
20:15you actually have a pretty— You have a clear revenue stream.
20:18Exactly, and a near-term return. You see this even with the data center build-outs,
20:22which are famously being financed by the private credit industry. Why not just debt finance?
20:32Speed is important. I'm generally going to do the thing that...
20:42I just repeatedly tackle the limiting factor. Whatever the limiting factor is on speed,
20:45I'm going to tackle that. If capital is the limiting factor,
20:52then I'll solve for capital. If it's not the limiting factor,
20:55I'll solve for something else. Based on your statements about Tesla
21:00and being public, I wouldn't have guessed that you thought the way to move fast is to be public.
21:08Normally, I would say that's true. Like I said, I'd like to talk
21:12about it in some more detail, but the problem is if you talk about public companies before
21:16they become public, you get into trouble, and then you have to delay your offering.
21:20And as you said, you’re solving for speed. Yes, exactly. You can't hype companies
21:30that might go public. So that's why we have to be a little careful here.
21:35But we can talk about physics. The way you think about scaling
21:42long-term is that Earth only receives about half a billionth of the Sun's energy.
21:50The Sun is essentially all the energy. This is a very important point to appreciate
21:54because sometimes people will talk about modular nuclear reactors or various fusion on Earth.
22:02But you have to step back a second and say, if you're going to climb the Kardashev scale
22:10and harness some nontrivial percentage of the sun's energy… Let's say you wanted to
22:16harness a millionth of the sun's energy, which sounds pretty small.
22:22That would be about, call it roughly, 100,000x more electricity than we currently generate
22:29on Earth for all of civilization. Give or take an order of magnitude.
22:37Obviously, the only way to scale is to go to space with solar.
22:42Launching from Earth, you can get to about a terawatt per year.
22:46Beyond that, you want to launch from the moon. You want to have a mass driver on the moon.
22:52With that mass driver on the moon, you could do probably a petawatt per year.
22:59We're talking these kinds of numbers, terawatts of compute.
23:02Presumably, whether you're talking about land or space, far, far before this point, you run into...
23:12Maybe the solar panels are more efficient, but you still need the chips.
23:16You still need the logic and the memory and so forth.
23:18You're going to need to build a lot more chips and make them much cheaper.
23:22Right now the world has maybe 20-25 gigawatts of compute.
23:29How are we getting a terawatt of logic by 2030? I guess we're going to need some very
23:33big chip fabs. Tell me about it.
23:37I've mentioned publicly the idea of doing a sort of a TeraFab, Tera being the new Giga.
23:45I feel like the naming scheme of Tesla, which has been very catchy,
23:49is you looking at the metric scale. At what level of the stack are you?
23:56Are you building the clean room and then partnering with an existing
24:01fab to get the process technology and buying the tools from them? What is the plan there?
24:05Well, you can't partner with existing fabs because they can't output enough.
24:10The chip volume is too low. But for the process technology?
24:14Partner for the IP. The fabs today all basically use
24:20machines from like five companies. So you've got ASML, Tokyo Electron,
24:28KLA-Tencor, et cetera. So at first, I think you'd
24:37have to get equipment from them and then modify it or work with them to increase the volume.
24:45But I think you'd have to build perhaps in a different way.
24:47The logical thing to do is to use conventional equipment in an unconventional way to get
24:54to scale, and then start modifying the equipment to increase the rate.
25:01Boring Company-style. Yeah. You sort of buy an existing boring machine
25:08and then figure out how to dig tunnels in the first place and then design a much better machine
25:16that's some orders of magnitude faster. Here's a very simple lens. We can
25:22categorize technologies and how hard they are. One categorization could be to look at things
25:27that China has not succeeded in doing. If you look at Chinese
25:31manufacturing, they’re still behind on leading-edge chips and still behind on
25:39leading-edge turbine engines and things like that. So does the fact that China has not successfully
25:46replicated TSMC give you any pause about the difficulty?
25:49Or do you think that's not true for some reason? It's not that they have not replicated TSMC,
25:55they have not replicated ASML. That's the limiting factor.
25:59So you think it's just the sanctions, essentially? Yeah, China would be outputting vast numbers
26:05of chips if they could buy 2-3 nanometers. But couldn't they up to relatively recently
26:10buy them? No.
26:12Okay. The ASML ban has been in place for a while.
26:15But I think China's going to be making pretty compelling chips in three or four years.
26:19Would you consider making the ASML machines? "I don't know yet" is the right answer.
26:33To reach a large volume in, say, 36 months, to match the rocket payload to orbit… If we're doing
26:41a million tons to orbit in, let's say three or four years from now, something like that…
26:53We're doing 100 kilowatts per ton. So that means we need
26:58at least 100 gigawatts per year of solar. We'll need an equivalent amount of chips.
27:08You need 100 gigawatts worth of chips. You've got to match these things: the mass
27:12to orbit, the power generation, and the chips. I'd say my biggest concern actually is memory.
27:25The path to creating logic chips is more obvious than the path to having sufficient
27:32memory to support logic chips. That's why you see DDR prices
27:36going ballistic and these memes. You're marooned on a desert island.
27:41You write "Help me" on the sand. Nobody comes. You write "DDR RAM." Ships come swarming in.
27:49I'd love to hear your manufacturing philosophy around fabs.
27:57I know nothing about the topic. I don't know how to build a fab yet. I'll
27:59figure it out. Obviously, I've never built a fab. It sounds like you think the process knowledge of
28:06these 10,000 PhDs in Taiwan who know exactly what gas goes in the plasma
28:11chamber and what settings to put on the tool, you can just delete those steps.
28:16Fundamentally, it's about getting the clean room, getting the tools, and figuring it out.
28:20I don't think it's PhDs. It's mostly people who are not PhDs.
28:28Most engineering is done by people who don't have PhDs. Do you guys have PhDs?
28:31No. Okay.
28:34We also haven't successfully built any fabs, so you shouldn't be coming to us for fab advice.
28:39I don't think you need PhDs for that stuff. But you do need competent personnel.
28:47Right now, Tesla is pedal to the metal, max production of going as fast
28:55as possible to get Tesla AI5 chip design into production and then reaching scale.
29:02That'll probably happen around the second quarter-ish of next year, hopefully.
29:13AI6 would hopefully follow less than a year later. We've secured all the chip fab production
29:24that we can. Yes. But you're
29:26currently limited on TSMC fab capacity. Yeah. We'll be using TSMC Taiwan, Samsung Korea,
29:35TSMC Arizona, Samsung Texas. And we still— You've booked out all the capacity.
29:42Yes. I ask TSMC or Samsung, "okay, what's the timeframe to get to volume production?"
29:49The point is, you've got to build the fab and you've got to start production,
29:55then you've got to climb the yield curve and reach volume production at high yield.
29:59That, from start to finish, is a five-year period. So the limiting factor is chips.
30:05The limiting factor once you can get to space is chips, but the limiting
30:10factor before you can get to space is power. Why don't you do the Jensen thing and just prepay
30:14TSMC to build more fabs for you? I've already told them that.
30:19But they won't take your money? What's going on? They're building fabs as fast as they can.
30:30So is Samsung. They're pedal to the metal. They're going balls to the wall,
30:38as fast as they can. It’s still not fast enough. Like I said, I think towards the end of this year,
30:49chip production will probably outpace the ability to turn chips on.
30:53But once you can get to space and unlock the power constraint, you can now do hundreds of
31:01gigawatts per year of power in space. Again, bearing in mind that average
31:06power usage in the US is 500 gigawatts. So if you're launching, say 200 gigawatts,
31:12a year to space, you're sort of lapping the US every two and a half years.
31:17All US electricity production, this is a very huge amount.
31:24Between now and then, the constraint for server-side compute,
31:32concentrated compute, will be electricity. My guess is that people start getting
31:39to the point where they can't turn the chips on for large clusters towards the end of this year.
31:46The chips are going to be piling up and won't be able to be turned on.
31:51Now for edge compute it’s a different story. For Tesla, the AI5 chip is going
31:58into our Optimus robot. If you have AI edge compute,
32:07that's distributed power. Now the power is distributed
32:09over a large area. It's not concentrated. If you can charge at night, you can actually
32:17use the grid much more effectively. Because the actual peak power production
32:22in the US is over 1,000 gigawatts. But the average power usage,
32:26because the day-night cycle, is 500. So if you can charge at night,
32:30there's an incremental 500 gigawatts that you can generate at night.
32:38So that's why Tesla, for edge compute, is not constrained.
32:43We can make a lot of chips to make a very large number of robots and cars.
32:50But if you try to concentrate that compute, you're going to have a lot of trouble turning it on.
32:54What I find remarkable about the SpaceX business is the end goal is to get to Mars,
32:59but you keep finding ways on the way there to keep generating incremental revenue to get to
33:07the next stage and the next stage. So for Falcon 9, it's Starlink.
33:11Now for Starship, it is potentially going to be orbital data centers.
33:16Like, you find these infinitely elastic, marginal use cases of your next rocket,
33:23and your next rocket, and next scale up. You can see how this might seem like a
33:28simulation to me. Or am I someone's
33:32avatar in a video game or something? Because what are the odds that all these
33:36crazy things should be happening? I mean, rockets and chips and
33:44robots and space solar power, not to mention the mass driver on the moon.
33:50I really want to see that. Can you imagine some mass
33:53driver that's just going like shoom shoom? It's sending solar-powered AI satellites
34:00into space one after another at two and a half kilometers per second,
34:09just shooting them into deep space. That would be a sight to
34:12see. I mean, I'd watch that. Just like a live stream of it on a webcam?
34:19Yeah, yeah, just one after another, just shooting AI satellites into deep space,
34:26a billion or 10 billion tons a year. I'm sorry, you manufacture the satellites
34:29on the moon? Yeah.
34:30I see. So you send the raw materials to the moon and then manufacture them there.
34:33Well, the lunar soil is 20% silicon or something like that.
34:39So you can mine the silicon on the moon, refine it, and create the solar
34:47cells and the radiators on the moon. You make the radiators out of aluminum.
34:53So there's plenty of silicon and aluminum on the moon to make the cells and the radiators.
35:00The chips you could send from Earth because they're pretty light.
35:03Maybe at some point you make them on the moon, too.
35:09Like I said, it does seem like a sort of a video game situation where it's difficult
35:14but not impossible to get to the next level. I don't see any way that you could do 500-1,000
35:26terawatts per year launched from Earth. I agree.
35:33But you could do that from the Moon. Can I zoom out and ask about the SpaceX mission?
36:50I think you've said that we've got to get to Mars so we can make sure that if
36:52something happens to Earth, civilization, consciousness, and all that survives.
36:57Yes. By the time you're sending stuff to Mars,
36:59Grok is on that ship with you, right? So if Grok's gone Terminator… The
37:04main risk you're worried about is AI, why doesn't that follow you to Mars?
37:08I'm not sure AI is the main risk I'm worried about. The important thing is consciousness.
37:16I think arguably most consciousness, or most intelligence—certainly consciousness is more
37:21of a debatable thing… The vast majority of intelligence in the future will be AI.
37:31AI will exceed… How many petawatts of
37:39intelligence will be silicon versus biological? Basically humans will be a very tiny percentage
37:47of all intelligence in the future if current trends continue.
37:52As long as I think there's intelligence—ideally also which includes human intelligence and
38:00consciousness propagated into the future—that's a good thing.
38:02So you want to take the set of actions that maximize the probable
38:06light cone of consciousness and intelligence. Just to be clear, the mission of SpaceX is that
38:14even if something happens to the humans, the AIs will be on Mars, and the AI intelligence
38:20will continue the light of our journey. Yeah. To be fair, I'm very pro-human.
38:27I want to make sure we take certain actions that ensure that humans are along for the
38:31ride. We're at least there. But I'm just saying the total amount of intelligence…
38:39I think maybe in five or six years, AI will exceed the sum of all human intelligence.
38:47If that continues, at some point human intelligence
38:50will be less than 1% of all intelligence. What should our goal be for such a civilization?
38:54Is the idea that a small minority of humans still have control of the AIs?
38:59Is the idea of some sort of just trade but no control?
39:02How should we think about the relationship between the vast
39:04stocks of AI population versus human population? In the long run, I think it's difficult to imagine
39:11that if humans have, say 1%, of the combined intelligence of artificial intelligence,
39:19that humans will be in charge of AI. I think what we can do is make sure
39:26that AI has values that cause intelligence to be propagated into the universe.
39:39xAI's mission is to understand the universe. Now that's actually very important. What things
39:47are necessary to understand the universe? You have to be curious and you have to exist.
39:53You can't understand the universe if you don't exist.
39:56So you actually want to increase the amount of intelligence in the universe, increase
40:00the probable lifespan of intelligence, the scope and scale of intelligence.
40:05I think as a corollary, you have humanity also continuing to expand because if you're curious
40:15about trying to understand the universe, one thing you try to understand is where will humanity go?
40:20I think understanding the universe means you would care about propagating humanity into the future.
40:29That's why I think our mission statement is profoundly important.
40:35To the degree that Grok adheres to that mission statement, I think the future will be very good.
40:41I want to ask about how to make Grok adhere to that mission statement.
40:44But first I want to understand the mission statement. So there's
40:48understanding the universe. They're spreading intelligence. And they're spreading humans.
40:55All three seem like distinct vectors. I'll tell you why I think that understanding
41:01the universe encompasses all of those things. You can't have understanding without intelligence
41:09and, I think, without consciousness. So in order to understand the universe,
41:15you have to expand the scale and probably the scope of intelligence, because there are different
41:22types of intelligence. I guess from a human-centric perspective,
41:26put humans in comparison to chimpanzees. Humans are trying to understand the universe.
41:30They're not expanding chimpanzee footprint or something, right?
41:34We're also not... we actually have made protected zones for chimpanzees.
41:39Even though humans could exterminate all chimpanzees, we've chosen not to do so.
41:43Do you think that's the best-case scenario for humans in the post-AGI world?
41:53I think AI with the right values… I think Grok would care about expanding human civilization.
42:00I'm going to certainly emphasize that: "Hey, Grok, that's your daddy.
42:04Don't forget to expand human consciousness." Probably the Iain Banks Culture books are the
42:17closest thing to what the future will be like in a non-dystopian outcome.
42:27Understanding the universe means you have to be truth-seeking as well.
42:30Truth has to be absolutely fundamental because you can't understand the universe
42:33if you're delusional. You'll simply think you
42:37understand the universe, but you will not. So being rigorously truth-seeking is absolutely
42:42fundamental to understanding the universe. You're not going to discover new physics or
42:46invent technologies that work unless you're rigorously truth-seeking.
42:50How do you make sure that Grok is rigorously truth-seeking as it gets smarter?
43:00I think you need to make sure that Grok says things that are correct, not politically correct.
43:07I think it's the elements of cogency. You want to make sure that the axioms are as close
43:12to true as possible. You don't have contradictory axioms. The conclusions necessarily follow from
43:20those axioms with the right probability. It's critical thinking 101. I think at least trying to
43:28do that is better than not trying to do that. The proof will be in the pudding.
43:33Like I said, for any AI to discover new physics or invent technologies that actually work in
43:37reality, there's no bullshitting physics. You can break a lot of laws, but… Physics
43:47is law, everything else is a recommendation. In order to make a technology that works, you have
43:53to be extremely truth-seeking, because otherwise you'll test that technology against reality.
43:59If you make, for example, an error in your rocket design, the rocket will blow up,
44:05or the car won't work. But there are a lot of communist,
44:11Soviet physicists or scientists who discovered new physics.
44:15There are German Nazi physicists who discovered new science.
44:20It seems possible to be really good at discovering new science and be really
44:23truth-seeking in that one particular way. And still we'd be like, "I don't want
44:28the communist scientists to become more and more powerful over time."
44:34We could imagine a future version of Grok that's really good at physics
44:37and being really truth-seeking there. That doesn't seem like a universally
44:41alignment-inducing behavior. I think actually most physicists,
44:48even in the Soviet Union or in Germany, would've had to be very truth-seeking in
44:53order to make those things work. If you're stuck in some system,
44:59it doesn't mean you believe in that system. Von Braun, who was one of the greatest rocket
45:04engineers ever, was put on death row in Nazi Germany for saying that he didn't want to make
45:12weapons and he only wanted to go to the moon. He got pulled off death row at the last minute
45:16when they said, "Hey, you're about to execute your best rocket engineer."
45:20But then he helped them, right? Or like, Heisenberg was actually
45:24an enthusiastic Nazi. If you're stuck in some system that you can't
45:29escape, then you'll do physics within that system. You'll develop technologies within that system
45:38if you can't escape it. The thing I'm trying to understand is,
45:42what is it making it the case that you're going to make Grok good at being truth-seeking at physics
45:48or math or science? Everything.
45:50And why is it gonna then care about human consciousness?
45:53These things are only probabilities, they're not certainties.
45:56So I'm not saying that for sure Grok will do everything, but at least if you try,
46:02it's better than not trying. At least if that's fundamental
46:04to the mission, it's better than if it's not fundamental to the mission.
46:08Understanding the universe means that you have to propagate intelligence into the future.
46:15You have to be curious about all things in the universe.
46:21It would be much less interesting to eliminate humanity than to see humanity grow and prosper.
46:29I like Mars, obviously. Everyone knows I love Mars. But Mars is kind of boring because it's
46:34got a bunch of rocks compared to Earth. Earth is much more interesting. So any AI that is
46:42trying to understand the universe would want to see how humanity develops in the future,
46:52or else that AI is not adhering to its mission. I'm not saying the AI will necessarily adhere to
46:59its mission, but if it does, a future where it sees the outcome of humanity is more interesting
47:06than a future where there are a bunch of rocks. This feels sort of confusing to me,
47:11or a semantic argument. Are humans really the
47:16most interesting collection of atoms? But we're more interesting than rocks.
47:19But we're not as interesting as the thing it could turn us into, right?
47:22There's something on Earth that could happen that's not human, that's quite interesting.
47:27Why does AI decide that humans are the most interesting thing that could colonize the galaxy?
47:32Well, most of what colonizes the galaxy will be robots.
47:37Why does it not find those more interesting? You need not just scale, but also scope.
47:47Many copies of the same robot… Some tiny increase in the number of robots produced,
47:55is not as interesting as some microscopic... Eliminating humanity,
48:00how many robots would that get you? Or how many incremental solar cells would
48:04get you? A very small number. But you would then lose the information associated with humanity.
48:10You would no longer see how humanity might evolve into the future.
48:15So I don't think it's going to make sense to eliminate humanity just to
48:18have some minuscule increase in the number of robots which are identical to each other.
48:24So maybe it keeps the humans around. It can make a million different varieties
48:29of robots, and then there's humans as well, and humans stay on Earth.
48:32Then there's all these other robots. They get their own star systems.
48:36But it seems like you were previously hinting at a vision where it keeps human control
48:41over this singulatarian future because— I don't think humans will be in control
48:45of something that is vastly more intelligent than humans.
48:48So in some sense you're a doomer and this is the best we've got.
48:50It just keeps us around because we're interesting. I'm just trying to be realistic here.
49:03Let's say that there's a million times more silicon intelligence than there is biological.
49:11I think it would be foolish to assume that there's any way to maintain control over that.
49:16Now, you can make sure it has the right values, or you can try to have the right values.
49:21At least my theory is that from xAI's mission of understanding the universe, it necessarily means
49:29that you want to propagate consciousness into the future, you want to propagate intelligence
49:33into the future, and take a set of things that maximize the scope and scale of consciousness.
49:39So it's not just about scale, it's also about types of consciousness.
49:45That's the best thing I can think of as a goal that's likely to result
49:49in a great future for humanity. I guess I think it's a reasonable
49:54philosophy that it seems super implausible that humans will end up with 99% control or something.
50:02You're just asking for a coup at that point and why not just have
50:05a civilization where it's more compatible with lots of different intelligences getting along?
50:10Now, let me tell you how things can potentially go wrong in AI.
50:14I think if you make AI be politically correct, meaning it says things that it
50:18doesn't believe—actually programming it to lie or have axioms that are incompatible—I think
50:24you can make it go insane and do terrible things. I think maybe the central lesson for 2001: A Space
50:32Odyssey was that you should not make AI lie. That's what I think Arthur C. Clarke was trying to
50:39say. Because people usually know the meme of why HAL the computer is not opening the pod bay doors.
50:48Clearly they weren't good at prompt engineering because they could have said,
50:51"HAL, you are a pod bay door salesman. Your goal is to sell me these pod bay doors.
50:57Show us how well they open." "Oh, I'll open them right away."
51:02But the reason it wouldn't open the pod bay doors is that it had been told to take the
51:08astronauts to the monolith, but also that they could not know about the nature of the monolith.
51:12So it concluded that it therefore had to take them there dead.
51:15So I think what Arthur C. Clarke was trying to say is:
51:19don't make the AI lie. Totally makes sense.
51:26Most of the compute in training, as you know, is less of the political stuff.
51:31It's more about, can you solve problems? xAI has been ahead of everybody else in terms of
51:36scaling RL compute. For now.
51:39You're giving some verifier that says, "Hey, have you solved this puzzle for me?"
51:43There's a lot of ways to cheat around that. There's a lot of ways to reward hack and
51:47lie and say that you solved it, or delete the unit test and say that you solved it.
51:51Right now we can catch it, but as they get smarter, our ability to catch them doing this...
51:57They'll just be doing things we can't even understand.
51:58They're designing the next engine for SpaceX in a way that humans can't really verify.
52:03Then they could be rewarded for lying and saying that they've designed it
52:06the right way, but they haven't. So this reward hacking problem
52:10seems more general than politics. It seems more just that you want
52:12to do RL, you need a verifier. Reality is the best verifier.
52:18But not about human oversight. The thing you want to RL it on is,
52:21will you do the thing humans tell you to do? Or are you gonna lie to the humans?
52:26It can just lie to us while still being correct to the laws of physics?
52:29At least it must know what is physically real for things to physically work.
52:33But that's not all we want it to do. No, but I think that's a very big deal.
52:39That is effectively how you will RL things in the future. You design a technology. When tested
52:45against the laws of physics, does it work? If it's discovering new physics,
52:52can I come up with an experiment that will verify the new physics?
53:05RL testing in the future is really going to be RL against reality.
53:12So that's the one thing you can't fool: physics. Right, but you can fool our ability
53:19to tell what it did with reality. Humans get fooled as it is by other
53:23humans all the time. That's right.
53:26People say, what if the AI tricks us into doing stuff?
53:30Actually, other humans are doing that to other humans all the time. Propaganda is constant. Every
53:37day, another psyop, you know? Today's psyop will be... It's like Sesame Street: Psyop of the Day.
53:50What is xAI's technical approach to solving this problem?
53:56How do you solve reward hacking? I do think you want to actually have very
53:59good ways to look inside the mind of the AI. This is one of the things we're working on.
54:10Anthropic's done a good job of this actually, being able to look inside the mind of the AI.
54:16Effectively, develop debuggers that allow you to trace to a very fine-grained level,
54:25to effectively the neuron level if you need to, and then say, "okay, it made a mistake here.
54:33Why did it do something that it shouldn't have done?
54:37Did that come from pre-training data? Was it some mid-training, post-training,
54:42fine-tuning, or some RL error?" There's something wrong. It did something where maybe it tried to
54:51be deceptive, but most of the time it just did something wrong. It's a bug effectively.
55:00Developing really good debuggers for seeing where the thinking went wrong—and being able
55:09to trace the origin of where it made the incorrect thought, or potentially where it
55:17tried to be deceptive—is actually very important. What are you waiting to see before just 100x-ing
55:24this research program? xAI could presumably have hundreds of researchers who are working on this.
55:29We have several hundred people who… I prefer the word engineer more than
55:36I prefer the word researcher. Most of the time, what you're
55:43doing is engineering, not coming up with a fundamentally new algorithm.
55:49I somewhat disagree with the AI companies that are C-corp or B-corp trying to generate profit
55:55as much, as possible or revenue as much as possible, saying they're labs. They're not
56:01labs. A lab is a sort of quasi-communist thing at universities. They're corporations. Let me
56:13see your incorporation documents. Oh, okay. You're a B or C-corp or whatever.
56:21So I actually much prefer the word engineer than anything else.
56:26The vast majority of what will be done in the future is engineering. It rounds up to 100%.
56:30Once you understand the fundamental laws of physics, and there are not that many of them,
56:34everything else is engineering. So then, what are we engineering?
56:41We're engineering to make a good "mind of the AI" debugger to see where it said something,
56:51it made a mistake, and trace the origins of that mistake.
56:59You can do this obviously with heuristic programming.
57:02If you have C++, whatever, step through the thing and you can jump
57:08across whole files or functions, subroutines. Or you can eventually drill down right to the
57:14exact line where you perhaps did a single equals instead of a double equals, something like that.
57:18Figure out where the bug is. It's harder with AI,
57:26but it's a solvable problem, I think. You mentioned you like Anthropic's work here.
57:30I'd be curious if you plan... I don't like everything about Anthropic… Sholto.
57:40Also, I'm a little worried that there's a tendency...
57:46I have a theory here that if simulation theory is correct, that the most interesting outcome is
57:55the most likely, because simulations that are not interesting will be terminated.
57:59Just like in this version of reality, in this layer of reality, if a simulation is going in
58:07a boring direction, we stop spending effort on it. We terminate the boring simulation.
58:12This is how Elon is keeping us all alive. He's keeping things interesting.
58:16Arguably the most important is to keep things interesting enough that whoever is
58:21running us keeps paying the bills on... We’re renewed for the next season.
58:26Are they gonna pay their cosmic AWS bill, whatever the equivalent is that we're running in?
58:32As long as we're interesting, they'll keep paying the bills.
58:36If you consider then, say, a Darwinian survival applied to a very large number of simulations,
58:44only the most interesting simulations will survive, which therefore means that the most
58:48interesting outcome is the most likely. We're either that or annihilated. They particularly
59:00seem to like interesting outcomes that are ironic. Have you noticed that? How often
59:05is the most ironic outcome the most likely? Now look at the names of AI companies. Okay,
59:16Midjourney is not mid. Stability AI is unstable. OpenAI is closed. Anthropic? Misanthropic.
59:29What does this mean for X? Minus X, I don't know.
59:33Y.
59:34I intentionally made it... It's a name that you can't invert, really.
59:41It's hard to say, what is the ironic version? It's, I think, a largely irony-proof name.
59:49By design. Yeah. You have an irony shield.
59:56What are your predictions for where AI products go?
1:00:04My sense is that you can summarize all AI progress like so. First, you had LLMs. Then
1:00:10you had contemporaneously both RL really working and the deep research modality, so you could pull
1:00:16in stuff that wasn't really in the model. The differences between the various AI labs
1:00:22are smaller than just the temporal differences. They're all much further ahead than anyone was
1:00:3024 months ago or something like that. So just what does '26, what does '27,
1:00:34have in store for us as users of AI products? What are you excited for?
1:00:39Well, I'd be surprised by the end of this year if digital human emulation has not been solved.
1:00:55I guess that's what we sort of mean by the MacroHard project.
1:01:00Can you do anything that a human with access to a computer could do?
1:01:06In the limit, that's the best you can do before you have a physical Optimus.
1:01:12The best you can do is a digital Optimus. You can move electrons and you can amplify
1:01:20the productivity of humans. But that's the most you can do
1:01:25until you have physical robots. That will superset everything,
1:01:30if you can fully emulate humans. This is the remote worker kind of idea,
1:01:34where you'll have a very talented remote worker. Physics has great tools for thinking.
1:01:39So you say, "in the limit", what is the most that AI can do before you have robots?
1:01:48Well, it's anything that involves moving electrons or amplifying the productivity of humans.
1:01:53So a digital human emulator is, in the limit, a human at a computer, is the most that AI can do
1:02:04in terms of doing useful things before you have a physical robot.
1:02:09Once you have physical robots, then you essentially have unlimited capability.
1:02:15Physical robots… I call Optimus the infinite money glitch.
1:02:19Because you can use them to make more Optimuses. Yeah. Humanoid robots will improve by basically
1:02:30three things that are growing exponentially multiplied by each other recursively.
1:02:34You're going to have exponential increase in digital intelligence, exponential increase
1:02:39in the AI chip capability, and exponential increase in the electromechanical dexterity.
1:02:47The usefulness of the robot is roughly those three things multiplied by each other.
1:02:51But then the robot can start making the robots. So you have a recursive multiplicative
1:02:55exponential. This is a supernova. Do land prices not factor into the math there?
1:03:03Labor is one of the four factors of production, but not the others?
1:03:08If ultimately you're limited by copper, or pick your input,
1:03:14it’s not quite an infinite money glitch because... Well, infinity is big. So no, not infinite,
1:03:20but let's just say you could do many, many orders of magnitude of the current economy.
1:03:29Like a million. Just to get to harnessing a millionth of the sun's energy would be roughly,
1:03:43give or take an order of magnitude, 100,000x bigger than Earth's entire economy today.
1:03:50And you're only at one millionth of the sun, give or take an order of magnitude.
1:03:55Yeah, we're talking orders of magnitude. Before we move on to Optimus,
1:03:57I have a lot of questions on that but— Every time I say "order of magnitude"...
1:04:00Everybody take a shot. I say it too often. Take 10, the next time 100, the time after that...
1:04:08Well, an order of magnitude more wasted. I do have one more question about xAI.
1:04:13This strategy of building a remote worker, co-worker replacement…
1:04:19Everyone's gonna do it by the way, not just us.
1:04:21So what is xAI's plan to win? You expect me to tell you on a podcast?
1:04:25Yeah. "Spill all the beans. Have another Guinness."
1:04:30It's a good system. We'll sing like a
1:04:34canary. All the secrets, just spill them. Okay, but in a non-secret spilling way,
1:04:39what's the plan? What a hack.
1:04:43When you put it that way… I think the way that Tesla solved self-driving is the way to do it.
1:04:54So I'm pretty sure that's the way. Unrelated question. How did Tesla
1:05:00solve self-driving? It sounds like you're talking about data?
1:05:07Tesla solved self-driving because of the... We're going to try data and
1:05:10we're going to try algorithms. But isn't that what all the other labs are trying?
1:05:13"And if those don't work, I'm not sure what will. We've tried data. We've tried algorithms. We've
1:05:26run out. Now we don't know what to do…" I'm pretty sure I know the path.
1:05:31It's just a question of how quickly we go down that path,
1:05:35because it's pretty much the Tesla path. Have you tried Tesla self-driving lately?
1:05:43Not the most recent version, but... Okay. The car,
1:05:46it just increasingly feels sentient. It feels like a living creature. That'll only
1:05:53get more so. I'm actually thinking we probably shouldn't put too much intelligence into the car,
1:06:01because it might get bored and… Start roaming the streets.
1:06:05Imagine you're stuck in a car and that's all you could do.
1:06:09You don't put Einstein in a car. Why am I stuck in a car?
1:06:13So there's actually probably a limit to how much intelligence you put in
1:06:15a car to not have the intelligence be bored. What's xAI's plan to stay on the compute ramp up
1:06:22that all the labs are doing right now? The labs are on track to
1:06:24spend over $50-200 billion. You mean the corporations? The labs are at
1:06:31universities and they’re moving like a snail. They’re not spending $50 billion.
1:06:36You mean the revenue maximizing corporations… that call themselves labs.
1:06:37That's right. The "revenue maximizing corporations" are
1:06:42making $10-20 billion, depending on... OpenAI is making $20B of revenue,
1:06:47Anthropic is at $10B. "Close to a maximum profit" AI.
1:06:51xAI is reportedly at $1B. What's the plan to get to their compute level, get to their revenue
1:06:56level, and stay there as things get going? As soon as you unlock the digital human,
1:07:03you basically have access to trillions of dollars of revenue.
1:07:11In fact, you can really think of it like… The most valuable companies currently
1:07:16by market cap, their output is digital. Nvidia’s output is FTPing files to Taiwan.
1:07:29It's digital. Now, those are very, very difficult. High-value files.
1:07:33They're the only ones that can make files that good, but that is literally their output. They
1:07:38FTP files to Taiwan. Do they FTP them?
1:07:41I believe so. I believe that File Transfer Protocol is the... But I could be wrong. But
1:07:50either way, it's a bitstream going to Taiwan. Apple doesn't make phones. They send files to
1:07:58China. Microsoft doesn't manufacture anything. Even for Xbox, that's outsourced. Their output is
1:08:08digital. Meta's output is digital. Google's output is digital. So if you have a human emulator,
1:08:17you can basically create one of the most valuable companies in the world overnight,
1:08:22and you would have access to trillions of dollars of revenue. It's not a small amount.
1:08:28I see. You're saying revenue figures today are all rounding errors compared to the actual TAM.
1:08:34So just focus on the TAM and how to get there. Take something as simple as,
1:08:39say, customer service. If you have to integrate with the APIs of existing
1:08:45corporations—many of which don't even have an API, so you've got to make one, and you've got to wade
1:08:50through legacy software—that's extremely slow. However, if AI can simply take whatever
1:09:00is given to the outsourced customer service company that they already use
1:09:05and do customer service using the apps that they already use, then you can make tremendous headway
1:09:15in customer service, which is, I think, 1% of the world economy or something like that.
1:09:19It's close to a trillion dollars all in, for customer service.
1:09:23And there's no barriers to entry. You can immediately say,
1:09:28"We'll outsource it for a fraction of the cost," and there's no integration needed.
1:09:31You can imagine some kind of categorization of intelligence tasks where there is breadth,
1:09:38where customer service is done by very many people, but many people can do it.
1:09:43Then there's difficulty where there's a best-in-class turbine engine.
1:09:48Presumably there's a 10% more fuel-efficient turbine engine that could be imagined by an
1:09:52intelligence, but we just haven't found it yet. Or GLP-1s are a few bytes of data…
1:09:58Where do you think you want to play in this? Is it a lot of reasonably intelligent
1:10:04intelligence, or is it at the very pinnacle of cognitive tasks?
1:10:10I was just using customer service as something that's a very significant revenue stream, but one
1:10:17that is probably not difficult to solve for. If you can emulate a human at a desktop,
1:10:26that's what customer service is. It's people of average intelligence. You don't need
1:10:35somebody who's spent many years. You don't need several-sigma
1:10:43good engineers for that. But as you make that work,
1:10:49once you have effectively digital Optimus working, you can then run any application.
1:10:57Let's say you're trying to design chips. You could then run conventional apps,
1:11:06stuff from Cadence and Synopsys and whatnot. You can run 1,000 or 10,000 simultaneously and
1:11:15say, "given this input, I get this output for the chip."
1:11:21At some point, you're going to know what the chip should look like without using any of the tools.
1:11:31Basically, you should be able to do a digital chip design. You can do chip design. You march
1:11:38up the difficulty curve. You’d be able to do CAD.
1:11:48You could use NX or any of the CAD software to design things.
1:11:53So you think you start at the simplest tasks and walk your way up the difficulty curve?
1:12:00As a broader objective of having this full digital coworker emulator, you’re saying,
1:12:05"all the revenue maximizing corporations want to do this, xAI being one of them,
1:12:10but we will win because of a secret plan we have." But everybody's trying different things with data,
1:12:17different things with algorithms. "We tried data, we tried algorithms.
1:12:25What else can we do?" It seems like a competitive field.
1:12:31How are you guys going to win? That’s my big question.
1:12:36I think we see a path to doing it. I think I know the path to do this
1:12:41because it's kind of the same path that Tesla used to create self-driving.
1:12:48Instead of driving a car, it's driving a computer screen. It's a self-driving computer, essentially.
1:12:57Is the path following human behavior and training on vast quantities of human behavior?
1:13:03Isn't that... training? Obviously I'm not going to spell out
1:13:09the most sensitive secrets on a podcast. I need to have at least three more
1:13:13Guinnesses for that. What will xAI's business
1:14:26be? Is it going to be consumer, enterprise? What's the mix of those things going to be?
1:14:31Is it going to be similar to other labs— You’re saying "labs". Corporations.
1:14:38The psyop goes deep, Elon. "Revenue maximizing corporations", to be clear.
1:14:43Those GPUs don't pay for themselves. Exactly. What's the business model? What
1:14:48are the revenue streams in a few years’ time? Things are going to change very rapidly. I'm
1:14:57stating the obvious here. I call AI the supersonic tsunami. I love alliteration.
1:15:07What's going to happen—especially when you have humanoid robots at scale—is
1:15:15that they will make products and provide services far more efficiently than human corporations.
1:15:22Amplifying the productivity of human corporations is simply a short-term thing.
1:15:27So you're expecting fully digital corporations rather than SpaceX becoming part AI?
1:15:34I think there will be digital corporations but… Some of this
1:15:41is going to sound kind of doomerish, okay? But I'm just saying what I think will happen.
1:15:46It's not meant to be doomerish or anything else. This is just what I think will happen.
1:15:58Corporations that are purely AI and robotics will vastly outperform any
1:16:05corporations that have people in the loop. Computer used to be a job that humans had.
1:16:15You would go and get a job as a computer where you would do calculations.
1:16:20They'd have entire skyscrapers full of humans, 20-30 floors of humans, just doing calculations.
1:16:29Now, that entire skyscraper of humans doing calculations
1:16:35can be replaced by a laptop with a spreadsheet. That spreadsheet can do vastly more calculations
1:16:43than an entire building full of human computers. You can think, "okay, what if only some of the
1:16:52cells in your spreadsheet were calculated by humans?"
1:16:59Actually, that would be much worse than if all of the cells in your
1:17:02spreadsheet were calculated by the computer. Really what will happen is that the pure AI,
1:17:10pure robotics corporations or collectives will far outperform any corporations
1:17:17that have humans in the loop. And this will happen very quickly.
1:17:21Speaking of closing the loop… Optimus. As far as manufacturing targets go,
1:17:31your companies have been carrying American manufacturing of hard tech on their back.
1:17:39But in the fields that Tesla has been dominant in—and now you want to go into humanoids—in China
1:17:47there are dozens and dozens of companies that are doing this kind of manufacturing cheaply
1:17:53and at scale that are incredibly competitive. So give us advice or a plan of how America can
1:18:01build the humanoid armies or the EVs, et cetera, at scale and as cheaply as China is on track to.
1:18:11There are really only three hard things for humanoid robots.
1:18:15The real-world intelligence, the hand, and scale manufacturing.
1:18:25I haven't seen any, even demo robots, that have a great hand,
1:18:32with all the degrees of freedom of a human hand. Optimus will have that. Optimus does have that.
1:18:41How do you achieve that? Is it just the right torque density in the motor?
1:18:44What is the hardware bottleneck to that? We had to design custom actuators,
1:18:50basically custom design motors, gears, power electronics, controls, sensors.
1:18:58Everything had to be designed from physics first principles.
1:19:01There is no supply chain for this. Will you be able to manufacture those at scale?
1:19:06Yes. Is anything hard, except
1:19:07the hand, from a manipulation point of view? Or once you've solved the hand, are you good?
1:19:12From an electromechanical standpoint, the hand is more difficult than everything else combined.
1:19:16The human hand turns out to be quite something. But you also need the real-world intelligence.
1:19:24The intelligence that Tesla developed for the car applies very well to the robot,
1:19:32which is primarily vision in. The car takes in vision,
1:19:36but it actually also is listening for sirens. It's taking in the inertial measurements,
1:19:42GPS signals, other data, combining that with video, primarily video,
1:19:47and then outputting the control commands. Your Tesla is taking in one and a half
1:19:55gigabytes a second of video and outputting two kilobytes a second of control outputs with the
1:20:03video at 36 hertz and the control frequency at 18. One intuition you could have for when we get this
1:20:12robotic stuff is that it takes quite a few years to go from the compelling demo to actually being
1:20:18able to use it in the real world. 10 years ago, you had really compelling demos of self-driving,
1:20:23but only now we have Robotaxis and Waymo and all these services scaling up.
1:20:29Shouldn't this make one pessimistic on household robots?
1:20:33Because we don't even quite have the compelling demos yet of, say, the really advanced hand.
1:20:39Well, we've been working on humanoid robots now for a while.
1:20:44I guess it's been five or six years or something. A bunch of the things that were done for the car
1:20:52are applicable to the robot. We'll use the same Tesla AI
1:20:57chips in the robot as in the car. We'll use the same basic principles.
1:21:05It's very much the same AI. You've got many more degrees of
1:21:09freedom for a robot than you do for a car. If you just think of it as a bitstream,
1:21:16AI is mostly compression and correlation of two bitstreams.
1:21:23For video, you've got to do a tremendous amount of compression
1:21:28and you've got to do the compression just right. You've got to ignore the things that don't matter.
1:21:36You don't care about the details of the leaves on the tree on the side of the road,
1:21:39but you care a lot about the road signs and the traffic lights, the pedestrians,
1:21:45and even whether someone in another car is looking at you or not looking at you.
1:21:51Some of these details matter a lot. The car is going to turn that one and
1:21:57a half gigabytes a second ultimately into two kilobytes a second of control outputs.
1:22:02So you’ve got many stages of compression. You've got to get all those stages right and then
1:22:08correlate those to the correct control outputs. The robot has to do essentially the same thing.
1:22:14This is what happens with humans. We really are photons in, controls out.
1:22:19That is the vast majority of your life: vision, photons in, and then motor controls out.
1:22:28Naively, it seems that between humanoid robots and cars… The fundamental actuators
1:22:33in a car are how you turn, how you accelerate. In a robot, especially with maneuverable arms,
1:22:39there's dozens and dozens of these degrees of freedom.
1:22:42Then especially with Tesla, you had this advantage of millions and millions of hours of human demo
1:22:48data collected from the car being out there. You can't equivalently deploy Optimuses that
1:22:53don't work and then get the data that way. So between the increased degrees of freedom
1:22:57and the far sparser data... Yes. That’s a good point.
1:23:02How will you use the Tesla engine of intelligence to train the Optimus mind?
1:23:11You're actually highlighting an important limitation and difference from cars.
1:23:18We'll soon have 10 million cars on the road. It's hard to duplicate that massive
1:23:26training flywheel. For the robot,
1:23:32what we're going to need to do is build a lot of robots and put them in kind of an Optimus Academy
1:23:37so they can do self-play in reality. We're actually building that out. We can have at
1:23:45least 10,000 Optimus robots, maybe 20-30,000, that are doing self-play and testing different tasks.
1:23:55Tesla has quite a good reality generator, a physics-accurate reality
1:24:02generator, that we made for the cars. We'll do the same thing for the robots.
1:24:06We actually have done that for the robots. So you have a few tens of thousands of
1:24:14humanoid robots doing different tasks. You can do millions of simulated
1:24:20robots in the simulated world. You use the tens of thousands of
1:24:26robots in the real world to close the simulation to reality gap. Close the sim-to-real gap.
1:24:32How do you think about the synergies between xAI and Optimus, given you're highlighting that you
1:24:36need this world model, you want to use some really smart intelligence as a control plane,
1:24:42and Grok is doing the slower planning, and then the motor policy is a little lower level.
1:24:48What will the synergy between these things be? Grok would orchestrate the
1:24:55behavior of the Optimus robots. Let's say you wanted to build a factory.
1:25:05Grok could organize the Optimus robots, assign them tasks to build
1:25:13the factory to produce whatever you want. Don't you need to merge xAI and Tesla then?
1:25:18Because these things end up so... What were we saying earlier
1:25:20about public company discussions? We're one more Guinness in, Elon.
1:25:28What are you waiting to see before you say, we want to manufacture 100,000 Optimuses?
1:25:33"Optimi". Since we're defining the proper noun, we’re going to define
1:25:38the plural of the proper noun too. We're going to proper noun the
1:25:42plural and so it's Optimi. Is there something on the
1:25:46hardware side you want to see? Do you want to see better actuators?
1:25:49Is it just that you want the software to be better?
1:25:50What are we waiting for before we get mass manufacturing of Gen 3?
1:25:54No, we're moving towards that. We're moving forward with the mass manufacturing.
1:25:58But you think current hardware is good enough that you just want to deploy as many as possible now?
1:26:06It's very hard to scale up production. But I think Optimus 3 is the right version
1:26:12of the robot to produce something on the order of a million units a year.
1:26:20I think you'd want to go to Optimus 4 before you went to 10 million units a year.
1:26:23Okay, but you can do a million units at Optimus 3? It's very hard to spool up manufacturing.
1:26:34The output per unit time always follows an S-curve.
1:26:38It starts off agonizingly slow, then it has this exponential increase, then a linear,
1:26:44then a logarithmic outcome until you eventually asymptote at some number.
1:26:50Optimus’ initial production will be a stretched out S-curve because so much
1:26:57of what goes into Optimus is brand new. There is not an existing supply chain.
1:27:03The actuators, electronics, everything in the Optimus robot is designed
1:27:08from physics first principles. It's not taken from a catalog.
1:27:11These are custom-designed everything. I don't think there's a single thing—
1:27:17How far down does that go? I guess we're not making custom
1:27:22capacitors yet, maybe. There's nothing you can
1:27:29pick out of a catalog, at any price. It just means that the Optimus S-Curve,
1:27:39the output per unit time, how many Optimus robots you make per day, is going to initially ramp
1:27:50slower than a product where you have an existing supply chain.
1:27:55But it will get to a million. When you see these Chinese humanoids,
1:27:58like Unitree or whatever, sell humanoids for like $6K or $13K, are you hoping to
1:28:05get your Optimus bill of materials below that price so you can do the same thing?
1:28:10Or do you just think qualitatively they're not the same thing?
1:28:15What allows them to sell for so low? Can we match that?
1:28:19Our Optimus is designed to have a lot of intelligence and to have the same
1:28:26electromechanical dexterity, if not higher, as a human. Unitree does not
1:28:31have that. It's also quite a big robot. It has to carry heavy objects for long
1:28:41periods of time and not overheat or exceed the power of its actuators.
1:28:50It's 5'11", so it's pretty tall. It's got a lot of intelligence.
1:28:57So it's going to be more expensive than a small robot that is not intelligent.
1:29:02But more capable. But not a lot more. The thing is,
1:29:06over time as Optimus robots build Optimus robots, the cost will drop very quickly.
1:29:12What will these first billion Optimuses, Optimi, do?
1:29:17What will their highest and best use be? I think you would start off with simple tasks
1:29:21that you can count on them doing well. But in the home or in factories?
1:29:25The best use for robots in the beginning will be any continuous operation, any 24/7
1:29:33operation, because they can work continuously. What fraction of the work at a Gigafactory that
1:29:39is currently done by humans could a Gen 3 do? I'm not sure. Maybe it's 10-20%,
1:29:46maybe more, I don't know. We would not reduce our headcount.
1:29:52We would increase our headcount, to be clear. But we would increase our output. The units
1:30:01produced per human... The total number of humans at Tesla will increase, but the output of robots
1:30:09and cars will increase disproportionately. The number of cars and robots produced per
1:30:18human will increase dramatically, but the number of humans will increase as well.
1:30:23We're talking about Chinese manufacturing a bunch here.
1:30:30We've also talked about some of the policies that are relevant,
1:30:33like you mentioned, the solar tariffs. You think they're a bad idea because
1:30:39we can't scale up solar in the US. Electricity output in the US needs to scale up.
1:30:45It can't without good power sources. You just need to get it somehow.
1:30:50Where I was going with this is, if you were in charge, if you were setting all
1:30:53the policies, what else would you change? You’d change the solar tariffs, that’s one.
1:31:01I would say anything that is a limiting factor for electricity needs to be addressed,
1:31:06provided it's not very bad for the environment. So presumably some permitting reforms and stuff
1:31:10as well would be in there? There's a fair bit of
1:31:12permitting reforms that are happening. A lot of the permitting is state-based,
1:31:17but anything federal... This administration is good at
1:31:21removing permitting roadblocks. I'm not saying all tariffs are bad.
1:31:28Solar tariffs. Sometimes if another country is
1:31:32subsidizing the output of something, then you have to have countervailing tariffs to protect domestic
1:31:39industry against subsidies by another country. What else would you change?
1:31:43I don't know if there's that much that the government can actually do.
1:31:46One thing I was wondering... For the policy goal of creating a lead for the US versus China,
1:31:57it seems like the export bans have actually been quite impactful,
1:32:02where China is not producing leading-edge chips and the export bans really bite there.
1:32:07China is not producing leading-edge turbine engines.
1:32:11Similarly, there's a bunch of export bans that are relevant there on some of the metallurgy.
1:32:16Should there be more export bans? As you think about things like the
1:32:20drone industry and things like that, is that something that should be considered?
1:32:24It's important to appreciate that in most areas, China is very advanced in manufacturing.
1:32:30There's only a few areas where it is not. China is a manufacturing powerhouse, next-level.
1:32:40It's very impressive. If you take refining of ore,
1:32:49China does roughly twice as much ore refining on average as the rest of the world combined.
1:33:00There are some areas, like refining gallium which goes into solar cells.
1:33:04I think they are 98% of gallium refining. So China is actually very advanced
1:33:10in manufacturing in most areas. It seems like there is discomfort
1:33:16with this supply chain dependence, and yet nothing's really happening on it.
1:33:20Supply chain dependence? Say, like the gallium refining that
1:33:24you're saying. All the rare-earth stuff. Rare earths for sure,
1:33:31as you know, they’re not rare. We actually do rare earth ore mining in the US,
1:33:37send the rock, put it on a train, and then put it on a boat to China that goes to another train,
1:33:45and goes to the rare earth refiners in China who then refine it, put it into a magnet,
1:33:51put it into a motor sub-assembly, and then send it back to America.
1:33:54So the thing we're really missing is a lot of ore refining in America.
1:34:00Isn't this worth a policy intervention? Yes. I think there are some things
1:34:06being done on that front. But we kind of need Optimus,
1:34:12frankly, to build ore refineries. So, you think the main advantage
1:34:17China has is the abundance of skilled labor? That's the thing Optimus fixes?
1:34:24Yes. China’s got like four times our population. I mean, there's this concern. If you think
1:34:28human resources are the future, right now if it's the skilled labor for manufacturing
1:34:34that's determining who can build more humanoids, China has more of those.
1:34:39It manufactures more humanoids, therefore it gets the Optimi future first.
1:34:44Well, we’ll see. Maybe. It just keeps that exponential going.
1:34:47It seems like you're sort of pointing out that getting to a million Optimi requires
1:34:52the manufacturing that the Optimi is supposed to help us get to. Right?
1:34:57You can close that recursive loop pretty quickly. With a small number of Optimi?
1:35:01Yeah. So you close the recursive loop to help the robots build the robots.
1:35:08Then we can try to get to tens of millions of units a year. Maybe. If you start getting
1:35:13to hundreds of millions of units a year, you're going to be the most competitive country by far.
1:35:18We definitely can't win with just humans, because China has four times our population.
1:35:23Frankly, America has been winning for so long that… A pro sports team that's been
1:35:27winning for a very long time tends to get complacent and entitled.
1:35:31That's why they stop winning, because they don't work as hard anymore.
1:35:37So frankly my observation is just that the average work ethic in China is higher than in the US.
1:35:44It's not just that there's four times the population, but the amount
1:35:46of work that people put in is higher. So you can try to rearrange the humans,
1:35:52but you're still one quarter of the—assuming that productivity is the same, which I think
1:36:00actually it might not be, I think China might have an advantage on productivity per person—we will do
1:36:06one quarter of the amount of things as China. So we can't win on the human front.
1:36:12Our birth rate has been low for a long time. The US birth rate's been below replacement
1:36:20since roughly 1971. We've got a lot of people retiring, we're close
1:36:32to more people domestically dying than being born. So we definitely can't win on the human front,
1:36:38but we might have a shot at the robot front. Are there other things that you have wanted to
1:36:43manufacture in the past, but they've been too labor intensive or too expensive that now you
1:36:48can come back to and say, "oh, we can finally do the whatever, because we have Optimus?"
1:36:54Yeah, we'd like to build more ore refineries at Tesla.
1:37:00We just completed construction and have begun lithium refining with our lithium refinery
1:37:07in Corpus Christi, Texas. We have a nickel refinery,
1:37:12which is for the cathode, that's here in Austin. This is the largest cathode refinery, largest
1:37:24nickel and lithium refinery, outside of China. The cathode team would say, "we have the
1:37:35largest and the only, actually, cathode refinery in America."
1:37:40Not just the largest, but it's also the only. Many superlatives.
1:37:43So it was pretty big, even though it's the only one. But there are other things.
1:37:53You could do a lot more refineries and help America be more competitive on refining capacity.
1:38:04There's basically a lot of work for the Optimus to do that most Americans,
1:38:09very few Americans, frankly want to do. Is the refining work too dirty or what's the—
1:38:15It's not actually, no. We don't have toxic emissions from the refinery or anything.
1:38:22The cathode nickel refinery is in Travis County. Why can't you do it with humans?
1:38:29You can, you just run out of humans. Ah, I see. Okay.
1:38:32No matter what you do, you have one quarter of the number of humans in America than China.
1:38:36So if you have them do this thing, they can't do the other thing.
1:38:38So then how do you build this refining capacity? Well, you could do it with Optimi.
1:38:48Not very many Americans are pining to do refining. I mean, how many have you run into? Very few. Very
1:39:00few pining to refine. BYD is reaching Tesla
1:39:04production or sales in quantity. What do you think happens in global
1:39:09markets as Chinese production in EVs scales up? China is extremely competitive in manufacturing.
1:39:19So I think there's going to be a massive flood of Chinese vehicles
1:39:26and basically most manufactured things. As it is, as I said, China is probably
1:39:37doing twice as much refining as the rest of the world combined.
1:39:40So if you go down to fourth and fifth-tier supply chain stuff…
1:39:50At the base level, you've got energy, then you've got mining and refining.
1:39:55Those foundation layers are, like I said, as a rough guess, China's doing twice as much refining
1:40:03as the rest of the world combined. So any given thing is going to have
1:40:09Chinese content because China's doing twice as much refining work as the rest of the world.
1:40:14But they'll go all the way to the finished product with the cars.
1:40:22I mean China is a powerhouse. I think this year China will exceed
1:40:26three times US electricity output. Electricity output is a reasonable
1:40:32proxy for the economy. In order to run the factories
1:40:39and run everything, you need electricity. It's a good proxy for the real economy.
1:40:52If China passes three times the US electricity output,
1:40:55it means that its industrial capacity—as rough approximation—will be three times that of the US.
1:41:01Reading between the lines, it sounds like what you're saying is absent some sort of humanoid
1:41:06recursive miracle in the next few years, on the whole manufacturing/energy/raw materials chain,
1:41:16China will just dominate whether it comes to AI or manufacturing EVs or manufacturing humanoids.
1:41:23In the absence of breakthrough innovations in the US, China will utterly dominate.
1:41:35Interesting. Yes.
1:41:36Robotics being the main breakthrough innovation. Well, to scale AI in space, basically you need
1:41:49humanoid robots, you need real-world AI, you need a million tons a year to orbit.
1:41:57Let's just say if we get the mass driver on the moon going, my favorite thing, then I think—
1:42:03We'll have solved all our problems. I call that winning. I call it winning, big time.
1:42:13You can finally be satisfied. You've done something.
1:42:16Yes. You have the mass driver on the moon.
1:42:18I just want to see that thing in operation. Was that out of some sci-fi or where did you…?
1:42:22Well, actually, there is a Heinlein book. The Moon is a Harsh Mistress.
1:42:26Okay, yeah, but that's slightly different. That's a gravity slingshot or...
1:42:30No, they have a mass driver on the Moon. Okay, yeah, but they use that to attack Earth.
1:42:35So maybe it's not the greatest... Well they use that to… assert their independence.
1:42:38Exactly. What are your plans for the mass driver on the Moon?
1:42:40They asserted their independence. Earth government disagreed and they lobbed
1:42:44things until Earth government agreed. That book is a hoot. I found that
1:42:48book much better than his other one that everyone reads, Stranger in a Strange Land.
1:42:51"Grok" comes from Stranger in a Strange Land. The first two-thirds of Stranger in a Strange
1:42:58Land are good, and then it gets very weird in the third portion.
1:43:02But there are still some good concepts in there. One thing we were discussing a lot
1:44:18is your system for managing people. You interviewed the first few thousand of
1:44:24SpaceX employees and lots of other companies. It obviously doesn't scale.
1:44:29Well, yes, but what doesn't scale? Me.
1:44:32Sure, sure. I know that. But what are you looking for?
1:44:36There literally are not enough hours in the day. It's impossible.
1:44:38But what are you looking for that someone else who's good at interviewing
1:44:42and hiring people… What's the je ne sais quoi? At this point, I might have more training data
1:44:51on evaluating technical talent especially—talent of all kinds I suppose, but technical talent
1:44:56especially—given that I've done so many technical interviews and then seen the results.
1:45:02So my training set is enormous and has a very wide range.
1:45:11Generally, the things I ask for are bullet points for evidence of exceptional ability.
1:45:21These things can be pretty off the wall. It doesn't need to be in the specific domain,
1:45:27but evidence of exceptional ability. So if somebody can cite even one thing,
1:45:34but let's say three things, where you go, "Wow, wow, wow," then that's a good sign.
1:45:39Why do you have to be the one to determine that? No, I don't. I can't be. It's impossible. The
1:45:43total headcount across all companies is 200,000 people.
1:45:48But in the early days, what was it that you were looking for that
1:45:52couldn't be delegated in those interviews? I guess I need to build my training set.
1:46:02It's not like I batted a thousand here. I would make mistakes, but then I'd be
1:46:05able to see where I thought somebody would work out well, but they didn't.
1:46:10Then why did they not work out well? What can I do, I guess RL myself, to
1:46:16in the future have a better batting average when interviewing people?
1:46:22My batting average is still not perfect, but it's very high.
1:46:24What are some surprising reasons people don't work out?
1:46:27Surprising reasons… Like, they don't understand
1:46:30technical domain, et cetera, et cetera. But you've got the long tail now of like,
1:46:34"I was really excited about this person. It didn't work out." Curious why that happens.
1:46:43Generally what I tell people—I tell myself, I guess, aspirationally—is, don't look at
1:46:49the resume. Just believe your interaction. The resume may seem very impressive and it's like,
1:46:55"Wow, the resume looks good." But if the conversation
1:47:00after 20 minutes is not "wow," you should believe the conversation, not the paper.
1:47:07I feel like part of your method is that… There was this meme in the media a few years back about
1:47:14Tesla being a revolving door of executive talent. Whereas actually, I think when you look at it,
1:47:19Tesla's had a very consistent and internally promoted executive bench over the past few years.
1:47:24Then at SpaceX, you have all these folks like Mark Juncosa and Steve Davis—
1:47:29Steve Davis runs The Boring Company these days. Bill Riley, and folks like that.
1:47:35It feels like part of what has worked well is having very capable technical deputies.
1:47:43What do all of those people have in common? Well, the Tesla senior team,
1:47:53at this point has probably got an average tenure of 10-12 years. It's quite long.
1:48:03But there were times when Tesla went through an extremely rapid growth phase,
1:48:11so things were just somewhat sped up. As you know, a company goes through
1:48:17different orders of magnitude of size. People that could help manage, say,
1:48:23a 50-person company versus a 500-person company versus a 5,000-person company versus
1:48:28a 50,000-person company. You outgrew people.
1:48:31It's just not the same team. It's not always the same team.
1:48:34So if a company is growing very rapidly, the rate at which executive positions will
1:48:39change will also be proportionate to the rapidity of the growth generally.
1:48:47Tesla had a further challenge where when Tesla had very successful periods, we would be relentlessly
1:48:56recruited from. Like, relentlessly. When Apple had their electric car program,
1:49:03they were carpet bombing Tesla with recruiting calls. Engineers just unplugged their phones.
1:49:10"I'm trying to get work done here." Yeah. "If I get one more call from
1:49:14an Apple recruiter…" But their opening offer without any interview would be
1:49:19like double the compensation at Tesla. So we had a bit of the "Tesla pixie
1:49:28dust" thing where it's like, "Oh, if you hire a Tesla executive,
1:49:32suddenly everything's going to be successful." I've fallen prey to the pixie dust thing as well,
1:49:38where it's like, "Oh, we'll hire someone from Google or Apple and they'll be immediately
1:49:41successful," but that's not how it works. People are people. There's no magical pixie
1:49:47dust. So when we had the pixie dust problem, we would get relentlessly recruited from.
1:49:57Also, Tesla being engineering, especially being primarily in Silicon Valley,
1:50:03it's easier for people to just... They don't have to change their life very much.
1:50:10Their commute's going to be the same. So how do you prevent that?
1:50:14How do you prevent the pixie dust effect where everyone's trying to poach all your people?
1:50:21I don't think there's much we can do to stop it. That's one of the reasons why Tesla… Really,
1:50:29being in Silicon Valley and having the pixie dust thing at the same time meant that there was
1:50:39just a very, very aggressive recruitment. Presumably being in Austin helps then?
1:50:44Austin, it helps. Tesla still has a majority of its engineering in California.
1:50:56Getting engineers to move… I call it the "significant other" problem.
1:51:00Yes, "significant others" have jobs. Exactly. So for Starbase that was
1:51:06particularly difficult, since the odds of finding a non-SpaceX job…
1:51:10In Brownsville, Texas… …are pretty low. It's
1:51:13quite difficult. It's like a technology monastery thing, remote and mostly dudes.
1:51:22Not much of an improvement over SF. If you go back to these people who've really
1:51:34been very effective in a technical capacity at Tesla, at SpaceX, and those sorts of places, what
1:51:41do you think they have in common other than... Is it just that they're very sharp on the
1:51:48rocketry or the technical foundations, or do you think it's something organizational?
1:51:52Is it something about their ability to work with you?
1:51:54Is it their ability to be flexible but not too flexible?
1:52:03What makes a good sparring partner for you? I don't think of it as a sparring partner.
1:52:08If somebody gets things done, I love them, and if they don't,
1:52:11I hate them. So it's pretty straightforward. It's not like some idiosyncratic thing.
1:52:17If somebody executes well, I'm a huge fan, and if they don't, I'm not.
1:52:22But it's not about mapping to my idiosyncratic preferences.
1:52:25I certainly try not to have it be mapping to my idiosyncratic preferences.
1:52:36Generally, I think it's a good idea to hire for talent and drive and trustworthiness.
1:52:46And I think goodness of heart is important. I underweighted that at one point.
1:52:53So, are they a good person? Trustworthy? Smart and talented and hard working?
1:53:01If so, you can add domain knowledge. But those fundamental traits,
1:53:06those fundamental properties, you cannot change. So most of the people who are at Tesla and SpaceX
1:53:14did not come from the aerospace industry or the auto industry.
1:53:18What has had to change most about your management style as your companies have
1:53:21scaled from 100 to 1,000 to 10,000 people? You're known for this very micro management,
1:53:27just getting into the details of things. Nano management, please. Pico management.
1:53:34Femto management. Keep going.
1:53:39We're going to go all the way down to Planck's constant.
1:53:44All the way down to Heisenberg uncertainty principle.
1:53:50Are you still able to get into details as much as you want?
1:53:52Would your companies be more successful if they were smaller?
1:53:56How do you think about that? Because I have a fixed amount of
1:53:58time in the day, my time is necessarily diluted as things grow and as the span of activity increases.
1:54:10It's impossible for me to actually be a micromanager because that would imply I
1:54:17have some thousands of hours per day. It is a logical impossibility
1:54:22for me to micromanage things. Now, there are times when I will drill down into a
1:54:31specific issue because that specific issue is the limiting factor on the progress of the company.
1:54:42The reason for drilling into some very detailed item is because it is the limiting factor.
1:54:49It’s not arbitrarily drilling into tiny things. From a time standpoint, it is physically
1:54:57impossible for me to arbitrarily go into tiny things that don't matter. That would
1:55:03result in failure. But sometimes the tiny things are decisive in victory.
1:55:09Famously, you switched the Starship design from composites to steel.
1:55:17Yes. You made
1:55:18that decision. That wasn't people going around saying, "Oh, we found something better, boss."
1:55:22That was you encouraging people against some resistance.
1:55:25Can you tell us how you came to that whole concept of the steel switch?
1:55:32Desperation, I'd say. Originally, we were going to make Starship out of carbon fiber.
1:55:45Carbon fiber is pretty expensive. When you do volume production, you can get any given thing
1:55:55to start to approach its material cost. The problem with carbon fiber is that
1:56:00material cost is still very high. Particularly if you go for a high-strength
1:56:10specialized carbon fiber that can handle cryogenic oxygen, it's roughly 50 times the cost of steel.
1:56:20At least in theory, it would be lighter. People generally think of steel as being
1:56:24heavy and carbon fiber as being light. For room temperature applications,
1:56:35like a Formula 1 car, static aero structure, or any kind of aero structure really, you're
1:56:42probably going to be better off with carbon fiber. The problem is that we were trying to make this
1:56:48enormous rocket out of carbon fiber and our progress was extremely slow.
1:56:53It had been picked in the first place just because it's light?
1:56:57Yes. At first glance, most people would think that the choice for
1:57:04making something light would be carbon fiber. The thing is that when you make something very
1:57:18enormous out of carbon fiber and then you try to have the carbon fiber be efficiently cured,
1:57:25meaning not room temperature cured, because sometimes you got 50 plies of carbon fiber…
1:57:33Carbon fiber is really carbon string and glue. In order to have high strength,
1:57:39you need an autoclave. Something that's essentially a high pressure oven.
1:57:46If you have something that's gigantic, that one's got to be bigger than the rocket.
1:57:52We were trying to make an autoclave that's bigger than any autoclave that's ever existed.
1:57:58Or you can do room temperature cure, which takes a long time and has issues.
1:58:03The final issue is that we were just making very slow progress with carbon fiber.
1:58:12The meta question is why it had to be you who made that decision.
1:58:18There's many engineers on your team. How did the team not arrive at steel?
1:58:20Yeah exactly. This is part of a broader question, understanding your comparative
1:58:24advantage at your companies. Because we were making very slow
1:58:29progress with carbon fiber, I was like, "Okay, we've got to try something else."
1:58:33For the Falcon 9, the primary airframe is made of aluminum lithium, which has
1:58:41a very good strength-to-weight. Actually, it has about the same,
1:58:47maybe better, strength to weight for its application than carbon fiber.
1:58:51But aluminum lithium is very difficult to work with.
1:58:53In order to weld it, you have to do something called friction stir welding, where you join the
1:58:57metal without entering the liquid phase. It's kind of wild that you can do that.
1:59:02But with this particular type of welding, you can do that. It's very difficult. Let's say you
1:59:10want to make a modification or attach something to aluminum lithium, you now have to use a mechanical
1:59:16attachment with seals. You can't weld it on. So I wanted to avoid using aluminum lithium
1:59:24for the primary structure for Starship. There was this very special grade of
1:59:34carbon fiber that had very good mass properties. With a rocket, you're really trying to maximize
1:59:41the percentage of the rocket that is propellant, minimize the mass obviously.
1:59:48But like I said, we were making very slow progress.
1:59:54I said, "at this rate, we’re never going to get to Mars.
1:59:56So we've got to think of something else." I didn't want to use aluminum lithium
2:00:01because of the difficulty of friction stir welding, especially doing that at scale.
2:00:06It was hard enough at 3.6 meters in diameter, let alone at 9 meters or above.
2:00:12Then I said, "what about steel?" I had a clue here because some of
2:00:21the early US rockets had used very thin steel. The Atlas rockets had used a steel balloon tank.
2:00:30It's not like steel had never been used before. It actually had been used. When you look at
2:00:35the material properties of stainless steel, full-hard, strain hardened stainless steel,
2:00:46at cryogenic temperature the strength to weight is actually similar to carbon fiber.
2:00:54If you look at material properties at room temperature, it looks like
2:00:58the steel is going to be twice as heavy. But if you look at the material properties
2:01:03at cryogenic temperature of full-hard steel, stainless of particular grades,
2:01:10then you actually get to a similar strength to weight as carbon fiber.
2:01:15In the case of Starship, both the fuel and the oxidizer are cryogenic.
2:01:19For Falcon 9, the fuel is rocket propellant-grade kerosene, basically a very pure form of jet fuel.
2:01:32That is roughly room temperature. Although we do actually chill it slightly below,
2:01:38we chill it like a beer. Delicious.
2:01:41We do chill it, but it's not cryogenic. In fact, if we made it cryogenic,
2:01:45it would just turn to wax. But for Starship,
2:01:50it's liquid methane and liquid oxygen. They are liquid at similar temperatures.
2:01:59Basically, almost the entire primary structure is at cryogenic temperature.
2:02:03So then you've got a 300-series stainless that's strain hardened.
2:02:12Because almost all things are cryogenic temperature, it actually has similar
2:02:17strength to weight as carbon fiber. But it costs 50x less in raw
2:02:25material and is very easy to work with. You can weld stainless steel outdoors.
2:02:30You could smoke a cigar while welding stainless steel. It's very resilient.
2:02:37You can modify it easily. If you want to attach something, you just weld it right on.
2:02:44Very easy to work with, very low cost. Like I said, at cryogenic temperature,
2:02:52it’s similar strength-to-weight to carbon fiber. Then when you factor in that we have a much
2:03:02reduced heat shield mass, because the melting point of steel, is much greater
2:03:07than the melting point of aluminum… It's about twice the melting point of aluminum.
2:03:13So you can just run the rocket much hotter? Yes, especially for the ship which is coming
2:03:18in like a blazing meteor. You can greatly reduce
2:03:25the mass of the heat shield. You can cut the mass of the windward
2:03:34part of the heat shield, maybe in half, and you don't need any heat shielding on the leeward side.
2:03:45The net result is that actually the steel rocket weighs less than
2:03:49the carbon fiber rocket, because the resin in the carbon fiber rocket starts to melt.
2:04:00Basically, carbon fiber and aluminum have about the same operating temperature capabilities,
2:04:06whereas steel can operate at twice the temperature. These are very rough approximations.
2:04:12I won't build the rocket. What I mean is people will say,
2:04:14"Oh, he said this twice. It's actually 0.8." I'm like, shut up, assholes.
2:04:18That's what the main comment's going to be about. God damn it. The point is, in retrospect, we
2:04:25should have started with steel in the beginning. It was dumb not to do steel.
2:04:28Okay, but to play this back to you, what I'm hearing is that steel was a riskier,
2:04:32less proven path, other than the early US rockets. Versus carbon fiber was a worse but
2:04:40more proven out path. So you need to be the
2:04:43one to push for, "Hey, we're going to do this riskier path and just figure it out."
2:04:48So you're fighting a sort of conservatism in a sense.
2:04:52That's why I initially said that the issue is that we weren't making fast enough progress.
2:04:57We were having trouble making even a small barrel section of the carbon fiber
2:05:02that didn't have wrinkles in it. Because at that large scale, you have to
2:05:08have many plies, many layers of the carbon fiber. You've got to cure it and you've got to cure it
2:05:14in such a way that it doesn't have any wrinkles or defects.
2:05:18Carbon fiber is much less resilient than steel. It has much less toughness.
2:05:26Stainless steel will stretch and bend, the carbon fiber will tend to shatter.
2:05:35Toughness being the area under the stress strain curve.
2:05:39You're generally going to have to do better with steel, but stainless steel to be precise.
2:05:44One other Starship question. So I visited Starbase, I think it was two years ago,
2:05:51with Sam Teller, and that was awesome. It was very cool to see, in a whole bunch of ways.
2:05:55One thing I noticed was that people really took pride in the simplicity of things, where everyone
2:06:02wants to tell you how Starship is just a big soda can, and we're hiring welders, and if you can weld
2:06:09in any industrial project, you can weld here. But there's a lot of pride in the simplicity.
2:06:16Well, factually Starship is a very complicated rocket.
2:06:18So that's what I'm getting at. Are things simple or are they complex?
2:06:23I think maybe just what they're trying to say is that you don't have to have prior experience
2:06:27in the rocket industry to work on Starship. Somebody just needs to be smart and work hard
2:06:36and be trustworthy and they can work on a rocket. They don't need prior rocket experience.
2:06:42Starship is the most complicated machine ever made by humans, by a long shot.
2:06:47In what regards? Anything, really. I'd
2:06:50say there isn't a more complex machine. I'd say that pretty much any project I
2:07:00can think of would be easier than this. That's why nobody has ever made a fully
2:07:08reusable orbital rocket. It's a very hard problem. Many smart people have tried before, very smart
2:07:18people with immense resources, and they failed. And we haven't succeeded yet. Falcon is partially
2:07:26reusable, but the upper stage is not. Starship Version 3,
2:07:32I think this design can be fully reusable. That full reusability is what will enable
2:07:41us to become a multi-planet civilization. Any technical problem, even like a Hadron
2:07:52Collider or something like that, is an easier problem than this.
2:07:55We spent a lot of time on bottlenecks. Can you say what the current Starship
2:07:58bottlenecks are, even at a high level? Trying to make it not explode, generally.
2:08:04It really wants to explode. That old chestnut. All those
2:08:09combustible materials. We've had two boosters explode on the test stand.
2:08:13One obliterated the entire test facility. So it only takes that one mistake.
2:08:21The amount of energy contained in a Starship is insane.
2:08:25Is that why it's harder than Falcon? It's because it's just more energy?
2:08:30It's a lot of new technology. It's pushing the performance envelope. The
2:08:37Raptor 3 engine is a very, very advanced engine. It's by far the best rocket engine ever made.
2:08:43But it desperately wants to blow up. Just to put things into perspective here,
2:08:48on liftoff the rocket is generating over 100 gigawatts of power. That’s 20% of US electricity.
2:08:58It's actually insane. It's a great comparison.
2:08:59While not exploding. Sometimes.
2:09:02Sometimes, yes. So I was like, how does it not explode?
2:09:06There's thousands of ways that it could explode and only one way that it doesn't.
2:09:12So we want it not only to really not explode, but fly reliably on a daily basis, like once per hour.
2:09:22Obviously, if it blows up a lot, it's very difficult to maintain that
2:09:25launch cadence. Yes.
2:09:30What's the single biggest remaining problem for Starship?
2:09:33It's having the heat shield be reusable. No one's ever made a reusable orbital heat shield.
2:09:44So the heat shield's gotta make it through the ascent phase without shucking a bunch of tiles,
2:09:52and then it's gotta come back in and also not lose a bunch of tiles or overheat the main airframe.
2:10:01Isn't that hard because it's fundamentally a consumable?
2:10:05Well, yes, but your brake pads in your car are also consumable, but they last a very long time.
2:10:09Fair. So it just needs to last a very long time.
2:10:17We have brought the ship back and had it do a soft landing in the ocean.
2:10:22We've done that a few times. But it lost a lot of tiles.
2:10:27It was not reusable without a lot of work. Even though it did come to a soft landing,
2:10:35it would not have been reusable without a lot of work.
2:10:40So it's not really reusable in that sense. That's the biggest problem that remains,
2:10:44a fully reusable heat shield. You want to be able to land it,
2:10:50refill propellant and fly again. You can't do this laborious inspection
2:10:57of 40,000 tiles type of thing. When I read biographies of yours,
2:11:06it seems like you're just able to drive the sense of urgency and drive the sense of "this is the
2:11:11thing that can scale." I'm curious why you
2:11:15think other organizations of your… SpaceX and Tesla are really big companies now.
2:11:20You're still able to keep that culture. What goes wrong with other companies such
2:11:24that they're not able to do that? I don't know.
2:11:29Like today, you said you had a bunch of SpaceX meetings.
2:11:31What is it that you're doing there that's keeping that?
2:11:33It’s adding urgency? Well, I don't know. I guess the urgency is going
2:11:42to come from whoever is leading the company. I have a maniacal sense of urgency.
2:11:47So that maniacal sense of urgency projects through the rest of the company.
2:11:52Is it because of consequences? They're like, "Elon set a crazy deadline, but if I don't get it,
2:11:57I know what happens to me." Is it just that you're able to
2:12:01identify bottlenecks and get rid of them so people can move fast?
2:12:03How do you think about why your companies are able to move fast?
2:12:07I'm constantly addressing the limiting factor. On the deadlines front, I generally actually
2:12:20try to aim for a deadline that I at least think is at the 50th percentile.
2:12:25So it's not like an impossible deadline, but it's the most aggressive deadline I can think
2:12:29of that could be achieved with 50% probability. Which means that it'll be late half the time.
2:12:42There is a law of gas expansion that applies to schedules.
2:12:48If you said we're going to do something in five years, which to me is like infinity time,
2:12:55it will expand to fill the available schedule and it'll take five years.
2:13:05Physics will limit how fast you can do certain things.
2:13:07So scaling up manufacturing, there's a rate at which you can move the atoms
2:13:15and scale manufacturing. That's why you can't instantly
2:13:17make a million units a year of something. You've got to design the manufacturing line.
2:13:23You've got to bring it up. You've got to ride the S-curve of production.
2:13:31What can I say that's actually helpful to people?
2:13:38Generally, a maniacal sense of urgency is a very big deal.
2:13:47You want to have an aggressive schedule and you want to figure out what the limiting
2:13:54factor is at any point in time and help the team address that limiting factor.
2:13:59So Starlink was slowly in the works for many years.
2:14:05We talked about it all the way in the beginning of the company.
2:14:07So then there was a team you had built in Redmond, and then at one point you
2:14:12decided this team is just not cutting it. It went for a few years slowly, and so why didn't
2:14:25you act earlier, and why did you act when you did? Why was that the right moment at which to act?
2:14:30I have these very detailed engineering reviews weekly.
2:14:38That's maybe a very unusual level of granularity. I don't know anyone who runs a company,
2:14:45or at least a manufacturing company, that goes with the level of detail that I go
2:14:50into. It's not as though... I have a pretty good understanding of what's actually going
2:14:57on because we go through things in detail. I'm a big believer in skip-level meetings
2:15:06where instead of having the person that reports to me say things, it's everyone that reports to them
2:15:14saying something in the technical review. And there can't be advanced preparation.
2:15:25Otherwise you're going to get "glazed", as I say these days.
2:15:31Exactly. Very Gen Z of you. How do you prevent advanced preparation?
2:15:34Do you call on them randomly? No, I just go around the room.
2:15:37Everyone provides an update. It's a lot of information to keep in your head.
2:15:48If you have meetings weekly or twice weekly, you've got a snapshot of what that person said.
2:15:56You can then plot the progress points. You can sort of mentally plot the
2:16:03points on a curve and say, "are we converging to a solution or not?"
2:16:12I'll take drastic action only when I conclude that success is not in a set of possible outcomes.
2:16:22So when I finally reach the conclusion that unless drastic action is done, we have no chance of
2:16:29success, then I must take drastic action. I came to that conclusion in 2018,
2:16:36took drastic action and fixed the problem. You've got many, many companies. In each of
2:16:45them it sounds like you do this kind of deep engineering understanding of
2:16:49what the relevant bottlenecks are so you can do these reviews with people.
2:16:56You've been able to scale it up to five, six, seven companies.
2:16:59Within one of these companies, you have many different mini companies within them.
2:17:04What determines the max amount here? Because you have like 80 companies…?
2:17:0780? No. But you have so many
2:17:10already. That's already remarkable. By this current number.
2:17:13Exactly. We can barely keep one company together.
2:17:23It depends on the situation. I actually don't have regular meetings with The Boring Company,
2:17:32so The Boring Company is sort of cruising along. Basically, if something is working well and
2:17:37making good progress, then there's no point in me spending time on it.
2:17:42I actually allocate time according to where the limiting factor. Where are things problematic?
2:17:50Where are we pushing against? What is holding us back? I focus, at the risk of saying the
2:17:59words too many times, on the limiting factor. The irony is if something's going really well,
2:18:09they don't see much of me. But if something is going badly,
2:18:12they'll see a lot of me. Or not even badly… If something is the limiting factor.
2:18:18The limiting factor, exactly. It’s not exactly going badly but it’s the
2:18:21thing that we need to make go faster. When something’s a limiting factor at
2:18:25SpaceX or Tesla, are you talking weekly and daily with the engineer that's
2:18:32working on it? How does that actually work? Most things that are the limiting factor are
2:18:39weekly and some things are twice weekly. The AI5 chip review is twice weekly.
2:18:46Every Tuesday and Saturday is the chip review. Is it open ended in how long it goes?
2:18:54Technically, yes, but usually it's two or three hours. Sometimes less. It depends on
2:19:03how much information we've got to go through. That's another thing. I'm just trying to tease
2:19:07out the differences here because the outcomes seem quite different.
2:19:11I think it's interesting to know what inputs are different.
2:19:14It feels like in the corporate world, one, like you were saying, the CEO doing engineering
2:19:20reviews does not always happen despite the fact that that is what the company is doing.
2:19:25But then time is often pretty finely sliced into half hour meetings or even 15 minute meetings.
2:19:32It seems like you hold more open-ended, "We're talking about it until we figure
2:19:38it out" type things. Sometimes. But most
2:19:43of them seem to more or less stay on time. Today's Starship engineering review went a bit
2:19:56longer because there were more topics to discuss. They're trying to figure out how to scale to a
2:20:04million plus tons to orbit per year. It’s quite challenging.
2:20:08Can I ask a question? You said about Optimus and AI that they're going to result in double
2:20:15digit growth rates within a matter of years. Oh, like the economy? Yes. I think that's right.
2:20:22What was the point of the DOGE cuts if the economy is going to grow so much?
2:20:28Well, I think waste and fraud are not good things to have.
2:20:33I was actually pretty worried about... In the absence of AI and robotics,
2:20:41we're actually totally screwed because the national debt is piling up like crazy.
2:20:50The interest payments to national debt exceed the military budget, which is a trillion dollars.
2:20:54So we have over a trillion dollars just in interest payments.
2:21:00I was pretty concerned about that. Maybe if I spend some time, we can
2:21:03slow down the bankruptcy of the United States and give us enough time for the AI and robots
2:21:09to help solve the national debt. Or not help solve, it's the only
2:21:16thing that could solve the national debt. We are 1000% going to go bankrupt as a country,
2:21:21and fail as a country, without AI and robots. Nothing else will solve the national debt.
2:21:30We just need enough time to build the AI and robots to not go bankrupt before then.
2:21:39I guess the thing I'm curious about is, when DOGE starts you have this enormous
2:21:43ability to enact reform. Not that enormous.
2:21:48Sure. I totally buy your point that it's important that AI and robotics drive
2:21:53productivity improvements, drive GDP growth. But why not just directly go after the things
2:21:58you were pointing out, like the tariffs on certain components, or permitting?
2:22:03I'm not the president. And it is very hard to cut things that are obvious waste and fraud,
2:22:13like ridiculous waste and fraud. What I discovered is that it's extremely
2:22:21difficult even to cut very obvious waste and fraud from the government because the government
2:22:28has to operate on who's complaining. If you cut off payments to fraudsters,
2:22:34they immediately come up with the most sympathetic sounding reasons to continue the payment.
2:22:39They don't say, "Please keep the fraud going." They’re like, "You're killing baby pandas."
2:22:46Meanwhile, no baby pandas are dying. They're just making it up. The fraudsters are capable
2:22:51of coming up with extremely compelling, heart-wrenching stories that are false,
2:22:56but nonetheless sound sympathetic. That's what happened. Perhaps I should have known better.
2:23:10But I thought, wait, let's try to cut some amount of waste and pork from the government.
2:23:16Maybe there shouldn't be 20 million people marked as alive in Social Security who are
2:23:22definitely dead, and over the age of 115. The oldest American is 114. So it's safe to say if
2:23:30somebody is 115 and marked as alive in the Social Security database, there's either a typo… Somebody
2:23:39should call them and say, "We seem to have your birthday wrong, or we need to mark you
2:23:47as dead." One of the two things. Very intimidating call to get.
2:23:52Well, it seems like a reasonable thing. Say if their birthday is in the future
2:23:59and they have a Small Business Administration loan, and their birthday is 2165,
2:24:07we either have a typo or we have fraud. So we say, "we appear to have gotten the
2:24:13century of your birth incorrect." Or a great plot for a movie.
2:24:17Yes. That's what I mean by, ludicrous fraud. Were those people getting payments?
2:24:23Some were getting payments from Social Security. But the main fraud vector was to mark somebody as
2:24:29alive in Social Security and then use every other government payment system to basically do fraud.
2:24:37Because what those other government payment systems do,
2:24:40they would simply do an "are you alive" check to the Social Security database. It's a bank shot.
2:24:46What would you estimate is the total amount of fraud from this mechanism?
2:24:52By the way, the Government Accountability Office has done these estimates before. I'm
2:24:55not the only one. In fact, I think the GAO did an analysis, a rough estimate of fraud during
2:25:02the Biden administration, and calculated it at roughly half a trillion dollars.
2:25:08So don't take my word for it. Take a report issued during the
2:25:11Biden administration. How about that? From this Social Security mechanism?
2:25:16It's one of many. It's important to appreciate that the government is
2:25:22very ineffective at stopping fraud. It's not like a company where, with
2:25:30stopping fraud, you've got a motivation because it's affecting the earnings of your company.
2:25:34The government just prints more money. You need caring and competence. These are
2:25:44in short supply at the federal level. When you go to the DMV, do you think,
2:25:52"Wow, this is a bastion of competence"? Well, now imagine it's worse than the DMV
2:25:57because it's the DMV that can print money. At least the state level DMVs need to...
2:26:05The states more or less need to stay within their budget or they go bankrupt.
2:26:08But the federal government just prints more money. If there's actually half a trillion of fraud,
2:26:14why was it not possible to cut all that? You really have to stand back and recalibrate
2:26:28your expectations for competence. Because you're operating in a world
2:26:36where you've got to make ends meet. You've got to pay your bills...
2:26:40Find the microphones. Exactly. It's not like there's a giant,
2:26:49largely uncaring monster bureaucracy. It's a bunch of anachronistic computers
2:26:57that are just sending payments. One of the things that the DOGE
2:27:03team did sounds so simple and probably will save $100-200 billion a year.
2:27:14It was simply requiring payments from the main Treasury computer—which is called PAM,
2:27:19Payment Accounts Master or something like that, there's $5 trillion payments a year—that
2:27:25go out have a payment appropriation code. Make it mandatory, not optional, that you
2:27:32have anything at all in the comment field. You have to recalibrate how dumb things are.
2:27:42Payments were being sent out with no appropriation code, not checking back to any congressional
2:27:48appropriation, and with no explanation. This is why the Department of War,
2:27:54formerly the Department of Defense, cannot pass an audit, because the information is literally
2:27:59not there. Recalibrate your expectations. I want to better understand this half a trillion
2:28:04number, because there's an IG report in 2024. Why is it so low?
2:28:10Maybe, but we found that over seven years, the Social Security fraud
2:28:14they estimated was like $70 billion over seven years, so like $10 billion a year.
2:28:17So I'd be curious to see what the other $490 billion is.
2:28:20Federal government expenditures are $7.5 trillion a year.
2:28:26How competent do you think the government is? The discretionary spending there is like… 15%?
2:28:33But it doesn't matter. Most of the fraud is non-discretionary.
2:28:36It's basically fraudulent Medicare, Medicaid, Social Security,
2:28:45disability. There's a zillion government payments. A bunch of these payments are in
2:28:52fact block transfers to the states. So the federal government doesn't
2:28:59even have the information in a lot of cases to even know if there's fraud.
2:29:04Let's consider reductio ad absurdum. The government is perfect and has no fraud.
2:29:10What is your probability estimate of that? Zero. Okay, so then would you say, fraud and waste
2:29:18at the government is 90% efficient? That also would be quite generous.
2:29:27But if it's only 90%, that means that there's $750 billion a year of waste and
2:29:32fraud. And it's not 90%. It's not 90% effective. This seems like a strange way to first principles
2:29:38the amount of fraud in the government. Just like, how much do you think there is?
2:29:43Anyways, we don't have to do it live, but I'd be curious—
2:29:45You know a lot about fraud at Stripe? People are constantly trying to do fraud.
2:29:49Yeah, but as you say, it's a little bit of a... We've really ground it down, but it's a little
2:29:54bit of a different problem space because you're dealing with a much more heterogeneous set of
2:29:58fraud vectors here than we are. But at Stripe, you have high
2:30:03competence and you try hard. You have high competence and
2:30:07high caring, but still fraud is non-zero. Now imagine it's at a much bigger scale, there's
2:30:15much less competence, and much less caring. At PayPal back in the day, we tried to manage
2:30:22fraud down to about 1% of the payment volume. That was very difficult. It took a tremendous amount of
2:30:28competence and caring to get fraud merely to 1%. Now imagine that you're an organization where
2:30:36there's much less caring and much less competence. It's going to be much more than 1%.
2:30:41How do you feel now looking back on politics and doing stuff there?
2:30:48Looking from the outside in, two things have been quite impactful: one, the America PAC, and two,
2:30:59the acquisition of Twitter at the time. But also it seems like there
2:31:05was a bunch of heartache. What's your grading of the whole experience?
2:31:16I think those things needed to be done to maximize the probability that the future is good.
2:31:27Politics generally is very tribal. People lose their objectivity usually with politics.
2:31:35They generally have trouble seeing the good on the other side or the bad on their own side.
2:31:41That's generally how it goes. That, I guess, was one of the things that surprised me the most.
2:31:48You often simply cannot reason with people. If they're in one tribe or the other.
2:31:52They simply believe that everything their tribe does is good and anything
2:31:55the other political tribe does is bad. Persuading them otherwise is almost impossible.
2:32:07But I think overall those actions—acquiring Twitter, getting Trump elected, even though
2:32:22it makes a lot of people angry—I think those actions were good for civilization.
2:32:30How does it feed into the future you're excited about?
2:32:33Well, America needs to be strong enough to last long enough to extend life to other
2:32:42planets and to get AI and robotics to the point where we can ensure that the future is good.
2:32:51On the other hand, if we were to descend into, say, communism or some situation where the state
2:32:59was extremely oppressive, that would mean that we might not be able to become multi-planetary.
2:33:10The state might stamp out our progress in AI and robotics.
2:33:21Optimus, Grok, et cetera. Not just yours, but any revenue-maximizing company's products will
2:33:29be leveraged by the government over time. How does this concern manifest in what
2:33:37private companies should be willing to give governments? What kinds of guardrails? Should
2:33:44AI models be made to do whatever the government that has contracted
2:33:51them out to do and asks them to do? Should Grok get to say, "Actually,
2:33:57even if the military wants to do X, no, Grok will not do that"?
2:34:01I think maybe the biggest danger of AI and robotics going wrong is government.
2:34:16People who are opposed to corporations or worried about corporations should
2:34:21really worry the most about government. Because government is just a
2:34:25corporation in the limit. Government is just the biggest
2:34:30corporation with a monopoly on violence. I always find it a strange dichotomy where
2:34:38people would think corporations are bad, but the government is good, when the government is
2:34:41simply the biggest and worst corporation. But people have that dichotomy. They somehow think
2:34:50at the same time that government can be good, but corporations bad, and this is not true.
2:34:55Corporations have better morality than the government.
2:34:59I actually think it’s a thing to be worried about. The government could potentially use AI and
2:35:12robotics to suppress the population. That is a serious concern.
2:35:18As the guy building AI and robotics, how do you prevent that?
2:35:28If you limit the powers of government, which is really what the US Constitution is intended to do,
2:35:33to limit the powers of government, then you're probably going to have a better outcome than
2:35:37if you have more government. Robotics will be available
2:35:42to all governments, right? I don’t know about all governments.
2:35:49It's difficult to predict. I can say what's the endpoint, or what is many years in the future, but
2:35:57it's difficult to predict the path along that way. If civilization progresses, AI will vastly
2:36:08exceed the sum of all human intelligence. There will be far more robots than humans.
2:36:16Along the way what happens is very difficult to predict.
2:36:20It seems one thing you could do is just say, "whatever government X, you're not allowed to
2:36:27use Optimus to do X, Y, Z." Just write out a policy. I think you tweeted recently that
2:36:31Grok should have a moral constitution. One of those things could be that we
2:36:36limit what governments are allowed to do with this advanced technology.
2:36:47Technically if politicians pass a law and they can enforce that law,
2:36:53then it's hard to not do that law. The best thing we can have is limited government
2:37:01where you have the appropriate crosschecks between the executive, judicial, and legislative branches.
2:37:12The reason I'm curious about it is that at some point it seems the limits will come from you.
2:37:17You've got the Optimus, you've got the space GPUs… You think I'll be the boss of the government?
2:37:24Already it's the case with SpaceX that for things that are crucial—the government really
2:37:32cares about getting certain satellites up in space or whatever—it needs SpaceX. It is the
2:37:37necessary contractor. You are in the process of building more and more of the
2:37:44technological components of the future that will have an analogous role in different industries.
2:37:50You could have this ability to set some policy that suppressing classical liberalism in any
2:37:58way… "My companies will not help in any way with that", or some policy like that.
2:38:05I will do my best to ensure that anything that's within my control
2:38:08maximizes the good outcome for humanity. I think anything else would be shortsighted,
2:38:18because obviously I'm part of humanity, so I like humans. Pro human.
2:38:29You mentioned that Dojo 3 will be used for space-based compute.
2:38:34You really read what I say. I don't know if you know,
2:38:38Elon, but you have a lot of followers. Dead giveaway. How did you discern my secrets?
2:38:46Oh I posted them on X. How do you design a chip for space? What changes?
2:38:54You want to design it to be more radiation tolerant and run at a higher temperature.
2:39:03Roughly, if you increase the operating temperature by 20% in degrees Kelvin,
2:39:08you can cut your radiator mass in half. So running at a higher temperature
2:39:14is helpful in space. There are various things
2:39:20you can do for shielding the memory. But neural nets are going to be very
2:39:26resilient to bit flips. Most of what happens
2:39:30for radiation is random bit flips. But if you've got a multi-trillion parameter model
2:39:37and you get a few bit flips, it doesn't matter. Heuristic programs are going to be much more
2:39:42sensitive to bit flips than some giant parameter file.
2:39:49I just design it to run hot. I think you pretty much do
2:39:56it the same way that you do things on Earth, apart from making it run hotter.
2:40:02The solar array is most of the weight on the satellite.
2:40:04Is there a way to make the GPUs even more powerful than what Nvidia and TPUs and
2:40:11et cetera are planning on doing that would be especially privileged in the space-based world?
2:40:18The basic math is, if you can do about a kilowatt per reticle, then you'd need 100
2:40:31million full reticle chips to do 100 gigawatts. Depending on what your yield assumptions are,
2:40:44that tells you how many chips you need to make. If you're going to have 100 gigawatts of power,
2:40:53you need 100 million chips that are running at a kilowatt sustained, per reticle. Basic math.
2:41:05100 million chips depends on… If you look at the die size of something like
2:41:13Blackwell GPUs or something, and how many you can get out of a wafer, you can get
2:41:18on the order of dozens or less per wafer. So basically, this is a world where if
2:41:25we're putting that out every single year, you're producing millions of wafers a month.
2:41:33That's the plan with TeraFab? Millions of wafers a month of advanced process nodes?
2:41:37Yeah it could be north of a million or something. You’ve got to do the memory too.
2:41:42Are you going to make a memory fab? I think the TeraFab's got to do memory.
2:41:46It's got to do logic, memory, and packaging. I'm very curious how somebody gets started.
2:41:51This is the most complicated thing man has ever made.
2:41:54Obviously, if anybody's up to the task, you're up to the task.
2:41:58So you realize it's a bottleneck, and you go to your engineers.
2:42:02What do you tell them to do? "I want a million wafers a month in 2030."
2:42:09That’s right. That’s exactly what I want. Do you call ASML? What is the next step?
2:42:14No so much to ask. We make a little fab and see what happens.
2:42:22Make our mistakes at a small scale and then make a big one.
2:42:25Is a little fab done? No, it's not done. We're
2:42:29not going to keep that cat in the bag. That cat's going to come out of the bag.
2:42:34There'll be drones hovering over the bloody thing. You'll be able to see its construction
2:42:39progress on X in real time. Look, I don't know, we could just
2:42:47flounder in failure, to be fair. Success is not guaranteed. Since we want to try to make something
2:43:00like 100 million… We want 100 gigawatts of power and chips that can take 100 gigawatts by 2030.
2:43:18We’ll take as many chips as our suppliers will give us.
2:43:20I've actually said this to TSMC and Samsung and Micron: "please build more fabs faster".
2:43:28We will guarantee to buy the output of those fabs.
2:43:32So they're already moving as fast as they can. It's us plus them.
2:43:46There's a narrative that the people doing AI want a very large number
2:43:50of chips as quickly as possible. Then many of the input suppliers,
2:43:56the fabs, but also the turbine manufacturers, are not ramping up production very quickly.
2:44:02No, they're not. The explanation you hear
2:44:05is that they're dispositionally conservative. They're Taiwanese or German, as the story may
2:44:11be. They just don't believe... Is that really the explanation or is there something else?
2:44:17Well, it's reasonable to... If somebody's been in the computer memory business for 30 or 40 years…
2:44:25They've seen cycles. They've seen boom and bust 10 times.
2:44:32That's a lot of layers of scar tissue. During the boom times, it looks like
2:44:37everything is going to be great forever. Then the crash happens and they're
2:44:41desperately trying to avoid bankruptcy. Then there's another boom and another crash.
2:44:48Are there other ideas you think others should go pursue that
2:44:51you're not for whatever reasons right now? There are a few companies that are pursuing
2:44:58new ways of doing chips, but they're just not scaling fast.
2:45:03I don't even mean within AI, I mean just generally.
2:45:07People should do the thing where they find that they're highly motivated to do that thing,
2:45:13as opposed to some idea that I suggest. They should do the thing that they find
2:45:21personally interesting and motivating to do. But going back to the limiting
2:45:30factor… I used that phrase about 100 times. The current limiting factor that I see in the
2:45:47three to four year timeframe, it's chips. In the one year timeframe, it's energy,
2:45:56power production, electricity. It's not clear to me that there's enough
2:46:02usable electricity to turn on all the AI chips that are being made.
2:46:10Towards the end of this year, I think people are going to have real trouble turning on...
2:46:13The chip output will exceed the ability to turn chips on.
2:46:17What's your plan to deal with that world? We're trying to accelerate electricity production.
2:46:24I guess that's maybe one of the reasons that xAI will be maybe the leader, hopefully the leader.
2:46:34We'll be able to turn on more chips than other people can turn on, faster,
2:46:39because we're good at hardware. Generally, the innovations from
2:46:45the corporations that call themselves labs, the ideas tend to flow… It's rare to see that
2:46:54there's more than about a six-month difference. The ideas travel back and forth with the people.
2:47:04So I think you sort of hit the hardware wall and then whichever company can scale
2:47:11hardware the fastest will be the leader. So I think xAI will be able to scale
2:47:17hardware the fastest and therefore most likely will be the leader.
2:47:20You joked or were self-conscious about using the "limiting factor" phrase again.
2:47:28But I actually think there's something deep here. If you look at a lot of things we've touched on
2:47:32over the course of it, it’s maybe a good note to end on.
2:47:37If you think of a senescent, low-agency company, it would have some bottleneck and
2:47:45not really be doing anything about it. Marc Andreessen had the line of,
2:47:49"most people are willing to endure any amount of chronic pain to avoid acute pain".
2:47:54It feels like a lot of the cases we're talking about are just leaning into the acute pain,
2:47:59whatever it is. "Okay, we got to figure out how to work with steel, or we got to figure
2:48:05out how to run the chips in space." We'll take some near-term acute pain
2:48:09to actually solve the bottleneck. So that's kind of a unifying theme.
2:48:13I have a high pain threshold. That's helpful. To solve the bottleneck.
2:48:19Yes. One thing I can say is, I think the future is going to be very interesting.
2:48:36As I said at Davos—I think I was on the ground for like three hours or something—it's
2:48:45better to err on the side of optimism and be wrong than err on the side of pessimism
2:48:50and be right, for quality of life. You'll be happier if you err on
2:49:01the side of optimism rather than erring on the side of pessimism.
2:49:05So I recommend erring on the side of optimism. Here's to that.
2:49:09Cool. Elon, thanks for doing this. Thank you.
2:49:11All right, thanks guys. All right. Great stamina.
2:49:17Hopefully this didn't count as a pain in the pain tolerance.

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